Now, as the president of the Fantasy Sports Trade Association, Charchian has seen the evolution of the game to the point where some 33 million players in the United States can see the touchdown just scored on television reflected in their matchup in a matter of seconds. And it’s cutting-edge technology that could create a whole new way of playing fantasy football.

“From the beginning, live scoring has been the killer app for fantasy sports and it’s gotten to a point where you can’t consider going into the marketplace without having that as an option,” Charchian says.

As fantasy football transitioned from the pen-and-paper era to the Internet, live scoring was somewhat of a premium – Yahoo!, for example, charged for it. But when sites like CBS and ESPN offered it for free, Yahoo! did it as well. “It was sending people to other services like ESPN, and secondarily, it became easier to offset the cost because the fantasy marketplace grew,” Charchian says.

Those numbers you see filling up your scoreboard every Sunday are thanks to STATS LLC, the Illinois-based company that works with Yahoo! to deliver live data, something they continue to focus on making as fast as possible.

“Our challenge on this side as we’ve delivered live data to more and more clients is we have to scale to deliver that load without adding any latency,” says Jim Corelis, STATS’ VP of product development. “Our customers want the data before you can see it on TV, which is possible with the video lags. And we’re delivering it as fast. We’ve put in a lot of redundancy in our systems to make sure that we handle the load from our perspective.”

Those immediate stats, however, are starting to go beyond yards, touchdowns and two-point conversions. Corelis explained that the company can now provide predictive analytics immediately. “So it’s not just what happened,” he explains, “but what’s going to happen next.”

That means there could be a growth in popularity with a type of fantasy game that doesn’t just allow users to change their lineup before the start of games. Instead, an owner could have a situation in which he or she subs out players on every offensive drive or every quarter. Those predictive analytics would be extremely useful in making decisions on who should move in and out of the lineup – let’s say  you own a pair of running backs on the same team and you want to pick the one more likely to score in the red zone. Those stats could help make that decision much easier.

In other words, it’s going to make fantasy that much closer to reality.

As a student, my biggest fear was that the teacher would call on me and I wouldn’t know the answer to her question. So, when I wasn’t 100% sure of an answer or didn’t have any facts to back up my opinion, I would sit as quietly as I could wishing to, somehow, make myself invisible. After all, nothing would be worse than fumbling around for information or being wrong in front of the entire class!

I soon learned that, if I wanted to succeed, I needed to be the first one to raise my hand with confidence – and the right facts.

“Fumblers” versus “fact finders”

When it comes to business intelligence, IDC identifies organizations as BI “fumblers” and “fact finders.” The fumblers base decisions on incomplete, unorganized data and opinions – and hope they never get called on to explain themselves. Whereas, the fact finders base decisions on accurate, structured, and up-to-the-minute information, supported by a well-defined BI and analytics strategy.

Which one are you?

According to Dan Vesset, program vice president of IDC’s Business Analytics research, fact finders are focused on providing a clear and visible BI and analytics strategy. As part of their strategy, they:

• Train employees on the meaning of data, the use of BI and analytics tools, and the use of analytics to improve decision making

• Invest in BI and analytics system design quality to rapidly respond to changing business conditions and user requirements

• Place a high level of importance (including funding) on a data governance group and associated policies

• Involve nonexecutive management in promoting and encouraging the design and use of the BI and analytics solution

• Align the organization to performance management methodology with a set of defined metrics and KPIs that influence user behavior

Why does it matter?

According to IDC, “as a group, fact finders are more competitive than fumblers.” “Eighty percent of the most competitive organizations are fact finders,” reported IDC, “almost thirty percent more than among the least competitive organizations.”

With a BI and analytics strategy, organizations can empower business users to make evidence-based decisions using the best possible intelligence about customers, finances, operations, suppliers, and market trends. And this can lead to improved business processes, increased productivity, and reduced costs – for a competitive edge.

Turn your organization into fact finders with the right BI and analytics strategy

To learn about the factors you should consider when creating a BI and analytics strategy and best practices for pervasive and highly impactful BI and analytics use, read the IDC Analyst Connection article “Best Practices for a BI and Analytics Strategy.”

So this won’t be just another excuse to post an image of Brad Pitt and make the observation that you can make better business decisions by running the correct algorithms. This isn’t about Big Data, faster analytics or mathematical models: it’s about the data gatherers themselves.

In the baseball story, the data gatherers are the nameless guys hunched over laptops, typing in each detail of every play, for game after game. Where was the ball pitched, was it a grounder or a fly, how was it fielded, etc. Amazingly it was done to this level of detail long before laptops.  In fact, the data was gathered a long time before it was used in a structured way.  Englishman Henry Chadwick –who was raised on cricket– developed the box score in the 1870s (and chose the letter “K” for a strike-out).

Without accurate data, there is no mining

Only once the data gatherers have done their work is there any data for the analysts and modelers to play with. The data-miners may have degrees in mathematical modeling, but Big Data can only take off once there is Data to actually work on.

The Data needs to be accurate, and normalised. In baseball it is pretty easy to identify a player by name, e.g. “Derek Jeter”. Jeter doesn’t ever change his name, but companies change their names all the time such as Rim becoming Blackberry. An analyst trying to answer “how much do we spend with Blackberry a year” must hunt through records for “RIM”, “R.I.M”, “Research in Motion” and many others.

Business networks are the data gatherers, and we are starting to develop the box scores of internet commerce. Instead of pitcher and batter, we know statistics of who is buying from whom, at what volume and transaction count. Just as in the Baseball Box Score, or the cricket Score book, each tiny interaction needs to be recorded so the larger picture can emerge. Business Networks are also becoming the holders of “canonical” data on companies, tracking name changes, mergers, Joint Ventures, cross-holdings and subsidiaries.

For example, say you are responsible for the Purchasing Operations at a bank, and your spend analysis tells you that you have 437 suppliers of Facilities Services. A business network can tell you how many suppliers the average bank has, allowing you to benchmark your supplier rationalisation program.

Or as a seller in a new market, you will be concerned about cash flow, especially how quickly your invoices are likely to get paid. The Network could tell you what the median DSO (a measure of invoice payment speed) is in this market.

Business networks are carefully filing away those box scores waiting for the statistician who will start to develop the algorithms.

And maybe Brad Pitt will play you in the movie.

Many times, the immediate need for business insight is scattered around the organization, and the response is addressed in isolation rather than as a company-wide imperative. Employees often perform their analysis in spreadsheets and resort to manual workarounds when existing tools and processes are inadequate.

Today, however, the requirement for financial and operational insight is growing as the business environment has become harder to predict. At the same time, workforces are stretched and there are fewer people available to do manual, time-consuming analysis. These pressures are at once acute and ongoing. For a holistic solution, you need to change both your data infrastructure and access to analytics.

How to Start Your Transformation

Some companies are just starting this journey, while others have already embarked on it. If your company is at the beginning of the journey, the following suggestions can help make the transformation quicker and more effective:

• Identify a specific business issue or issues that can drive particular financial value in your organization— such as customer segmentation, fraud detection, supply chain optimization, social media analytics, or DSO reduction
• Analyze and fully understand the business processes that impact this issue
• Integrate all of the data necessary to drive insight throughout those processes
• Provide information and tools in easy-to-use and flexible formats to key stakeholders in the business process ecosystem

How to Expand to the Enterprise Level

Starting small and focusing on a particular problem can improve confidence in the relevancy of these initiatives. If you’ve done this already in one or more specific areas, you’re probably able to take action at the enterprise level:

• Establish a best-practices center to create a standardized analytics environment
• Develop a data strategy that ensures business users can access all relevant information in real time or near real time
• Identify existing analytics tools that can be rationalized to create cost-savings opportunities
• Provide integrated information in an easy-to-use and flexible format, available on any device, to those users who can impact key strategic goals

Executives in all companies are discovering that the decisions that impact their financial and operational performance the most are made by business contributors who combine good judgment with actionable insight.

Read the rest of my blogs in the How to Get from Data to Decision series, and let me know what you think. What have been your greatest challenges as you leverage “Big Data” analytics?

So far in the series, I have charted a course for creating value in the sports and entertainment industry through analytics. I have advocated a 0000ff;">0000ff;">holistic approach to paint the full picture that goes beyond the basic game-day stats, and I have underscored the important role that fan engagement plays in shaping the 0000ff;">0000ff;">fan experience.

And finally, in my last post, I touched on the design thinking methodology as a framework to embark on this journey, including the power of proof of plays that can leverage the philosophy of “Fail early and often” to design better solutions. If I were able to pique your curiosity and get you excited enough that you started asking questions, the next logical piece in this puzzle would involve the crucial step of making it happen.

Rapidly changing business requirements by fast-moving markets, trends, and consumer sentiments require businesses, including sports organizations, to stay ahead of the curve. How fast we move to deliver business value is critical to executing the game-winning play when all we may have is seconds left on the clock. Therefore, traditional design, delivery, and implementation of technology solutions may become insufficient.

0000ff;">0000ff;">Rapid deployment solutions (RDS) can help sports and entertainment organizations sprint to value and insight. In its simplest form, RDS is about delivering the right level of functionality at the right time. Here are some of the ways to take advantage of RDS to deliver unique analytics solutions.

Run on all three cylinders of analytics

Whether we are trying to solve complex problems or answer basic questions that have been around for decades, we need to run on all three cylinders of analytics: insight into right data for the right role and at the right time. If we have all the data but we cannot deliver it for the right role, it is like finding treasure while stranded on an island but not being able to obtain water. If we have the data, and have it for the right role, but we can’t get it until the next quarter, it may be too late by then to take action.

Reinventing the wheel does not make sense

It does not make any sense to start from scratch when we can leverage experience that comes from years of implementing best practices, which include not only technical knowhow but also deep insight into processes and specific functions needed to execute a particular role. This is important because an unlucky break may cause us to slip and fall as we charge to the net, but we must avoid unforced errors at all costs. We want a trusted partner that can guide us in delivering successful solutions that can be implemented in weeks and months, not in quarters or years.

Integrated solutions that can scale up

Technology must be delivered as an integrated solution that can scale up. Can you imagine a coach putting a game plan into effect without a fully coordinated effort from all teams or players? Whether the sport is basketball, in which five players must effectively play the zone defense, or football, where multiple coaches are ready to jump in with suggestions in response to what the game may throw at us in the last seconds of the quarter, when it matters the most, we want everything and everyone to be aligned, working off the same playbook.

The same way we would want to put up points early in the game, we want integrated solutions with immediate effect and with the ability to scale up. This means we build on the right foundation.

Imagine what would happen if we offer fans Wi-Fi at a sports venue, but after building up excitement, we quickly run out of bandwidth on opening night. Or we promise a smart fan app that delivers awesome analytics complemented by multimedia, but the query or the video takes forever to load. Or we offer a fast pass for concession orders, but the lack of integration with our other business systems results in only a partial picture of our revenue streams.

Accelerated implementations allows us to sprint

Time to delivery is critical not just for achieving lower TCOs and higher ROIs; it can also help us gain a competitive advantage, whether in the form of new products that improve the fan experience or of internal business processes that provide increased efficiencies.

Think for a moment about how this may play out in sports: Except when it comes to hardcore fans, a sports organization is competing with numerous options for alternative entertainment, whether it is the couch at home or the sports bar downtown. And, of course, we may not be talking about a team that is on a hot winning streak or that just acquired a hot player. This point becomes even more important for sports such as golf, where the event takes place over a longer period and unfolds under more demanding conditions, so that it may not be suitable for all fans to attend.

Finding the strategic edge that makes a sports organization unique is vital. The ultimate goal should be to increase overall fan engagement — not only for current fans but also for new fans from all walks of life — judging our performance by how successfully we convert casual fans into regular attendees.

I subscribe to the notion that each team is unique not just in its basic attributes (makeup of the players, the city it represents, the size of the venue) but also in how it designs and delivers its overall fan experience: win or lose, playoffs or not. Thus, the ability to drill down into data sets to uncover unique insights should not be dismissed like a draft pick left for the last round.

Innovative technology solutions and analytics can bring about real opportunities for growth and profitability. As Steven Birdsall, SVP and general manager of global OEM and RDS at SAP, so eloquently puts it, “An opportunity cost is an opportunity lost.”

Stay tuned for my next post in the series, Sports And Analytics: Insight Through Mobile Dashboards.

While the role of an executive sponsor is critical in all tech projects, several aspects of mobile BI technology make it easy for executive management to be involved closely and play a unique role.

Moreover, although the CIO or the CTO plays a critical role in making sure the right technology is acquired or developed, the executive sponsorship from the business side provides the right level of partnership in order to run on all three cylinders of BI: insight into right data for the right role and at the right time.

Why Do We Need an Executive Sponsor?

We need executive sponsorship because, unlike grassroots efforts, business and technology projects require a top-down approach. Whether the strategy is developed as part of a structured project or as a standalone engagement, the executive sponsor delivers three critical ingredients:

1. The mobile BI strategy is in line with the overall business strategy.
2. The required resources are made available.
3. Necessary guidance is provided in order to stay the course.

Is Having an Executive Sponsor Enough?

Having an executive only on paper isn’t enough, however. How much commitment an executive sponsor makes and the leadership he/she provides has a direct impact on the outcome of the strategy. Thus, the ideal executive sponsor of a mobile BI initiative is a champion of the cause, an ardent mobile user, and the most active consumer of its assets.

What Makes an Ideal Executive Sponsor for Mobile BI?

How does the executive champion the mobile BI initiative? First and foremost, he/she leads by example—no more printing paper copies of reports or dashboards. This means that the executive is keen not only to consume the data on mobile devices but also to apply the insight derived from these mobile assets to decisions that matter.

Using the technology demonstrates firsthand the mobile mindset that sets an example for the rest of the direct reports and their teams. In addition, by recognizing the information available on these mobile BI assets as the single version of the truth, the executive provides a clear and consistent message for everyone to follow.

Is Mobile BI Easier to Adopt by Executive Sponsors?

Without a doubt, mobile BI, just like mobility, is conducive to a wide range of users, starting with executives. Unlike the PC, which wasn’t mobile at all, and the laptop, which provided limited mobility, tablets and smartphones provide a perfect combination of mobility and convenience. This ease of use makes these devices an ideal candidate in winning over even those executives who may have been initially uneasy to include mobile BI in their arsenals or to use them in their daily decision-making activities.

The mobility and simplicity may give the executives additional incentives to get involved in the development of requirements for the first set of mobile BI assets because they can easily see the benefits of having access to critical information at their fingertips. These benefits include an additional opportunity for sales and marketing to use mobile BI to showcase new products and services to customers (an approach that reflects the innovation inherent in the use of this technology).

Bottom Line: Executive Sponsorship Matters

The most important goal of a mobile BI strategy is to enable faster, better-informed decision making. Executive sponsorship matters because with the right sponsorship, the mobile BI initiative will have the best chance to drive growth and profitability. Without this sponsorship—even with the most advanced technology in place—a strategy will face an uphill battle.

What other aspects of executive sponsorship do you see playing a role in mobile BI strategy?

Stay tuned for my next blog in the series, Ten Mobile BI Strategy Questions: Security.

For more on mobile BI, read my other blogs in the series. 

We know that online retailers like Amazon have the ability to track our browsing habits and purchase history. They know how long we linger on a product page, which options we are comparing, and if we abandon our shopping cart without making a purchase.

Now it’s possible for brick-and-mortar retailers to have similar insight into the shopper experience thanks to the latest 3D sensor technology installed in shopping malls and retail stores.

Vizualize, specialists in shopper intelligence, combine the latest 3D sensors with sophisticated analytics to help retailers in more than 40 countries better understand which items are most attractive to shoppers, how they interact with displays and which products they reject or select for purchase.

Michael MacMillan, CEO of Vizualize, explains how his company is helping retailers improve the shopper experience, as well as the bottomline:

A small change to the retail environment can have a significant impact on sales. Based on the results of Shopper Tracking, a Vizualize client in the confectionary business found that by shifting the display of chocolate bars just 6 feet in a new direction they were able to increase product sales by 1.5 percent.

 How Do they Do It? 

Vizualize employs the latest generation of 3D infrared sensors to produce images with amazing pixel-level detail.  Not only can these devices see in the dark, their depth perception enables them to distinguish between people and other objects with impressive accuracy.

They can count people moving at any speed or direction through doorways, passages or open space. And they can even be trained to distinguish between employees and customers.

Window Shopping:  Just looking, thanks

The latest technology from Vizualize also gives retailers the ability to analyze the behavior of window shoppers. Sensors can distinguish between passers-by who had the opportunity to see and viewers that actually engage with a display.

With the use of advanced facial detection the system can even determine a shopper’s gender, and general age group.

If all of this is making you more than a little uncomfortable with the idea of sensors monitoring your every move, you may be relieved to know that Vizualize systems employ image blurring to protect the identity of individual consumers.

Getting the Big Picture 

It’s through the consolidation of sensor tracking data that retailers are really able to see the big picture – patterns in shopper behavior that reveal how consumers experience the store. It can help them answer such fundamental questions as:

• How much time do shoppers spend in the store (dwell time)?
• Where do they stop?
• How much time do they spend in different categories and aisles
• Where are the “hot spots”
• Are there choke points that impede traffic?
• What is the traffic to end caps, fixtures, kiosks?
• What percentage of shoppers convert?

The answers to these questions are provided by sophisticated analytical programs that generate metrics at the individual store level as well as consolidated insights across multiple locations operated by global brands.

