One of the most useful ways to define an approach is to contrast it with other established approaches.  This is particularly true in information technology where many approaches offer similar benefits — increased agility, operational efficiency, reduced costs, etc.  Decision Management is often compared with various kinds of Business Intelligence and with Business Process Management.  Because Decision Management uses Business Rules and Data Mining or Business Analytics it is also compared with these approaches individually.  While there are similarities in each case, and while the benefits overlap, there are crucial differences.

Decision Management is not the same as Business Process Management.

Business Process Management is about making the steps and sequence of processes explicit and then managing and automating these in software.  While processes do contain decisions, and simpler decisions like routing are often managed using the same software, BPM is not focused on decision making.  There is some common technology also — business rules and analytics are often used in processes — but the fundamental difference of being process- rather than decision-centric makes BPM very different.

Decision Management is more (and less) than Business Rules.

While Decision Management uses a business rules approach to defining the logic of a decision and Business Rules Management technology, there is more (and less) to it than business rules.  While business rules are important to decisions, and critical to many, the use of information and analytics is likewise important.  Business Rules also have broad applicability in areas such as data quality, process control, and more.  These uses of business rules are also different from Decision Management.

Decision Management is not the same as Analytics.

Data mining and analytics are important to Decision Management.  Without them it is hard to take what can be learned from historical data and effectively apply it to decision making.  Data mining and analytics, however, can be used in other ways.  In particular they can be used in a more passive way in the sense of providing better reports and deeper understanding.  Data mining to show managers and knowledge workers what their data is telling them and predictive analytics embedded in reports to extrapolate the future, not just report on the past, are very valuable uses of these techniques.  Decision Management is different because they are combined with business rules to become active — to drive the decision making explicitly.

Decision Management is not the same as Business Intelligence.

There are many ways to use data to improve decision making, and Business Intelligence is a broad umbrella term used to describe many of them.  There are several flavors, and it is worth considering how Decision Management compares with the various types of BI.

Strategic BI involves the use of analytics to improve long-term planning and the decision making of executives.  Dominated by reports driven by the budget process and the company organization structure, this kind of data-driven decision making tends to focus on long-term changes and trends.  While aggregation and summarization dominate, the use of data mining and analytics to create more insight in this context is increasing.  This kind of BI blurs into more Tactical BI as it moves down the organization.

Tactical BI is focused on shorter-term planning within lines of business.  Dashboards and a focus on KPIs become dominant, along with support for knowledge workers building their own reports and running ad-hoc queries.  This kind of BI supports decisions that get made repeatedly but that are not completely repeatable.  There is often a template or process defined for these, and the BI tools used fit into that template.

These two kinds of decision making correspond to what Neil and I called Strategic and Tactical decisions in our book Smart (Enough) Systems.  And this focus clearly differentiates them from Decision Management with its focus on Operational decisions.

It gets more complex with Operational or Pervasive BI.  These terms have become popular recently, and the definitions have not completely settled down.  You can regard Operational BI as applying standard BI tools for operational reporting from near real-time data feeds.  Operational BI thus focuses on the use of Business Intelligence tools and techniques to deliver information to people running the operations of a business.  The people who receive the information can then manipulate it and explore it to some degree, using both expert-oriented and more business-friendly tools.  Pervasive BI is about taking this to the extreme of providing these tools to everyone, even to customers and consumers.

In both cases, though, decision making remains "outside" the system and guided only by the experience of the user and by the policies, procedures, and regulations read by the people using the tools.  As you move further down the organization, and even outside it, this becomes more problematic — there are more people to train on policies or regulations, compliance becomes harder, and analytic skills become rarer.  In contrast, Decision Management is focused on the automation of these decisions — either all the way to action or to the presentation of relevant, appropriate options.  The policies, procedures, regulations, and best practices are captured inside the system, and information is not presented so much as used to drive the decision.  So, while the decisions are similar (operational, transactional, high-volume, front-line decisions) the approach is quite different.

The common factor that differentiates Decision Management in all these areas is a ruthless focus on decisions.  Decisions, and the identification and automation of them, are what make Decision Management different.  Decision Management begins with the decision in mind at all times and is focused on delivering the most precise, most consistent, most agile decision possible.  While it shares techniques and tools with other approaches, it is this focus on explicit decisions and decision making that primarily differentiates it.

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