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***Editor's Note: Curious how insurers can increase agility while keeping costs down? Click here.



Introduction

It is an inconvenient reality that many BPM suites are too slow for today's increasingly data intensive environments. Their poor performance is exacerbated by the use of a workflow-only architecture; a "Web services-centric" definition of SOA; overly general and inappropriate models for business rules and poor software technology for translating models into executive processes.

A BPM Suite (BPMS) allows users to collaboratively model and automate business processes. These will generally include tools for the design and automation of processes, workflows and business rules together with integration, a forms generator and facilities to simulate, optimize and continuously improve the developed system.

The aim of such tools is to enable business transformation and to increase the adaptability or "agility" of companies so that they can respond quickly to changes in the market. The majority of products, however, have tended to focus exclusively on long-lasting human workflows, leaving the data-intensive processes to the wayside.

Middleware tools can be used to process transactions quickly, but while they offer the necessary speed and integration, they do not enable business users to be directly involved in the design or ongoing maintenance of their business applications. It is this feature of BPMS tools that produces many of their benefits.

As the business world becomes more data-intensive due to regulatory demands, mergers and acquisitions and the move to more centralized processes, the need to process large volume transactions is increasing rapidly and BPMS tools that cannot cope with these volumes will have restricted applicability. We will now look at those tools within a BPMS which can have a major effect on performance and suggest a new approach that addresses both worlds.

Business Rules

The popular "atomic and indivisible" approach to rule formulation is one in which all business rules are reduced to a set of simple conditional statements. This approach results in thousands of rules being needed and any change of data causing hundreds of them to be activated. This has two consequences:

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