Mobility: The Next New Thing for Retail Analytics 

In recent years merchants have felt threatened by the increase in showrooming – when consumers check-out a product in-store while using their mobiles to compare and purchase online.  But retail marketers are beginning to recognize that the proliferation of mobile devices is not so much a threat as a potential boon to retail business.

Mobile data about shopper behavior opens new possibilities for understanding shopper preferences at an individual level.  According to OgilvyAction, 70% of purchase decisions are made within 3 feet of the shelf.  Leveraging the consumer’s mobile device as a personal delivery channel for real-time offers gives marketers new opportunities to influence shopper behavior at that critical point of decision.

The future of retail marketing lies in combining the sensibility of 3D sensors with real-time in-store offers delivered via mobile.  It’s the retailers who take advantage of these twin technologies who will win the next round in the ongoing battle for market share.

Join me on Twitter (@eagaines) or connect on LinkedIn or Google+.

Read more from Business Innovation >>

Until recently, financial analysis-based decision making relied on a historical view of aggregate data –understanding yesterday’s events to influence tomorrow’s performance. Highly skilled data scientists took summary-level information – analytic engines would be quickly overrun by larger or more detailed volumes of data – and analyzed promotion effectiveness, capital ROI, or quarter-end results.

While useful, this approach posed several problems:

• Analysis was by definition backward looking.
• Summary-level analysis could easily mask anomalies that, if identified, would change the course of a decision.
• Because analytic tools were so difficult to use, only a few specialists could use them, which created bottlenecks and limited dissemination.

Today’s Analytics

Today, enterprise data is growing exponentially – and raising questions on how to manage Big Data for enterprise value. Powerful in-memory computing makes it possible for businesses to analyze large volumes of data in real time at the most granular levels. Easy-to-use tools put sophisticated predictive analytics, once the domain of senior business analysts, into the hands of employees across the organization.

The result: More people get deeper insight more often, increasing their ability to affect business performance.

The data exists, and the technology is ready, to help you a make the most of your information. The next step is yours.

Today, organizations face multifaceted problems and need to quickly realize the value of their technology solutions whether it’s for business intelligence (BI) or other technology implementations. Business challenges have become more complex and are often riddled with ambiguity that makes it harder to address using traditional methodologies in analytics and beyond.

As a result, to answer the most pressing business questions, we’re still left with an age—old problem—balancing functionality against scarce time and resources. Rapid deployment solutions (RDS) can provide an alternative method by providing an accelerated timetable and the right foundation to scale up as needed.

RDS Finds the Equilibrium Point

In its simplest form, RDS is about delivering the right level of functionality at the right time. What RDS aims to do is find the right balance between the high-priority functionality needed for the urgent business requirement and the immediate value delivery for maximum impact.

For example, organizations may be willing to give up optimal functionality for a solution with reduced functionality if it means they can get there faster because this results in immediate value and insight. The complexity of the analytics foundation may shape the desired solution. However, the flexibility to give up functionality may depend on how critical the business function is where the speed may play a less important role.

On the other hand, the more functionality we put in, the costlier the solution becomes. The additional functionality may increase the implementation time to deliver the increased capabilities, which would impact both the time and resources needed. The key question becomes: Will the additional investment required in time and resources deliver immediate value or does it result in diminishing returns for additional functionality which becomes increasingly costly?

The Business Questions We Ask Today May Become Irrelevant Next Month

Accelerated implementations, whether they’re planned for analytics or for business systems, are no longer a nice-to-have. Time is of the essence. Rapidly changing requirements for the business to adapt to market changes and consumer trends may force it to move faster than ever before.

We can’t execute boldly and manage risk if it’s going to take us a year to get there. It doesn’t matter what the engagement is, or whether we’re asking questions about a new product or a marketing campaign— what matters is that what’s in front of us that requires answers through insight into data doesn’t remain static for a long time.

Thus, time becomes a critical element of RDS to realize immediate value. For analytics, this may, for example, translate into rapid development and implementation of mobile BI assets that provide timely insight before the next wave of business challenges washes out current assumptions and changes equations that were considered safe.

Why Reinvent the Wheel?

Delivering proven business value through experience and industry best practices is the only way to deliver insight in analytics that is both relevant and timely. Why would we want to reinvent the wheel when we can work with technology leaders and partners that can help us deliver the proven business value?

When I consider the importance of experience, I think of an example that occurs around the holidays. Let’s say you have three kids, and they all want the same toy that requires assembly. So, you order three of the toys and begin the assembly the night before you need them. The first one may take an hour to finish. The second one may take you 30 minutes. By the time you get to the last one, you are competing against time.

The same analogy applies to business and delivering analytics and technology solutions in general. When it comes to integration of business and technology, which our organizations may depend on, we don’t want to try to build it with a user manual in our hand. Instead, we want a trusted partner that can guide us in delivering successful solutions that can be implemented within weeks and months, not within years.

Bottom Line

RDS with accelerated implementations through tools, configuration content, templates, and educational material dramatically shortens development cycles by delivering initial results within weeks and months.

Where do you see RDS adding value to your business?

Many companies believe they have an analytic strategy when what they actually have is an architecture, which is important but certainly not all you need. An analytic strategy requires continuous strategic and tactical business alignment, defining and updating your analytic strategy in view of your business goals and objectives, and analyzing and updating your current analytic roles, analytic skills, business processes and technologies. It also requires governance— data and development— as well as an effective data strategy.

One of the hurdles our customers most often face is change management. How do they move business users from a reporting mindset to a truly analytic, and ideally predictive, one? Other hurdles entail how to address increasing business user frustration in meeting analytic reporting requests, as well as IT’s challenge in meeting them. IT can address these hurdles by developing prototypes and sandboxes to help business users make the transition and to lead by example.

Start your Strategy with Executive Dashboards 

This can begin with executive dashboards—selected key metrics and leading indicators that can be drilled through for root cause analysis, and complemented by self-service reporting and data exploration. Creating relevant business views of data that support self-service is a win-win tactic; business users get the data sets they need and, because they can fulfill their own reporting, this takes the development load off the IT staff. Additional supporting operational dashboards should be developed jointly with the business areas, both to build ongoing collaboration and consensus and to avoid a “field of dreams”( i.e. if you build it they will come) failure.

As business users become more comfortable with data exploration and increase their analytic skills, they’ll adopt predictive tools. They’ll use them not only to look at what has happened and why, but also to drive action to what they want to have happen and adjust tactics as they monitor opportunities and risks in real, or near, real time. Innovation techniques such as design thinking can be used to help identify leading indicators and predictive indicators.

The road from data to decision is a journey that is best enabled by an analytic strategy.

Business analytics has evolved dramatically from its humble beginnings in core data warehousing more than 30 years ago. It’s evolved from simple, decision support solutions to advancements in business intelligence (BI), enterprise performance management (EPM), and, most recently, intelligent data concepts and predictive analytic applications. Companies have embraced the value of analytics – solutions that combine data and analysis to drive insights – to help them differentiate, become more competitive, and make better business decisions.

A Pervasive Culture of Analytics

Almost everyone in your company can benefit from some form of analytics. Employees want to make their jobs easier – and you want them making better decisions. In the past, powerful analytic tools were reserved for a select few who had mastered data manipulation and reporting. Such data gurus were the only ones who could build the complex models required – making them de facto gatekeepers. Today, however, technology puts powerful models and endless data sources into more hands, supporting better decisions from the shop floor to the C-level suites.

Thanks to advancements in cloud and mobile technologies, analytics are now available everywhere from your basic laptop to the latest smart device – and from client sites to buyer meetings to airport dinners. Mobile access puts near-real-time analytics at the fingertips of all employees, all the time. Key performance indicators (KPI) were once watched monthly, quarterly, or annually. Now, real-time analytics enable daily analysis of emerging trends. And exception reports identify areas in need of change, from the monthly financial close to the highest-level corporate KPIs.

No Data Off Limits

It takes little time for an organization to amass a large historical database. Until recently, translating database data structures into meaningful analytics was time consuming and difficult. Analytic solutions simply eliminated much of the detail in exchange for a timely analysis of summary data. Find a variance, and you’d have to engage a team of experts to dive into the details.

With in-memory computing, however, data volume and level of detail are no longer limiting factors. Every piece of data can be used, generating richer, more meaningful analysis. Users quickly find meaningful information within the analytic system. And analysis of big data lets companies skimp on neither volume (important for establishing trends) nor detail (important for investigating variances). Intelligent data also makes it possible to analyze social media, incorporating and analyzing unstructured data such as customer product reviews, satisfaction surveys, and Facebook posts.

Getting Sophisticated Just Got Easier

Now that sophisticated analytic capabilities no longer have to be built from scratch, both business users and IT departments benefit.

• More frequent modeling. In the past, running and modeling multiple simulations was difficult and time consuming, limiting the number of scenarios that could be run. Today, in-memory computing advances allow nearly unlimited simulations. Scenarios can be run as often as needed to update forecasts or provide input into planning processes. And predictive capabilities let you model multiple scenarios to forecast performance or recalculate enterprise risk.

• Faster to deploy without customization. New technologies such as cloud and mobile solutions enable IT to lower the cost of ownership and deployment while providing more powerful tools to business users. Cloud-based solutions make it easy to deploy flexible numbers of users and licenses, reducing total cost of ownership. Mobile solutions mean that analytic applications can be pushed out to more users even as they travel or work in the field. In-memory computing lets IT capture and store massive amounts of highly detailed data, make it available to any user, and easily access it for later analysis. Together, cloud, mobile, and in-memory technologies dramatically enhance the capabilities of analytic solutions. Powerful functionality can be quickly deployed, includes more compelling data, and doesn’t require months of customization.

How have these trends in Analytics impacted your organization recently?

We expect our hospitals to care for the sick, but for years they have been suffering from a chronic condition of their own.  What ails them can’t be found in the IDC-10 codes, but it dramatically affects their organizational health. Call it Fractured Data Syndrome – the condition that occurs when health data exists is separate silos, seriously impairing the ability of hospitals to make strategic decisions about the health of their organization as well as their patients.

Although hospitals are awash in data, comparatively few have been able to bring their disparate collections together into an integrated platform.  Patient data, healthcare provider records, and financial results frequently exist in separate spheres, with ownership assigned to different parts of the healthcare system, including vendors, partners, and consulting organizations.

The Cure for Fractured Data Syndrome

An integrated data platform is essential to gaining a clear understanding of the correlation between internal performance and market dynamics. While traditional point solutions applied to stand-alone data collections may provide deep vertical analysis, they fail to capture the breadth of insight available when internal data is consolidated and combined with publicly-available external data.

The current trend toward integration of data silos is being driven by the increased use of electronic health records (EHRs). Information formerly stored in offline patient files may now be combined with financial and administrative information to give hospitals a more comprehensive view of the quality of both inpatient and outpatient care.

When EHRs are combined with GIS mapping data, demographic information, population-based planning, physician performance ratings, ACO and Medicare files and competitor intelligence, hospitals have greater insight into the dynamics of their local market and how changes to internal organization and protocols can result in improved patient outcomes and increased financial strength.

Health Data Analytics For Strategic Decision-Making

Healthcare analytics are increasingly shifting from traditional reporting tools utilized by hospitals to a more holistic approach to business intelligence (BI). And it’s a trend that has been rapidly accelerating over the past few years.  Frost & Sullivan analysts predict that advanced health data analytics will continue to grow significantly, increasing from a 10% adoption rate in 2011 to an anticipated adoption rate of 50% by 2016.

Sophisticated BI software is now available that can visualize the results of advanced analytics and present them as actionable information, giving hospital personnel the ability to make more strategic decisions.

Customized dashboards featuring key performance indicators keep the focus on what matters most and the ability to drill-down to root causes opens the way to greater understanding of the correlations among variables.

The Major Challenge to Change

The greatest challenge to realizing the benefits of healthcare analytics is not technical but organizational.  In order to bring all of the data together, hospitals will need to foster a climate of cooperative change.

This may require that contracts with some vendors and partners be re-negotiated in order to more clearly define ownership and access rights to the data. And the combination of confidential patient information with external data sources will demand attention to privacy concerns and compliance with HIPAA legislation.

While there is no denying that the road to change is likely to be a multi-year journey, many hospitals are well on their way. The smoothest route to success begins with consolidation of core health data, gradually expanding the scope of the project to include data collections that sit outside hospital walls.  Early, demonstrated success will help pave the way for additional investment in health data analytics. And at the end of the journey, hospitals can expect to have the information they need to make informed decisions that will improve their financial health as well as their patient outcomes.

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As the legend goes, the era of so-called Sabermetrics began when a watchman at a pork and beans cannery named Bill James began publishing his writings about Major League Baseball, using statistics to, in some cases, destroy preconceived notions about the sport and prove answers to thought-provoking questions he posed.

Decades later, a James disciple named Nate Silver predicted how states would vote the next U.S. president into office using mathematical models. In 2008, those methods predicted 49 of the 50 states’ results correctly. When Barack Obama was re-elected in 2012, he was a perfect 50-for-50.

The thread that connected James and Silver as sultans of statistics? Baseball.

Silver started his rise to fame as a member of Baseball Prospectus, which continued the work of James and his contemporaries — he referred to the approach as Sabermetrics after the Society for American Baseball Research (SABR), the organization to which James belonged that focused both on the history and analysis of the sport. It may have been acceptance of data-based decision-making by many teams that removed the nerdy stigma that came along with them.

When Michael Lewis‘ landmark book “Moneyball: The Art of Winning an Unfair Game” hit the shelves in 2003, it thrust the spotlight on the unlikely success of the Oakland A’s and how they embraced a different kind of thinking backed by emphasis on Sabermetrics. It was a bestseller devoured by some readers who weren’t necessarily baseball fans. But the book and subsequent 2011 feature film based on the non-fiction hit provided another message that went beyond the numbers.

“There are parts of ‘Moneyball’ that are worth focusing on that tie less to individual metrics and more to the philosophy that you should go against the grain and find out new market inefficiencies and react thusly, as opposed to adhering to a very static mindset on what works,” says Jay Jaffe, who writes for Sports Illustrated’s site and has also worked for Baseball Prospectus. “And I think that’s largely the legacy.”

Jaffe also pointed out the Boston Red Sox and their pair of championships in the last decade as another turning point. And who happened to be an employee for both of those wins? None other than Bill James.

But why did a stick-and-ball game have an impact that’s spread beyond sports? SABR president and former PepsiCo president Vince Gennaro says the parallel rise of big data in sectors like business and government is responsible.

“Thirty years ago, we had box scores and we’re trying to figure out who’s a good player and who’s not a good player. We’ve come so far that what teams are looking to do today with information is extraordinary,” he says.

But it’s not just the minds in the game that can mine endless bytes of data or utilize the kind of info captured by cameras on every single pitch in a season. “Fans are so much more sophisticated than we all were 20 years ago in terms of access to information.”

There’s another theory, one that’s not overly analytical: applying mathematical concepts to something that’s the opposite of academic — especially a sport with such a rabid fan base — brings this kind of thinking from the fringes to the mainstream.

“Baseball has become a wonderful platform to advance the analytics cause because so many people in companies can relate to it or are baseball fans,” Gennaro says.

In other words, it’s a common denominator.

For the next ten weeks, I want to chart a course that will highlight the key questions you need to ask before embarking on a mobile BI journey. This is the critical first step in validating mobile BI readiness for any organization, whether it’s a Fortune 500 company, a small-to-medium enterprise, or a small team within a large enterprise. The size or the scope of the mobile BI engagement doesn’t negate the need for, or importance of, the preflight checklist.

Think about this for a moment. Would a flight crew skip the preflight planning because it expects only a small number of passengers on the flight? No, and we shouldn’t skip it either. We want to evaluate and identify any issues before the takeoff.

It doesn’t matter in what order you answer these questions. What matters is that you consider them all as you work to develop a comprehensive strategy that will set you up for success.

1. Executive Sponsorship
Do we have an executive sponsor? It starts and ends with executive sponsorship. As with any engagement, this not only ensures alignment with business strategy but also the attainment of required resources.

2. Security
How do we mitigate risks associated with all three layers of mobile BI security: device(s), mobile BI app, and data consumed on the app? Is there an existing corporate security policy or framework that can be leveraged?

3. Enterprise Mobility
Do we have either a formal enterprise mobility strategy that we need to align with or a road map that we can follow?

4. Technology Infrastructure
Can our current IT and BI infrastructure, which includes both hardware and software, support mobile BI? Are there any gaps that need to be addressed prior to going live?

5. Design
Do we have the know how to apply mobile BI design best practices, whether it’s for dashboards or operational reports? Does the existing software support effective use of metadata and modeling to leverage the “develop once, use many times” design philosophy?

6. Talent Management
Do we have internal talent with the required skill set that includes not only technical expertise but also soft skills such as critical thinking?

7. Support Infrastructure
Do we have a sufficient support infrastructure in place to ensure that both business (content, analysis) and technical (access, installation) challenges are addressed in a timely manner? Do we have the right resources to develop effective documentation? Can we leverage existing IT and/or BI resources?

8. Communication
What will be our communication strategy in the pre-and post-Go Live phase? How will we update the user community on a regular basis?

9. Business Processes
Are there any business processes that need to be updated, changed, or created to support the mobile BI assets? Are these changes feasible and can we complete them prior to development to ensure proper testing and validation?

10. System Integration
Are there any requirements or opportunities for integration with other internal apps, business systems, or processes?

Many of these topics are not unique to mobile BI. Moreover, additional areas of interest such as project management or quality assurance (testing) are assumed to be part of the existing IT or BI framework. Although these initial questions may seem extensive at first, their primary purpose is to provide a checklist.

