See-Saw – Data scientists vs Data Interpreters

An “unbalanced see-saw” between data scientists/analysts vs data interpreters, is it for real? Do you think that in upcoming years , the job market will be flooded with data analysts/scientists due to the overexposure hype received in the last few years? Your thoughts will be appreciated in the comment box below.

Well ,my personal belief in few years or even sooner , Yes the job market will be flooded with data scientists, and, the value of data analysts/scientists may potentially decrease. However, this is not primarily because of the overexposure of the discipline. The awareness of the need for data science skills is a good and necessary ingredient to create value from data in effective, innovative ways. To be more precise, it also doesn’t mean you do not focus of being a “good” analyst by worrying of this futuristic notion. The problem lies elsewhere.

There was a research paper done on this subject on big data study by McKinsey’s Business Technology Office in various industry sectors and one of the insights they highlighted was that there will be a shortage of talent necessary for organisations to take advantage of small/big data with deep analytical skills as well as analysts and leadership managers with the know-how to interpret and utilise the analysis to make better effective and informed decisions.

Guess what ! its already 2020 . And since then, the interest in doing data science has increased steeply, as shown in the trend graph below as per McKinsey. .Sadly there is a lot less interest in being the analytically literate manager to be able to interpret results (and this is a global issue and not limited to a specific industry). And therein lies the problem with the new data scientists entering the market. In my view, the market is not still equipped to utilize them.

I think it is time to realize that the bottleneck in the job market is not just the oversupply of data analysts/scientists, it is also the undersupply of the right people to manage them, and perhaps most importantly the people who will use the output of data analysts/ scientists to make better decisions or embed data science capabilities in an organisation. Personally, i believe in data universe, technology and technique has a vital role to play and it’s important to differentiate. Not one technology/technique may suit all your needs in the data analytical world (just like any other BI tool/technology ) as they are only the enablers, but it is important to forecast what you would want to achieve after defining a problem statement and effectively enforce the suitable technology and technique to churn the results. However the win point here is the simplicity in narrating a data story so as to enable an easy path for your line managers or C- suite executives to interpret these results.

One of the possible practices which I follow and would like to share is to keep reading, learning and test your skills practically and narrate the story results to someone who is neutral to the subject. If he/she is able to understand what you are trying to narrate, that’s your achievement. Else, you have a learning curve to adapt this skill. Watch out for my next article in sharing some of my personalised best practices in data story telling and guidelines towards interpretation.

In conclusion- There could be sizeable data analysts/ scientists in our organizations, but the true treasure is to own the art of narrating a data story and interpreting them, regardless of big data or small data world . Do you agree?

February 28, 2021

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