The stages of data science maturity

In their Field Guide to Data Science, Booz Allen Hamilton describe the various phases of data science maturity. I quite liked this picture:


Booz Allen Hamilton, 2015, p. 35

There are other similar models which describe the progression from traditional business intelligence through to advanced analytics (i.e. descriptive measures of historical data through to predictive and prescriptive models). I really like Booz Allen Hamilton’s model however, as I feel it better captures an organisation’s culture and attitudes towards their data.

It is probably fair to say that most companies are beavering away in the collection phase and are likely to be making some efforts in the description phase. Making that advance into the discovery phase is a little trickier though – there is the widely reported talent shortage and this transition marks a shift in company culture as well. Two significant barriers.

There is a clear role for people who love data to help companies make this transition from descriptive efforts into discovery and prediction. Ironically, I find myself actually evangelising the opposite move in my current environment. Here there is so much emphasis on collecting data and diving straight into discovering ‘new or novel science’ that we don’t spend nearly enough time just looking at the data and understanding its properties, or thinking about what / why / how we might go about both asking and answering interesting questions.

If nothing else, this image really cemented the importance of each layer and that flashy models and brilliant predictions are only part of the story.


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