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Getting Big Value from Big Data

Big data is becoming more and more available in our daily life, changing the way companies analyze and report on business metrics, trends, and performance. Reporting expectations are also climbing – senior managers want the ability to include more data, run faster and deeper analyses, and get reports more frequently. Employees need to do this analysis quickly and efficiently, without any interruption or downtime associated with waiting for IT.

You need a way to keep up with these changes – to drive business in real time and to unlock the value of your big data. Just buying new hardware and adding storage space is neither affordable nor sustainable. Instead, you need to handle large amounts of data very efficiently, analyze it wherever and whenever you want, and share results via a collaboration platform.

To generate the most value from big data, companies need strategies and technologies that help them break free of infrastructure limitations to support new business processes. Roles and responsibilities for business intelligence (BI) have to be redefined and employees must be empowered to create their own reports or analytic cases. With the SAP Analytics platform, you can put your big data to work, tapping instantly into massive amounts of information from virtually any source – including powerful in-house quantitative information and valuable qualitative context from customers, suppliers, and other stakeholders. Make more accurate decisions, combining state-of-the-art interactive analysis (such as deep behavior and data pattern analysis or predictive analysis of data streams and social media) with valuable insights into transaction history. And expertly manage and use all data regardless of source, processing technology, or number of users.

SAP Analytics allows you to:

•    Identify new business opportunities through high-performance analytics that drive deeper real-time insights

•    Flexibly deliver immediate insight into massive volumes of data at any level of granularity and dimension

•    Use in-memory analytics to enable real-time analysis of both structured and unstructured data

Let’s look at how SAP Analytics, plus the right reporting and planning tools, can solve complex business problems.

Analyzing Big Data in Social Media

A few years ago, social media was an emerging trend. Today, social media forums such as Facebook, Twitter, and LinkedIn give you access to a wealth of real-time opinions about product experiences and brand performance. Imagine an airline, on sensing an uptick in negative sentiment across social media channels, looking into the situation and seeing a planeload of passengers stuck on the tarmac, unable to return to the gate. The airline could then respond in the moment with free drinks, mileage credits, flight discounts, or on-the-fly online rebooking. Suddenly the online buzz changes from “I’m never flying this airline again” to “This airline cares about its passengers and is making things right.”

In addition to social media platforms, SAP Analytics allows you to analyze, news sites, blogs, shopping and review sites, and support forums, so you can react to sentiment trends in the moment – changing behavior and mitigating damage.

Liquidity Risk Management in Banking

Banks often struggle to capture and analyze cash flow across a complex array of financial products – information that lets them make informed market decisions. For example, a bank may receive and analyze regional investment rating information each week, then make recommendations to buy, sell, or hold. The problem: week-old (or even day-old) data can reflect outdated economic or political conditions. In contrast, a bank that can access rating information and cash flows in real time can adjust its financial products, or inform customers to change their investments, before the situation changes.

With instant transparency into massive amounts of cash flow and liquidity risk data, banks get responsive intraday liquidity risk management, real-time access to liquidity information, and the ability to stress-test scenarios as needed. SAP Liquidity Risk Management based on SAP HANA gives banks in-the-moment access to deep customer insights, risk exceptions, and issues, so they can leverage real-time data. The result: more accurate planning and budgeting, faster reporting, and better investment decisions.

Risk Analysis in the Oil Industry

Liquidity risk management can benefit any large corporation. Take a large oil producer that manages and funds maintenance and upgrades on hundreds of multi-million dollar drilling platforms. Without the ability to analyze the platforms’ combined cash flow, it had to borrow money for each investment. Now, in-memory computing lets the oil company run daily reports on the combined cash flow from all platforms. The improved visibility made it possible for the company to self-finance many upgrades, saving considerable in financing fees – an immediate return on its SAP HANA investment. With advanced analytics, treasury departments are able to simulate different investment scenarios in real time with very detailed data.

Final Big Data Considerations

Many companies have copious amounts of data, yet struggle to combine structured and unstructured data for immediate analysis. The SAP Analytics platform powers analysis and reporting of big data, dramatically improving decision making and reporting speeds. To get the greatest value from your data, you also need an overall BI strategy that helps you document new reports, KPI ownership, and user roles and responsibilities.

SAP professional services can help you make the most of big data and establish relevant BI strategy, architecture, competency center, and governance. The adoption of SAP rapid-deployment solutions for big data – including several with social media analysis built into standard reports – can help you get SAP Analytics up and running quickly.

To find out more about how SAP Analytics Services can help your company develop a targeted roadmap that unlocks business value from your big data, please visit us online.

What is keeping you from including valuable big data in your daily analysis?

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