Combinations are More Important Than Single Skills

Why is the deficit to be estimated so high? Because Data analytics requires a combination of skills - statisticians, behavioural psychologists, business analysts and technologists - which are difficult to find. How does this process work?

Statisticians are required to make sense of the huge data in the databases. They will use their knowledge of statistics to find  strange correlations between two seemingly unrelated sets of data. For instance, the risk profile of a single adult is higher than rhe risk profile of married adult, for say giving , automobile loans or housing loans. This can be discovered from the available data in banks by statisticians. However behavioural psychologist is required to confirm that this data is not mere correlated data. It has got underlying causes to justify this. So he needs to find the causative factors and confirm the hypothesis that 'risk profile of single adult is infact higher due to xyz factors'.

Once this is established, business analysts converts this 'information' into useful 'knowledge' by finding business figures: single adult customers, married customers, returns from both customers, the growth of these customer population in last three years and so on. He makes a business case and converts this 'knowledge of single adult behaviour' into 'business strategy'. Technologists then comes into the picture to ensure that 'all the different applications' which are sitting in different technologies ( ERP, BI and front end softwares) are integrated to produce the desired result.

Of course, this process does not happen in the above sequence only. The process can be initiated by anyone, while others have to pick up from that place. The dependency between the four skills is however very very high.And that is what makes it difficult for companies to find 'good data scientist'.

So who are data scientists?
Are they statisticians, psychologists, business analysts or technologist? Because the dependency between these four skills is very high, if you start as a business analysts, you have to learn a lot about 'statistics' to converse intelligently with 'statisticians'; a lot about 'technologies' to understand the limitations and benefits of different technologies and a bit about behaviour psychology (something one reads now in the books of Friedman) to understand what 'needs' to be understood. In other words, a business analysts must have 'sufficient' depth in the other three areas to become a data scientists (to make the process between the four skills seamless and transperant). If you are a technologist, you must acquire sufficient depth in the other three areas, and so on.