Analytics 3.0 is coming…with big impacts on business models and the supply chain!

In my last blog, I discussed some of the insights from the Chief Analytics Officer conference in Chicago this past week, and highlights from Tom Davenport’s presentation on “Analytics 3.0”.  We covered some of the elements of the early years of analytics, and emphasized the point that given the time and effort being used to get data structured and subdued, there is not much effort being directed at actually analyzing the data using more complex math models, and this is the real opportunity that exists.  Enter “Analytics 3.0”.

The current data analytics environment is smoking hot!  People at the conference are all pushing away from consulting, but moving increasingly to creating data products for the new digital analytics marketplace.  “I don’t want to be managing, I want to be on the bridge with Captain Kirk!”   This has resulted in a strong urgency and a “land grab” situation, where people are moving quickly.  There is also a strong push towards tools that open sourced and scraped from Internet where data is free.   A number of different attitudes are beginning to emerge…..

The challenge of course is to try to build analytical capabilities in a world where there is already a set of existing analytical capabilities and technology – and that is where Analytics 3.0 is moving.   Davenport notes, “Anybody can create database services but the challenge is moving traditional analytics and big data, to analytics that can help people to run the enterprise!  This means having data that is produced in real-time, not backwards looking historical reviews.”

And this is where it gets exciting…. as Tom noted, (somewhat crudely…) “Analytics in the past was a pimple on the rear end of the economy! But Analytics today IS the data economy – and it doesn’t work without the people in this room!” Analytics 3.0 calls for a new way of thinking about analytics that are embedded in products and services and are communicating back to the enterprise.  For example, a logistics company, CH Robinson, is tagging fruit containers with sensors to determine if the fruit is spoiling, allowing them to determine who was responsible for storage at that location and avoiding payment of waste factors if the shipper wasn’t responsible for it.  Cement companies are putting sensors in cement, which allows them to determine when it is beginning to dry in transit, and how to re-route the cement to the job site.  In such instances, analytics is not just about supplying data (which information providers already do), but also about providing insight and services.

Consultant Stan Davis has dubbed data from products and services that emerges “information exhaust” because reams of such information escape every corporation, a supposedly useless asset, but in fact this is data that can be converted into insight.  To drive insight, begin with the data you have available, and explore what problems can be solved.  Such problems require individuals known as “Data Scientists” – who know how to exploit data sets in ways that can be applied to business problems.

At UPS, the history of telematics began with complaints from Teamsters, who complained that analytics was ruining their lives by telling them what to do throughout the data!  Similarly at Schneider, fuel sensor analytics tell drivers and operations how much fuel is left in the tank, and where they need to refuel based on the lowest prices and optimization of the route.  Historically, the driver made these decisions, so Analytics 3.0 is beginning to have some pushback.  Which raises an important point – analytics will bring about significant change in the way that people work, which may require a good deal of change management.  UPS analytics data may inform a supervisor that based on predictive analytics, a particular driver may be more likely to have an accident based on driving habits.  Now imagine the conversation between the supervisor and the driver!  What will this look like?  And what will the Teamsters have to say about that?  Analytics will create a new dynamic and will put a new set of responsibilities on people who are driving the analytical models.

There is no doubt that Analytics 3.0 is something companies are making a huge investment in.  GE is investing $2.5B on data modeling, with 200 data scientists that are embedding analytical sensors in jet engines, locomotives, and other equipment that can help drive predictive maintenance and discover operational issues more quickly, which will in turn improve safety.  And Google is working on analytics for driving the new car…. but according to Davenport, what are the major automotive companies doing?  Nothing….”let Google work on it…”.  Imagine the biggest thing in your industry coming along, and the decision is to let others to work on it.  Leading companies are moving ahead with driving investments in analytics, including biopharmaceutical companies and healthcare insurance companies.

It’s time to jump in to Analytics 3.0…. before others do…