I subscribe to the notion that strategy planning for any engagement—not just IT projects— should invite questions that promote critical thinking. Only by encouraging questions can we make sure that we ask the right questions.

What key questions do you see as critical to the development of a comprehensive mobile BI strategy?

Stay tuned for my next blog in the series, Ten Mobile BI Strategy Questions: Executive Sponsorship.

If you want to read more on mobile BI, you can find other blogs in the series here.

I’ve been passionate about analytics for over twenty years – but my head is still spinning with the amount of change currently going on in the analytics industry. Here’s my quick personal view of the top 10 trends in Analytics and Business Intelligence for 2013 — what did I miss?

1. Analytics And Business Intelligence Are Still #1

According to Gartner’s latest CIO survey, the top business priority is back to enterprise growth, and analytics and business intelligence remains the number one technology priority for 2013. And the next three technologies on the priority list (mobile, cloud, and collaboration) are all key areas for analytic innovation.

2. Increasing Analytic Maturity

Thanks to greater industry maturity and new technology opportunities, most organizations are making steps from Descriptive Analytics (“what happened?”) and Diagnostic Analytics (“why did it happen?”) towards Predictive Analytics (“what will happen”) – with Prescriptive Analytics (“how can we make it happen”) as the next frontier.

3. In-Memory is Ripping Up The Old Rules

In-memory computing is providing an opportunity to rethink information systems from scratch. According to Gartner, in-memory: “isn’t only about SAP HANA, isn’t new, isn’t unproven, isn’t only about big companies, and isn’t only about analytics”:

“In-memory computing will have a long-term disruptive impact by radically changing users’ expectations, application design principles, and vendor’s strategy”

4. Breaking Down Old Barriers

In-memory breaks down long-standing analytics barriers. For example, in-memory computing platform SAP HANA supports structured and unstructured data in a single system, and includes a sophisticated, embedded text analysis engine. Predictive or advanced analytics no longer requires a separate system – powerful analytic algorithms are available directly in-memory, without any unnecessary data movement, and thousands of times faster than disk-based predictive system.

5. Operations and Analytics Are No Longer Separate

For forty years, operational systems and analytic systems have been separate because of technology limitations. That’s now changing with in-memory platforms. For example, with SAP Business Suite on HANA, transactional data is written directly to memory, where it is instantly available without any of the analytic compromises that have plagued earlier “real-time” analytics.

6. Big Data is a Big Deal

In addition to traditional “transaction data”, it’s now feasible to analyze “interaction data” (events before, after, and around a transaction, such as the products that were considered but then not purchased) and “observation data” (such as data streamed from sensors). Algorithms such as MapReduce and projects such as Hadoop have introduced new opportunities for storing and analyzing data that was previously ignored because of technology limitations. Actuaries are finding new careers and glory as “data scientists”. These new technologies have more than proved their worth in niche or standalone systems, but need to better integrated with existing corporate environments.

7. Analytics Moves To The Core

Analytics is no longer an afterthought to your transaction systems — it’s the heart of your future information infrastructure. The data you are storing now you will still have in 15 or 20 years time, while your applications may be long gone. The next generation of information infrastructures will combine big data, transactional data, analytic data and “content” into a single, coherent set of services that Gartner calls an “information capabilities framework”:

“The information capabilities framework is the people-, process- and technology-agnostic set of capabilities needed to describe, organize, integrate, share and govern an organization’s information assets in an application-independent manner in support of its enterprise information management (EIM) goals.”

SAP is working on this vision with the “real time data platform”, combining SAP HANA with Hadoop, Sybase ASE, Sybase IQ, Sybase ESP – and (crucially) end-to-end information governance.

8. Optimizing the User Experience

Today’s information consumers demand the same ease-of-use and immediate access they get in the consumer world. Business people want to be able to grab and mix information on the fly, without having to wait for it to be loaded into a corporate data warehouse. Data discovery tools such as SAP Visual Intelligence cover this essential demand – without sacrificing the corporate needs for enterprise governance. And of course, people expect a smooth, mobile-ready BI experience with integrated social collaboration, and the option of using a cloud-based infrastructure.

9. Information as an Asset

Along with all the technology changes, there have been big changes to analytics culture. Information is no longer a byproduct of manufacturing processes – it is fast-becoming a key part of the products themselves. Today’s retailers and service providers want to offer “customer experiences” that are tailored to individuals, optimized for the moment, and coherent over time – and that requires powerful new data platforms. As information becomes part of revenue generation, interest in information and control over budgets are swiftly moving to the business units, rather than traditional IT. This is creating new opportunities, but also new IT pressures and organizational issues.

10. The Revenge of Information Governance

As the technology gets more and more powerful, it becomes even more important to fix one of the oldest and least tractable barriers to successful BI: the pain of integrating multiple sets of quality data. Better integration between “big data,” traditional analytic systems, and transaction systems must also involve investments in data governance and solutions such as SAP Information Steward.

What did I miss? Add a comment below…

The Next Round of the Analytics Revolution

If you’d like to find out more about any of these trends, don’t hesitate to contact me, and I’ll help point you to the best experts available. If you’re interested in SAP Analytics technology, should follow the Business Intelligence areas of the SAP Community Network, subscribe to the SAP Analytics Blog, follow @sapanalytics or @timoelliott on Twitter, and join us at the analytics campus of SAPPHIRE NOW and ASUG 2013 in Orlando, May 14-16 to explore industry changes in depth, hear about companies that are implementing analytics in new way, and talk face-to-face with the experts.

[Note that a version of this post originally appeared on the SAPPHIRE NOW area of the SAP Community Network]

You’re driving down the highway on your way to work. As you approach, a billboard changes to show an advertisement for your favorite coffee shop, two miles away. After you pass, the billboard switches to show a trailer for the new Disney movie, just as a school bus rounds the corner.

These scenarios sound like something out of the far future (insert obligatory Minority Report reference here), but they’re here now. Companies are using their customers’ locations to more accurately target ads toward them.

If done poorly, these “location-aware” ads can feel like a privacy violation. If done right, it’s a win-win: consumers appreciate receiving timely, relevant ads and deals right when they’re in the best position to buy.

Popular Shopping Apps Use Geo Location Targeting

Need proof consumers are embracing these technologies?

A Nielsen analysis of shopping apps named location-aware app Shopkick the fourth-most popular app, behind only eBay, Amazon, and Groupon. Apps like Shopkick provide valuable data for retailers and brands.

They learn more about their customers’ shopping habits and entice buyers back. And consumers get rewards, deals–or maybe just a map. That’s the pitch behind Aisle411, which works with Walgreens and a number of grocery chains.

“If you’ve ever been lost, wandering through a, for example, Home Depot store, trying to find a list of items you’re after,” Aisle411 is for you, says CEO Nathan Pettyjohn. “You pull out your phone and we show you an in-store map where you can find the product …. At the moment of truth, you’re in the store and you’re ready to buy … we can help you. We can communicate or influence your decision.”

That influence often comes in the form of complementary deals. If a user searches for directions to the pasta aisle, Aisle411 might pop up a notice that sauce, or Parmesan cheese, is on sale–along with directions to the neighboring aisle. Or if a user checks milk off her shopping list, an ad might pop up saying, “While you’re here, don’t forget eggs.”

While an in-store map and helpful coupons are great for consumers already in the store, what about brands that want to get their customers’ attention earlier?

Last year, GM filed a patent for a system that would serve targeted ads on digital billboards. In other words, the system should be able to determine that, for example, more than 50 percent of drivers on a certain road are heading to Myrtle Beach, S.C., so the billboard should display an ad for golf clubs or a restaurant.

The system might also be able to learn seasonal patterns. “Vehicles in the state of Michigan are more likely to travel to an apple orchard during the months of October and November than any other months,” the patent stated. So apple orchard ads might show up for cars leaving Michigan cities during those months.

Yes, apple orchards already know to advertise in the fall and companies marketing their services to tourists already buy billboard space along tourist routes. But the patent also describes technology to also determine the age and demographics of people in the car.

In other words, “Burma Shave” is out. Individual ads just for you–thanks to your smartphone, your car, and some savvy retailers–are in.

Therefore, fan experience matters for two main reasons: Smart technology solutions provide opportunities for greater fan engagement. On the other hand, advanced analytics as part of an integrated business intelligence framework deliver insight for faster, better-informed decision making for sports organizations.

I always like to start with the basic question “What is fan experience in the context of technology and analytics?” To me, it is the end-to-end experience that starts before you purchase the ticket (whether you are a season ticket holder, have courtside seats, or sit in the nosebleed section), and it does not end when the season is over — championship or not. The time you spend at the venue has to be the most awesome experience each time you are there. No excuses! This is the goal of fan engagement on and off the field. With the advancement of technology, especially in the area of mobile solutions, sports organizations today can pursue a number of avenues to get engaged with their fans.

If you are saying to yourself that this strategy is not much different than that of, for example, a retail or consumer products company, you are right on. Just as Starbucks and Target aim to get closer to their customers and try to answer questions like “Why?” and “How?” sports organizations invest a lot of time and resources to improve the overall fan experience. The same way, for example, Starbucks goes out of its way to provide a unique experience before the customer even walks into one of its stores, sports organizations seek to do the same for their fans. It is well understood that getting closer to customers (fans) and trying to understand their specific wants and needs shape better products and services offered to them.

Let’s take a closer look at some examples in sports.

It starts with the ticket

How did you buy it? Can you get it on your smartphone or print it from your computer, the same way you would purchase a boarding pass? Is there a smart app that can organize not just the attendance information but also everything else you will need or use that day? Is the app integrated to provide you a single point of consumption, whether it is the parking ticket you will pay, hot dogs you may purchase, or the jersey you want to get for your friend who could not make it?

Parking experience

How was parking? On your way, did you get a notice to go to lot C because lots A and B are full, so you can get to the game more quickly? Being directed to a specific parking lot can be the equivalent of a supermarket cashier beckoning you to a checkout lane that’s just opened up: If you register your app and purchase your parking ticket before the game, you may be just a bar code away from a parking space.

Entering the venue

Did you get a welcome message acknowledging you by your name when you entered the venue? How about a map of the stadium, and if you enabled the location function, can you see how close you are to your seat? Are you able to connect to the Wi-Fi available at the venue?

At your seat

Once you get to your seat, can you place a concession order without leaving it? Most important, can you claim a reserved spot in an express lane to pick up your order so you spend less time in line and more time enjoying the game? Did the online concierge ask you to check in when you arrived so that your seat number can be included for prizes and drawings throughout the event and after you go home?

During the game

The home team takes a timeout, and you want to look up a player who is playing really well tonight. On your fan app, can you find everything you want to know about that player? While you are browsing, you notice a yellow star that pops up on the bottom of the screen. It neither blocks your view nor interrupts your browsing experience, but you wonder what it is, and you tap on it. Wow, your dad’s favorite player’s jersey is on sale! And it is 30% off, a deal good only through the end of the second quarter. First, this is a perfect birthday present for him. Second, the game is about to resume. Can you take advantage of this offer right from your seat because you registered at home and all you need is a single tap to complete the purchase? Let’s not forget about the restrooms. What if you had the chance to check out your fan app to determine how long the restroom lines are in your section?  Do you go now and miss 15 minutes of the game, or do you wait?

Let’s recap. By the time the game is over — we won by, the way — there are numerous transactions which generate data that can be turned into insight for shaping and improving the fan experience:

1) Ticket purchase

2) App registration

3) Parking ticket

4) Concierge

5) Concession

6) Location services

7) Merchandise

8) Game-day promotions

9) Wi-Fi services

10) Use of app during the game

11) Loyalty-program points

If we think for a moment about the amount of data that can be captured for one fan throughout the day or evening and multiply that by all the fans who attended and multiply that by each game of the season, the number of questions we can ask starts to increase rapidly. These data sets alone may not carry the full value or tell the whole story. But, aggregated and analyzed across multiple business dimensions over time, they can provide priceless opportunities in getting closer to the fan.

At a high level, an integrated fan-engagement and analytics model provides a 360-degree fan profile that combines transaction data (date of last visit, attendance statistics, merchandise purchases, etc.) with fan-engagement dimensions (membership profile, ticket holder status, etc.). Moreover, when you incorporate the campaign data and correlate it against the social media statistics, interesting questions can surface that would otherwise be not possible to entertain.

Next, we apply data mining and predictive analytics, and we have the chance to discover new opportunities — for example, unique combinations of products and services that can be bundled for effective campaigns. While I am at it, let me stretch the envelope and pose this question: How about asking some of these questions in real time, whether we do so with concession offers for that night, offers for future tickets combined with other promotions, or merchandise purchases that can be customized for the individual fan?

As I wrote in my second post in this series, how about having these answers not after everyone goes home or before the season ends but before the game is over and before the playoffs begin? Making faster, better-informed decisions is what drives growth and profitability in today’s highly competitive markets.

Stay tuned for my next post in the series, Sports And Analytics: Innovate With Design Thinking.

Before an organization can execute these high-level strategies, it must have the following:

• An existing BI framework that can be leveraged
• Current technology (hardware and software) used for BI that support mobile capabilities
• A support infrastructure to address technical challenges.

If an organization meets these minimum prerequisites, then there’s a greater chance for success. Thus, the higher the level of BI maturity, the better a head start an organization gets on its mobile BI journey.

“Mobile-Only” Strategy 

A “mobile-only” strategy reflects a strong commitment, or all-in approach, by the management team to mobile BI, or mobility in general. This may be due to a specific reason, such as the relevance of mobility in a particular industry or the opportunity to create a strategic advantage in a highly competitive market. Or a company may decide that mobility needs to be a vital part of their vision.

However, in order for this strategy to be successful, it requires a commitment that results in both championing the cause at the board or senior management level and making the necessary resources available for execution at the tactical level.

In reality, this approach doesn’t necessarily translate into creating a mobile version of every analysis or shutting down all production lines for PC-based outlets for reporting and analytics. Instead, it reflects a strong emphasis on establishing scalable mobile consumption paths for analytics, and it signals a willingness to exploit a mobile-first mindset.

“Key Asset(s) First” Mobile Strategy 

Organizations that aren’t ready or don’t have the resources for a mobile-only strategy may be forced to pursue a less ambitious approach. This would enable such organizations to supplement their existing BI portfolio with key analyses delivered in mobile BI, resulting in a smaller initial investment and reduced pressure to overhaul large stacks of assets, so to speak.

With the “key-assets-first“ strategy, the spotlight is on finding key BI areas of focus that can both return the maximum value when delivered effectively on mobile platforms, and can directly support the execution of the business strategy in the short-term. For example, the business strategy may include expansion into a new market, and the mobile BI may deliver analytics to help sales teams to sell more and provide management with insight into forecast and pipeline.

To me, this is the most flexible strategy because it doesn’t commit to an all or nothing approach. Most importantly, it differentiates between what may be conducive to mobile-ready consumption and what can produce the maximum impact by ignoring those assets with marginal returns on investment.

“Key Group(s) First” Mobile Strategy 

A “key-group-first” strategy makes a considerable commitment to arm a particular group or groups in an organization with a complete set of capabilities that can be delivered in mobile BI. This hybrid strategy identifies the best candidate group(s) for mobile BI and delivers an end-to-end solution. At minimum, it may consider the existing BI framework, the BI culture (history in terms of successes and failures), the BI adoption across the enterprise, and the current BI asset portfolio to develop this more comprehensive approach.

For example, sales teams, which travel and spend a lot of time in the field, tend to benefit most from mobility. If they’re selected as the target group, the mobile BI strategy’s goal will be to provide them with a comprehensive package of sales-centric BI assets. Thus existing capabilities in enterprise mobility for sales teams may compliment not only the delivery of new mobile BI resources but also the greater adoption of mobile BI content.

Mobile BI Bottom Line 

The fundamentals don’t change–a smart mobile BI strategy needs to contribute to growth or to profitability. In order to deliver the true business value for mobile BI, all three strategies must embrace the common objectives of an integrated mobile intelligence framework. They must leverage the technology’s strengths as well as minimize its weaknesses within a supported infrastructure.

The mobile intelligence framework can’t exist separately from, or independent of, the organization’s business or technology strategy.

What is your start-up strategy for mobile BI?

Stay tuned for my next blog in the series, Ten Questions To Develop Your Mobile BI Strategy.

To steer your professional services firm successfully, you need the right information about internal as well as external conditions. But it’s easy to get bogged down by limited data access, hard-to-use tools, slow calculations, and inefficient distribution of results. Let’s take a quick look at some of these challenges, their implications for your business, and what you can do to overcome them.

• Limited access to data. Information on past operational performance is often locked away in application silos and heterogeneous systems. Analysts must use manual data compilation and calculation – which can make it hard to plan and analyze project performance. Instead, you need a way to deliver trusted, decision-quality information to a variety of applications and business users.

• Difficult-to-master analytic tools. At today’s speed of business, you can’t waste time on outdated business tools with multiple interfaces and steep learning curves. Users must be able to perform analysis and present visualizations via intuitive, self-explanatory tools – with little or no IT involvement.

• Slow results. Analysis can get bogged down by two things: the amount of data and the complexity of calculation. Consider a typical project-costing analysis that, for each individual resource, requires a complicated calculation based on HR data (including labor costs, billing rates, and cost centers), client contract specifics, and time and expense data. To ensure maximum profitability, you need to make quick, accurate decisions about staffing and project scoping.

• Inefficient information distribution. Reports can be difficult to produce and slow to distribute, leaving decision makers without valuable performance insights. What information is shared is mostly “pushed” to employees via e-mails, alerts, and static reports. Today’s mobile technologies, however, allow firms to move from a push environment to interactive, connected employee communication.

