Today’s “A:360” podcast answers the question “What is an enterprise data strategy?”. I describe what an enterprise data strategy is, what it must include, and some of the key points it needs to address. A well-defined enterprise data strategy will become the foundation of your analytics success and is of critical importance in your efforts to become a data-driven organization.
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Hey everyone! Welcome to today’s A:360. My name is Brewster Knowlton, and today we’re going to be answering the question: “what is an enterprise data strategy?”
There’s a great website, Dataconomy, that has a definition of an enterprise data strategy that I love. They define it as:
A comprehensive and actionable foundation for an organization’s ability to harness and leverage data.
There are a couple of words in that definition that stick out to me.
The first is comprehensive. If we’re talking about building a strategy for the entire organization – an enterprise data strategy – we can’t just focus on one or two areas. We can’t just focus [only] on lending and marketing. Or operations alone, or marketing and operations. We have to focus on the entire organization.
The data strategy has to be comprehensive.
The other important word in that definition is actionable. The data strategy has to be actionable. It can’t just be a “binder-on-the-shelf” type of strategy where it’s a big book, there’s a lot of words, there’s a lot of pages but it’s not really saying much.
A data strategy has to be actionable. It has to address the technical aspects. It has to address the cultural aspects. It has to tell you how you’re going to go from point A (where you are today) to point B (where you want to be as a data-driven organization). Not just in theory, but in practice. You have this strategy, but how do you execute on that strategy?
Having an actionable foundation built into a data strategy is absolutely critical.
Building an enterprise data strategy, a data strategy that’s going to help you evolve from a not particularly data-driven organization to one that is can be a pretty intimidating task. Here are a few things to start with that might get the ball rolling.
First, compile a data inventory. Go through all of the different applications you have, all of the different databases, and start asking people if they have access databases or important Excel spreadsheets that they’re maintaining on their own computers. Start by getting an inventory of all those data sources that are out there.
After you build this data inventory, I’d recommend building a report inventory. This is very similar to the data inventory except instead of identifying all the data sources, you want to identify all of the reports – specifically, the recurring [reports].
This is important because it helps you identify where the majority of your reporting efforts are being focused within the organization. It also tells you what type of return you might be able to get early on by automating some of these reports. ROI and analytics can be a tricky thing to calculate sometimes. However, for organizations that are just starting their data and analytics journey, automating recurring reports and manual processes that take a lot of time are great ways to generate FTE savings up front and start to show the value of analytics.
There are quite a few other key concepts that have to go into your data strategy, most of which we will talk about in later podcasts. But, the last thing I want to talk about today is the importance of developing a data dictionary. I’ve addressed this a number of times in my previous podcasts and my repetition just emphasizes the importance of it.
You have to define key terms like member, service, product, household, or any other key term that’s critical to your business and for the analytics that might be generated
You’re already analyzing your organization’s data needs, wants, challenges, and getting a picture of where you are and where you want to be. Building in some definitions and recommendations for those definitions is advised because it starts to get people to understand the importance and the impact that their decision-making on these key definitions will have going forward.
In summary, an enterprise data strategy is, and I’ll repeat this definition from earlier, a “comprehensive and actionable foundation for an organization’s ability to harness and leverage data.” It has to be comprehensive in that it looks at the entire organization, not just one or two specific departments. And, it has to be actionable. Instead of just addressing these high level conceptual or theoretical principles of data and analytics, it has to address the technical aspects. It has to address the cultural impacts, which we’re going to talk about later on. It has to address KPIs, which we have discussed before, and actually, in our next podcast, we’re going to talk about ensuring you have the right KPIs. We brought up data governance and talked about key definitions.
An enterprise data strategy should become your blueprint for going from where you are today as an organization with data to becoming a data-driven organization.
Thanks for tuning into today’s A:360!
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