Participant Questions From the Recent Data and Analytics Webinar: Round 2

On March 15, 2017, we held a free, live webinar to share the findings and insights from our latest MIT Sloan Management Review Data and Analytics Initiative research report, “Analytics as a Source of Business Innovation.” The report summarizes our findings about the increased ability to innovate with analytics and how it is producing a surge of benefits across industries.

If you missed the webinar, the recorded version is available on our website. Thanks to everyone who participated in the webinar — we had a great turnout.

During the webinar, many, many participants asked questions. Unfortunately, we had time to answer only a few during the webinar itself. Last month we answered some remaining questions, and this month we’ll cover a few more. We’ve paraphrased some of the questions to provide context, combine similar questions, and anonymized them.

I read recently that everyone needs to be educated in analytics, starting with K-12 education. Does everyone actually need to know more about analytics, and will this happen in the near future?

We certainly see rising importance of analytics skills in our research — our 2015 report, “The Talent Dividend,” focuses on that topic. As businesses increasingly rely on data to create value, the use of analytics will become more pervasive in organizations. At a minimum, increasing education in analytics will help managers better understand potential biases and discern strong evidence from weak.

But we’ve also found that it isn’t easy. It is hard to keep up with the pace of analytics. In our article “Minding the Analytics Gap,” we describe how the rate of increasing sophistication of analytical results that companies produce exceeds the rate at which organizations are able to consume those results — in other words, it’s easier to develop more complicated analyses than it is for everyone in the organization to understand those complexities. This indicates that increasing education in analytics is important, but it’s also a difficult process.

How do we deal with sparse data in innovation?

Of course, many areas currently have massive volumes of data available. For example, sensors can sample data many, many times per second. Or internet browsing activity can quickly generate detailed logs. Or smartphones — even cameras in mannequins’ eyes — can record shopper behavior.