A New Approach to Steering Strategic Enterprise Data Initiatives – Part 2 of 8

In Part 1 of this series, I introduced the challenge that senior executives often face when trying to get a handle on large-scale data transformation initiatives.  In this post, I’ll outline the questions that senior management should ask to start to get a sense of the scope and scale of the work, so that they can start playing an effective steering role.

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The simple steering questions

As a senior executive, what do you need to know about your data?

This is where you should start: the all-important scoping and status tracking questions.

Don’t worry about how your teams will work out the answers – we’ll get to that later.  The important thing for you at this stage is to start with the right questions, because they will help steer the actions of your teams in the right way, to get to the right outcomes.

So, I would suggest that some good first questions, at a high level, could be:

• What data is in scope?
• How much of the in-scope data is “OK” and how much of it isn’t “OK”?
• How many systems are in scope?
• How many in-scope systems are “OK” and how many aren’t?
• How many issues have you logged about each of the above?
• How many issues have been resolved and how many are still open?

Initially, I’d recommend leaving it at that (or start with a similarly short list of questions).  At the highest level, you should be able to get answers to these questions, before you start drilling down.

It should be possible to get the answers to these questions presented simply, on a single page.  For example, they could be visualised as three dials, presenting:

• Number of data elements (split by those that are “OK” vs. those that aren’t)
• Number of systems (split by those that are “OK” vs. those that aren’t)
• Number of issues (split by those that are still open vs. those that have been closed)

This gives you a simple, tangible starting point.  From here, you can ask a whole range of sensible questions:

• How did you choose the list of data and systems that are in scope?  What have you left out and why?
• What does “OK” mean for your data and systems?  What’s the impact of not being “OK”?
• How have your prioritised your data, systems and issues?
• What are you doing to make more of your data and systems “OK” and to resolve more issues?

Do you see how simple yet hugely powerful these questions are?  You don’t need to be a data expert, and these questions enable you to set the boundaries of your data initiative in a very clear way, and to start steering the rationale for the scope and approach for prioritising the backlog of work that needs to be done.

With this structure, you can take control of the decisions about what’s in and out of scope.  You can decide on how priorities are set and challenge why some things are at the top of the priority list and others are lower down.  You can challenge the progress that is being made in improving the “OK-ness” of your data and systems, the speed at which issues are being resolved, whether you have the right people on point for sorting things out or not.

Each issue on your issues list should address something that moves your data and/or systems into a more “OK” state.  Everything on your list of data, systems and issues should have an owner assigned, who can be held to account for any actions required against whatever it is that they are accountable for.  This enables very clear decision making about where efforts are placed to drive the most value.

But surely, it’s more complicated than that!

It is, but mainly at an implementation level.

At an executive steering level, you shouldn’t need to know all of the detailed ins and outs about how the data management outcomes are achieved: you just need to know enough to be able to challenge and steer effectively.  It is true that you will need a certain level of understanding about how the approach is being followed to get to these high-level answers, some of the basic things that drive the definition of “OK” for your organisation and the criteria for prioritisation, to be able to challenge and steer – and this basic level of understanding will be provided in subsequent posts.  However, keeping it simple and precise is key to avoiding over-complication of the issues.  It’s easy to get lost in detail, and the good thing about this approach is that it brings simplicity and clarity.

At an implementation level, you will need appropriately qualified people to deliver the work and to ensure the complexities are understood and dealt with appropriately.  You will need people who can explain why something is taking as long as it is to progress and why there are dependencies between various actions that are important to deliver the improvement work successfully.  There often are lots of complications and challenges in delivering the underlying work, but that doesn’t change the status that can be monitored and steered at this level.

Coming up in Part 3…

In part 3, I’ll explain a bit more about how the answers to the high level questions about data need to be compiled, to enable even more insightful steering of the work.

In the posts that follow after Part 3, I’ll do the same for systems and issues.  I’ll deliberately keep this to data, systems and issues initially; but after these topics, I’ll expand to explain more generally about the importance of aiming for precision, not perfection; and finally will also expand this approach out further, to cover other lists of things, which support a more comprehensive and holistic data management approach.  There’s a lot to cover – but I promise it’s well worth sticking with it, because this approach provides a great level of clarity and control over your data initiative.