In a recent conversation with a colleague, we talked about people’s natural tendency to focus on the newest and coolest tools for data analytics. People often want to believe that if you invest in the bright and shiny toy for a very specific purpose, your problems are going to be over. But, of course, it’s never that simple—especially when it comes to data analytics. Many people have learned the hard way that chasing a bright, shining analytics toy can get you into a lot of trouble.
While we always should be open to embracing leading-edge technologies, we have to embrace them within the context of a strong data analytics foundation. So my advice starts there. When you want to ramp up your analytics capabilities, focus first on your foundation, and then build up from there.
What’s in a strong foundation for data analytics? For most organizations, the analytics foundation begins with core technologies, such as conventional relational databases for enterprise systems, a versatile data lake built around a technology such as Hadoop, and sophisticated data analytics solutions.
But these technologies alone are only part of the foundation. A strong data analytics foundation also includes comprehensive governance and security policies. Today, growing numbers of people within an enterprise need access to data. To meet those access needs in a secure manner, you need to have the right governance mechanisms in place. You need the ability to monitor the information you are making available to people and to maintain tight control over who sees what.
It’s also important to understand how people are going to use data. Companies around the world are actively focused on collecting all the data they can collect and consolidating it into data lakes. That’s good, but that’s not enough. In many cases, companies don’t understand how they are going to use all of the data they are collecting. This shortcoming points to the need to focus on use cases. By itself, a data lake doesn’t make data valuable to the organization. The value stems from the use case.
Consider, also, where you will conduct the analysis for different types of data. In the traditional static data world of years ago, enterprises brought all their data into an enterprise data warehouse. In today’s hybrid world, that rigid approach no longer makes sense. Depending on the data, analytics now can run at the core, the edge, and in the cloud. This means you need a hybrid architecture that allows you to take data to analytics and to take analytics to data.
Performance considerations also come into play when building a robust analytics foundation. Simple access to data isn’t enough. Users and systems need fast access to data, and that requirement points to the need to focus on access points and their ability to keep up with the pace of your business.
The big message here is to think about the basics. Step back and take a holistic view of your environment. Focus first on your foundation. Embrace multiple tools for a hybrid analytics ecosystem. Use the right tools for the job. Don’t hit a nail with a screwdriver. Don’t try to make tools do things they weren’t designed to do. And think about who needs access to data, how they will access it, and how you will govern that access.
With the rapid evolution of technologies for capturing and analyzing big data, we are at the dawn of an era of exciting advances in the way we use information, connect with customers, and gain a competitive advantage. Companies that win in this new all-data world will all have one thing in common: a strong foundation for data analytics.
For a look at how Dell transformed a complex data landscape by focusing on standardized data sets, governance and culture change—before technology solutions—read the case study “Unlocking data’s value for better insights and decisions.”
Shawn Rogers is the Chief Research Information Officer for the Information Management Group at Dell Software.
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