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Why machine learning is the new BI

http://www.cio.com/article/3058980/business-intelligence/why-machine-learning-is-the-new-bi.html

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.

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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.

They’re the kind of tools that are currently used by marketing or risk analysis teams for understanding churn, customer lifetimes, cross-selling opportunities, likelihood of buying, credit scoring and fraud detection. Those users aren’t going away. “Many telcos want to get from being reactive to being proactive,” says Oberoi. “They want a system where they can say ‘tell me which of these customers, based on their customer profile and calling pattern, is going to churn’.”

But Gartner says almost every business unit is going to be interested in these tools and he agrees that matches the customers for Cortana Analytics. “The people we’re talking to are changing. I’m having a lot more dialog with line of business decision makers. We see a lot of budget moving to line of business teams.” He recently spoke with customers at Microsoft’s Convergence conference in Europe. “Of the five customers I met, at least three were line of business and two of those had a charter for driving digital transformation and their company’s innovation agenda.”

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Predictive maintenance has got a lot of attention but there are other key uses like predicting demand and finding problems in service or product quality using anomaly detection, as well as decision support systems. Those are questions like “what might happen” and “what should I do?” says Oberoi.

“The fundamental customer challenge hasn’t changed; how do I go from the data that I have, to getting some insights, to actually enabling some an action or driving something forward?” What the tools in Cortana Analytics can do is reduce the number of manual steps you have to take to get to those actions.

“The basic way to go from data to decisions is a static report that says what happened. If I’m in sales and I want to look at what my sales look like by region for the last quarter, that’s my static report, and then there’s some manual steps I take before I go make a decision,” he explains.

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“The next piece of it is not just something happened but why it happened.

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