Video
With increasing importance of data for business use, companies must respond to the advanced analytics imperative. Chris Brahm, who leads Bain's global Advanced Analytics practice, shares six principles, which if implemented correctly, can help companies reach their full potential of advanced analytics.
Read the Bain Brief: Closing the Results Gap in Advanced Analytics—Lessons from the Front Lines
Read the transcript below.
CHRIS BRAHM: Getting full potential value on advanced analytics is not an easy endeavor. And yet it's an endeavor that every large-scale enterprise needs to undertake. And so how do you actually do that? We see a set of principles deployed that really define the difference between those that are capturing full potential and those that that are not.
It starts with you have strategic alignment. And aligning where you're putting your analytic focus with the strategy and the economics of the company. It then moves to designing those analytics with the last mile of adoption in mind. It's important to look beyond traditional analytics. This space and the state of the art is moving very, very quickly. Whether it's in R&D, whether it's in sales, whether it's in marketing, whether it's in manufacturing, you need to look for new datasets that are emerging.
New techniques like advanced machine learning techniques, new deployment models, whether it's visualization or in-process inline analytics—all those things matter. You need to look ahead to where things are going, rather than how you've traditionally done analytics. Then it moves to test, learn, and iterate. Analytics work on an experience curve.
Version 1 of an algorithm is never as powerful as the version of that two years hence. Some algorithms like credit-rating algorithms have been in process for 50 years of improvement. So you need to take a test-and-learn and iterate mindset with respect to advanced analytics—any use case. Then you need to manage the transition towards an advanced analytics-enabled enterprise with purpose and focus.
These are a new set of capabilities for any organization. They require shifts in talent, they require shifts in your operating model, they require shifts in your technology, in your data foundation, and in how you engage in problem solving analytically involving the right technical analytic resources, but also the right line of business resources. So all that needs to come together, and the links in the chain will define. And the weakest link in that chain will define how well you do that and how quickly you close the value realization gap.
Read the Bain Brief: Closing the Results Gap in Advanced Analytics—Lessons from the Front Lines