Enterprise Analytics are the Backbone of a Strong Corporate Data Strategy
If you’re following the SAP Data Champion Roundtable series I’m fortunate enough to host on LinkedIn Live, you’ll know that my guests have a wonderful way of distilling complex topics into simple quips. The title of this blog post is a prime example. It’s a quote from Ronald van Loon, CEO and Principal Analyst at Intelligent World, from our discussion about enterprise analytics.
My question was a simple one: What are enterprise analytics? Ronald’s answer was much more complex, yet encapsulated in a simple statement. “Enterprise analytics are the backbone of a strong corporate data strategy.” Struck by this succinct statement I had to ask, is it really that simple?
It can be, according to Maribel Lopez, Founder and Principal Analyst at Lopez Research, and my second esteemed guest for episode five of the series. Her outlook on enterprise analytics is what set the stage for Ronald’s statement. In her eyes, enterprise analytics are far from unfamiliar or unknown—rather, they’re something we’re learning to understand.
The combined expertise of Ronald and Maribel yields amazing insight into how simple the world of complex enterprise data analytics can be with the right outlook. In the latest episode of SAP Data Champion Roundtable, we discuss what companies can do to wrangle analytics into a strong corporate data strategy.
Organizations are seeing data differently
Making a complex topic simple starts with a fundamental understanding of it. For many enterprise companies, modern analytics demand a complete re-teaching of the fundamentals, thanks to the Big Data boom and the technologies fueling it.
Today, enterprise analytics are a gargantuan topic because enterprise data is infinite. Depending on the company, it can amount to hundreds of data sources streaming millions of data points—all in real-time. This data becomes the basis for decision-making at every level of the business. It results in exponential potential for analytics that seems even bigger now that companies are realizing the potential of de-siloed data.
“We’ve had enterprise analytics for a while, but what’s different today is that organizations are thinking differently about data and analytics. They don’t want it in silos or in specific groups. They want to be able to look at an organizational view of their data,” says Maribel. “Where and how they do analytics is different now, too. There’s much more of a sense of real-time data analytics, near where it’s happening so that you can have actionable insights in that moment.”
Data as an active, dynamic, integrated experience across an organization is more complex than it’s ever been in the past. Yet, the purpose of data remains the same: to empower better decision-making. Today, from an enterprise, top-down level, organizations are beginning to see holistic data as imperative. This is where Ronald’s quip about data strategy becomes real.
“It’s all about extending end-to-end data and analytics capabilities across the entire enterprise, so that the business is using the tools and the technologies to help them get the most out of real-time data, not from all the different silos anymore,” says Ronald. “This way, decisions are based much more on data-driven facts from a corporate perspective than on data silos with their own analytics. If you look to the corporate strategies and the analytic strategies, they become much more intertwined right now than they were ever before.”
Therein lies the first opportunity to simplify enterprise analytics. Companies need to see data differently: as a driver of corporate strategy, not just an affirmation of effort.
Better processing power means more insights
Part of the reason companies are seeing data differently is because they have access to more data generation tools than ever before. There may be millions upon millions of data points, but collecting, aggregating and accessing that data is simpler than ever. Look around you right now. Laptop. Smartphone. Tablet. Smart watch. These things are likely within reach, and they represent the amazing accessibility enterprise companies have to data.
“Think about all the connected devices we have. A lot of those devices have, not just connectivity, but the ability to format and process data on the device or very local to the device,” says Maribel. “Very specific lines of businesses can now have better insight, because they have more data to put into their applications.”
According to Maribel and Ronald, the data-processing devices we use today—and that companies use on an enterprise level—have opened the door to better integration. There may be much, much more data these days, but companies are getting better at assessing it at the point of use. From silos to application-specific deployment, data is becoming more meaningful because we have access to it where and when it’s useful.
