Business analysts won't be able to use complex predictive analytics systems without help. Expecting this is like asking someone to create a report using Java. That’s the upshot of Doug Henschen’s recent article in InformationWeek, “Analytics Vendors Must Make Prediction Easier, Forrester Says.”
The Forrester Wave report to which Henschen refers calls on the established cadre of business intelligence vendors to improve the accessibility of their analytical capabilities. It says that SAS needs to "provide more sophisticated solutions for real-time analytics, such as stream processing [and] offer predictive modeling tools that business analysts find more usable.” Forrester suggested IBM make its total portfolio "less confusing" and create more solutions that "customers can use out of the box,” which Henschen interprets as code for “find a way to do this without the usual army of consultants.”
Conventional vendor offerings weren’t the only tools taking a beating. The R programming language, while highly sophisticated, was judged by Forrester as “difficult to learn and only appropriate for direct use by data scientists and high-level analytics professionals.”
The article concludes that one of the most promising ways forward is to take advantage of automation and machine learning, specifically, to “automatically test a variety of algorithms and implement the most effective ones without forcing users to go through complex, iterative testing.”
At AgilOne, we could not agree more. The premise of our cloud-based predictive analytics platform is that business users should be able to use all their data to make on-the-spot actionable decisions, such as strategically interceding in negative purchasing patterns before a customer is lost, or upselling a new customer with a product they actually want. We welcome the analysts’ call for predictive analytics tools that are not only more powerful, but more effective in helping business users put data to work.