Predictive analytics is not just about forecasting what’s coming down the pike. It’s also about keeping the bad alternative futures from happening. If you can see the nasty things that might happen far enough in advance, you have a better chance of neutralizing or squelching them entirely.
In fact, many real-world applications of predictive analytics are “interdictive,” a term often used in military and law enforcement contexts to refer to tactics that delay, disrupt, or shut down an adversary’s forces or supply routes before they can do damage. Anti-fraud is one of the principal interdictive applications of predictive analytics technology. Companies everywhere rely on data mining to determine who’s been engaging, alone or in groups, in stealing money, supplies, finished goods, cellular airtime, and other valuables — and also where they’re likely to strike next. Likewise, anti-terrorism efforts rely on predictive models to sift through massive collections of historical and real-time intelligence in a Jack Bauer-like race against time and imminent disaster. You best believe that social network analysis is a key weapon in your arsenal for predicting and interdicting these sorts of malignant social patterns.
We are sometimes so focused on details that we forget to think clearly. Nothing new there; it’s still a story about trees and forest. A few years ago, this was clearly the case when I met with one of the first vendors of run book automation. My first thought was that it was very similar to workload automation, but I let myself be convinced that it was so different that it was obviously another product family. Taking a step back last year, I started thinking that in fact these two forms of automation complemented each other. In “Market Overview: Workload Automation, Q3 2009,” I wrote that “executing complex asynchronous applications requires server capacity. The availability of virtualization and server provisioning, one of the key features of today’s IT process [run book] automation, can join forces with workload automation to deliver a seamless execution of tasks, without taxing IT administrators with complex modifications of pre-established plans.”In June of this year, UC4 announced a new feature of its workload automation solution, by which virtual machines or extension to virtual machines can be provisioned automatically when the scheduler detects a performance issue (see my June 30 blog post “Just-In-Time Capacity”). This was a first sign of convergence. But there is more.
Automation is about processes. As soon as we can describe a process using a workflow diagram and a description of the operation to be performed by each step of the diagram, we can implement the software to automate it (as we do in any application or other forms of software development). Automation is but a variation of software that uses pre-developed operations adapted to specific process implementations.
One of the key findings from this Forrester Wave is that a growing range of CRM vendors have incorporated deep analytics features into their customer service capabilities. Most provide embedded, out-of-the-box business intelligence (BI) features such as reporting, query, online analytical processing, dashboarding, scorecarding, and key performance indicators prebuilt to support their customer service applications. That’s no surprise, because these core BI features enable enterprises everywhere to keep track of how well they’re providing customer service across diverse CRM interaction channels and to identify opportunities to improve satisfaction, retention, upsell, agent productivity, and other key metrics.