Cheryl Wilson, Eugene, Oregon
Program Manager for WebSphere ILOG BRMS in IBM Software Group
Brett Stineman, Foster City, California
Senior Product Marketing Manager for WebSphere ILOG BRMS and WebSphere Business Events in IBM Software Group
Are you confused after hearing similar messages regarding the roles of business rule management (BRMS), business events processing (BEP) and business analytics (BA), especially when it comes to business process management (BPM)? These decision management technologies can be used individually or in combination to accelerate process improvement; each bringing a set of unique capabilities to the table. This article helps to clarify their special roles in making processes more responsive, more intelligent and more automated. It also offers practical tips on getting started with decision management.
Let’s start with a business scenario to illustrate the application of business process management (BPM) and the requirement for decision management in a company.
A healthcare insurance company has made a significant investment in streamlining its claims processing and adjudication system, anticipating improved employee productivity through redesigned processes and automated orchestration using business process management (BPM). After a year of using the BPM-orchestrated process, the company has significantly reduced the average time to process a claim and has increased its provider network (and the corresponding number of claims) without additional headcount in its claim processing division – great news.
Six months later, an auditing team uncovers a previously undetected multimillion dollar billing fraud scheme; they also discover that this fraud is increasing in frequency. This bad news is a result of the following problems with the claims processing system:
This is a decision management problem.
What do we mean by decision management?
Decision management is a growing practice of combining software and expertise to automate and improve decision-making within critical business systems. It involves both being able to make the best possible decision at the current moment based on data and situational context, as well as being able to use data to discover insights that can be used to continually improve and automate decisions over time. Examples of decision management applications include:
These decisions may be fully automated, for example, through an online application or a self-service point-of-sale system, or they may be used to provide decision support to people, for example, through a customer relationship management (CRM) system used at a call center, branch or store location, or in the back office.
Three primary technology areas fit within the Decision Management approach, as shown in the following table. While these three technologies areas are highly complementary, they can be used separately or together as part of a decision management solution. Each area brings a different focus and strength to automating and improving operational decisions.
|Decision Management Technology Areas||Description||Technologies|
|Situational awareness and decision execution||The ability to identify significant events taking place across business systems and provide precise decision responses based on the context of a customer interaction, transaction, or process||Business rule management and business event processing.|
|Business intelligence monitoring and reporting||The ability to provide decision support to people and systems through historical and current views of business operations||Business activity monitoring, reporting dashboards, data warehousing and associated segmentation/analysis|
|Analytics||The ability to discover insights in data that can be used to implement improvements to decision execution||Predictive analytic modeling, constraint and mathematical optimization, and neural networks|
As they relate to process improvement, the technologies of business rules, events and analytics have key roles to play. These technologies can be used to streamline process design and execution, enable more responsive actions within processes, as well as to enforce consistency across different processes that are subject to common business policies and/or regulatory requirements.
Understanding the roles of business rules, events and analytics
Business rule management and business event processing
Business rules and business events provide key operational capabilities for automating and managing decisions. While they have proven their value individually, these technologies are highly complementary, and both focus on enabling intelligent and responsive decision automation.
By using these two technologies together, organizations can flexibly create solutions that can detect and react to defined data patterns as they occur and provide the appropriate decision response based on a variety of factors, including an organization’s business policies and best practices or regulatory requirements.
For more information about how business rules and events can improve the timing and quality of operational decision-making, read the white paper titled, “Working smarter through intelligent, responsive decision automation.“
Business analytics provide key analytical capabilities that bring additional insight and oversight to improve complex decision-making. The types of analytics are as follows:
Here in a nutshell are the key distinctions between rules, events, analytics and process management:
To learn more about using business analytics together with BPM for better business results, read the white paper titled, “Using Business Process Management and Business Analytics together for smarter work.”
