I am a faculty member for the International Institute for Analytics and recently published two new briefs on how to use decision requirements modeling in the successful application of analytics, especially (but not solely) more advanced analytics such as data mining or predictive analytics.
- Decision Discovery for a Major Business Function
This outlines how an organization can identify decisions, document them and understand their dependencies and business contexts as a way to drive successful broad application of analytics.
- Using Decision Discovery to Manage Analytic Project Requirements
This drills down in more detail to show how a model of decision requirements can play a key role in providing the business understanding that an analytic project needs to build effective analytic models.
These are available only to members but you can get a fair amount of background on the topic from my blog (To make better decisions you need to manage those decisions for instance), my column on Information Management (Requirements for Advanced Analytics for instance) and our recorded webinars.
I have also published a brief based on our Predictive Analytics in the Cloud research and a couple of use cases – Reducing Customer Churn and Building Loyalty at YouSee, a leading provider of Cable TV and broadband in Denmark (there is a video interview on the SAS site) and Consumer Knowledge at US Cellular, smaller US carrier that competes with what amounts to a customer intimacy strategy.
Going further back I published other briefs including:
- Critical Issues in Applying Analytics at Production Scale
One of the most powerful uses of data mining and predictive analytics is to apply these techniques to operational, transactional systems. This means applying analytic models and results on a production scale.
- The Role of Decision Services in an Enterprise Architecture
Standard Enterprise Architecture approaches do not lend themselves to analytic deployment as they lack the concept of a decision and of architectural components that implement those decisions.
- Automating Decisions: Business, Analytics & Technical User Roles
Applying analytics to operational decisions creates challenges and forces new roles on business, IT and analytic staff.