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Author's profile photo Chris Hallenbeck

Digital Transformation and the Data Warehouse

There has been a lot of talk recently about digital transformation for businesses and all the advantages it brings. A great example of a company going digital in a big and successful way can be found in Under Armour, a major player in the athletic wear space who saw a new opportunity emerging around what they call “connected fitness.” Using sensors embedded within the clothing itself, athletes can access more comprehensive and detailed fitness data than a handheld or wrist-attached device. The company now provides not only the athletic wear to track fitness, but the online infrastructure that allows athletes to track and measure their performance, communicate results, compete with other athletes, find running groups that match their level, and so on. Under Armour has redefined their business while showing the world how elegant the process of digital transformation can be.

What, you might wonder, does any of that have to do with data warehousing? As a matter of fact, that is the very question a lot of businesses are asking. If your business has been modified to better leverage digital technologies, or if yours is a digital-native business that never needed transforming, you might be skeptical about what role a legacy technology like a data warehouse can do for you.

An old-school solution?

And you might be right to be skeptical. When you hear the words “data warehouse,” what do you picture? Do you think of a monolithic environment with a relational database at the center supported on one end by a server for extracting, transforming, and loading data and on the other end by a server for reporting and businesses intelligence? Sure, back in the day, such environments were pretty good at answering known questions using familiar and highly structured datasets. But they weren’t necessarily great at integrating new kinds of data or answering completely unexpected questions.

Dealing with those kinds of challenges—which, of course, are the kinds of challenges that businesses are constantly running up against today—required a lot of time and work and, more often than not, a generous share of costly infrastructure. Yet even with all that, the results were sometimes less than adequate. Or the modified environment worked for a while, and then needed another expensive and time-consuming upgrade.  Isn’t it true that whatever benefits a data warehouse may have once brought to businesses, they have since been bypassed by more open, flexible, and cost-effective technologies that are a better fit for the world of digital business? Things like Hadoop, Spark, and Kafka?

Well, to answer that we should maybe step back and consider a more fundamental question.

What is a “data warehouse,” anyhow?

There is a major problem with the above “old-school” takedown of the data warehouse. It’s based on a misunderstanding of what a data warehouse is, and what its true value proposition is to the organization it supports. TechTarget provides a definition of data warehousing that can help to clear up this misunderstanding:

A data warehouse is a federated repository for all the data that an enterprise’s various business systems collect. The repository may be physical or logical.

What’s important about this definition is what’s not there. There’s nothing about ETL, data models, relational databases, or expensive servers. A data warehouse is where a business puts its business data. Period. And the definition goes even further than that. That’s an intriguing start for a definition, but there seems to be something missing. After all, what does a business do with this repository? Why are they putting data in there? BI and data warehousing guru Wayne Eckerson answers that question by defining the data warehouse in even more abstract terms:

At its heart, a data warehouse is not a technology or tool. It is primarily a business process that unites an organization in electronic form (i.e., through data) so it can function as a single entity, not a conglomeration of loosely coupled fiefdoms. [Emphasis added.]

Of course, once you start thinking in terms of “uniting the organization in electronic form,” you’re talking about a technology that fits squarely within a discussion about digital transformation for business. Some have argued that the data warehouse is the original big data technology. Eckerson may have one-upped that argument, putting the data warehouse at the center of a digital business model.

Turning the lights on

With all that in mind, it probably doesn’t make sense to ask whether a data warehouse would be right for your business. Chances are, your business is using some kind of repository for business data, and some kind of process for bringing that data together to support decision-making and other internal processes. The truth is, whether your environment is built on licensed software or open source, whether you deploy on-premises or in the cloud, and whether you keep all your business data in Hadoop or MYSQL or NoSQL—or whatever—you already have a data warehouse. Maybe you just didn’t see it there.

It’s only when you turn on the lights and take a close look at the data warehouse you already have that you can assess whether it’s the right fit for your business. In his book, Data Warehousing in the Age of Big Data, data warehousing thought leader Krish Krishnan argues that the best way to evaluate the effectiveness of a data warehouse is not to look at architecture, but rather business impact. What opportunities is the data warehouse enabling the business to take advantage of, and what opportunities are being missed? His criteria for evaluating a data warehouse environment include:

  • Gaining competitive advantage
  • Reducing operational and financial risk
  • Increasing revenue
  • Optimizing core business efficiencies
  • Analyzing and predicting trends and behaviors
  • Managing brand presence, channels, and reputation
  • Managing customer expectations proactively

For many years now, the data warehouse—including the traditional version described earlier, with all its limitations—has been at the heart of most organizations’ efforts to make improvements in these areas. Implemented correctly, a data warehouse can ensure data integrity and security, putting the whole organization on the same page and providing fast answers to complex questions. And now a new generation of data warehouse is emerging that is architected to meet the demands of modern data infrastructures and the pace and complexity of business in the digital era. .

This is how a company like Under Armour can completely transform their business—addressing all the criteria Krishnan cites above, while adding a new one: successfully implementing whole new business models / lines of business—by deploying a real-time solution built on SAP HANA. Under Armour’s fully integrated in-memory environment enables the company to deliver products to retailers faster and stay on top of supply and demand as it changes by the hour. And beyond that, it allows them to maximize customer satisfaction via a targeted and personalized service, one that helps customers improve their performance, achieve their fitness goals, and stay connected with a broader community of athletes.

As relevant as ever

Successes like these demonstrate that the data warehouse is as relevant now as it ever was. However it might be architected, and whether it relies on traditional enterprise technologies, big data technologies, or an emerging class of real-time technologies for digital business, the data warehouse has an important role to play both for businesses that were “born digital” and for those that are now taking their first steps towards digital transformation.

Explore next-generation data warehousing solutions from SAP.

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