erwin Expert Blog

Don’t Fool Yourself About Data Management

The early stages of adopting a data strategy often involve an ad-hoc approach to data management. Rather than invest in a suite of new tools, businesses tend to make do with what they have already, starting small and eventually formalizing the approach.

In many cases, this could be the best approach – or at least better than wasting an investment on shelfware, right?

But if your business wants to use its data effectively, a point inevitably will come when the data has outgrown the makeshift means in which it is managed.

data management

Harder to Manage and Share

Enterprise architecture (EA), for example, can start as a collection of Visio files, Excel spreadsheets and PowerPoint slides, but it’s never long before the EA starts spilling from the Office Tools and onto the desk – literally. It’s not uncommon to see EA represented as Post-it Notes haphazardly scattered across a workstation.

For a while, an enterprise architect might be able to maintain this approach. But when the information needs to be shared with the wider business to influence decisions and strategy, the lack of structure can make the findings difficult to comprehend.

This not only slows down time to markets and leads to inaccurate analysis and results, but it also undermines EA’s value in the eyes of stakeholders and decision-makers. If they can’t clearly see the business outcomes, then why bother investing more money in the discipline?

Harder to Analyze

Taking this approach also limits the potential analysis an organization can even carry out. With the Office Tools approach, even though the software all falls under the “Office” bracket, the files and systems are still disparate.

Traditionally, this was less of an issue for EA in years gone by. Foundational EA, as we refer to it today, was and is about support, rather than innovation. Businesses weren’t really actioning an EA initiative to give insight into where they could innovate, the likelihood of disruption, and how that disruption could be capitalized on.

EA was more about “legacy” IT tasks like keeping the lights on, highlighting redundant systems and processes, and trimming fat to keep costs low. In other words, it was more concerned about the current state of the business and less about what needs to be done to achieve the desired future state.

In-depth and all-inclusive analysis required to maximize the data’s potential benefits, needs to be stored in one repository.

Harder to Maintain

And what happens if your enterprise architect leaves? His/her work may be rendered useless to the business due to the lack of formality.

So don’t fool yourself about data management. Given all these consideration, you need to invest in effective data management so your business can truly capitalize on its data and the valuable insights it will provide.

Data Management - Enterprise Architecture & Data Modeling White Paper