Building Models That Turn Trained Data into Business Insights
While the world of software continues to evolve with the introduction of additional layers of abstraction, the complexities appear to melt away, offering exciting new opportunities for people with varying backgrounds and technical capabilities. With the rise of no-code platforms, more people are able to easily build solutions for their business challenges, large and small.
This isn’t to say that software development or the supporting concepts required for it have become obsolete, that’s unlikely to happen any time soon. Some might say that it has been this exposure to innovative tools, such as no-code platforms, that has driven the growth of new “power users” across industries, known as citizen data scientists. Gartner defines a citizen data scientist as “a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics.”
As any citizen data scientist knows, in order to generate key business insights one needs not only a good set of models but also the data that is fed into them. This data - providing that it’s the right types, that there’s sufficient quantity, and with proper preparation and utilization - is just as important for the production of business solutions as the models used to analyze it. And the most under-utilized data to date is text-based or unstructured data.
