Needless to say, we live in the age of data. It’s not because, miraculously, we have somehow started to create more data. No, we still walk, attend lecturers, and go to grocery shops. The real reason is our improved ability to collect the data. Daily, heck, hourly, we use our smartphones, leaving behind a track of data via usage of countless of apps – Gmail, Youtube, Spotify, Facebook, WhatsApp, etc. According to Bernard Marr, 83% of professionals say data is making existing services and products more profitable, and 60% feel that data is generating revenue within their organisation. Talk about mainstream adoption potential. The data is, simply, making the world spin right now and although we probably won’t see a single-event breakthrough in how we use data, it will gradually pervade all aspects of life and business and establish itself as a normal thing.
It’s clear then that the potential of changing the world is there (and I would even argue that the change is already bound to happen). To explain how this will change the world, we should start with already established position of a Chief Data Officer (i.e. data scientist), a hot new job, which companies are incorporating into their structures.
According to IBM, data scientist is someone who “is inquisitive, can stare at the data and spot trends”. A difference to a traditional statistician is a strong business acumen and the ability to deal with “multiple disparate sources [of data]” (there are other, more fun definitions that you can read at The Guardian. Even until now everybody understood that data is important, but only hiring and nurturing a data-biz professional puts data from background to foreground, as something that needs to be seriously considered a part of your business. This means understanding that if you are a grocery store, your business is no longer only fast-moving consumer goods, but also data on your customers. Your business’ value proposition stays the same, but the process of how you achieve it can significantly change (improve) with the usage of data.
Here are numerous case studies of how companies leverage (big) data to their advantage. One for all – Tesco collecting 70 million data points related to refrigerators and using them to monitor performance, estimate when machines need to be serviced and do a more proactive maintenance cutting down on energy costs. As I said above, the usage of data did not change that Tesco is primarily a grocery store, but it helped reduce costs and, I assume, improved quality of refrigerated goods because of better machine management. The idea of a data scientist is creating more of these insights, in-house, and cheaper, because she knows the business and the data.
Now imagine a step further – similar insights would not need to rely on a single data scientist or a single team of data scientists. Everyone (well, not the grocers, but manager-level positions) in the business would have the ability to look at the data and develop some insights within their positions. As I mentioned in the introduction, business is not a homogenous thing. What you do and how you do it changes from engineering to marketing department, or from a store in Kensington to a store in Ealing (London’s poverty profile). Obviously, there are data and insights which are company-wide, but many of the useful insights would be hyper-localised and a single team without that hyper-localised knowledge (that a manager working there for a few years has) would find it impossible to access this knowledge.
In my eyes, then, citizen data science is about accessing an additional level of data. Official definition by Gartner (see Note 2) talks about a person who creates models that leverage prescriptive predictive analytics. The person who is able to perform simply and moderately sophisticated analytic tasks. At the moment, we are starting to access big data and leverage it to great benefits, to both businesses and customers. Other technologies on the Gartner Hype Cycle, like advanced analytics, will provide yet another way of accessing knowledge/data, informing on tools for citizen data scientists as well. In line with this movement, I don’t think that citizen data science (and I am repeating myself now) will be a major breakthrough, but it will enable us to drive businesses to ever-increasing productivity, enabling better products and better services.
Thus how will citizen data science really change the world? Subtly, but once gained, the newly-found freedom of leveraging complex data by ourselves, without rigorous training in statistics or data skills, will not be easy to give up. And when the Internet of Things hits in its full force, and we will have big data in our homes (and basically everywhere) as well, we might even see the rise of personal data science; a natural, almost mundane, extension by then.