Level Up: How to Scale Business Rules to the Enterprise

Enterprise users of business rules, or even a BRMS, should be aware of the severe limitations of rules when scaling up systems. In contrast, the decision model approach improves the enterprise scalability of business rules by providing a high-level business organization of logic missing in business rules. It also provides a precise, measurable alignment between rules and business goals. Lastly, it prioritizes the business value of automation over the ‘rush to technical detail’ often seen with a rule-based technique. Upgrading from rules to decisions represents the safest way to grow your systems.

The Challenge: How to Scale Business Rules

Business rules are a proven method of automating business operations that promotes transparency, agility and accountability. They are a more approachable means of representing the logic of business operations that business experts can use. However, my experience (of over 25 years) has shown that business rulesets, even those administered using Business Rule Management Systems (BRMS), don’t scale. They become very hard to manage and understand once they reach a certain size and complexity.

Although small, very tightly focused rule sets can be practical for simple business domains, large rulesets associated with enterprises are challenging to create and even more complicated to maintain. Small rule sets that become large over time present the most difficulty. They risk becoming excessively complex or the sole preserve of a few key individuals (‘high priests’) who alone understand them—defeating a key objective of business rules—to make operational business logic tangible to business experts, not just IT.

Why does the rule approach fail to scale?

  • The ‘rush to technical detail’: a business rule approach encourages policymakers to focus on rule implementation prematurely before considering their business decisions’ broader motivation, rationale and structure. This approach is like starting to build a house by laying bricks rather than understanding the market demand, design, drawing plans and establishing foundations.
  • Poor dependency management: large rule sets give rise to a growing and poorly understood set of inter-dependencies between rules. When rules are modified, these interdependencies lead to unintended consequences, making the ruleset brittle and reducing its agility over time. Rules don’t make dependencies explicit.
  • Insufficient transparency: the bewildering size of a rule set, the use of technical (rather than business) terms and style for expressing rules, and a poor connection between rules and the business make the meaning and motivation of rules more obscure.
  • Lack of value focus: the technical emphasis on individual rules often loses sight of the business motivation. It makes tracking the value delivered by automation very hard.

These problems arise for the same reasons: a lack of high-level organization in BRMS rule sets, which are frequently little more than a ‘bag of rules’, and a disconnect between rules and the business context (e.g., process, motivation and performance indicators).

How Decision Modelling Addresses These Issues

Decision Modelling is a technique for representing business decisions in a precise, standardized format that addresses these issues. It focuses on decision motivation and requirements—the big picture— before implementation. It documents dependencies diagrammatically, supports a precise representation of business logic, and succinctly defines business decision services. Specifically, decision modelling:

  • Encourages the development of rule sets to be more focused on the definition and requirements of end-to-end business decisions—saving time and ensuring alignment with business goals.
  • Focuses on business, not technical representations—making rules more straightforward to understand and identifying what needs to change as business needs evolve.
  • Uses a visual representation of the ‘high-level’ structure of a rule set right from the beginning—helping users understand how rules collaborate to determine an overall outcome and how any given rule can be decomposed into subordinates.
  • Allows you to see the dependencies between all rules and understand the impact of changing a rule or data input.
  • Establishes the traceability between rules and business process models, data models, business motivation models and regulatory, policy or legal frameworks.

The visual depiction of decision dependencies also helps to:

  • Make the ruleset more accessible to business users by offering a view that elides technical detail and is specifically designed for business analysts.
  • Support selective views of business logic, which allows business users to answer specific questions about business policy.
  • Scale the rule sets to larger sizes without confusion.
  • See patterns of dependency that could be harmful before they take root (e.g., lots of dependencies on a single, volatile rule or data item).
  • Offer a ‘top-down’ means of designing new decision services and rule sets instead of ‘jumping to detail’ with individual rules. Unlike rules, decisions can directly specify the interface and implementation of lightweight decision services.
  • Help those looking for an existing rule or the most appropriate place to fit a new rule to offer the desired flexibility. This facility supports reuse and prevents repetition.
  • Understand what data is (and is not) required to support decision-making.

A decision-based approach also has the following benefits over business rules:

  1. Additional verification: business rules verification, where available, checks rules individually, ensuring they are self-consistent. Decision verification does this but also verifies that individual rules are mutually This contextual verification is much more thorough because it considers how rules work together.
  2. Explicit about influences: decisions, unlike rules, can capture all sources of external influence. This facility allows enterprise decision models to track their obsolescence or maintenance requirements.
  3. Follows an industry standard: there are standards for business rules, but few commercial products support them. All popular rule management systems use their own rule representation standard. However, the Decision Model and Notation (DMN) standard for decisions is widely supported by many vendors. This standardization means a community of consultants, a body of best practice and off-the-shelf working models for specific domains are available to companies using decision modelling, regardless of the tools they use.

Bottom Line

The high-level structure, concise documentation of rule dependencies, and the standardized, transparent definition of business logic supported by decision modelling mean that the technique can scale to solve more significant problems than business rules alone. In short, improving the scalability, business alignment, and robustness of business rulesets is just one of the beneficial applications of decision modelling that can scale business rules and enhance their life expectancy.

If you are interested in this or other benefits of decision modelling, please don’t hesitate to contact us.

About the Author

Jan Purchase has been working in investment banking for 20 years during which he has worked with nine of the world’s top 40 banks by market capitalization. In the last 13 years he has focused exclusively on helping clients with automated Business Decisions, Decision Modelling (in DMN) and Machine Learning. Dr Purchase specializes in delivering, training and mentoring all of these concepts to financial organizations and improving the integration of predictive analytics and machine learning within compliance-based operational decisions.

Dr Purchase has published a book Real World Decision Modelling with DMN, with James Taylor, which covers their experiences of using decision management and analytics in finance. He also runs a Decision Management Blog www.luxmagi.com/blog, contributes regularly to industry conferences and is currently working on ways to improve the explainability of predictive analytics, machine learning and artificial intelligence using decision modelling.

purchase@luxmagi.com   |   @JanPurchase   |   https://www.luxmagi.com

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