Data Analytics and Machine Learning: Driving Speed to Insight

No question about it: Data-driven organizations are clearly on to something.

Such organizations are three times more likely to report significant improvement in decision making, according to a PwC Global Data & Analytics survey, which polled 1,135 executives. Research by MIT’s Center for Digital Business uncovered similar results in interviews with executives at 330 North American businesses. “The more companies characterized themselves as data-driven, the better they performed on objective measures of financial and operational results,” MIT’s Andrew McAfee and Erik Brynjolfsson reported in Harvard Business Review.

So what do analytics leaders—and their data-driven initiatives—need to succeed? Specifically, they need to be able to easily integrate more data sources, harness machine learning and advanced technology for faster, more sophisticated analyses, and extract insights that will improve business performance. Ultimately, they need to make the transition from data to action. 

Organizations that are already making that leap are transforming not only their businesses, but, in some cases, their industries. As just one example, consider the success of Uber, which uses algorithms for real-time monitoring of traffic and trip times to balance demand and supply for ride sourcing—and to adjust fees accordingly.

That kind of transformation is no surprise to researchers like Alex “Sandy” Pentland, MIT’s Toshiba Professor of Media Arts and Sciences. Pentland has said big data’s power resides in the fact that it reflects how humans behave rather than what they believe. By using analytics and machine learning to analyze the data trail that people constantly create—whether it’s from mobile phone location records, online browsing and purchasing, or credit-card purchases— organizations can obtain more insights (and more valuable insights) to continuously improve the customer experience. In addition, they can do so faster—often without human intervention.

“Traditionally, we have relied on experts to gather these insights from data,” says Sagnik Nandy, distinguished engineer at Google. “A data-driven organization wants this to happen automatically.” 

To achieve that speed to insight, analytics leaders face massive challenges in three areas: accumulation, analysis, and action.

Read our exclusive report to learn more about how data analytics and machine learning can help organizations overcome those challenges, gain key insights, make better business decisions, and ultimately gain competitive advantage.