Powerful new tools

You need a solution that allows you to pull timely data from a range of sources, cleanse it, perform complex analysis quickly, then distribute it to employees when and where they need it, on the devices they prefer. In its 2011 Professional Services Maturity Benchmark study, SPI Research found that only 29% of firms surveyed were using standard business intelligence tools to better understand their businesses.

Know thyself

But that number is changing quickly. According to SPI cofounder and study coauthor David Hofferberth, “The number of Professional Services firms using business intelligence is expected to grow even faster now that organizations have significantly integrated their application infrastructure. This allows them to more efficiently display and report data to optimally measure and transform the organization.” My forecast: Firms that use modern BI tools to understand their businesses will set themselves apart from the competition – and reap the rewards.

What’s bogging down your analysis?

Join the conversation about SAP for Professional Services:

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Last week, in the first installment of my Sports and Analytics series, I detailed how similar sports and entertainment organizations are to their counterparts in more conventional industries such as retail or consumer products. In this second installment, I want to expand on this point and discuss key components that are part of a more holistic approach when it comes to running a sports (teams, leagues, venues) and entertainment (media organizations such as ESPN or Golf Channel) organization that promotes data-driven decision making.

In this context, when we think of the role of any CEO in an industry, we recognize at least three basic needs for data and actionable insight: strategy, execution, and measurement. In their simplest form, the strategy states the direction, the execution is where the rubber meets the road, and the measurement allows for adjustments and course correction.

I recognize that there is more to it than this simplistic view of running a multimillion-dollar enterprise, but if we go along with this explanation, we will conclude that neither the strategy nor the measurement can be one-dimensional for the execution to produce the desired results.

This is where an integrated business intelligence framework may come into play: connecting diverse data points in order to draw a complete picture of the state of the enterprise in real time and to enable faster, better-informed decision making on mobile platforms that can be delivered on the premises or in the cloud. Thus, the paradigm shifts to Any Time, Any Place, Any Device. I refer to this byword of the 21st century as the AAA standard.

This equation comes in two main parts:

• The Business Strategy, which aligns with the organization’s vision and resources

• The Technology Solution, which contributes to the execution of the strategy and enables the effective measurement of the results

Hence, a holistic approach requires that both parts work in tandem to provide a 360-degree value proposition not only in generating considerable ROIs but also in building a solid foundation for actionable insight in all facets of the business.

Therefore, the need for real-time insights is best met with an integrated and enhanced analytics approach that presents opportunities in three key areas of the sports and entertainment business:

Game Statistical Data

The data sets, well known in this area, include game stats for players and teams. The granularity of the data changes depending on the type of organization. For example, a typical franchise team in the NFL, NBA, MLB, or FIFA may collect data down to each player’s individual performance both on game day and in practice, including scouting and other external data sources. On the other hand, an organization such as the PGA or LPGA may be primarily concerned with official statistics determined by the rules and format of the game.

For example, in golf, each player’s tournament stats are much more prearranged than a player’s stats in football or basketball. Once a golfer starts a round, the statistics by which all comparisons are made will be within fixed parameters except for the outcome, because each round of the tournament, each hole on the course, follows a predictable layout, so to speak.

In comparison, football or basketball yields completely different data sets, albeit still in structured format (for example, in football, we may pay attention to runs or interceptions; and in basketball, we may track fouls, assists, or blocks), but ultimately, any of these dynamics can change as a football or basketball game unfolds. A player may play one minute or the entire game. And where unlimited substitutions are allowed, it can be impossible to predict. Thus, the unique data sets in each game exponentially increase the possible combinations that can be analyzed in real time. Finally, Moneyball-like analytics may take center stage in draft rooms or in booths above the field that may resemble a control room, with analysts who put the pieces together in real time while watching banks of computer and television screens.

Business Transaction Data

These data sets align with traditional goals of an enterprise to optimize business processes (sales, finance, HR, etc.) in order to drive revenue growth and profitability. For example, rudimentary revenue analyses may look at the number of tickets sold and at revenue figures sliced and diced by different business dimensions such as ticket, campaign, or revenue type (advertising, merchandising, concession, etc.), as well as the time dimension required for trending and seasonality analysis.

These are mostly data streams that capture transactional data from revenue-generating activities the organization manages. Again, they can vary depending on the type of organization. For example, a Major League Baseball team that owns a stadium may be interested in concession data. Questions to consider may include “How many hot dogs are sold?” or — similar to concern about a hotel’s occupancy rate — “What is the capacity utilization?” (number of seats sold vs. stadium capacity). And which sections of the stadium or arena appear to be underutilized? When you combine these analyses with marketing-campaign data for ticketing and merchandise, simple questions can get interesting, and the effort to seek insight from these data troves can get complicated.

However, before we go there, how about we entertain these two basic questions:

• As a sports organization, do we even have the capability to ask these basic questions?

• If we do, can we answer them in real time for actionable insight not after everyone goes home or before the season ends but before the game is over and before the playoffs begin?

Social Media Data

The final area of interest is with the data collected through social media, whether it is through established channels such as Facebook and Twitter or through mobile apps developed by the sport organizations or other third-party companies. Of the three areas discussed so far, because of the unstructured nature of the data, this is the one that poses the greatest technical challenge for integration.

Unlike a ticketing or merchandising transaction, where the data captured — a purchase made for a seat or a football jersey — are already in easy-to-process and easy-to-query form (for example: a ticket includes game date, section, seat number, etc., and the football-jersey purchase includes price, size, color, etc.), the data for social media would require special processing before it can be ready for analysis.

To count the number of likes or tweets may be easy, but to uncover insights and trends in fan preferences, emotions, and behavior may not be as simple. As the use and spread of social media accelerates, so do the analytical capabilities to extract insight from across the social Web. We now have technology solutions that can extract insights from user postings including likes, dislikes, and the intensity of buyers’ (fans’) emotions. How about tracking and optimizing the social media components of marketing campaigns? It is not far-fetched to correlate changes in customer (fan) opinion with campaigns, promotions, news, and other events.

Stay tuned for my next blog in the series, “Sports and Analytics: Fan Experience Matters.”

However, if mobile BI is going to enable organizations to drive growth and profitability, it requires, as with any other technology initiative, an integrated strategy. Moreover, organizations must deliver the power of mobile BI through innovation and without disruption.

Just as we know that mobile isn’t just about one or two sexy apps, the step to gain the ability to deliver reports on a mobile device alone isn’t synonymous with mobile BI.

In order to deliver the true business value of mobile BI, organizations need to formulate a carefully thought-out mobile BI strategy that not only leverages the technology’s strengths but also minimizes its weaknesses within a supported infrastructure. The mobile intelligence framework can’t exist separately from or independent of the organization’s business or technology strategy.

Determine Your BI Maturity

However, the way in which the organizations should go about realizing the benefits of mobile BI may depend on the state of their enterprise mobility and the level of their BI maturity. Some basic questions need to be addressed before they hand out smartphones and tablet devices or allow bring your own device (BYOD) options. These preflight checks can help identify any gaps and prompt the necessary prerequisites to safeguard critical data assets and ensure success.

Source: ITScore Overview for Business Intelligence and Performance Management, Gartner (August 2010)

Implement a Mobile BI Road Map

Organizations need to develop a formal mobile enterprise strategy and a road map to provide the strong foundation that mobile BI needs. A mobility strategy requires an integrated approach with all mobile initiatives across the company, which forces standardization, thereby reducing support and maintenance costs. A typical organization, for example, may start with a device strategy that differentiates or mixes the use of company-issued devices and BYOD.

It gets more complex as you drill down into the architecture requirements for integrating data and processes with other business systems. A decision about the infrastructure —whether to keep it on the premises, for example, or host it in the cloud—will need to be made, and answers to questions such as “How do we secure corporate data, especially on the BYO devices?” will need to be found.

Know Your Risks for Mobile BI Readiness

An organization’s BI maturity level, on the other hand, will highlight the risks for mobile BI readiness. It will dictate whether a company can exploit its current information-delivery infrastructure and leverage a culture of data-driven decision making. Organizations with a higher level of maturity are more likely to design, implement, and execute a successful mobile BI strategy because they already enjoy the resources and expertise, which allows them to deliver actionable insight effectively and consistently.

Is your organization ready for Mobile BI?

Stay tuned for my next blog in the series, 3 Strategies To Get Started With Mobile BI.

Companies were searching for Social Media Professionals

Since crafting the perfect job description and wrangling in a wave of innovators who have driven social media strategies across large companies and are figuring out the magic of social media ROI, companies have cracked the code and are finding what they need in a social media professional. Now, companies are moving on and are now searching for a new species - Data Scientists.

A Data Scientist or Data Analysis professional is the new black

These rare, hard-to-find employees require a very special skill set that most employees who have been in the workforce on the data or the marketing side have not developed. This combination of left-brain analytics and right-brain creativity will allow these data experts to not only figure out how to collect and analyze big data, but also what to do with it and how to translate it into terms and statistics that make sense to a corporate strategy and present to the company or the c-suite. In addition, these professionals must understand computer code, have excellent interpersonal skills, and use Powerpoint effectively. Who are these people? Where are these people?

After doing an informal content analysis of a few data scientist job descriptions, here are a few main (and interesting) skills that seem to be key to finding the “right” fit.

• Database and data mining experience – knowing how to collect and process data
• Ability to review secondary research and uncover algorithms
• Understand this sentence – “Appropriate statistical methodology such as binary choice, other generalized linear and nonparametric models or data reduction techniques such as clustering and factor analysis” and be able to do what it means
• Be a thought leader and innovator in the industry
• Ability to [effectively] present data findings in Powerpoint
• Understand the engineering team
• Understand the marketing team
• Be a strong communicator with excellent interpersonal skills

And, here is an excerpt of one Data Scientist job description, “As Data Scientist, you’ll wrestle large data sets to the ground and force them to tell interesting – and true! — stories about [company's] games and their players.”

Hmmm….

Here are a few of my favorite jobs that probably have some of the coolest data:

• Data Scientist for the CIA
• Data Scientist at The New York Times
• Research Scientist at Yahoo!
• Sr. Data Scientist at Redbox

In a Forbes post, Maribel Lopez lists a number of recent statistics about mobility. “While we could debate the numbers, the trend is clear,” she writes. ”The pace of mobile adoption across devices and applications is accelerating.”

Mobility is no longer a nice-to-have option. Instead, it’s become a must for many businesses. Many surveys support this view. According to the Accenture CIO Mobility Survey 2013, “79% of respondents cited mobility as a revenue-generator and 84% said mobility would significantly improve customer interactions.”

The Evolution of Mobile Business Intelligence

With this paradigm shift comes the natural extension of business intelligence (BI) to mobile business intelligence (mobile BI) or mobile intelligence. This term may mean different things to different people, and it’s sometimes used interchangeably, but your perception of mobile BI will be influenced primarily by your understanding of BI.

In my post “What Is Business Intelligence?” I defined BI as the framework that enables organizations of all sizes to make faster, better-informed business decisions. Mobile BI extends this definition and puts the emphasis on the application of mobile devices such as smartphones or tablet computers. Therefore, you can argue that the fundamentals remain unchanged—Mobile BI is the enabler that, if designed, implemented, and executed effectively, can help organizations drive growth and profitability.

However, the way organizations go about realizing the true value of mobile BI may depend on the state of their enterprise mobility (for example, whether or not a formal mobile enterprise strategy and a road map exist) and the level of their BI maturity.

Harnessing the Power of Mobile Business Intelligence

Mobile BI is more prevalent and more relevant today because the gap between the experience of traditional BI content consumed on a desktop PC and that accessed on a mobile device is disappearing rapidly. We now talk about the gap between a smartphone and a tablet device. The tablet devices are getting smaller both in size and weight to compete with our smartphones. Rapid growth in areas such as the cloud, in-memory technology, big data, and predictive analytics are fueling this innovation cycle. As a result, companies are looking for ways to harness the power of mobile BI through innovation and without disruption.

As businesses face more obstacles and are forced to deal with more complex challenges, they increasingly require greater mobile access to more processed data coming from both structured sources (such as sales data by markets and geography), and unstructured sources (like social media or email data that can’t be easily queried with traditional tools and technologies). Companies at the leading edge seek to gain the edge to exploit mobile BI to support a workforce that’s becoming more and more mobile.

Mobile BI Can Become a Key Differentiator

According to IDC, the “world’s mobile worker population will reach 1.3 billion, representing 37.2% of the total workforce by 2015.” The share of the mobile workforce is even higher if we focus on the business roles such as sales, where mobility is a critical component for success. Business models that rely on insight thru outdated or limited capabilities can no longer compete in an ever-increasing global market, which simply dictates mobile execution.

Today, there’s no doubt that both for-profit and not-for-profit organizations must deliver more for their customers and stakeholders. In this context, mobile BI can become a key differentiator in helping organizations cope with both the complexity and the real-time challenges they face with the execution of their strategy. It’s a transformative force that has the power to change how businesses deliver value today, because mobile BI further breaks down the walls of information silos, thus dramatically extending the ability to gain actionable insight thru data-driven analyses for all decision makers at all levels of an organization.

Where do you see Mobile BI adding value to your organization?

Want to dig deeper into mobile BI? Stay tuned for my next blog in the series, Mobile BI Doesn’t Mean Mobile-Enabled Reports.

The data created by Hawk-Eye’s tracking is converted by SAP software to provide statistical analysis that enables coaches and players to measure performance, and identify players’ strengths and weaknesses.

At the Sony Open Tennis, it was strikingly obvious that coaches, analysts, and fans were reaping the benefits of this technology collaboration. Everything from the new mobile app to the broadcaster’s commentary, the  analytics platform was being used to figure out exactly why players were performing a certain way, and how they could compete against their next opponent.

Tennis sensation, Milos Raonic, is proof of the power of analytics applied to the sport of tennis. Appearing at the 2013 SAP Open as the tournament’s two-time singles champion, Raonic and his coach used the Hawk-Eye data with SAP analytics to plan his performance.

“This was my third career appearance in San Jose, and the more match information my coach, Galo Blanco, and I could access, the better we could prepare,” said Milos Raonic.

Tennis Channel commentator and ATP Board Member, Justin Gimelstob, uses the analytical information when watching matches and discussing the strategies behind the plays to help fans better understand the games, and the players.

At the Sony Open Tennis, Justin said, “It’s about telling the story of the ‘why’ and not just reporting the ‘what.’ This is a whole other layer of the game that can give a player the edge.”

As the technology advances, SAP is planning to provide a more customizable and interactive dashboard to report the hard statistics and subjective analysis. This use of data will continue to propel the sport into using a more calculated and analytic approach.

For many of these companies, it’s a big waste of money.

Let’s be rational about this. What customer wants to tweet to his or her friends about toothpaste or B2B procurement services? (Please repeat after me: “None of them.”)

So what do we do when our customers just aren’t that into us? We come to grips with the fact that social media isn’t all about us, it’s all about them. Companies should be much more focused on listening to customers than they are on talking to them.

Here’s an example that I heard at a recent SAP Forum:  some Consumer Packaged Goods (CPG) companies like General Mills have invested in analyzing the social media conversations of their various target demographics. In one case the trends showed that mothers are concerned about protecting gluten-sensitive kids from getting sick. That led General Mills to begin prominently labeling its appropriate foods “gluten free,” which helped drive more sales, according to Marcus Shingles of Deloitte, who spoke at the Forum about research he has done with CPG companies about market analytics.

The increasing power of analytics should not be used with customers alone, however. We must also listen to the market, as SAP CMO Jonathan Becher pointed out recently. That means developing sophisticated analytical capabilities that enable us to make better decisions across the business. Here are some best practices that I picked up at the Forum for doing that:

• Know what your competitors are doing with analytics. In CPG, for example, a few companies have constantly been pushing the envelope of analytics for decades. Now that the analytical possibilities are exploding, that’s a gap that competitors need to close—fast.

• Let customers do the data gathering. Gigwalk is an example of how CPG companies can crowdsource tasks that normally cost them a fortune in employees’ time and labor. In a typical Gigwalk example, customers get a nominal fee to walk through stores and monitor things like stock outs and competitive placement.

• Keep an eye on startups. Gigwalk is just one example of the many startups blooming in the fallow field of analytics. “Three or four CPG companies have worked with all of the startups,” said Shingles. “Other companies need to start doing the same if they want to keep up.”

• Create an analytics sharing model. Companies need easy ways to share analytical information across the organization to improve and speed up decision making. But they also need to share analytics nerds, who are in short supply right now. Shingles recommends establishing an analytics center of excellence.

• Keep the insights simple and visual. Analytics is for nerds, by nerds. To make it accessible to humans (and salespeople), put it all in a visual format—it’s how our brains like to process information.

• Speed translates into margin. What good is insight if it comes too late to act? Especially in social media, companies must be ready to decide and act quickly—which means the data must be processed quickly. German home shopping network HSE24 uses real-time data from CRM and social media to determine the right up-sell and cross-sell products to offer customers while representatives have them on the phone. In another example, Fox saw that the lead ape in Rise of the Planet of the Apes was trending more on social media than the human actors. So Fox ripped down all the human posters and replaced them with shots of the ape.

What do you think? Have you stopped talking about yourself in social media yet?

This post originally appeared on the SAP Community Network

It’s almost time for the match.

ATP professional Rajeev Ram is warming up. His coach,Craig O’Shannessy, is quietly pacing next to the court, reading notes, and reviewing how he has prepared Ram for battle. The match is about to begin and O’Shannessy feels confident that his preparation and match analysis is going to give Ram he edge he needs to come out on top.

