In the thirteen years that have passed since the beginning of 2007, I have helped ten organisations to develop commercially-focused Data Strategies ^[1]. I last wrote about the process of creating a Data Strategy back in 2014 and – with the many changes that the field has seen since then – am overdue publishing an update, so watch this space ^[2]. However, in this initial article, I wanted to to focus on one tool that I have used as part of my Data Strategy engagements; a Data Maturity Model.

A key element of developing any type of strategy is knowing where you are now and the pros and cons associated with this. I used to talk about carrying out a Situational Analysis of Data Capabilities, nowadays I am more likely to refer to a Data Capability Review. I make such reviews with respect to my own Data Capability Framework, which I introduced to the public in 2019 via A Simple Data Capability Framework.

Typically I break each of the areas appearing in boxes above into sub-areas, score the organisation against these, roll the results back up and present them back to the client with accompanying commentary; normally also including some sort of benchmark for comparison ^[3].

A Data Maturity Model is simply one way of presenting the outcome of a Data Capability Review; it has the nice feature of also pointing the way to the future. Such a model presents a series of states into which an organisation may fall with respect to its data. These are generally arranged in order, with the least beneficial state at the bottom and the most beneficial at the top. Data Maturity Models often adopt visual metaphors like ladders, or curves arching upwards, or – as I do myself – a flight of stairs. All of these metaphors – not so subtly – suggest ascending to a high state of being.

Here is the Data Maturity Model that I use:

The various levels of Data Maturity appear on the left, ranging from Disorder to Advanced and graded – in a way reminiscent of exams – between the lowest score of E and the highest of A. To the right of the diagram is the aforementioned “staircase”. Each “step” describes attributes of an organisation with the given level of Data Maturity. Here there is an explicit connection to the Data Capability Framework. The six numbered areas that appear in the Framework also appear in each “step” of the Model (and are listed in the Key); together with a brief description of the state of each Data Capability at the given level of Data Maturity. Obviously things improve as you climb up the “stairs”.

Of course organisations may be at a more advanced stage with respect to Data Controls than they are with Analytics. Equally one division or geographic territory might be at a different level with its Information than another. Nevertheless I generally find it useful to place an entire organisation somewhere on the flight of stairs, leaving a more detailed assessment to the actual Data Capability Review; such an approach tends to also resonate with clients.

So, supposing a given organisation is at level “D – Emergent”, an obvious question is where should it aspire to be instead? In my experience, not all organisations need to be at level “A – Advanced”. It may be that a solid “B – Basic” (or perhaps B+ splitting the difference) is a better target. Much as Einstein may have said that everything should be as simple as possible, but no simpler ^[4], Data Maturity should be as great as necessary, but no greater; over-engineering has been the downfall of many a Data Transformation Programme.

Of course, while I attempt to introduce some scientific rigour and consistency into both my Data Capability Reviews and the resulting Data Maturity Assessments, there is also an element of judgement to be applied; in many ways it is this judgement that I am actually paid to provide. When opining on an organisations state, I tend to lay the groundwork by first playing back what its employees say about this area (including the Executives that I am typically presenting my findings to). Most typically my own findings are fairly in line with what the average person says, but perhaps in general a bit less positive. Given my extensive work implementaing modern Data Architectures that deliver positive commercial outcomes, this is not a surprising state of affairs.

If a hypothetical organisation is at level “D – Emergent”, then the Model’s description of the next level up, “C – Transitional”, can provide strong pointers as to some of the activities that need to be undertaken in order to ratchet up Data Maturity one notch. The same goes for if more of a stepped-change to say, “B – Basic” is required. Initial ideas for improvement can be further buttressed by more granular Data Capability Review findings. The two areas should be mutually reinforcing.

One thing that I have found very useful is to revisit the area of Data Maturity after, for example, a year working on the area. If the organisation has scaled another step, or is at least embarked on the climb and making progress, this can be evidence of the success of the approach I have recommended and can also have a motivational effect.

As with many things, where you are with respect to Data Maturity is probably less important than your direction of travel.

If you would like to learn more about Data Maturity Models, or want to better understand how mature the data capabilities of your organisation are, then please get in touch, via the form provided. You can also speak to us on +44 (0) 20 8895 6826.

 

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Notes

[1]	In case you were wondering, much of the rest of the time has been spent executing these Data Strategies, or at least getting the execution in motion. Having said that, I also did a lot of other stuff as per: Experience at different Organisations. You can read about some of this work in our Case Studies section.
[2]	The first such article is Data Strategy Creation – A Roadmap.
[3]	I’ll be covering this area in greater detail in the forthcoming article I mentioned in the introductory paragraph.
[4]	There is actually very significant doubt that he actually ever uttered or wrote those words. However, in 1933, he did deliver a lecture which touched on similar themes. The closest that the great man came to saying the words attributed to him was: It can scarcely be denied that the supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience. “On the Method of Theoretical Physics” the Herbert Spencer Lecture, Oxford, June 10, 1933.

Another article from peterjamesthomas.com. The home of The Data and Analytics Dictionary, The Anatomy of a Data Function and A Brief History of Databases.

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