Syndicated from BeyeNetwork
In a great post on 8 things to keep in mind on predictive analytics, some folks from Diamond Management & Technology laid out some things to keep in mind that I really liked. Here they are with my comments – you can get more detail on each from the series of posts with which they followed this initial one.
- Understanding the cost of a wrong decision helps target investments
Absolutely, though I still think that finding a decision you can tie to an executive’s compensation plan works better.
- Strategic and operational decisions need different predictive
modeling tools and analysis approaches
.. and deployment approaches. I divide decisions into strategic or direction-setting ones, tactical or day-to-day management ones and operational or transactional ones. Particularly with the latter, which are crucial, you need to think about how the models will be deployed if they are to add value.
- Integration of multiple data sources, especially third-party data,
provides better predictions
Yup, but don’t just integrate your data – begin with the decision in mind and integrate to support it.
- Since statistical techniques and tools are mature, by themselves
they are not likely to provide significant competitive advantage
True. It is their ability to turn YOUR data into YOUR insight that does.
- Good data visualization leads to smarter decisions
.. at the strategic and tactical level and to better models at the operational level – decision making at the operational level is too high-speed, too automated for much in the way of visualization to be useful a the moment of decision.
- Delivering the prediction at the point of decision is critical
- Prototype, Pilot, Scale
Of course – don’t forget to scale the deployment piece too
- Create a predictive modeling process & architecture
Yes. And map it to your IT development process if you want to impact operational decisions embedded in your enterprise IT infrastructure.
A great list!