“You have light bulbs that can report energy utilization, for example. Or, if you’re a manufacturing plant, you have equipment that’s connected that can read the temperature or vibrations of the gear itself, giving you vital information. If you’re in a scenario where you’re in a truck, you’ve got the logistics information—how’s that truck performing? Where is it in terms of its location? What’s the tire pressure?” says Maribel. “All these come together and produce data that can be analyzed and filtered into the appropriate application, so you can take the right action with more information than you had in the past.”
It’s important that strategy is at the core of this. Harkening back to Ronald’s encapsulation of analytics as the backbone for data strategy, it’s vital for companies to establish methods, modes and means for drawing on data in a meaningful way.
“It doesn’t matter what type of technologies an organization invests in—if they haven’t established a successful data analytics strategy that spreads across the entire enterprise, they cannot maximize the value and impact that the data will bring to them,” says Ronald.
Data strategy leads to informed action
According to my guests, a corporate data strategy is what bridges the gap between the plethora of data out there and the better decision-making capabilities companies see in it. Companies have data and they have goals, but many are still learning how to promote action to connect them. Part of this problem has to do with disparate data architecture. It’s hard to form a strategy when you’re only looking at part of the picture. The first step in a unified data strategy is, well, unifying data!
“There’s still quite a gap between what’s required and where organizations are. What’s important is to develop the right strategies to build your successful architecture—one that aligns stakeholders with the plans and strategies you have,” says Ronald. “Instead of mapping your organization’s entire data future, start with a small case. Learn from it. Understand it. Get specific results that you want. These types of strategies—these quick-start strategies—are important. Then, you have the ability to scale it to an enterprise perspective.”
There’s no better context for this concept than the COVID-19 pandemic. Maribel expounds on the idea of simple case studies bridging into bigger strategies, and how decisive action is the result of access to data.
“These are questions organizations had to deal with very rapidly, and those that had a good data and analytics infrastructure were able to have a good handle of where they were today. They were able to do things like scenario planning to see what their business might become,” says Maribel. “I think we’re now seeing them as data analytics champions. They’re gold at this moment right now because they’re the people driving the business forward.”
These data analytic champions are exactly the models the SAP Data Champion Roundtable series focuses on. Chatting with Ronald and Maribel, it’s clear that more and more companies are finding a seat at the table because they’re empowering these data analytic champions within their organization. In this way, enterprise data strategy is both top-down and bottom-up. Top-down, executives need to enable their organizations to be data-first and data-forward. Bottom-up, every company needs a strong foundation in data readiness.
“If you have a good foundation, you can further build on that and roll out your data strategy much faster. When it comes to business leaders, they need to attempt analytics adoption across the business. It’s important right now that they have this foundation,” says Ronald.
Data strategy helps business thrive, not survive
Coming off the crippling effects of a global pandemic, many businesses are happy to have survived. They’ve turned to data to help them pivot and adapt—and now this strategy is helping them to go from surviving to thriving in their new mode of operation. It’s fueling a major interest in developing a long-term data strategy.
“If you don’t [leverage your data], it’s going to be really hard for you to create any kind of competitive advantage moving forward,” says Maribel. “Companies need to do that with a directed goal, not just throwing data at a question and seeing s what comes out of it.”
And so, we’ve come full circle: Enterprise analytics are the backbone of a strong corporate data strategy. To create the strategy you need analytics, to get analytics you need data, to get data you need architecture, and to get architecture, you need a data-first mindset. Like most things, the concept is simple, but the underlying effort to capitalize on it takes a great investment.
I hope you’ll join me for this full episode of Data Defined: SAP HANA Innovations episode 5, “Leveling Up Your Business with Enterprise Analytics,” to learn more about how companies can put themselves on the path to a strong corporate data strategy, using enterprise analytics as the foundation.
Stay up to date by registering for the Data Defined: Monthly Bytes newsletter, catch-up on the latest episodes here, or take a deeper dive by reading SAP’s latest guide, “The Ultimate Guide to Enterprise Analytics.”