Machine Learning & Data Analysts: Seizing the Opportunity in 2018
Undoubtedly, 2017 has been yet another hype year for machine learning (ML) and artificial intelligence (AI). As ML and AI become increasingly ubiquitous in many industries, so does the proof that advanced analytics significantly improve day-to-day operations and drive more revenue for businesses.
Yes, it’s true – enterprises worldwide have shown us time and time again that there is major potential for industry change with ML and AI. But in order to bring about that change, there must be strategy involved. It’s not enough to assemble a large data team and expect the results to come; in fact, becoming a truly data-driven enterprise at the core has proven to be more of an organizational challenge than a technical one. Becoming successful with data requires collaboration across teams, placing new concepts – such as reusability and reproducibility of data models – at the heart of the business.
While these technological and organizational evolutions are happening across the globe, there are several areas in which they are gaining particular momentum. One is in Germany, where the previous Minister for Economic Affairs, Sigmar Gabriel, announced in 2016 that “data is the commodity driving our digital age.” As dawn breaks on a new calendar year, it seems truer than ever. According to a recent study by Bitkom Research and KPMG, approximately 60 percent of German companies have already managed to either reduce risk, reduce costs or increase revenue through the use of data science (including ML and AI). The Mittelstand is slowly but surely understanding its value, and big data technologies are inching towards widespread adoption.
Yet, despite this revolution, 77 percent of German companies still rely on small data tools (like Excel and Access) for ad-hoc data analysis. There’s still a long way to go, but this figure is down 10 percent compared to 2015, which shows promise. Businesses that were ill-equipped in the past to manage large amounts of data are in the process of “gearing up.” This trend coincides with a rise in the adoption of data science platforms.
What are data science platforms, exactly? Well, in order to scale, data teams need staff, structure, efficiency, automation and a deployment strategy; data science tools facilitate these requirements.