O’Shannessy, an Australian tour coach and Director of the Brain Game – a sports science company that specializes in video analysis of tennis matches that teaches patterns of play that dominate the game – has a coaching style deeply embedded in data analytics.

Through his in-depth analysis of match video where he uses Dartfish Match Tagging software to strategically identify and analyze clips that are the pivotal points in his players’ matches, O’Shannessy has a dedicated approach to strategically improve his players’ performances based on data analysis, statistics, and hard facts. He believes in players, but more importantly, he believes in technology.

O’Shannessy heavily relies on the Hawk-Eye/SAP integration and the reports generated from that technology, which shows ball and player movement, the results of each shot, and the patterns of play from both players that feed into his effective and logical coaching approach. O’Shannessy focuses on the four main areas of data analysis – serve reports, return contact point, shot placement, and player movement (court coverage). Each aspect of performance and opposition analysis leads to a more accurate and aggressive strategy for increased performance in competition.

While reviewing a post-match report, O’Shannessy mentioned that, “I can tell this strategy worked,” and, “This data is exactly what I need.”

Without SAP analytics, it’s all opinion and guesswork. To separate his team from the pack, O’Shannessy relies on the analytics data, since too often, perception can cloud analysis by the emotions of competition.

An aspect of the data that O’Shannessy really values is the historical and current match review data comparisons that he can show players, and in time, reflects their improvement which reinforces the strategic coaching he has implemented. This greatly helps the players from an emotional standpoint. By showing them hard data analysis of their progress, they are motivated to keep working on the small details of their play and recognize the nuances of change that help their performance.

Overall, O’Shannessy feels that we are just scratching the service of data analysis in the sport. He is excited about the future of the technology in SAP’s hands since he knows these advancements will be pivotal in transforming the coaching behind the players. O’Shannessy believes that SAP is globally changing tennis by making analytics the centerpiece of the sport.

Real-Time Room for Improvement in Old School Service

Recently, I was downtown on a weekend trying to find an available parking spot. Various mobile apps are available to show me locations of garages and street parking zones. But I had no way of getting accurate-to-the-second information on which street or garage parking slots were unoccupied. So I had to drive around randomly searching.

I encountered another old-school headache when I tried to change some of my utility services. I went onto the utility website and discovered, after several failed attempts, that while I could add services via the website, I couldn’t drop anything without speaking to a representative.

I suspect this was a business policy designed to force an intervention so an agent could try and talk me out of reducing my services and monthly bill. And the result of this policy? I wanted to call them up and discontinue every single service because they didn’t engage with me in a simple, convenient, internet-only manner.

Organizations That Innovate Offer Real-time Service

These difficulties aren’t necessary in today’s world, where the technology is available to transform services and transactions from old school and annoying to smart and real time. Low cost sensors, predictive analytics, and new “Big Data”-powered applications are being used by innovative organizations to dramatically alter their business models. Obviously, an organization also needs to alter its business policies and procedures to correctly leverage these new innovations – otherwise it just shows broken processes to more people and with less lag time.

But the companies that are successful in engaging intelligently and instantaneously are getting more of my and everyone else’s business. Such companies provide more responsive service and better tailored offerings in a very cost-effective manner. Online retailing, same day shipping, and movies/books/television on demand are just the beginning. Even long established business models like parking are being radically transformed.

The Parking Holy Grail

So where does that leave the question of my parking problem? In my city and most others, all the traffic meters are now being wired to accept credit cards. Which begs the question— how difficult would be to add a sensor and connect the meter to the network to show if the spot is occupied or not? The answer—not difficult at all.

A handful of companies and municipalities are beginning to implement the “smart parking” concept. If you’re lucky enough to live in one of them, you can now use your mapping application to see open slots and even reserve them. It will improve service (easier to find spots), increase revenue (demand-based pricing), and lower congestion and pollution (less parking space hunting). Everyone wins! Or will win as these new concepts are tested, refined, and go into large scale rollouts. (See the New York Times article, “The Learning Curve of Smart Parking” for more information.)

How is your organization becoming “smarter”?

I followed up by asking if anyone knew what Neilsen ratings were and only two half up/ half down hands raised without full confidence and with a puzzled look, which gave me reinforcement of what I already knew. Neilsen ratings, once a matter of great interest to consumers, are losing authority.

Now, the main audience of Neilsen ratings is focused more on advertisers and networks, but this all converges when figuring out and navigating user behaviors and what really matters to all audiences.

A recent article I read in Wired magazine, Welcome to the Platinum Age of TV, discussed the allure of television beyond the traditional television. Basically, the engagement of an audience with a TV show goes far beyond actually watching the show.  Although Neilsen ratings are trying to change this for 2013-1204 (check out their new cross-platform report),  they just do not provide the full picture of a show’s popularity and reach because essential data is lacking in their analytics.

Now, this isn’t to say that the current Neilsen ratings do not have merit – because they do. They still serve as the standard for gauging show watching/behaviors and reviewing the scope of an audience, but as viewers who appreciate shows on multiple screens, it’s not about what we watch when the show is available – it’s about what we watch when we are available.

(Note:  For 2013, Neilsen has identified and will measure a new group of American households called “Zero-TV,” which do not fit the definition of the traditional TV household used in Neilsen studies.)

We live in the world where everything is at our fingertips and available when we need and want it. In fact, I am writing this post aboard a Delta Airlines flight from my bluetooth keyboard attached to my iPad at 10.21 p.m. with a pile of PDFs and magazines on the empty seat next to me.  We know that what we want will be accessible when we are ready, and that is how we choose to consume ALL media. We also want to tell everyone about it using our online reach (read: social networks). We want to voice our opinion and tell our friends what we liked, don’t like, and engaging in a community with similar interests allows us to connect – albeit virtually.

There are a few companies who are working to measure the social media TV chatter (check out Bluefin Labs, recently acquired by Twitter) by tracking hashtag trends and mentions. Through a combination of this data and traditional ratings, Neilsen could create a new scale of measurement, which will redefine the social impact of TV viewership and lead to more interactive and crowd-sourced programming.

So, for production and for networks – will these additional analytics of user engagement, beyond what is collected through the Neilsen sample, change TV programming? Will that information provide a better snapshot into a show’s performance, audience interaction and interest, and ultimately lead to consumer-driven programming that goes beyond viewing habits and into analyzing sentiment and community?

Time will tell.

Coaches have always worked to plan plays by factoring in those statistics to provide a snapshot of the competition, and to plan a winning game strategy.

It’s about revealing the opponent’s weakness and making that your strength (think about baseball and how a coach will send a left-handed player with a good on-base percentage and speed, to hit against a left-handed pitcher, to result in a better chance of getting the player on base and scoring a run). The technology used to calculate that information is a combination of statistical equations and cross-tabulations.

Fast forward to 2013, and the statistical data associated with planning a winning sports strategy is now at the next level. It’s the advancement and acknowledgement of new technologies that are enabling today’s athletes and coaches to, not only plan a winning strategy, but to make a player better in every aspect – from their techniques through to their game mentality.

In tennis, technology has been an influencing factor in some key plays such as determining if that ball is in or out. This type of technology has created a definitive way of calling plays and leading to more accurate outcomes – something all sports players and fans can appreciate (I mean, what did we do before instant replay?).

Now, technology has gone beyond making more accurate calls and is transforming the game into one of specific player analysis, strategic planning, and reviewing historical and predictive data to decrease the winning margins and increase the chances of an underdog catching up to a superstar.

There are four main areas of data analysis that coaches and players are using to determine patterns of their players and opponents, and transform the game by changing their strategies as they approach game planning:

• Serve Direction – through the technology, first and second serve is tracked and plotted to show the performance of a player on each serve attempt. Post-match, this data is transformed into a visual representation of where shots are placed, and through cross tabulations, predictive analysis can show how a player will perform based on their personal patterns.

• Return Contact Point – the contact point shows the strike point and where a player returns the serve, which reveals their patterns and individual ability to return different types of serves. This is important for a coach to review and provide feedback on how to improve in specific situations.

• Shot Placement – it’s paramount to know and evaluate how each ball is hit and where it is placed. This part of the analysis shows if the hit was a slice or topspin, total number of unforced errors, and the groundstroke speed.

• Movement (court coverage) - the court coverage analysis produces a heatmap showing the players’ movement and how much of the court was covered through the match. Understanding where a player is standing (their comfort zone) and needs to stand, in comparison to where their opponent will be hitting can result in a much better return percentage.

Technology goes hand-in-hand with tennis and transforming the sport to take it to the next level. As the reporting structure for the data advances, and coaches and players start implementing the changes guided by the report implications, the sport will continue it’s competitive appeal and keep players strategically improving their games.

The release in 2011 of the movie Moneyball (based on the book Moneyball: The Art of Winning an Unfair Game, by Michael Lewis, published in 2003) made the use of analytics a popular subject for public consumption.

In the movie, the general manager of the Oakland Athletics baseball team, Billy Beane (played by Brad Pitt), struggles to put together a competitive team as the franchise faces financial challenges. Beane employs an approach called sabermetrics, which relies on evidence-based analysis in areas such as evaluating players, measuring game activity, and scouting. Thanks in part to this methodology, the team wins 20 consecutive games — a record in the American League.

The advancement of technology, especially in the areas of big data, in-memory computing, the cloud, and mobile, further put the spotlight on analytics in the sports and entertainment industries. Today, these technology solutions make it possible for sports organizations to effectively leverage their rich data assets and take their analytical capabilities to the next level beyond the rudimentary game, player, or scouting stats.

When we think about sports organizations in this context, we may realize that they are not much different than traditional businesses. Sports organizations face some of the same challenges as their counterparts in more conventional industries such as retail or consumer products. They operate in similar terrains, where they need to effectively manage their scarce resources and deliver value to their customers and other stakeholders.

One of the main reasons why sports and analytics is a perfect combination is the data sources that include data points from topics as diverse as game scores and attendance and revenue figures. These considerable data repositories are conducive for cross-fertilization and until now have been mostly untapped.

Moreover, just as, for example, a consumer-products company may be interested in understanding all aspects of their customers (products, stores, or service offerings), sports organizations seek greater insight in understanding all aspects of their fans, whether they study fan engagement, team performance, or venue management.

An integrated business intelligence (BI) approach that brings these unique and diverse data points together gives sports organizations the ability to see the big picture and execute growth faster with better-informed decisions.

It enables them to address new business models and complex challenges, which increasingly require greater access to data coming from sources both structured (for example, revenue and attendance data) and unstructured (such as data collected on fan experience from social media that cannot be easily queried with traditional tools and technologies).

Innovative technologies and strategic BI solutions, which go beyond just game-day analytics to manage big data on mobile platforms and which can be delivered on the premises or in the cloud, can not only transform their operations but also continue to deliver value-added products and services, improving the overall fan experience.

In my next post, I will discuss sports analytics in more detail and highlight recent examples of implementations in a variety of sports.

A recent benchmark study conducted by Ventana Research revealed the need for smarter business intelligence tools designed specifically to address the needs of business users. 63% of study participants rated usability as the most important consideration for evaluation of BI software, well ahead of functionality (49%), manageability (47%), or even reliability (46%).

The results of the study reflect the frustration of many business users who find the advanced capabilities of BI software designed for analysts too cumbersome for their needs. Business users of BI placed greater importance on interactive and collaborative features that help them identify and take action on the most important information for their area of business without requiring IT or analyst involvement.

Top Usability Features on the Business User’s Wish List

1. Customizable Mobile Alerts

Whether they are on the road or simply immersed in the daily demands of business, users want BI systems that will alert them to a potential problem as soon as it is appears.

Among the most highly-valued features demanded by business users is the ability to quickly review their most important KPIs and set customized thresholds. Whenever the value of a key metric moves outside a specified range, BI users want to be notified by an alert sent via email or directly to their mobile device.

2. Contextualized Analytics

If you’ve ever been baffled by an operating manual written exclusively in pictograms you will understand the business user’s desire for text explanations in straight-forward language that can provide a context for dashboard graphics.

BI business users want text in addition to graphics so they can absorb the information as easily as they read a newspaper or scan a website. By capitalizing on the brain’s ability to process both language and visuals, next-generation BI software can greatly increase usability with text explanations that can be customized to the specific variables of a user’s business as well as their preferred language.

3.  Collaborations Tools for Annotating and Sharing

Business users need an integrated solution for annotating and collaborating on the results of their analysis. I have personally spent countless hours transferring the results of advanced analytics to a format that is more congenial to sharing (usually Microsoft PowerPoint.)

Business users need to share analytics in a form that allows them to annotate key points about a chart or graph and easily distribute it to colleagues or clients.  As social business becomes increasing the norm, BI software with integrated social features will make current practices obsolete. It will no longer be necessary to repeat a presentation many times over in order to socialize analyses across a large organization.

Why Are These Features Not Available In All Business Intelligence Software?

While many of these features are available in the best of Business Intelligence software, it begs the question: why are they not standard in all BI applications?  Mark Smith, CEO and Chief Research Officer for Ventana Research has this to say:

For years, BI software providers have stated there was no demand for capabilities like collaborative and mobile to make things easier for business users, but my analysis indicates this was because they were getting feedback only from IT organizations and IT industry analysts who do not research or understand the way business professionals operate and how they want to become smarter in how they communicate and collaborate, including via mobile technology.

It’s time to change that pattern by recognizing how business users consume and share the results of analysis. By incorporating the best of mobile and social business practices, BI for business users is evolving to meet the demand for enhanced usability.

Additional Resources:

Learn how SAP Business Objects is leading the way with Business Intelligence software designed to meet the top usability requirements of business users.

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Are you a business user with a pet peeve about Business Intelligence software?  What improvements would you like to see in the next generation of BI software?

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 Join me on Twitter (@eagaines) or connect on LinkedIn or Google+.

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Let it be known, then, that very few things will lull your audience into a boredom-induced sleep (or at least a trance-like daydream) than boring slides.

You could have the most interesting data to present. You might have a charismatic stage presence. All of it could be undone in a second with a bad slide presentation. It’s like putting on white athletic socks and blue shoes to go with your expensive, hand-tailored black suit. It’s a small detail, maybe, but if it’s all wrong, it’s really all wrong.

Let’s look at some common slide blunders to help us understand the mistakes we’re making and why it’s important to rectify those situations so that the focus and interest remains on our data.

#1. Too much information on one slide

Much like they want to do with blog posts and other online content, your audience wants to skim and scan. As you talk about your data and give more details about its relevance to them, they can take notes to supplement shorter bullet points and informational highlights with the information they feel would be most relevant to their purposes, so don’t feel the need to drill down on every minute detail.

Also keep in mind that the more time they spend reading your slides, the less time they spend listening to what you have to say – and then your charismatic stage presence doesn’t make much of a difference. Keep that font size pretty large so that you can’t overcrowd your slides and you’re already making strides toward getting more attention on your data.

#2. Painful color schemes

There are some color combinations that you just shouldn’t do. Yes, your company colors might be white and yellow, and on your logo, that might look great. But when someone is trying to read white on a yellow background (or vice versa), they’re not going to last very long before giving up. This also holds true for other really bright colors, and just about anything on black (yes, even white). A black background is going to bug out your audience’s eyes. If their eyes get tired, they get sleepy, and they stop listening.

Keep your color schemes simple, tasteful, and easy to read.

#3. Moving through the slides too quickly

Sometimes presenters have a tendency to speed through the information at hand. If you’re presenting data, keep in mind that many (if not all) of the numbers and figures might be easier for you to comprehend than they are for your audience.

Aside from the obvious – moving through the slides too quickly means that people don’t have enough time to read them, even if they are short and able to be skimmed and scanned – moving through the slides too quickly means that you’re not focused on making sure your audience understands the data. If they don’t understand it, they can’t possibly fathom its impact or anything it might imply.

Next time you’re presenting data, slow down. Explain everything well – just because it seems obvious and simple to you doesn’t mean that it will to everyone present. Take the time to invite questions and get everyone on the same page.

#4: Not varying slide content.

Everyone has a way in which he or she best processes new information. For some people, it’s visual. Others are auditory learners.

So many terrible slide shows consist of title, text, title, text, title, text. It becomes monotonous and the audience loses interest – fast. For the ones who do well by listening and reading, they might hang on a bit longer than the others, but ultimately, you need to be trying to appeal to everyone.

Include images, charts, videos, and other supplemental information that will help your audience to understand the data and its importance. Numbers can be very dry for a lot of people, so the more you’re able to make your data presentation a work of “educational info-tainmen,” the better you’ll hold their attention and the more memorable your data will be.

And when your data is memorable, it’s used and shared. This helps to build up not only your name, but trust in the work that you do, which could potentially lead to further opportunities in the future.

#5: Having too many slides.

Again, you have to remember here that numbers can baffle people. Data can be confusing – if it didn’t require any kind of explanation or elaboration, you wouldn’t need to present it.

If you include too many slides, your audience is going to feel overwhelmed with information (especially if you have 127 slides that are all overcrowded with text… and especially if those are all information slides that are impossible to read because of the loud color schemes). If the audience feels overwhelmed, they shut down.

The absolute last thing you want is to be presenting your data to people who could really use it and have them shut down. Failure to process. Does not compute.

It spells trouble. If your data is used, there’s a good chance it will be misused or that you will be misquoted or misunderstood (or any other mis- that leads to frustration).

To prevent your audience from being overwhelmed from the start (or from about forty-five minutes into the presentation), keep the slide count low.

Want to dazzle your audience? Give up the traditional slides completely

PowerPoint slides can be really useful, but we’ve become so accustom to them and they almost never stop being so bland. Sure, there are ways you can drastically improve them (see some of the suggestions above, for example), but if you really want to wow your audience, try getting rid of them. Period.

