The shortage of qualified data scientists is often highlighted as one of the major handbrakes on the adoption of big data and AI. But a growing number of tools are putting these capabilities in the hands of non-experts, for better and for worse.
There’s been an explosion in the breadth and quality of self-service analytics platforms in recent years, which let non-technical employees tap the huge amounts of data businesses are sitting on. They typically let users carry out simple, day-to-day analytic tasks—like creating reports or building data visualizations—rather than having to rely on the company’s data specialists.
Gartner recently predicted that workers using self-service analytics will output more analysis than professional data scientists. Given the perennial shortage of data specialists and the huge salaries they command these days, that’s probably music to the ears of most C-suite executives.
And increasingly, it’s not just simple analytic tasks that are being made more accessible. Driven in particular by large cloud computing providers like Amazon, Google, and Microsoft, there are a growing number of tools to help beginners start to build their own machine learning models.
These tools provide pre-built algorithms and intuitive interfaces that make it easy for someone with little experience to get started. They are aimed at developers rather than the everyday business users who use simpler self-service analytics platforms, but they mean it’s no longer necessary to have a PhD in advanced statistics to get started.
To learn more, please visit: The Democratization of AI Is Putting Powerful Tools in the Hands of Non-Experts
