I caught up with DataInfoCom recently – a research-oriented software company headquartered in Austin, Texas. Their focus is on what they call Predictive Decision Management. Their software product, OSMOSYS, delivers predictive decisioning over the Internet – Decisions as a Service or DaaS as I call it. Their customers include a couple of well known, Fortune 50 corporations across a fairly broad range of solution areas (new product introduction, channel demand, field service, parts logistics, customer service, etc.). Like me, they see a need to make decisions based on the underlying predictions, not just deliver predictions. For instance, DataInfoCom can forecast demand for specific products at specific stores, so their customers can ship the right products, at the right price, and at the right time to avoid discounting or returns. In customer service, customer satisfaction, first contact resolution and brand loyalty (net promoter score) are predicted so that customer service can make the best up/cross-sell offers at the right time during the interaction.
OSMOSYS is an enterprise software product that contains models that predict what will happen, when and why. On top of these models are actionable recommendations as to how to avoid predicted issues and take advantage of predicted opportunities. To deliver on this they perform predictive analytics, root cause analysis, optimization and what-if simulations. It involves applied statistics, machine learning, operations research and business rules. They have built their own implementations of a wide range of analytic algorithms. The platform aims for automated and “touchless” decisions and allows business managers to easily build in their own specific constraints, business rules, etc.
The basic process goes like this:
- They use an extensive set of predictive analytics techniques (logistic regression, time series, decision trees, neural networks, etc.) to predict KPIs at different, future time horizons of interest.
- These predictions are then fed to a root cause analysis engine that assesses why the predictions are the way they are – it finds the top reasons with their contribution. These could be decision variables – knobs that the business can turn – or something outside the control of the business (managed using tags on input data and constraints).
- The optimization layer then varies the high impact decision variables to preempt a predicted issue or benefit from a predicted opportunity, while conforming to the constraints based on rules. Simplistic examples include maximization of value of an interaction given a cost constraint, or minimization of cost for a given customer satisfaction constraint.
- OSMOSYS recalibrates its algorithms continually to ensure accurate predictions and useful decisions as the process being governed by OSMOSYS changes.
When touchless or fully automated is not desirable they offer a what-if simulation environment that allows the various predictions, decision variables, etc. to be varied by an expert user to see what the impact on the affected KPIs would be.
Activating OSMOSYS takes 30 days of set up, data cleansing, data integration, model tuning and adding rules/constraints – fewer for areas where DataInfoCom has a lot of experience. As a DaaS solution there is no software to install, the recommended decisions are just fed into existing systems.