- Business Intelligence (1)
- Methodology (3)
- Operations Research (4)
- Risk (2)
- Vision (1)
Purposing Intechné
At the recently concluded INFORMS 2008 Practice Meeting, multiple colleagues asked about our vision for Intechné. Quite simply, our vision for the company is to reliably deliver smart decision-making capability to our clients.
It goes without saying that smart business decision-making involves advanced analytical techniques from the fields of Operations Research, Statistics, and Artificial Intelligence. These include Predictive Analytics and Data Mining (to detect correlative, possibly causal, relationships in historical data), Monte Carlo Simulation and Decision Analysis (to simulate the impact of such relationships and tease out key sensitivities in anticipative decision-making) and various flavors of Optimization (both mathematically-driven algorithms and less-structured, heuristic approaches). But the application of a wide spectrum of techiques does not necessarily guarantee smart decisions. Intechné differentiates itself by explicitly focusing on an often overlooked issue in applying advanced analytics in the enterprise: Risk.
Viewed in the context of applying advanced analytics to business improvement, risk is like the weather: everybody talks about it, but nobody does anything about it. Or, nothing systematic at any rate. At the purely technical level, approaches such as mathematical optimization produce “brittle” decisions; very small changes in input can produce dramatically different recommendations. Perhaps that’s not of concern in the few situations where human operators are not in the associated decision chain. But in general, analytics are used to support decision, not execute them. Unexplainable, non-intuitive, or volatile decisions often force operators to work around their decision-support systems, or even completely ignore them. For instance, we found a sophisticated SAP/APO installation essentially ignored by its users (demand planners at a Food & Beverage company) because it couldn’t auto-profile different product types. While the overall MAPE was ok, sales forecasts for individual products diverged from reality in unexpected ways.
When it comes to delivering decision-support technology based on advanced analytics, a host of implementation risks arise beyond standard IT development risks. For instance, the response times of constraint programming models can decay exponentially with input size. (This is quite different from, as an example, rule-based decision engines.) Encountered unexpectedly, such non-responsiveness leads to expensive and disruptive modeling/algorithmic rework at an advanced project stage.
All in all, the business of delivering smart business decision-making is characterized by these, and many other risks. In informal feedback from colleagues and clients, we find that project mortality in this area is unacceptably high: about one in three advanced analytics projects fails to perform to expectation.
An active risk management orientation lies at the core of our vision for Intechné. In forthcoming communications we describe how this orientation is incorporated into our practice culture, and how it has been shown to improve client results.