My focus – in my columns as in my work – is on Decision Management Systems. This exciting new class of systems put predictive analytics to work improving the day-to-day decision-making that drives operational success.

These systems are different from the typical information systems that run your business today and in one of my previous columns I outlined their four principles  – that they are focused on decisions, agile, analytic and adaptive. These systems are exciting, at least in part, because they are the most effective way to apply predictive analytics.

To build these systems, you can and should continue to use many of the technologies you use to build traditional operational systems. The data infrastructure that drives your analytic systems is also a critical building block for decision management systems. However, most organizations will find that they must adopt additional technologies to be successful in building these systems. The technologies are not new, having been proven in real-world deployments, but they are not widely used in organizations today. These technologies are the subject of a new report –the Decision Management Systems Platform Technologies Report – available from the Information Management website.

The report gives an overview of the additional capabilities required to develop decision management systems – capabilities to manage decision logic, embed predictive analytics, and optimize, simulate and monitor decision performance over time. It also begins the discussion of the key characteristics of suitable products, identifies some best practices and discusses some use cases for decision management systems. The report wraps up with a list of available vendors, some guidelines on picking vendors and an appendix introducing the topic of decision management systems for those who have not read my book.

The core of this report is its discussion of the additional capabilities organizations require to be successful developing decision management systems:

  • Managing Decision Logic. Decision management systems require the definition of decision-making logic – how a particular decision should be made given the systems understanding of the current situation. Decision management systems require that decision logic is managed in a way that delivers design transparency, so it is clear how the decision will be made, and execution transparency, so it is clear how each specific decision was made. For most organizations this means adding a business rules management system or other decision management capability to replace the traditional coding of logic.
  • Embedding Predictive Analytics. Most decision management systems should take advantage of the information available to improve the accuracy and effectiveness of each decision. Decision management systems cannot use visualization and reporting technologies to understand the available information and must therefore embed predictive analytic models derived from historical data. This shift from presenting data to humans so that they can derive insight from it, to explicitly embedding analytic insight in systems using predictive analytic techniques means that organizations will need to adopt a predictive analytic workbench or equivalent functionality. For performance and scalability, many organizations are also adopting in-database analytic infrastructure.
  • Optimization and Simulation. Many decisions consume constrained resources (like staff, product inventory or service capacity) and most organizations want to optimize their results given these constraints. Therefore, you need optimization and simulation technologies to manage trade-offs and to ensure that decisions are made in a way that produces the best possible results given the constraints on decision-making.
  • Monitoring Decisions. It‘s not often possible to tell how good a decision will turn out to be right away, so monitoring decisions and their outcomes is critical. Most organizations will find that they will use their existing performance management and data infrastructure to conduct much of this analysis. However, the use of the capabilities discussed above will allow the explicit logging of decision-making and outcomes for detailed analysis. Organizations will also need to conduct and manage experiments in decision-making.

An organization that is established in developing decision management systems will ultimately adopt technologies for all these capabilities. There are literally dozens of vendors with suitable products (they are listed in the aforementioned report). Today the products that deliver these capabilities belong to a number of overlapping categories, some of which are focused in one area of capability and some of which cut across the boundaries. Organizations may  find it useful to have more than one product with the same kind of capability, while others will standardize on a single product. Some organizations will focus on vendors that have all the capabilities in a single product or suite, and others will mix and march. (Future releases of the report will drill into more detail on how to find the right product mix.)
Decision management systems are high ROI systems. They used to also be high cost, but the wide range of products available and the maturity and effectiveness of those products are bringing these powerful systems squarely into the mainstream for IT and analytic organizations. It’s time to take control of the decisions that drive your business every day and to develop systems that will be active participants not just passive repositories of your data.

Register or login for access to this item and much more

All Information Management content is archived after seven days.

Community members receive:
  • All recent and archived articles
  • Conference offers and updates
  • A full menu of enewsletter options
  • Web seminars, white papers, ebooks

Don't have an account? Register for Free Unlimited Access