Data scientists look for the proverbial “needle in the haystack,” with the haystack, in this case, being the mass of data collected by a company. They are in this pile of data to find the most valuable and relevant intelligence for their company.
And the haystack is growing. The volume of data is exploding, and there aren’t enough humans to keep up with the pace, and, more importantly, not enough human brain power to deal with the mass of constantly changing and growing data available to consider. Because of this, the work of data scientists can now benefit from machine learning since it provides the real-time execution and speed needed to turn insights into actions.
NGDATA has found that, to date, the ratio of data scientists compared to machines has been 70/30, but because of the continuous growth of data, the roles are now becoming reversed to 30/70, as machines can do more work in a shorter amount of time.
This may seem a worrying trend, but it doesn’t have to be. Data scientists will always be necessary to obtain insights and to translate the needs of the market, but it’s the real-time execution part that has been missing in the past. With machine learning supplementing a piece of the process, data scientists can focus on what they do best – understanding intelligence.
With the rise of machine learning, the role of the data scientist will progressively evolve into two parts: either into more programming at a lower level to work to optimize machine learning, or to a more strategic role where they look at big picture, via the outputs from machine learning, to predict future directions for the company at a higher-level. And that right there, is the value that only a data scientist can bring.
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