As the demand for data increases, the need for coding experience becomes less clear
By Kumesh Aroomoogan, Co-Founder and CEO, Accern
We’ve been told for years that the prevalence of data will require an ever-increasing number of data scientists. As our world has become more digital, more and more data has become available that can guide our decision-making – creating information opportunities that were never possible before, while requiring new expertise to deliver. one direction. After all, data is only useful if it can provide actionable value. Without someone to interpret it, data alone is not very useful.
But does everyone really need to know how to code to access the benefits of data? Not too long ago the answer was an obvious and resounding yes. As McKinsey predicted in its 2011 Big Data report, the United States would need 190,000 data scientists and likely face a shortage of skilled and experienced talent. While it is true that there is a shortage of people with analytical expertise, the magnitude of this shortage is probably not what was once expected.
Why wouldn’t they have seen it coming? The introduction and widespread adoption of codeless, low-code technology, which allows you to access the benefits of data without having deep data engineering expertise. Essentially, data scientists have gotten so good that they can train models to adapt and customize for almost any purpose. As a result, the end users of the data do not need to be the ones creating their own models. If everyone needed to have more than a cursory understanding of the data, they wouldn’t.
And this comes at an important time for the financial services industry. Alternative data has grown dramatically in recent years as the technology now exists to analyze business performance in real time, looking at metrics such as credit card spending, satellite imagery, usage volume Slack and Microsoft Teams, weather, and more. New data sets have emerged that offer asset managers, investors and insurers a view of market performance that was not possible even 12 months ago. At Accern, for example, we analyze over 500 signals (comprised of newspaper articles, blogs, SEC files, etc.) around the world, every second.
As the use and expectations of data have become more sophisticated, so has the demand for data that can provide this crucial advantage. Expectations for faster information tailored to specific needs have become the norm, as many of us cannot imagine a world without the real-time information to which we have become accustomed. For example, we no longer need to rely on quarterly financial statements to know how a business is doing. There is real-time data that provides a window into foot traffic patterns, mobile app downloads, keyword usage and more. This allows us to know at all times the likely health of a business so that we can make better predictions about its ability to meet financial goals.
Fortunately, as the use of data increased, the field grew so that data models could be created and pre-built for specific use cases. The best and brightest minds are now creating the tools that make the benefits of data and AI a reality for the masses. Data scientists are now striving to make data more accessible, which is only fueling demand.
Will we need an ever-increasing number of coders and developers to create the tools that can help us take advantage of the growing data opportunity? Probably not. Demand is certainly increasing and we are facing a talent shortage, but I expect the field of data science to adapt faster than many of us anticipated. And, with codeless, low-code technology, data scientists empower teams to solve the biggest challenges in their respective industries.
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