Ready for Analytics 3.0? Predictive Analytics Software Gets You There

April 02, 2013

Tom Davenport is a key thought leader in the analytics space whose work I have been following. When I saw he had written an article called “Ready for Analytics 3.0?” I jumped on it. Davenport gives us a guided tour of the journey from Analytics 1.0 to 2.0 and now 3.0. He traces the evolution of analytics, talking about changes in the types of data in use, internal, external, structured, and unstructured, as well as advances in technology such as in-memory databases and predictive analytics software. Every bit as interesting is the movement of analytics from a back-office function to the center of the C-suite. As I read this, I thought about how true this is for those of us in marketing – analytics is right at the center of every marketing plan and every conversation.

Davenport sees Analytics 1.0 stretching across a long time period --  about 1954 to 2009. It’s characterized largely by the use of internal data from traditional systems of record. Models, when they were created, took several months to generate. Analytics was a back office function and almost no one competed with analytics, a topic that Davenport wrote a groundbreaking book about in 2007.

Around 2010, we enter Analytics 2.0, and we start hearing about big data. Companies start using Hadoop to process all that unstructured data and we see visual analytics begin to come to the fore, which he sees as a form of descriptive analytics. In this phase, descriptive analytics is still crowding out predictive and prescriptive analytics. Predictive analytics makes predictions about future events; prescriptive analytics uses business rules to suggest actions that we should take based on those predictions. Both of these types of analytics heavily leverage machine learning.  But I’m getting ahead of myself: that’s part of Analytics 3.0.

Analytics 2.0 also heralded a shift in the place of analytics in the organization. Analysts move closer to the business and competing with analytics becomes the norm. Data scientists are in high demand.

Lots of people would say we are still in Analytics 2.0, but Davenport suggests multiple reasons why he believes that we have entered Analytics 3.0. He gives several reasons:

  • Companies are combining numerous internal and external data sources to drive predictive and prescriptive models
  • Software that leverages agile analytical methods and machine learning is taking data from all of these sources and driving faster predictive and prescriptive analytics
  • Analytics is playing a central role in driving strategy to the point that it’s entering the C-suite: take a look at how many organizations now have a Chief Analytics Officer

It’s an exciting and challenging time to be a marketer. We have so much data available; the potential is high for us to devise more effective marketing actions. But honing in on the most meaningful information to take just the right action is the key. Analytics 3.0 will take us there. And with all the attention coming from the C-suite, as Davenport says, we’ll actually compete with analytics.