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  • Writer's pictureAlastair Majury

DataRobot Fills the Data Analyst Gap

Artificial intelligence and machine learning are gaining ground within a range of industries. From entertainment, sports, healthcare, finance and real estate to manufacturing, education and e-commerce, automated processes are necessary for companies to stay competitive and gain business insights. However, the supply of data analysts and data scientists are not capable of filling the need for data-driven business insights. Enter DataRobot, start-up specializing in automated data analysis solutions.

Unicorn Status and Early Entry

Tom de Godoy and Jeremy Achin, company founders, entered the field of artificial intelligence at the right time. Achin understands that the artificial intelligence market is set to boom. DataRobot has been under Achin and de Godoy's direction since 2012, but the surge in industry adoption of AI and machine learning modeling has led to the company's $431 million in funding. The unicorn is on its way to dominating a market.

Expansion, Acquisition and Growth

DataRobot's platform offers companies a platform to run their own data analysis models without the need for a data analyst. It's a plug-and-play approach that enables a business analyst who has received basic DataRobot training to provide data and ask a business question that the data set will answer. In some instances, the AI-powered platform can provide an answer in hours or weeks rather than months or years.

DataRobot's technology is constantly improving and offering more features. The company has acquired several smaller machine learning firms and leveraged their technologies to fine-tune their own processes. Since data analysis is a process with many steps, each acquisition serves a different step in the process. The result is an end-to-end automated data analysis platform replete with inconsistency and bias modeling.

DataRobot's platform has generated over a billion data models across a range of industries. As more models are built, the platform gains greater sophistication. This places DataRobot in a position to gain more funding to develop robust software solutions that cater to specific industries or that serve to streamline complex data analytic processes. The more models DataRobot generates, the greater its AI capabilities become, which makes it difficult for late entries into the AI space to gain higher ground.

The need for automated data analysis is filling a skills gap, the question of whether the gap will ever be filled by human data scientists remains to be seen.

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