What competences will companies need in order to develop and commercialise Citizen Data Science?

There is possibly a great number of competences that are needed for any company to develop and commercialise a new product. Here, I list three of the key competences that I identified, indicating in brackets whether they relate to commercialisation or development.

Security (commercialisation)

With the trend of moving towards Cloud, the ability to present not only working, but also secure solutions is an essential quality for any company hoping to offer data analytics. First, security concerns are limiting companies’ adoption of cloud and mobility. Second, consumers, also owning to major data breaches at Ashley Madison or TalkTalk, are increasingly aware of the security of their data. The source states that three quarters are concerned with the amount of personal data shared with brands online. Third, stringent official regulations are in plans and compliance will have to become more of a priority in order to persuade businesses that adopting a particular solution will not carry hidden costs of potential bureaucratic investigations.

User experience (UX) (commercialisation)

If it is important to provide a great UX to those who understand the data, then it is even more important to offer seamless UX to those, who might feel overwhelmed by the content. Take up of the software depends on ease and pleasure (as a function of time spent on analysis to the usefulness of the result) of use. It’s not enough then to just have a powerful tool – it also needs to be enjoyable to work with, otherwise it will be difficult to persuade employees to use it, posing a barrier to company-wide adoption. This importance of this criterion can be substantiated by the fact that Gartner Magic Quadrant specifies User Interface (UI) when talking about company’s profile. For example, UI as Dell’s strength, but KNIME’s weakness (kind of).

Advanced analytics (development)

To keep citizen data science relevant, the tools will need to be able to offer ever improving ability to crunch complex data. From big data to Internet of Things to predictive analytics, enabling the same level of users to access more and more complicated analysis might reach its limits one day, but the later that happens, the more business value and profit there is to be found in citizen data science solutions.