A New Approach to Steering Strategic Enterprise Data Initiatives – Part 1 of 8

A common challenge for senior executives when sponsoring a large-scale data management initiative, is how to get a handle on just how much work there is to do, how long it will take and how much value it will drive.  In this series of posts, I'll introduce a simple approach to enable senior management to do just that, without needing to become data experts themselves.  I call it the “list-based” approach…

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Is it all just too big a problem?

With an increasing number of laws and regulations forcing companies to make data management a Board-level priority, providing senior management with the mechanisms to understand the state of their data and enabling them to steer actions to manage it appropriately are imperative.

More importantly, it is now increasingly widely recognised that businesses not only run on data, but can use their data to drive massive value.  The ability to leverage new digital services, Artificial Intelligence (AI), Machine Learning (ML) and many of the new technologies that could bring significant competitive advantage and open up totally new markets and opportunities to engage with customers in new and innovative ways, are totally dependent on high quality, well managed data.

However, in large organisations, especially those that have evolved over many years, there are often so many different systems, so many different processes... and as a result, so much, often disparate and inconsistent Data, that getting a handle on the challenge of managing an enterprise’s data effectively, at scale, can appear to be a very daunting task.

When sat with Boards and Executive Committees reviewing a new data management programme’s grand plans, common questions that are often met with totally inadequate answers are:

• What’s the current status?  Where are we now?  What are the implications of that current status?
• Where do we want to get to?  How far have we got to go?  How much cost and effort will it take to get to there?
• How long will it take?  How do we know that the right resources are being deployed in the right ways to get there?

Why are the answers to these answers often so inadequate?

Quite simply, because it is true that there’s a very high volume of “stuff” to get a handle on and manage: many types of data, many inter-connected systems, many different data flows, many different processes, lots of projects and change, various teams and people working on these things all the time, various third parties and contractual arrangements… and if you’re not careful, this mess of “stuff” very quickly becomes overly complicated and difficult to get your head around.

This is often compounded by the fact that the people working on data programmes are often either very technical, meaning that they know how to articulate the challenge in complex, detailed, bottom-up ways; or are non-technical and don’t really get how things work, so either over-simplify the challenge or explain it in an inaccurate and confusing way.  Even when you have experienced data professionals who get the technical detail and can speak in a way that business stakeholders understand, it’s not uncommon for them to struggle with sizing the challenge and explaining plans and progress in a way that enables senior management to play an effective steering role.

The good news is, there are a range of tried-and-tested methods for assessing large-scale data management initiatives; for getting a handle on the volume of work, applying some logic to prioritise efforts and for tracking progress in such a way that non-techies can start to get their heads around what's going on and to steer the work from a well-informed and intelligent position.

In this series of posts, I’m going to introduce a somewhat new way of thinking about this problem.  When I explain the new approach, some data professionals may feel that they already do what I’m describing and may contest whether the ideas I’m sharing are new.  It is true that what I’m going to outline is not totally new in the world of data management and most data professionals will immediately recognise what I'm talking about, because it’s often necessary that this approach exists in some way, especially on large-scale data projects, even if just implicitly.  Yet, I still find that many organisations are not making use of this simple and highly powerful and effective approach – so I hope that explaining it in this slightly different way will be helpful.

At its core, what I'm going to describe is so simple, some will wonder why they haven't embraced the ideas already.  I'm hoping that, once you've got your head around the basic principles of this approach, your eyes will be opened to just how simple it can be to get a level of control over your data management initiative.  This will be particularly revealing if you have made various attempts in the past and didn't realise what was possible.  You will see that you can apply this same technique to a range of different content types and assets.  I will touch on a few common examples to bring the ideas to life, but once you have got the concepts, you will quickly see how you could apply the same idea in a number of different scenarios.

If you're a data management professional, this approach will give you a way of communicating very large and often very complex topics in a far simpler, more quantifiable way.

If you're a senior executive, you'll be able to get your head around a range of problems in this space, in a very similar way to the way that you do with any other business problem and will then be able to play an effective role in steering and sponsoring the efforts required to deliver the transformational improvements that your organisation is aiming to achieve.

Coming up in Part 2…

In part 2, I’ll outline the questions that senior management should be asking to start to get a sense of the scope and scale of their data initiative, so that they can start playing an effective steering role.