It is no big secret that data, data science, AI and ML are continuously entering our daily lives. According to a number of leading research companies and institutions, data science is the key enabler of digital transformations, and further states that by 2022, as much as 90% of corporate strategies will explicitly mention information as a critical enterprise asset and analytics as an essential competency.
As a consequence, data scientists are in great demand, and there seems to be not enough of them .
Still, despite the enthusiasm for data science in many companies and industries, a recent survey of CEO’s ), showed that whilst they recognize that data science is likely to be or become a key driver of their business, achieving the benefits that data science, AI and ML may bring to their companies, is still a bit of a struggle.
Despite all the upbeat messages brought forward by these research studies, many business people and domain experts may still find it hard to achieve any benefit from data science. This may be attributed to two key points.
The first one is a more general point, and that is comprehending and envisioning what can be achieved with data science. Put differently, many C-level executives, business people or domain experts, may not always realise the insights which are likely to be hidden inside the data they hold, or in the data they can easily get a hold off.
Even if the data is not perfect, a good data scientist will be able to extract insights and knowledge, which will be very useful for business people or domain expert. Realising that data does not need to be perfect for very useful insights to be gained, will already provide a very good starting point. An inquisitive minded business person or domain expert, is a second way to start unlocking the potential of data science. With a mindset of asking a simple question – “Given the data we have, which insights could we get out of it ?” – a manager or domain expert will open up an entirely new path of insights.
The second point, is related to the technical nature of data science. In many ways, the business people and data scientists find it hard to connect. The business person may not always understand the data science process, and may find it quite technical and mathematical. Therefore, the outcome of the data process, may not be used by the business or the domain expert. On the other hand the data scientist may not always understand what the business colleague or the domain experts would like to achieve, or find it hard to communicate her or his findings in an easy-to-understand format. A key element of attention hence is to bring the two fields much closer together. And these days, that can be achieved much more easily than before. For example, many universities and training institutions already offer data science courses to business managers and domain experts. On the other hand, data scientists can for example also be trained up into understanding the needs of their business colleagues or domain experts.
Before anything else, creating a successful cooperation between the business or domain expert, and the data scientists, is the key ingredient towards unlocking the vast potential hidden inside anybody’s data. It is this cooperation which will lay the fundament to the digitally transformed journey which we are all upon.