Some of the questions from our March 15, 2017, webinar went unanswered, so we’re addressing them in upcoming blogs.
On March 15, 2017, MIT Sloan Management Review held a free, live webinar to share the findings and insights from our latest 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 for free, on-demand viewing. Thanks to everyone who participated in the webinar — we had a great turnout.
During the webinar, many participants asked questions. Unfortunately, we had time to answer only a few during the webinar itself. But we didn’t want to leave so many unanswered. So, instead, we’ll answer some of the questions in this and an upcoming post. We’re sorry that we still won’t be able to get to them all, but we hope we’ve covered the ones asked by the most people. We’ve paraphrased some of the questions to provide context, combined similar questions, and anonymized them.
How does one embed data analytics in business processes that predominately rely on qualitative data, such as sales leads?
In our 2016 report, “Beyond the Hype: The Hard Work Behind Analytics Success,” we discussed the blending of analytics and intuition. That report quoted Jim Sprigg of InterContinental Hotels Group, who said, “Intuition versus data is a false dichotomy.” The core of the idea is that approaches that rely on either quantitative or qualitative data alone will likely fall short of approaches that combine both. So to figure out how to embed, it may be helpful to first think about two “why” questions:
- First, why do they rely on qualitative approaches? The business processes you mention may predominantly rely on qualitative data; is the word “predominantly” used because people have tried incorporating analytics but haven’t found them useful? Or is it because that’s historically how the processes have been done. The path toward using more analytics (or perhaps not) can be quite different depending on circumstances.
- Second, why embed analytics? I think (of course) that there are many good reasons that one would embed more analytics. But what does the organization hope to obtain? Management involves allocation of resources. Given that the impetus in some areas will be greater than others, the approach may be more tempered (for example, pilots or small-scale training) or more wholehearted (such as formal centers of excellence or incentive programs).
A final note is that the report this year does look at breakouts by functional area. Business processes in all areas are embedding analytics in processes to at least some degree. Going forward, it is difficult to imagine that all areas won’t increasingly involve analytical approaches.
Combining two related comments from participants brings us the following:
Is this resurgence in competitive analysis primarily attributed to the fact that technology is readily available to measure, collect, and analyze data? And I love that “Going Forward” points are all about the organization and structure, impacts and behaviors. It isn’t about technology; it’s about the people.
We were intrigued to see how the competitive advantage measure changed this year. When you think about how readily available technology is to measure, collect, and analyze data, my expectation for this trend actually would be a continued reduction in competitive advantage — but an increase in business value. Increased access to technology should improve the ability of organizations to improve processes and remove waste, thereby increasing the ability of organizations to create more outputs from fewer inputs. As technology is more available, more organizations should be able to do these activities, reducing the ability of any single organization to gain advantage.
But that isn’t what we found this year. Instead, competitive advantage rose.
One aspect of this finding is the increased ability to specialize and apply analytics in the specific areas where the organization creates competitive advantage. Another aspect was the points we noted in the “Going Forward” section — as technology becomes more readily available, then other aspects become more differentiating. These other aspects are not equally distributed throughout industries. I’ve gone into more detail behind this transition in an article “Data Analytics Makes the Transition From Novelty to Commodity.”
Do you have examples of organizations that shifted their core business from something else to becoming solely focused on data analytics?
GE is one example that receives a lot of attention. We go into more depth around its transition in “GE’s Big Bet on Data and Analytics.” But GE is far from the only example. PrintFleet Inc., based in Kingston, Ontario, is an example from the 2014 report, “The Analytics Mandate”; PrintFleet has transitioned from a focus on replacing printing supplies to using its data to help organizations manage printing operations in general. In addition, a recent case study on South Africa’s Nedbank Ltd. shows that some of its business is changing; see “A Data-Driven Approach to Customer Relationships.” In this case, Nedbank transitioned from internal use of its Market Edge tool to developing it as a standalone data product. These changes are fascinating to see and learn about.