Getting literate in the language of the future

As we all know, digitalisation of everything is decreasing barriers to entry and increasing the speed of change in almost every perceivable industry. Unfortunately, this probably also means the half-life of opportunity exploitation is also shrinking. The organisations that seem to be best at leveraging opportunities in the market are the ones that are able to identify and act the quickest.

They identify and act quickly… based on data. That’s why we hear about data-driven organisations and data-driven decision support. OSS collect enormous amounts of data every year. But it’s only those who can turn that information into action who are able to turn opportunities into outcomes.

Data is the language of the future (well today too of course), so literacy in that language will become increasingly important. I’m not expecting to become a highly competent data scientist any time soon, but I’m certainly not expecting to delegate completely to mathletes either.

The language of data is not just in the data sets, but also in the data processing techniques that will become increasingly important – regression, clustering, statistics, augmentation, pattern-matching, joining, etc. If you can’t speak the language, you can’t drive the change or ask the right questions to unearth the gems you’re seeking. Speaking the language allows you to take the tentative first steps towards machine learning and AI.

As a consultant, I see myself as a connector – of ideas, people, concepts, solutions – and I see OSS largely falling into the same category. But the consultancy, and/or OSS, skills of today will undoubtedly need to be augmented with connection of data too – connection of data sets, data analysis techniques, data models – to be able to prove consultancy hypotheses with real data. That’s where consultancy and OSS is going, so that’s where I need to go too.

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