Reading the recent Harvard Business Review article from Tom Davenport et al., it occurred to me that next best offer (NBO) is actually a subset of what my colleague Jim Kobielus calls “next best action” (NBA).
And when you couple that predictive thinking with advances in process mining (see Wil van der Aalst’s post and the “Process Mining Manifesto”), it clearly becomes possible to optimize operations dynamically on the fly. First of all, the organization could mine the existing system (the transaction logs of traditional systems or a newly implemented BPM/CRM system) to identify what happens today. This then enables you to identify the outcomes that are most interesting (or those you want to achieve) and then optimize the NBA accordingly.
We take for granted a process definition where the next action is predetermined by the arc of the process definition. But if we can do NBO in 200 milliseconds, we can also do NBA in a similar time frame. Directed arcs in process models and the business rules that go with them start to become a little redundant. This sort of combination (mining and NBA) enables wide-open goal-oriented optimization for all sorts of processes, not just those related to marketing and cross-sell/upsell ideas.
So to the prediction: I think we already have a few BPM/dynamic case management (DCM) platforms that can do NBA on the fly; so the next step, which I expect to see in 2012, is to see this predictive capability dynamically combined with process mining technologies. These platforms will also need to leverage complex event processing (CEP) and social technologies to really make them usable. Of course, all of that needs to be delivered in a compelling and accessible package.
We are starting to see the tip of this iceberg in the dynamic/adaptive case management area; no doubt, some of the usual suspects will claim they do it already.
However, I think it will take a bit longer (say by 2015) before we see business models that really connect the dots to leverage this sort of capability. What seems to be generally missing is the management mindset to take advantage of this fundamentally new capability. When I posed notion this internally, our practice leader, Kyle McNabb, responded with this:
Here’s some food for thought based on recent discussions between our peers in the Customer Intelligence role team and folks at the Harvard Business Review, the New York Times, and the Financial Times. To summarize the frank discussions we had a few weeks ago with those editors about big data: All this technology is great, but organizations that don’t treat data literacy as a competency are doomed to be laggards. Western organizations have outsourced or simply removed too many important roles and skill sets over the past few years that would help address data literacy — business analysts, data analysts, and market researchers.
Most organizations now don’t have enough internal personnel who can objectively help their business identify which questions to ask, how data (including big data) can help answer those questions, and — more importantly — how analyzing data in different ways identifies new questions and answers that people are not now considering. These people have to be able to translate data into business terms and vice versa. All this technology without people skilled in data literacy (including the data scientists Jim Kobielus has blogged about) is akin to arming a baby with a machine gun. I’d be interested in understanding what firms like Nordstrom have done in terms of skills to make this work.
This blog originally appeared at Forrester Research.
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