Automating intelligent decisions with rules and predictive analytics
Integrating a BRMS with predictive analytics and advanced reporting technologies allows businesses to use predictive models as part of a rule-based, automated decision. This improves decisions related to areas such as risk, fraud and propensity-to-buy. Predictive analytics help uncover changes to consumer behavior or market dynamics, things that are essential in determining how an organization should be running its business. Predictive analytics don’t, however, ensure that operational systems behave optimally to meet the organization’s requirements. This is where business rules come in, providing a different but complementary set of capabilities. The following table compares and contrasts business rules and predictive analytics:
|Business Rules||Predictive Analytics|
What we are certain of:
Explicit statements of corporate policies and human expertise
What we are uncertain of:|
The probability of events and conditions occurring in the future
What to do:
Decide what actions to take (or not take)
What is likely to happen:|
Must use other techniques/technologies to decide what to do with this insight
Easy to understand, natural language expressions:
Understood and managed by business users and IT
Mathematical (usually statistical) expressions:|
Understood and managed by statisticians
Directly deployed and executed by the BRMS in the operational environment:
No translation/coding required
Deployed and executed in various ways:|
Manual coding, SQL, PMML and sometimes through a real-time scoring service
Another way of improving the intelligence of rule-based decision automation is to combine the execution audit trail from the BRMS with data from other applications and processes. By aggregating data and making it available as highly meaningful dashboards and reports, organizations can more easily determine business performance, trends and potential problems. This information enables subject matter experts within an organization to determine where business rules may need to be refined, or to recommend where further analysis is required in order to improve rules.
How decision management helps to improve process management
The decisions that matter the most to business processes are operational decisions such as those that determine eligibility, manage risk or detect customer opportunities. By automating and improving these opportunity- and risk-based decisions – especially the higher volume ones that change frequently and need to be made quickly or through an automated channel – you can significantly and continuously improve process outcomes, such as cross-sell/upsell acceptance rates, straight-through processing or first call resolutions.
BPM is used to define and orchestrate the various tasks and services that comprise the end-to-end business process, whereas the BRMS manages and executes automated decisions at specific points in the process. In most cases, the BRMS is exposed to BPM through web services that are invoked by the process to make a decision that has direct influence on how the business operates. The BRMS is not just a technology to do simple routing rules inside a business process, but is instead used to automate complex, highly-variable decisions that take place at different points in a process or across different processes within an organization.
A central focus of decision management is to improve the alignment between business requirements and IT systems, and to keep this alignment flexible and continuous to adapt to changing business needs. This, in turn, enables business agility, and allows processes to be continually improved over time. By combining BPM with decision management capabilities, you can:
The need for improved processes requires the ability to easily and quickly change the decisions that drive them – decisions that can help processes maximize efficiency, make employees more productive, and improve interactions with customers, partners or suppliers. Rules, events and analytics bring critical capabilities for making processes more responsive, more intelligent and more automated, all of which can bring competitive advantage.
Two practical tips for getting started with decision management
1. Start with your high-value decisions first.
If you want to improve process outcomes, look to your high-value business decisions first, especially the higher volume, frequently changing decisions. Those decisions about customer offers, pricing, shipping, service, and eligibility can make your year, or break your year if you don’t make them fast enough or well enough. They add up, transaction by transaction, customer by customer, and can act as an opportunity or risk multiplier depending upon whether you get them consistently right or not.
The following conditions can signal a need for additional decision support:
2. Start small – one meaningful decision at a time.
Start small, applying rule and/or event-based decision automation to a particular process or application in a pilot setting. Starting with a meaningful decision, you can deploy a subset of your business decision system to demonstrate and validate the implementation process. Taking a practical and incremental approach is designed to deliver faster return on investment. The key to success is to establish the appropriate initial scope with the help of IBM experts, in collaboration with your business and IT teams.
Learn how the Quick Win Pilot for BRMS can help you get started.
Improved decision management is crucial to giving businesses the agility and decision-making power to succeed in a dynamic, uncertain and highly competitive environment. This article provides a quick overview of how BRMS, BEP and analytics can be used together, or individually, to automate and improve operational decision-making, offering businesses in virtually every industry the ability to work smarter and achieve better business outcomes.