That's all been made possible by using the analysis tools in BI applications which allow enterprises to dig deeply into their data to find patterns, information and the stories behind the information.
"Traditional BI has been like driving and looking into your rearview mirror" to see where you have been and what's back there, Hopkins said. "Now, the direction of BI is forward, you are able to drive and look out through the windshield" to see what's happening next.
For today's enterprises, this is a huge boon for using BI data.
Traditional BI has asked questions, gathered data, cleaned and structured the information and put it into forms that could be queried and filtered to produce valuable business information that could be used to plan strategies and make decisions. "Traditional BI is about creating reports and making inferences about the past and the future," Hopkins said. "It's not as cheap as we would have liked and it takes longer than we would have liked, but it generally meets that need."
Better means of BI have been coming for the last five or 10 years, he said, and those ideas are showing up more and more today in viable products that do more for enterprises.
Instead of going through the data sets and reacting to what's already happened in a business, the latest BI systems can do more by anticipating what will happen in the future and help plan for those expectations, based on the old data that's already been collected.
"The whole idea of predictive analysis like this has been around awhile," Hopkins said. "It's not a new science. It's here and more changes like this are coming."
Essentially, new generations of BI applications will give enterprises additional and better information to use and analyze.
Hopkins discusses this in his research paper: "Big Opportunities In Big Data: Positioning Your Firm To Capitalize In A Sea Of Information."
"We began seeing it five to 10 years ago when airlines started setting ticket prices using sophisticated models," Hopkins said. "Hotels followed suit, based on predictive models. Even your credit scores are done this way and retail stores are using it to see what they should stock in the future based on customer needs."
With older BI methods, businesses were only looking at the past, then collecting, massaging and running statistical models on the data. "What we're seeing now is that a new generation of predictive analytics is developing to allow us to not have to do so much massaging and filtering."