Instead, maybe give Prezi a try. It’s the same slide concept, but it’s interactive. You can make the slides more visually appealing while still conveying your information, so you’re reaching a broader range of learning styles and increasing retention rates for your data.

The Prezi below encompasses a lot of the characteristics of a good presentation that we’ve discussed here in this post. It’s very to-the-point with its information, using lots of bullet points where it gives text. But it’s not all text. Note the use of infographics – still presenting data, but through a popular medium created to help people understand that information. And because one of the features of a Prezi is that you can have the presentation zoom in, this one focuses on little bits of information at a time, making it more easily digestible for your audience, and allowing you to speak at length about one thing before moving on.

Finally, this isn’t a long presentation either. It presents the relevant data, gives some bullet points to highlight, and then moves on. This is great as an introductory slideshow, one in which you might be first introducing your data to an audience.

What are some of your tips for great presentations that maximize the effect of your data? We’d love to hear your thoughts!

image credit: blognewschannel.com

Face it – the sports world will never be less competitive. There will never be a time when everyone in professional sports is a winner, and there will always be amazing athletes who are champs, heroes, and role models. Unfortunately, there will also be athletes who are the antitheses of what we expect and crave as fans…

As athletes feel the pressure to perform at their optimal ability, they sometimes turn to the wrong supplements that may push them beyond their natural limits, and potentially right off the edge. But for other athletes, they get their edge by strategizing a better performance and using the right supplements.

In the sport of tennis, the performance enhancer of choice isn’t steroids or banned substances – it’s data. It’s analyzing personal performance data post-match, and reviewing opposing data pre-match to strategize where the first and second serves should land, the optimal place to stand on the court when returning a serve, and how hard to hit that match point to win.

Enter Hawk-Eye and SAP analytics - the perfect marriage of data and analysis that produces a full statistical and visual understanding of player performance, through serve stats, match plays, and ball placements. This technology creates a layer of information that addresses and answers the “why” of players’ performances instead of just the “what.”

Former player (Mixed Doubles Grand Slam Champion), current commentator on the Tennis Channel, and ATP Board Member, Justin Gimelstob, uses the analytical information when broadcasting matches and discussing the strategies behind the plays.

From a broadcaster perspective, Gimelstob says, “Fans can see what is happening, but they want to know why its happening – why the player hit a certain way or why they are standing in a specific space…I can use this information to educate fans.”

From a player and coach perspective, Gimelstob points out that the margins to win a match are minimal and it’s really all in the subtleties in techniques that wins the game, which can be created by analyzing data and implementing small changes.

The subtleties include everything from the serve and return approach, to showing specific data to a player that will boost a his/her confidence because it is hard facts, not just opinions, and shows how to win. The data also reveals if player is playing more aggressively or defensively, based on how he/she is hitting the ball. As we all know (and as the players recognize), the numbers don’t lie.

The data collected from Hawk-Eye and SAP analytics provide the hard data – those numbers and facts that, when analyzed, can show historical data about what happens when a player stands in one space through the sets and a predictive analysis of what could happen. The data combined with an analysis of the competition can lead to a change of strategy that enhances a player’s performance. The result is the difference between a player who is good, and a good player who plays a strategic game and wins.

That is the benefit of the real performance enhancer – data-driven winning performances.

Of course, saying you’re getting on their level and actually getting on it can be two completely different things. Prior to any kind of data presentation, publication, or explanation, it’s imperative that you do a little bit of investigative work.

Step 1: Identify your target audience

If you’re giving an actual presentation of some sort, this step needs to go beyond a list of names. Find out what the people on that list do and why they would care about your data. Regardless of whether the data is being presented or published, though, you still need to identify your audience. That’s how you tailor the information. Dig deeper by asking yourself the following questions to help you identify your target audience:

• In what industry does my audience work?
  • Narrow this down as far as possible. For example: Marketing → online marketing → social media marketing → social media account managers.
  • Consider the breakdown in terms of small, medium, and large business. Certain data might be more relevant to a large enterprise than it will be a small business.
• What positions do people in my audience hold?
  • Think: C-Suite, middle management, entry-level, etc.
• What level of education does my audience hold?
  • Also consider which degrees your audience has. People who are wired for math and science will understand data in a completely different way than people who have studied in the more creative humanities fields. This breaks down to left-brain/right-brain.
• In what geographic location does my audience reside?
  • Above all, knowing this helps you tailor your presentation. If you’re presenting to a bunch of suburban housewives, conveying your information in a way that would better appeal to a rural farmer won’t create any connection for them.

Step 2: Understand your audience

Understanding your audience means applying some psychological and sociological principles. If you want to present data in a way that makes sense to them, or if you want to target a specific audience in published reports, it’s important to learn about who they are.

• What are the major issues facing my audience?
  • What are their biggest pain points?
  • What questions keep them up at night?
• What information would help them to better perform their jobs?
• What information do they want to know?
• How can I explain things in terms that make sense to them?
  • For this one, look back at who they are, what they do, where, how, when, and why they do it.

Step 3: The Presentation

Whether it’s an actual presentation at a conference, a boardroom meeting in which you present your findings, or a publication of some sort, use the information from the first two steps to help you tailor your data presentation.

• If you’re dealing with a high-level audience who will understand the terminology, feel free to go into more depth as you explain the data.

• If your audience is mixed or might not understand the concepts you’re presenting, make sure to speak in layman’s terms. Avoid using jargon when possible, and make sure to explain the data using terms and examples to which the audience can relate.

• Mix visuals in with your presentation. While some people can comprehend facts and figures just fine aurally, others will better understand by seeing the analytics presented visually. Be sure to explain any data visualizations that might not be immediately clear to the audience.

• Connect the information being presented to its real world applications. How can the audience use this information on a regular basis? Cite examples based on the kind of work they do and other details specific to them.

Pay close attention to that last bullet. It’s the big one!

Without a way to apply the material, the data will remain isolated information for a lot of people. You might help them win a round of trivia at the pub sometime, but they might not fully understand how the data applies to them in their everyday work lives.

The long and the short of it is this: if you don’t know your audience, it’s nearly impossible to reach them. Data and analytics can be complicated – the concepts, the information, even the process by which you found them – so it’s important that you’re able to convey all that in as simple terms as possible. If you want your audience to get the most out of your data presentation, you must give the findings some practical application; show them why they should care and how this information affects them. You can’t reach an audience you don’t know. And if you don’t reach them, your data presentation falls on deaf ears. When that happens, too much of everyone’s time is wasted all around. Don’t be that presenter.

I’m going to leave you with this SlideShare presentation from Altimeter because I think it exemplifies the concept of knowing your audience nicely. Given the thorough explanations throughout, we can deduce that this particular presentation was created with a specific audience in mind, though one that might hold a range of knowledge. To bridge those gaps, Altimeter starts to corral everyone and get them on the same page pretty early on in the presentation. Additionally, given the description of the presentation provided by Susan Etlinger, we know that Altimeter researched the audience and knew exactly who it was presenting to.

How do you tailor your data presentations to your audience? What steps do you take to learn as much as possible about them beforehand so that you can help them to apply that information? We’d love to hear your thoughts in the comments! 

image credit: Leanne W. Smith

In such a lopsided relationship, one would expect the balance of power to favor Goliath Inc. But when it comes to customer intimacy, it is the individual decision-maker that the enterprise must woo if the relationship is destined to prosper.

In a recent study commissioned by FICO (the folks who calculate your credit score), Forrester research conducted a survey of 266 business decision-makers in marketing, fraud, and collections across eight countries.  The overwhelming majority of respondents (87%) reported that their top priority was improving the relationship with their customers. 

What Is Customer Intimacy?

“Customer intimacy” is marketing shorthand for understanding your customer’s preferences and habits so well that you can anticipate what they want, how they want it, and when they want it. This “intimate” knowledge of an individual’s preferences and history is vital to the development and maintenance of a long-term customer relationship.

But creating customer intimacy when one partner is a large, multi-national corporation can be a tricky business.  It is not always easy to judge when exercising knowledge about a customer’s location, habits and preferences moves beyond the level of “engaging” into the realm of the “creepy.”  As in any relationship, customer intimacy grows over time with repeated interactions built on a foundation of trust.

The Challenge of Customer Intimacy

Meeting the challenge of customer intimacy requires moving beyond broad generalizations about “customer types” to the highly specific details of engaging with customers as individuals.

The large companies represented in the Forrester survey currently rely on broadly-defined segments that lump many customers together based on a handful of demographic characteristics.

In order to move to micro-segments, and ultimately to segments of one, they need better data, new analytical capabilities, and automated decision-making to reduce insight-to-action time.

3 Keys To Building Customer Intimacy

The large enterprises represented in the Forrester study are putting their investment in three key areas that are essential to driving greater customer understanding and engagement. Here’s how they are focusing their efforts:

1.)  Gather more data, better data.  To really understand customer preferences, it takes a broad set of demographic, social, contextual, and behavioral data. This means marrying the traditional customer information available in the corporate data warehouse with external data coming from mobile and social media channels.

2.)  Use analytics to put customer preferences in context.   With a broader base of available data, analysis of past customer behavior patterns can provide a rich historical context for predicting the likely preferences of new or returning customers.   Location data and attitudes reflected in social media “likes” offer a more nuanced picture of individual preferences than was previously possible.

3.)  Automate the insight-to-action cycle.  Large enterprises can no longer afford to take months to convert new customer insights into fresh marketing campaigns with new offers and eligibility requirements directed to new segments of the market. They require rule-based “decision engines” to evaluate conditions, calculate responses and automate the execution of marketing campaigns delivered to the individual customer.

Customer Engagement: Right Message, Right Time, Right Place

Once you have established a deeper understanding of your customers as individuals, the next challenge is determining when, how, and where to engage with them.  The goal is to deliver the right message to the right customer at the right time and location.

Sophisticated modeling techniques applied to customer data provides the foundation for customer engagement at a more personal level. Deeper knowledge of the habits, preferences and status of individual customers creates new opportunities for cross-sell and up-sell. And eligibility for specific offers can now be determined from data about a customer’s relationship with the company or legal requirements in their location.

What’s Ahead?

While 67% of the companies represented in the Forrester study are already engaged in measuring the outcomes of their customer modeling efforts, only 14% reported using metrics about customer preferences and decisions to optimize running campaigns. The vast majority (85%) were still using metrics strictly for reporting on past business performance.

But that is about to change as more organizations are shifting their perspective from past performance to real-time optimization.

Those companies that invest in real-time analytics capabilities will gain the competitive advantage by optimizing in-market campaigns based on customer responses, and continually improving the decision rules that automate the insight-to-action cycle.

By building a history of timely and consistent interactions with individuals instead of demographic types, the winners will see real payoff in the form of improved business efficiency and greater customer loyalty.

———

Join me on Twitter (@eagaines) or connect on LinkedIn or Google+.

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As you read this post, you probably fall into one of these three categories:

• You know exactly what BI is because you eat, sleep, and breathe it every day. BI is in your business DNA.

• The term means nothing more than the name of an exotic tech cocktail that might have pierced your ears, figuratively speaking of course.

• You‘re somewhere in between the two extremes. You’ve been exposed to the term, but haven’t had a chance yet to fully digest it or appreciate it.

Do you have something to learn about BI? Let’s roll up our sleeves and get to work.

To begin with, BI looked very different when I started my career in the early ’90s. You couldn’t look it up on a mobile device smaller than a floppy disk. Moreover, you couldn’t Google it, Bing it, or Yahoo it. Today, the keywords “business” and “intelligence” together return more than 250 million results on Google, though few will be relevant to you, nor will you have time to go through them. Nevertheless, the ease and the speed at which you are able to query large volumes of recorded data to make faster, better-informed conclusions puts the question at hand in perspective.

Scratching the Surface

Beginners to BI should start their research with the definition. Wikipedia’s definition of BI is a good place to start, and from it you get the sense that BI includes tangibles such as hardware and software as well as intangibles such as people, culture, processes, and best practices.

Continuing on the Wikipedia page, you can find out about the origins of the term. In 1958, Hans Peter Luhn, an IBM researcher, defined the term as “the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal.” By the ‘90s, the term had become more widespread. At CIO.com, BI is defined as “an umbrella term that refers to a variety of software applications used to analyze an organization’s raw data.”

Digging Deeper

Next, you can dig a little deeper by performing what I call a rapid-research exercise to glance at the web sites of BI companies that develop the technology. In this way, your searches can transition from text-based and definition-centric explanations to visually rich and appealing presentations, including graphs and charts. This is where BI dashboards take center stage. Not surprisingly, the emphasis on mobile that showcases tablets and smart phones becomes apparent by pictures of BI artifacts shown on mobile devices. Additional references pop up for Big Data and Cloud. Both are hot technology terms that gained popularity in the last few years.

As you research and connect the dots, you can start to build your own definition of BI. This will be influenced by your own unique background, your experiences with technology (with or without BI), and, possibly, a perception layered with your biases. However, in the end, your definition of BI may still fall short.

Hitting the Core

Ultimately, BI is about decision making. In its simplest and purest form, I define BI as the framework that enables organizations of all sizes to make faster, better-informed business decisions. 

I don’t claim that this particular definition of BI is better or more comprehensive than others. But it does provide a direct and concise answer with less emphasis on technology and more focus on business, people, and decision making.

When it comes to defining BI or technology in general, we need to put the focus on business and people more often. In this context, business decisions should be complemented by technology that promotes actionable insight, and not the other way around. BI is not a miracle pill.

BI alone does not solve business problems or cure corporate infections. Instead, BI is the enabler that, if designed, implemented, and executed effectively, can help organizations drive growth and profitability. 

What is your definition of BI?

Every time I read a new book, my perspective is altered for the better. For all the social media analysts out there, it’s actually imperative that you understand each type of key performance indicator in other areas of a business. Why? Because social media is one piece of the puzzle.

Here are the top 4 books to read to propel your career forward:

1.        Data-Driven Marketing by Mark Jeffrey

Mark Jeffrey is the Director of Technology Initiatives and Senior Lecturer in the Center for Research in Technology and Innovation at the Kellogg School of Management. He’s also the Managing Partner of Agile Insights LLC, a marketing and technology consultancy.

In this book, Mark breaks down the 15 metrics everyone in marketing should know. Not only this, he provides a ton of case studies on how organizations have adopted data driven marketing technology and processes. Finally, he provides a bunch of formulas and excel sheets to get you started. This is by far my favorite out of this list.

2.      How to Measure Social Media by Nichole Kelly

This is, hands down, the best book on measuring and presenting social media data to Executives’ liking. Nichole is a pioneer in aligning social media to the bottom line and is the president of SME Digital, the digital agency division of Social Media Explorer. She shows you how to create measureable strategies, implement the proper technology, and create reports that compare social media with other marketing channels. If you ever want to get more budget for your social media program, this book is a must read.

Some of the key questions you will be able to answer if you implement the practices in the book:

• How much revenue did social media activities generate?
• Are social media prospects more likely to convert to customers?
• Which status update delivered the highest conversion rate?
• How long do we retain new social media customers?
• Do they spend more or less than customers from other channels?
• Do they make repeat purchases more often than other customers?

3.      Social Media Metrics by Jim Sterne

Jim Sterne is one of the early digital analytics thought leaders. He is the founder of the eMetrics Marketing Optimization Summit and cofounder of the Web Analytics Association.

Jim breaks down how social media metrics fit in with traditional web metrics. He’s been around the block, so you’ll get a historical view of how web analytics have evolved and how to implement social media metrics into your web measurement processes.

4.      Analytics at Work by Thomas Davenport, Jeanne Harris, and Robert Morrison

Analytics at Work was written by three very smart authors, all with years of valuable experience. Thomas H. Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College and has been involved in writing thirteen other books. Jeanne G. Harris is Executive Research Fellow and a senior executive at Accenture’s Institute for High Performance in Chicago. Robert Morrison has been leading business research in professional service firms for over twenty year and is a coauthor of Workforce Crisis.

The book illustrates how organizations can gain a competitive advantage by mastering these five practices:

• Use data more effectively to glean valuable analytical insights
• Manage and coordinate data, people, and technology at an enterprise level
• Understand and support what analytical leaders do
• Evaluate and choose realistic targets for analytical activity
• Recruit, hire, and manage analysts

So there you go. I definitely suggest picking up a copy of each book if you really want to impress people at work with your knowledge. Remember this: everything ties back to business goals. If what you’re doing isn’t measurable and tied to business goals, you won’t be doing it for much longer.

Just a side note: I am in no way compensated for sending you to the authors’ Amazon pages if you buy the books. I truly love the work they produced, and I admire them.

image credit: charuzu

Intrinsically, you know that you need good data. But how far do you need to go to tidy up dirty data? What are the real costs incurred if you DON’T have clean data? 

The most common data quality question is, “We’re still making money, so how bad can it really be?”

Consider the following scenario. You made the “mistake” of hiring good people. Rock stars, even. They’re smart people, and they want to make sure you keep making money. As processes break and bad reports are generated, they want to fix them. So they do. Herein lies the “mistake.” Now, all kinds of shadow, manual processes are happening in your company to make the data fit-for-use.

This is how data quality affects labor productivity. When data is incorrect, bad things happen. Bills don’t get paid on time, shipments get returned, and so on. That’s not the end of the story, either. Now an employee has to step in and start a new process—for example, one that handles return shipments. When data quality is improved, however, much of this work can be avoided.

