In Jim Baum’s keynote at the EnZee Universe conference  Intelligent Economy was central and it featured elsewhere in the conference. Not always by that name, but with the same essential characteristics – a mass of data and imaginative use of that data beyond the core application it was gathered for. I’ve written before about the analogy with human intelligence - that babies have all the data but they haven’t developed the capability to deal with it intelligently. With us humans (and I’m assuming we have that in common, no offence to my more exotic readers if it’s a false assumption) we gradually learn to do that sort-of automatically until we reach the rational point where we can choose to learn more skills, acquire more data. Organizations entering the nascent Intelligent Economy are already rational (ok, stay with me, i know there are exceptions) and can choose. That was Jim’s final slide on Monday, “think big”. The organizations that choose to exploit the data they have more fully, will have an advantage. For example, retailers who have big loyalty card programs started them primarily to buy loyalty (there’s a clue in the title) but increasingly they, unlike their competitors with less well-developed loyalty schemes, can now do market basket analysis over time for an identified individual customer – genuine micro-segments of one! And that's Jim’s real message, echoed elsewhere, is what do you do next? What new capability do you choose to acquire? What further data do you choose to collect?

Sticking with my retail example, is the next step in-cart technology for the customer to insert their loyalty card into so it can display their usual shopping list? Or custom suggestions? Or shortest route through the store for the list? Or automatic scanning and tick-off of each item as it drops into the cart? Stephen Baker’s book is packed with examples of mind-boggling, but possible ideas like this.

One of the threads that Stephen Baker and others have emphasized is the importance of the big-brained analysts who will be needed to build the models that enable the complex analytics that drive some of these scenarios. But it occurred to me that the limited supply of such mathematicians would be a barrier to progress. A bit like the early assessment of telephone adoption - that if everyone in the US had a phone then one third of the population would be working as switchboard operators (Is that an urban myth? I couldn’t track it down. Let me know if you can source it). That didn’t happen and i suspect the parallel scenario won’t happen; apart from anything else, quantitative analytics is a bit more intellectually demanding than operating a manual switchboard. In the BBBT forum on Tuesday Richard Hackathorn suggested one way to achieve the necessary scalability will be the development and productization of industry-specialized models. These might then be operated, combined, incorporated into business processes. I’m looking forward to the technology advances, Richard’s suggestion and/or others, that will create the breakthrough for mass adoption of deep analytics. But meanwhile the existing technologies and opportunities provide ample scope for the next wave.

And yes i know i'm a Brit using an American English word in my title, but i suspect i have more readers for whom Diaper is the right word.