Why machine learning is the new Business Intelligence

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Business intelligence has gone from static reports that tell you what happened, to interactive dashboards where you can drill into information to try and understand why it happened. New big data sources, including Internet of things (IoT) devices, are pushing businesses from those reactive analytics — whether you look back once a month to spot trends or once a day to check for problems — to proactive analytics that give you alerts and real-time dashboards. That makes better use of operational data, which is more useful while it’s still current, before conditions change.

“There’s a demand for real-time dashboards,” says Herain Oberoi from Microsoft’s Cortana Analytics team. “A lot of businesses want to get the pulse of their business. But dashboards show things that have already happened.”

That’s why fastest growing area is predictive and other advanced analytics, according to Gartner. Its latest Magic Quadrant for advanced analytics predicts that by 2018 more than half of all large organizations around the world will use advanced analytics (and algorithms built on them) to compete.

Advanced, predictive analytics are about calculating trends and future possibilities, predicting potential outcomes and making recommendations. That goes beyond the queries and reports in familiar BI tools like SQL Server Reporting Services, Business Objects and Tableau, to more sophisticated methods like statistics, descriptive and predictive data mining, machine learning, simulation and optimization that look for trends and patterns in the data, which is often a mix of structured and unstructured.

Read the source article at InfoWorld