Productivity Can be a Hard Sell

First, let’s assume you’ve been capturing your metrics. Turns out, no one’s tracking all the metrics they want. And there’s no one golden metric that’s perfectly defined, monitored, and speaks to high business value.

If you’re one of the many who’s not tracking metrics and constantly reporting business value achieved through your efforts, you can use the Gartner and Ventana statistics below to help you quantify results. How many business initiatives did you undertake last year? How many delivered on-time, on-target, and have increasing adoption? How many do you plan to undertake next year? (Ask your chief intelligence officer or program management office for these numbers.) Then have a discussion. If you’re normal, 40 percent of those projects will fail. Investing in the quality of the information feeding those initiatives is an imperative.

And don’t overlook that second stat. You spend a lot of money hiring smart people, making sure they have great compensation packages, conducting surveys on employee satisfaction, and so on. But are those rock star employees doing rock star work? Or is most of their time spent on data-related tasks to get them access to information? Talent retention is key to IM success.

Executive Momentum

Do you have executive support for your data quality initiative? Do you have support to start figuring out the true extent of your data quality problems? Do you have support from both business and IT in defining pragmatic, high-value solutions to the data quality problems? This level of executive support can be not difficult to attain and also difficult to maintain as executive focus shifts. Aside from becoming a full-time data politician, what can you do? You could get yourself a chief data officer. All the cool kids are doing it.

At a customer meeting this week, only one company even had Data Steward roles defined. None had Data Scientists, much less a Chief Data Officer. What about your company?

There are lots of mobile applications available that allow skiers and snowboarders to keep track of their exploits on the slopes, including RealSki, SkiTracks, and GPS Kit.

But I wanted to have full control over analyzing the data, so I instead opted for a general-purpose application called Sensor Data from Wavefront Labs. As the name implies, the app can record ALL of the information available from the iPhone’s five different sensors — and then export it as an easy-to-read data file.

Here’s an example of one of the runs – I pressed the “start capture” button at the top of the lift, choosing the option to record a line of data every 1/2 second. At the end of the day, I exported the file from my iPhone to my PC, and opened it up in the SAP Visual Intelligence data discovery tool.

From there, I was able to quickly and easily create a series of graphs and charts showing different aspects of the run:

The fun part was synchronizing the analysis with video I took with a helmet-mounted GoPro camera – watch the video below to see more about the process, and what happened during the highlighted area in the chart!

If you were intrigued, you should take SAP Visual Intelligence for a spin with the free trial offer, or the new $99 personal version that gives access to text and Excel files.

“When am I ever going to need to know this?”

For me, this was calculus. For you, maybe it was the MLA style guide, proper thesis statement construction, or chemical compounds. The point is that we couldn’t see the long-term practicality of investing in this information. Because of that, we saw no reason to absorb it enough to really learn it, and it definitely didn’t keep our interest. Rote memorization helped us to pass the test (hopefully). After that? Well, all that information sort of just slid effortlessly off of our Teflon-coated brains.

There was one thing that could potentially make a difference here: the teacher. If the teacher connected the information to something tangible, something we could identify as a practical, real world connection or application, we were more inclined to actually learn the information they were presenting to us. Hands-on experience could help us to actually start learning to use this information beyond the classroom walls.

How to Make Your Data Presentation More Effective

If you’re presenting data, the situation is very much the same. Though you might be enthusiastic about your research and subject matter, it’s important to understand that just because you’re presenting it to an audience doesn’t mean that it will resonate with each person who hears or reads it. In fact, you should expect that your findings will have a different impact on everyone who hears them. Some will be inspired while others’ interest will be piqued. Some will genuinely want to understand the data, but will feel frustrated if you don’t connect it.

As someone who presents data, you have a certain set of responsibilities to those who are consuming it. For one thing, you want your data to be accurate, meaning that the tools you use to run analytics and crunch numbers should be as up-to-date and inclusive as possible.

But even more than that, you have to be able to convey that data in a way that will make sense to your audience. Everyone learns differently, so it’s important that you connect the real world applications for your data in as many ways as you need to in order to reach your audience. By sharing information, you’re providing the tools to affect change or growth. Some people might just need a crash course on using those tools before they go out and do something brilliant with them. Others might need a mere refresher.

Show some real world examples of the data in action. Provide solid numbers and concrete evidence of its importance. Make suggestions for how the data could be used. Connect it to specific industries and professions. Use visualizations. Let people know why it’s important that they have this information.

You could give people all the numbers, examples, and analytics in the world, but if you’re not connecting them to anything, it’s not going to make a bit of difference. Data must connect to be effective.

Ultimately, your ability to help your audience make those connections will determine how well your data is used, and therefore how far its reach extends. If your information isn’t actionable, it’s unfortunately not nearly as effective as it could be.

Take a look: This SlideShare presentation from HubSpot is one good example of how you could take your data and use it to demonstrate its practicality to your audience. In this presentation, HubSpot shows data – charts, stats, and so forth – and then uses that information to support certain facts. For the audience (business fields), the facts and data are rooted to the real-world issues that affect their companies, so they get a sense of why that information is important to them and how they can use it for change and growth.

What steps are you taking to help others connect your data and discover its real world applicability? Feel free to share your thoughts in the comments!

image credit: Valens Point

Companies have embraced the value of analytics – solutions that combine data and analysis to drive insights – to help them differentiate, become more competitive, and make better business decisions. Today, at the forefront of analytics, SAP helps develop and bring to market the latest technology innovations while keeping a close watch on emerging trends.

A Pervasive Culture of Analytics

Almost everyone in your company can benefit from some form of analytics. Employees want to make their jobs easier – and you want them making better decisions. In the past, powerful analytic tools were reserved for a select few who had mastered data manipulation and reporting. Such data gurus were the only ones who could build the complex models required – making them de facto gatekeepers. Today, however, technology puts powerful models and endless data sources into more hands, supporting better decisions from the shop floor to the C-level suites.

Thanks to advancements in cloud and mobile technologies, analytics are now available everywhere from your basic laptop to the latest smart device – and from client sites to buyer meetings to airport dinners. Mobile access puts near-real-time analytics at the fingertips of all employees, all the time. KPIs were once watched monthly, quarterly, or annually; real-time analytics now enable daily analysis of emerging trends. And exception reports identify areas in need of change, from the monthly financial close to the highest-level corporate KPIs.

No Data Off Limits

It takes little time for an organization to amass a large historical database. Until recently, translating database data structures into meaningful analytics was time consuming and difficult; analytic solutions simply eliminated much of the detail in exchange for a timely analysis of summary data. Find a variance, and you’d have to engage a team of experts to dive into the details.

With in-memory computing, however, data volume and level of detail are no longer limiting factors. Every piece of data can be used, generating richer, more meaningful analysis. Users quickly find meaningful information within the analytic system. And analysis of big data lets companies skimp on neither volume (important for establishing trends) nor detail (important for investigating variances). Intelligent data also makes it possible to analyze social media, incorporating and analyzing unstructured data such as customer product reviews, satisfaction surveys, and Facebook posts.

Getting Sophisticated Just Got Easier

Now that sophisticated analytic capabilities no longer have to be built from scratch, both business users and IT departments benefit.

•  More frequent modeling. In the past, running and modeling multiple simulations was difficult and time consuming, limiting the number of scenarios that could be run. Today, in-memory computing advances allow nearly unlimited simulations. Scenarios can be run as often as needed to update forecasts or provide input into planning processes.  And predictive capabilities let you model multiple scenarios to forecast performance or recalculate enterprise risk.

• Faster to deploy without customization. New technologies such as cloud and mobile solutions let IT is able to lower the cost of ownership and deployment while providing more powerful tools to business users. Cloud-based solutions make it easy to deploy flexible numbers of users and licenses, reducing total cost of ownership (TCO).  Mobile solutions mean that analytic applications can be pushed out to more users even as they travel or work in the field. In-memory computing lets IT capture and store massive amounts of highly detailed data, make it available to any user, and easily access it for later analysis. Together, cloud, mobile, and in-memory technologies dramatically enhance the capabilities of analytic solutions. Powerful functionality can be quickly deployed, includes more compelling data, and doesn’t require months of customization.

Learn More

To find out more about how analytics services from SAP can help your company develop a customized roadmap that supports adoption of new technologies over time and unlocks the business value of analytics please visit us online.

Meanwhile perhaps you could share with us how these trends in Analytics impacted your organization recently?

When you’re presenting any kind of data, it’s important to remember not to overwhelm your audience with an over-abundance of facts and figures. This is especially true if you’re giving only brief overviews or introductory information. It often happens that, because people presenting data are so interested in and enthusiastic about their subject matter, they forget that not everyone is able to wrap their minds around such concepts as easily.

To avoid being the presenter who doles out information that goes over everyone’s heads, try using smaller, “snack-sized” bits of data when presenting. This will give your audience relevant information that’s easier to digest, but it can also help your presentation skills. How so?

It forces you to focus on what’s most important

There is absolutely nothing wrong with being excited about the work that you’re doing, much less the data that you’re mining from various analytics and research endeavors. But it’s also important to remember that, depending on your specific areas of focus in a presentation, not all of it will be relevant. When you focus on smaller, snack-sized bits of information, you have no choice but to pare down your findings to what is the most important and relevant information for this particular presentation. This means that your audience gets the most interesting and compelling info when it comes to the topic at hand, and because they don’t feel inundated with data, they’re more likely to retain that which is most important.

It opens up options for future presentations and content marketing

When you’re only using the most important pieces of data in your presentations, it opens up a few options for you to keep your audience engaged. First, depending on the reason for your presentation, as well as for the kind of presentation that you’re giving, it opens up the possibility of follow-up engagements. If you have a lot of relevant data, you might choose to disperse it over time, so as not to overwhelm your audience with too much information at once.

You’ve also got the option to do some content marketing. If you present the most interesting and relevant data, maybe you make a full report available to participants who are interested in learning more. Maybe you do a series of blog posts or videos in which you discuss more of the data over time. This gives people the chance to receive all of the data, but at a more agreeable pace that allows them to better process it.

‘Snack-Sized’ means presentation materials that are easy to skim and scan

Like it or not, we are a society of skimmers and scanners. Our attention spans just don’t last for a long time (reports vary from 7 to 20 minutes). For that reason, as a presenter, it’s your job to make sure you’re keeping people interested without wearing them down. This often means shifting gears (and focus) on a pretty regular basis.

How does this help your presentation skills? Well, it sort of ties in with the first point about getting you to focus on what’s most important. When you determine the most important facts and figures to present, you can more easily tailor your presentation materials to your audience (remember, it’s always smart to know what kind of audience you’re dealing with prior to a presentation). This gives them materials that they can annotate as you speak, and easily skim and scan afterwards, allowing them to blog about it or use the data in other ways, as well. Don’t forget that the materials you provide are just as important, especially if you want to make a lasting impact so that your audience doesn’t forget the information they’ve been given. Here’s a perfect example from slideshare.

Web Reporting vs. Web Analysis from Dennis Mortensen

Remember: ultimately, it’s all about striking a balance. You don’t want to present with so much data that your audience is overwhelmed and can’t follow you, but you also don’t want to skimp on the details. Not providing enough information means you’re taking a risk on just wasting everyone’s time, which is certainly not what you want to be doing. Find the balance with “just enough” relevant and interesting data.

What benefits have you noticed when using smaller bits of data in your presentations? How has it helped you as a presenter? We’d love to hear your thoughts in the comments.

image credit: smbc-comics

Predicting the future is tricky business – there’s always the risk that time will exposure your folly. But with every new year, pundits and authorities continue to step out on thin ice and announce their predictions for the future.

While chance alone will favor some forecasts, there are always those that are far off the mark. Take, for instance, these famously false predictions on the future of technology:

1876:  This ‘telephone’ has too many shortcomings to be seriously considered as a means of communication.  — from an internal Western Union memo. 

1880:  Everyone acquainted with the subject will recognize it as a conspicuous failure.  — Henry Morton, President of the Stevens Institute of Technology, passing judgment on Thomas Edison’s light bulb. 

1895: Heavier-than-air flying machines are impossible.  — Lord Kelvin, President of the British Royal Society, mathematical physicist and engineer. 

1909:   That the automobile has practically reached the limit of its development is suggested by the fact that during the past year no improvements of a radical nature have been introduced.  —  ”Scientific American”,  Jan. 2, 1909. 

1923:  There is no likelihood man can ever tap the power of the atom. — Robert Millikan, winner of the Nobel Prize for Physics. 

1926:  While theoretically and technically television may be feasible, commercially and financially it is an impossibility, a development of which we need waste little time dreaming. — Lee De Forest, U.S. inventor and radio pioneer commenting on the future of television.

1936:  A rocket will never be able to leave the Earth’s atmosphere.  — The New York Times. 

1943:  I think there is a world market for maybe five computers.  — Thomas Watson, Chairman of IBM.

1961:  There is practically no chance communications space satellites will be used to provide better telephone, telegraph, television or radio service inside the United States.  — T.A.M. Craven, U.S. Federal Communications Commissioner.

Predictive Power: Then and Now

How could so many experts in their field be so sure and yet so wrong? To be fair, many of the sources quoted above were speaking in terms of what was likely in their present (or, at least, their near-term future.)  And all of them pre-date the powerful new possibilities for predictive insight available to today’s businesses.

In a recent benchmark study by Ventana Research, 86% of those surveyed assert that predictive analytics will have a major positive impact on their business, and 68% of the participants currently using predictive analytics report they have already realized a competitive advantage.

So How Are Companies Using Predictive Analytics?

Early adopters of predictive analytics have taken a “customer first” approach to predictive analytics by focusing on new ways to generate revenue and increase customer satisfaction.

43% of the organizations who participated in the study are using the results of predictive analytics to make product recommendations and offers designed to up-sell or cross-sell their current customers. By combining customer information with marketing and sales data, they are able to tap into new trends and opportunities as they emerge.

What’s more, the 40% of companies who are now evaluating or planning to incorporate social media data in their analyses are moving even closer to the goal of real-time insight into customer behavior.

Top 5 Things Predictive Analytics Can Do For You

Predictive analytics can give your organization increased insight across mission-critical areas of the business, from marketing and customer service to supply chain and human resources.

Think of it as x-ray vision giving you the power to see through walls and grasp the bigger picture. Using predictive analytics you can:

1.) Instantly predict market trends and customer needs

2.) Create customized offers for each segment and channel

3.) Predict how market-price volatility will impact your production plans

4.) Foresee changes in demand and supply across your entire supply chain

5.) Proactively manage your workforce

Are You still Steering Through The Rear-View Mirror?

The classic approach to business intelligence has been limited by both the available data and analytical tools. While reports were useful for understanding past business performance and drill-down analyses might unveil important correlations, they remained focused on what was visible in the rear-view mirror.

Predictive modeling takes a forward-looking perspective, giving you a view into what might be ahead. Combine that future perspective with real-time analytical capabilities and now you can move out of the past and into the future with the ability to predict and take action ahead of the curve.

This infographic brings the power of predictive analytics into sharp focus:

Additional Resources:

• To learn more, download the complementary executive summary of the Ventana Research benchmark study (registration required.)

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There’s been a lot of media attention in recent months on big data analytics and its potential to radically transform business. Futurists and big thinkers predict dramatic innovations in technology and business over the next 20 years.

There’s no denying that big data and the Internet of Things presents a dazzling vision of the future. But the truth is, in the real world of business, adoption of big data analytics is still in its infancy.

The IBM Institute for Business Value, in partnership with the Saïd Business School at the University of Oxford, recently conducted a study of more than 1100 business and IT professionals in 95 countries. The results provide a realistic picture of how business enterprises are using big data analytics in the here and now.

Some Surprising Results

One of the surprises coming out the study was how few of the respondents were using big data analytics in conjunction with social media. Despite the many press reports, it turns out that less than half of those with active big data initiatives reported collecting and analyzing social media data. Most of the current big data efforts are focused, instead, on sourcing and analyzing existing internal sources of data.

This is in keeping with the pragmatic approach of business leaders who’ve recently weathered a global recession. Despite the heady media hype, they are methodically building out their big data capabilities and applying their enhanced analytics to familiar collections of internal data.

Many of the organizations that are early adopters of new technology for storing and analyzing big data have been handling huge stores of data for years.

Take, for instance, the global telecommunications company that collects billions of call records per day, the oil exploration company that analyzes terabytes of geologic data, or the stock exchanges that process millions of transactions per minute.

What’s new is not necessarily the size of the data stores, but the addition of new sources of unstructured data that do not fit into traditional data warehouses.

Now, with the latest tools for advanced analytics, even those organizations accustomed to managing big data are able to achieve new depths of insight with greater speed and accuracy.

Focus First on Customer Insight 

Nearly half of the organizations in the study identified customer analytics as the top business priority driving their investment in big data. By better understanding individual preferences and behavior, they expect to improve the customer experience and develop a deeper level of engagement between the company and its customers.

This focus on customers first is fundamental to the business case for investing in big data analytics. In the current fiscal environment big data initiatives are subject to more than the usual level of financial scrutiny.

Successful proposals are able to show an enterprise-wide blueprint for implementation and a forecast of early returns based on measurable outcomes.  Projects that receive the green light often start with existing data and build out analytics capabilities in order of business priority.

The Analytics Talent War

As big data capabilities grow, previously unexplored data can present new opportunities for greater insight into customer behavior. Advanced analytics techniques combining both structured and unstructured data can shine a light on the dark data that accumulates in log files and archives.

To successfully mine these and other potential sources of business information, organizations will need data scientists who combine advanced analytic skills with deep industry knowledge. Talent of this caliber is rare and may require years of development beyond the formal training available in recently-established data science programs.

