What are the barriers to adoption of Citizen Data Science?

There are two main aspects of citizen data science – the “citizens” and the data technology. Both are needed to unlock the true potential of citizen data science, and by extension, both can act as significant barriers.

Citizens

Let’s take the “citizens” first. Although I was talking about “almost everyone”, in the end, the employees will need some kind of inner motivation/drive to want to work with the data. They will also require basic education in understanding data which they could develop and apply to the business. This might be either provided by the business itself (increasing cost of adoption of citizen data science) or, by a stretch of imagination, the future education systems will implement the basic data skills into national curricula (as it has happened with programming). Clearly, those two solutions are not mutually exclusive, and since the latter is a long-term one, in the next at least 10 to 20 years the businesses wishing for citizen data science will have to think of a way to impart essential data skills onto the employees. Third solution is to push for hiring employees who are better equipped to handle the data (as was already happening in 2014). In theory, this leads the university academics (possibly the most responsive curriculum) to adjust the courses to reflect the needs of the employers (also happening, check the previous source).

The most sceptical account of citizen data science, and by analogy a barrier to its adoption, that I have read comes from this piece, in which the author essentially says that whatever the technology, as long as the person operating it does not have a proper skillset for data, the results/insights will never be trustworthy. Personally, however, I think that he might be missing a bigger picture about the evolving technology.

Technology

The reason I hold such belief is that of the pair citizen-technology, Gartner believes that what will help citizen data science is the technology that will enable people with minimal data skills create valuable business insights. Today already the data tools are becoming more user friendly, also becoming geared towards non-expert users. It remains to be seen if the technology will evolve to such phase that would incite a mainstream adoption of the citizen data science. IBM, however, already claims to offer solution that scales this barrier – IBM Watson Analytics (as do SAS, Dell, KNIME and RapidMiner, as you will [HERE – reference to another blogpost]. Still, it is unclear how much of an average Joe one can be to still usefully utilise Watson. In this video (dated November 2015), IBM claims to have half a million users which they categorise as “citizen analysts”. Unfortunately, because there are no specific definitions for who “citizen data scientist” or “citizen analyst” really is, it’s difficult to say that Gartner and IBM mean the same thing. For the time being, then, I think that it is fair to say that we are not there yet, but that the future looks optimistic.

Legacy Processes

Third barrier of adoption are legacy processes – the legacy software, the legacy decision making and the legacy workforce. Obviously, this is mostly relevant only to the really big companies, but they are also important in advancing technology to mainstream adoption. For them, every change in how they operate represents a huge initial costs. Just imagine. With approx. hundreds of managers, every single one would need the relevant software, as well as service that software. All of them would also need to know how to use it effectively, so a training workshop for these purposes would likely be set up. Those without quantitative skills would need to be trained. Then internal protocols would need to be updated to include the feedback loop through which the company could utilise the insights created by these citizen data scientists. I could continue, but the bottom line is that it would be incredibly costly and therefore it constitutes a barrier to adoption by the big companies. Unless there would be a significant improvement (also expressed in terms of money saving/profit) to be made from such move, large companies have a long way before fully embracing the framework of citizen data science, if it ever goes mainstream, that is.