Citizen Data Scientist – why not stay by Data Scientist only?

Science. Scientific. Scientist. These almost magic-like words carry, for most people, a heavy connotation of respect. Politicians use those terms to substantiate their claims, while media like to use them to gain credibility in their articles. It’s been roughly 150 years that the term scientist replaced the term ‘man of science’ – about the time that there was a shift in the paradigm of what constitutes a “scientist” – and citizen science might just be the push in the right direction.

In 2015, Gartner Hype Cycle – a reputable source for emerging technologies – included Citizen Data Science among those technologies currently rising to popularity. The reason was simple – recent years saw a massive emergence of data science, and many firms have now created positions such as Chief Data Scientist with which they aim to create unique insights for their business, from the data that their company collects through day-to-day operations. There is, however, a huge bottleneck here – our ability to process the data is not catching up to our ability to create and collect these data, not least because we would have insufficient technology. IBM Spark and Hadoop are well-established competitors in the field of Big Data, but because Big Data does not only mean a huge amount of data, but also a huge variety of data, we are still only coming to terms with how to process them. Individual companies need individual solutions, but even if we imagine that we do have that, we still need someone that would make sense of the data – i.e. derive the actual insights. The reason companies are employing their own data scientist is pragmatic – being within the firm positions these people best for understanding the business itself, its specificities and niches, which in turn enables the scientist to look at the data through a lens of business expertise.

Here comes the catch, however. A business is usually not a homogenous thing. Sure, in an ideal world, every single employee in the firm is aligned by the firm’s single value proposition. But even then, the way a marketing department sees the business is different to the way the engineers see it which is different to how the executives think about it. And with these different perspectives, you can expect as many types of data as well. A Chief Data Scientist, then, although knowledgeable about the data and the business, still has a limited way of operating. What’s the solution then? More Chief Data Scientists? Hardly, as these are extremely scarce and quite pricey. Better technology, possibly AI? Maybe, but we are still nowhere near substituting actual human professionals with AI.  Take business professional and teach them how to work with data? Yes, partially. While it is clear that additional data education can never substitute a proper one, it is surprisingly easy to acquire useful data-handling skills. And this is exactly where the citizen data science is heading.

Actually, a bit further, to be honest. What Gartner claims is that it is not the data-handling education which will drive citizen data science – it should be improved technology which will be user-friendly, require minimal training and provide enough power to enable even non-data-scientists drive valuable insight from the data that they have. Still further, having these people recognise the value of data, implementing these insights into business practices should also become easier, helping to drive everything, from engineering to sales. That is the “citizen data science”, and that is what we will look at in the next few posts.