As demand for advanced analytical skills grows, your big data blueprint needs to look beyond the technology infrastructure to include provisions for talent recruitment and development of existing resources.

Where to Begin?

The authors of the study provide general recommendations based on an organization’s  place on the big data adoption curve. The continuum ranges from the early stages of education and exploration to the more mature phases of engagement and execution.

Learn where your organization falls on the path to big data adoption and what steps to take  on your progression to the next level of maturity. Download the executive report: Analytics: The Real World Use of Big Data (registration required).

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Lothar Burow likes simplicity. As the head of business intelligence at the Bayer Group, he wants every employee – not just the controlling experts – to be able to perform their own analyses. And this makes him part of a growing trend.

Really, it should work as easily as a mobile search function for a restaurant: You install the app – like one from Yelp – on your smartphone, activate geolocation services, and then, at the mere tap of a finger, you get the Italian, Greek, and Thai restaurants around the corner displayed with ratings.

If Lothar Burow (60) has his way, it won’t be long before business intelligence in companies – that is, the detailed analysis of business performance and results – works in a similar way. “Let’s take a look at the global financial crisis of 2009,” says the head of business intelligence at the Bayer Group, headquartered in Leverkusen, Germany. “Then, it was important to get information as fast as possible about how demand was developing in the regions.” And also about any possible signs of economic recovery and, for example, when it might be possible to end reduced working hours at the plants, he adds.

How to improve usability?

However, in Burow’s opinion, the commonly available front-end tools did not provide what was needed. Burow, a physicist who has been working in the business intelligence field for more than 15 years, more often than not encountered “a dry world of numbers and poor usability.”

So what should be done?

“Of course,” says Lothar Burow, head of business intelligence at Bayer, “iOS improved usability, but it works on PCs, too.”

Business intelligence requirements

Forrester analyst Boris Evelson recently outlined some important business intelligence trends in his blog. Three of them are: the best tool for each job instead of IT standards, do-it-yourself rather than get it delivered, and mobility as the BI mantra.

Everyone should be in a position to understand the pie charts and bar graphs on the screen and to create them themselves. The tool itself – be it software like SAP BusinessObjects Web Intelligence software and SAP BusinessObjects Design Studio, or tools from competitors Tableau Software or MicroStrategy – hardly matters to Burow. “Good usability, high performance, and availability,” are for him the three magic specifications for business intelligence at the Bayer Group. “It’s important for the complexity to disappear into the background,” he says.

Nevertheless, that doesn’t necessarily mean aiming to be an Apple clone. “Of course,” says Burow, “iOS improved usability, but it works on PCs, too.”

Business intelligence solution

Viewed in this light, iOS and its outstanding usability elevated tablets to the measure of all things, but for Burow the mobile connection is only the last step in the development. Beginning with the back end, which is mainly dominated by SAP NetWeaver Business Warehouse, Burow takes an approach that, above all, leaves options open with regard to the front-end tool. Even when he was responsible for BI only in the area of material science, a number of different tools were in use until, finally, the custom development roi (PE) came into being. It was developed on the Flex platform and initially for PCs only. Burow invariably found that generating a report was too much work. “You had to go through an endless Customizing tree before you eventually got a report out of it,” he says, “not to mention drag-and-drop functionalities.”

In 2007, Bayer’s controlling department finally started scouring the market for a tool that could execute a report without help from the IT division. WebFOCUS from the New York-based company Information Builders was the name of the solution they found. As far as the functionalities were concerned, it was “a dream,” but more JavaScript code was required than expected, so the increasing costs finally meant that continuing BI on this basis was not possible. “As a result, we didn’t look at any more tools, but developed our own,” Burow says.

Not the lowest common denominator, but rather specific needs – especially concerning usability – were finally the reason for preferring the homegrown solution.

BI tip

Business intelligence consultancy BI Scorecard, based in Sparta, New Jersey, believes that other companies will also take such a path in the future. If the functionalities of the major providers are not sufficient in terms of suitable dashboards, decision-makers will mix different tools and integrate them into an individual BI landscape.

The competition will become increasingly tough, as even large IT companies will have to get used to keeping up with small specialist enterprises and software designers. That’s why Bayer’s BI man Burow stresses the importance of integration capabilities for his environment. “No one can guarantee that our custom development will still be the best solution for us in a year’s time,” he says. The cards will be reshuffled again and decisions will be made according to new criteria.

Today, however, Bayer is planning to roll out its BI solution throughout the group, before preparing it for iPads, Android tablets, and mobile devices based on Windows 8. Maybe then the Bayer employees will have the “Yelp BI” experience that Burow wants for the future.

Each person seems to believe they are the atypical expert in the subject matter, offering up ideas that range from the fairly well thought out: we should cancel the football preseason; to the ridiculous: basketball would be so much better if they had trampolines (amazingly, this thought has come true).  

Well recently when the NBA announced a partnership with SAP it got me to doing just this, hypothesizing about ways that analytic software could improve the game of basketball as we know it.

SAP and the NBA have already begun innovating,  providing fans with up-to-date stats and in-depth analysis on the NBA website. And this is just the beginning – there are so many areas where innovation could potentially change the way the game as we know it is played.

We are seeing a general influx of technology in all of the sports we watch today and there is no reason to think that trend will not continue to increase. With that in mind, I’ve come up with three different ways in which technology could change the NBA as we know it today.

1.       Understanding Player Value

One of the biggest pieces of a general manager’s job is signing and acquiring talent, and one of the trickier parts of this comes when they must sign players to lengthy deals of four or five years.

Right now there is no good way of projecting what players will become three or four years down the line and it often leads to a paradox.  Teams are paying the most money for older players who sit on the bench which leaves them without enough money to retain their younger talent who are playing the majority of the minutes.

If the team were to turn to a predictive analysis type tool they could a player’s performance and better understand exactly when players start to break down and under-perform.

A team could hypothetically take all of the NBA’s past player data and start identifying trends for age and performance; sorting through these massive amounts of records to sort it into divisions like position and game played.

They could then perform regression analysis on these data points to understand not only when players will begin to break down but specifically at what rate they will decline each year. By better understanding these trends, general managers can then correlate the specific player’s salaries with their projected output, ultimately fixing the paradox and leaving teams with room to pay their younger talent on the court.

2.       Managing NBA Talent

The big data trend could also be used to manage the rapidly growing amount of talent that is being developed both overseas and in the NBA’s Developmental League. As the game of basketball becomes increasingly more global with the NBA now reaching into both Asia and Europe, the influx of talent is only going to increase over the years and the most successful general managers will be the ones that can harness this growth. Managers could have dashboards that would allow them to sort quickly from league to league and have watch lists of all the players they want to keep an eye on.

Along with overseas, the NBA announced this year that their Developmental League (referred to as the D-League) would now begin to be affiliated with specific teams. So teams like the Boston Celtics would now be able to utilize the talent from the Maine Red Claws (who comes up with these names?).

As the D-League continues to grow and the NBA becomes much more like baseball in the sense of having a minor and major league, it will become increasingly important for General Managers to have visibility not only into the performance of their own team but also the talent they have on their developmental team. Data mining tools will be extremely important to help them accurately assess players that they can’t always see in person.

3.       Coaching

While the first two areas have focused largely on the front office operations; perhaps the largest impact that technology could have is on the coaching side. For anyone who has ever watched a basketball game, you have no doubt seen the camera pan over to a huddle and watched as the coach draws up a play on his whiteboard. With the boom of tablet devices in the past two years, I have to think that the whiteboard days are numbered.

Already at Duke University, Coach Kryzweski has given each of his players and his coaching staff tablets this year in place of playbooks. While he is one of the first I’ve heard to do this, I am sure he will not be the last, and as this becomes a growing trend, the importance of mobile analytics will expand rapidly.

Coaches could use mobile apps to monitor players minutes much easier than they do now, having alerts set up to notify them when a player has reached his threshold.  They could utilize real time statistics to understand the tendency of the opposing coaches play calling.

Image when it came down to the final two minutes the coach could go to his tablet and say with 73% certainty that the ball will go to their star player for a drive to the hoop based off all of their previous plays. Heat maps would allow coaches to understand exactly where they need to shift their defense; something no white board could ever tell them. Ultimately coaches would be able to react to information that they now typically review after the game or in the film room the next day- except they’d have it in real time as it happened.

Throughout the history of sports, technology has played a vital role in how the game is played. It seems undeniable that the next big adjustment will be the introduction of real time analytics and the use of mobile analytics on the sideline by the coaching and training staff .

Measure Past, Present and Future

Most analytic applications have historically focused on measuring past performance on a quarterly, monthly, weekly, or daily basis at best. Nowadays, technology makes it easier than ever to go beyond lagging indicators, and organizations can no longer afford not to leverage the knowledge of what’s happening in their business right now or to predict what may happen in the future.

Understanding future trends can help your organization do more accurate planning and foresee issues before they become a problem that causes business disruption. There’s a lot of untapped knowledge contained in historical data patterns that you can harvest by using predictive analysis software.

Today, real-time analytics are more real, affordable, and possible than ever before. You can do analytics at the same time you’re doing transactions. Let’s say you’re a sales person… just imagine the power of having up-to-the minute information of all sales opportunities that are closing around the world – and from your mobile device!

It’s good to know how much you sold during the last few quarters, but it’s even better to have real-time visibility on sales operations and demand, and to predict how an increase in sales may impact your ability to fulfill orders in the future.

Not Everything Can be Measured with Numbers

Some farmers claim their eggs are healthier because their chickens are happier than those of other chicken farmers. But, can a farmer really quantify the return on investment of raising happy chickens? I can’t measure the value of my professional network just by counting how many connections I have in LinkedIn. My point is, there are many things that are very hard to measure but that doesn’t mean we have to ignore them all together.

Advanced analytics provide tremendous value in these cases where quantitative measuring can’t be obviously done. For example, sentiment analysis can be used to perform customer behavioral analysis or to better understand the influence of market forces on your brand, your products, and ultimately your bottom line.

Sentiment analysis is a great, advanced analytic method to help companies understand customer attitude towards their brand, or even for political leaders to take a pulse on their community. (I’m not sure it can be applied to chickens though.)

Leverage Industry Best Practices in your Analytics

Did you ever wish you knew which key performance indicators the best-run companies in your industry use to measure and optimize performance? You can find out. There‘s a lot free best practices content that you can use as a blueprint to help you get started with your analytics (such as industry data models, reports and dashboard samples). Whether you’re a large insurance company looking to optimize your claims recovery or a small research firm wanting to manage research funds more effectively, you’ll greatly benefit from use cases that showcase how analytics can be applied to your unique business challenges. You don’t need to start from scratch.

It’s time to put your analytics glasses on to fully leverage what your data is hiding! If your organization has been suffering from nearsightedness, real-time analytics will be a great place to start. If, on the other hand, your issue‘s been farsightedness, then predictive analytics will put you on the right track.

Because when it comes to analytics, it pays (and a lot) to go beyond historical reporting. I’d like to hear your stories about how you are evolving your analytics to deliver more business insight.

“…there’s only one commercially available platform capable of real time performance. It’s the ideal system for simulation. It solves complex problems from sensor data at speeds that are compatible with real-time control. Because of its ability to reduce large volumes of data at tremendous speeds, the system easily handles even the most difficult problems. Furthermore, it offers many other outstanding characteristics, including: superb operating efficiency, large capacity, great versatility, the ability to interface with a wide variety of different types of data. For more information about the system, or for information about ways in which you might apply the system to your particular problems, contact us… “

In fact, it’s a slightly-edited version of the copy from this 1956 ad for a Remington Rand Univac…

Image source: the awesome http://www.dvq.com

My conclusion? Not that marketing is dumb, but that the fundamental business need for real information, in real-time, hasn’t changed in 60 years. The technology has taken a massive leap forward — but so has the complexity of the modern world, leaving us “back where we started” (but at a far more advanced level).

The new in-memory platforms are “ripping up the old rules” according to analyst firms like Gartner — and yet we’re getting back to the fundamentals of what we’ve always wanted.

It’s an arms race: ever since the abacus, the cycle has been the same: computing technology improves, we use it do to great new things, and we move on to the next great problem that technology can’t (yet) solve.

The Univac was part of the real-time business information revolution — and in-memory platforms like SAP HANA are too.

I happened to be doing a puzzle with her last weekend, and I thought about how information governance is really a puzzle that many people are afraid to start. Here are a few different “puzzle solving” strategies that I can suggest for you to keep in mind as you begin to put the information governance puzzle together:

What size is the puzzle? How do we define our Information Governance (IG) initiative?

• Many organizations opt to start in a smaller, more focused area, such as with their marketing or operational data.

• Once they’ve earned buy in and success with their IG philosophy, they begin to expand their reach across the company.

Can I do it alone or is the puzzle big enough to need help? How do I get collaboration between IT and business?

• The business units own the data but are seldom involved in its management so there’s a disconnect when it comes to information ownership, accountability, and the analysis.

• By helping empower the business to own and manage its data, an information governance strategy improves employee efficiency and productivity.

• Involving both sides immediately in the planning phases is not optional— it’s a requirement for success.

How do I know if all the pieces of the puzzle are here? I don’t want to get started and find a piece missing. How can I manage governance with confidence when I can’t rely on the quality of the information?

• Fragmented data and managing information outside the context of the business process are problems IT has faced within the data warehousing environment for decades.

• Governing information within the business process when the data is created is key. This way, data quality issues don’t have the opportunity to propagate to different systems across the enterprise.

• You need to manage disparate data and ensure the right regulatory compliance measures and reporting are implemented so there’s one version of the information to rely on.

Hesitant to Start?

Honestly, the best way to start a puzzle is to just open the box, dump out all the pieces, and dig in. Your information governance strategy is the same. Just keep some of these points in mind as you “open the box.”

Then once you start to look at your organization’s data and dig in, you will begin to formulate a plan for IG success.

I’d like to know how your organization has achieved success by beginning an information governance initiative, or even what hurdles you may be facing.

A logistics company wanted to know how to re-route its trucks if it has three or four breakdowns in a single day. It already has ways to modify routes for one or two breakdowns, but three or four became a data analytics problem, said Michael Goul, chair of the Information Systems Department of the W.P. Carey School of Business at Arizona State University.

The university, which with 74,000 students currently tops Ohio State in their perennial jockeying for the largest university in the country, is starting a data scientist program, a master of science in business analytics (MS-BA) program in the fall.

“A lot of analytics will be embedded in processes,” said Goul. The school said that the MS-BA degree will address the boom in big data and its use in companies’ social media feedback, networking with customers and in their own manufacturing systems. The Master of Science in Business Analytics is a joint effort between the school’s information systems and supply chain management programs.

Arizona State’s data science program has been developed with a local business advisory council; IBM, American Express, Honeywell, the Mayo Clinic, Intel, General Dynamics US Air and Edward Jones and Charles Schwab have major facilities around Tempe and Phoenix.

“People on our boards are working in those companies and see the need,” said Goul. The program is among the first accelerated business analytics master programs in the U.S. Tuition is $29,600 for Arizona residents and $44,100 for out of state residents.

“Companies are trying to figure out how to make ‘big data’ and business analytics into a strategic advantage as they collect more information about operations and customers,” said Robert Mittelstaedt, dean of the W. P. Carey School of Business. ““Companies are trying to figure out how to make ‘big data’ and business analytics into a strategic advantage as they collect more information about operations and customers. They also want people with a good mix of industrial engineering knowledge and command of supply chains for efficiency, cost savings and risk reduction.”

The nine-month masters program will take university graduates with STEM — science, technology, engineering and math — degrees into a program with a strong emphasis on analytics and business. The program has attracted a lot of attention from students, he said.

“A STEM student coming in from math or statistics will get challenged by a lot of that business knowledge, and a business students with a degree in economics will be challenged by the data manipulation,” added Goul. “So you might have someone in your class who is an expert in one topic, but you might be more of an expert in the next class.”

With a projected need for 4.4 million data analysts worldwide by 2015, analytics is a prime career path, he said.

“This is an opportunity to jumpstart a career in leveraging data and computer models to solve complex business problems, evaluate scenarios, predict outcomes and support decision-making,” added Goul. “Only about a dozen business-analytics programs like this are available in the United States.”

Jerry Oglesby, who heads up SAS’s coordination with university programs in analytics, said a growing number of universities offer analytics degrees, certificates and coursework.

“SAS has collaborated with North Carolina State University and Louisiana State University to launch Master of Science in Analytics degrees. We are also engaging with the University of South Carolina and Lehigh University to create Analytics Centers and masters programs.” he said.

The new LSU MSA program follows a successful pilot during the 2011-2012 school year, where all graduates found employment within weeks of graduation. Employers include major companies such as Amazon, Bank of America and SAS. LSU’s program closely mirrors the Masters of Science in Analytics at North Carolina State University which worked with SAS to launch the nation’s first analytics master’s degree.

“All 38 graduates of NCSU’s 2012 class had job offers before completing the program; more than half received at least three, said Oglesby. “Ninety-two percent had positions upon graduation.”

Other schools are following suit, though not always with a full-fledged master’s in analytics. SAS has partnered with colleges to create more than 40 certificate programs around the world. Some use a hybrid of statistics and data analysis offerings. Kennesaw State University offers a Master of Science in applied statistics, a minor in applied statistics and data analysis and a SAS certificate in statistical analysis. To date, 60 students have earned SAS certificates and of the 104 master’s program graduates, all who qualify to work in the US are employed.

University of North Carolina at Charlotte (UNCC) has also launched a data science program and one in bioinformatics, which looks like it is becoming a hot field.