Search DMU Library

Categories

Problems Addressed by Business Rules

Time to take a fresh look at business rules. What problems do they address? Why are they still so relevant? How will they relate to AI and machine learning?

Introduction

Business rules cover a very broad space.  Across the entire space, there is one central idea — business logic should not be buried in procedural programming languages.  Call it rule independence.

Why is rule independence important?  Because rules entangled in procedural code will never be agile.  Rules change all the time — and in a digital world, the pace of change is always accelerating.  How an organization can stay on top of it is the central question in business agility.

That has not to say business logic is simple.  Just the opposite.  It can often be quite complex.  So why entangle it in procedures and code, which must attend to all sorts of other complex concerns, ranging from platforming, messaging, synchronization, data access, user interfaces, and more?

Separation of Concerns

Business rules are all about disentanglement — a natural separation of concerns.  What advantages does that have?  For one thing, separation allows subject matter experts to be involved directly in the validation and verification of the business logic before it is implemented.  For another, business can be empowered to make changes directly to the business logic after deployment.  Doing so provides a degree of control to business staff — and in the process, liberates IT resources for other work, focused on the technology.

Do not forget about compliance.  Often, compliance people are among the strongest supporters of the business rule approach, since with business rules, results can be directly traceable.  If someone (such as a business partner or regulator) wants to knowthe reasona business result was produced, instead of program code or (usually sparse) documentation, they can inspect the actual business rules that produced the results.  Fast, efficient, trustworthy validation.

Business Rules and Business Processes

To understand all the areas that business rules cover requires looking at the things that business processes must (or should) address.

  • Decisions.  Suppose the organization wants to implement dynamic pricing, taking into account multiple factors about the customer, the product, current demand and supply, size of the order, etc.  What better way to organize the business logic than as a set of rules?  Doing this allows business to analyze the structure of the decision using an appropriate decision model, then express the rules in some decision table(s) with clear outcomes.  Recognizing that decisions are a separable part of process models has been one of the most important contributions of the business rule approach.
  • Case Management.  Businesses often organize activities around concepts that have predictable stages or states.  A classic example is the medical case of a patient.  The same thinking, however, arises in many situations — for example, products developed in stages, orders that progress through predictable states, complaints, inquiries, break-downs, purchases, etc.  What better way to coordinate allowable movement between states or stages than by explicit rules expressing what constraints must be satisfied for each category of situation?  Without business rules, managing each category effectively as a distinct aspect of the processes would prove difficult or impossible to accomplish.
  • Data quality.  To achieve high data quality, the data must reflect business results produced correctly from the start.  Business rules help ensure correct business outcomes by guiding business behavior.  Processes only ensure someone does the correct things; business rules ensure those things are done correctly.  Therefore, business rules always play either a direct or an indirect role in data quality matters — there is no way to escape it.  Business rules give the prominent profile to data quality matters that are absent in processes alone.
  • Digitalization.  Despite the digitizing of much data and processes, many gaps remain in processes where manual intervention is still required, including simple tasks such as moving data from one form or format to another.  In the past few years, robotic process automation (RPA) has focused on this challenge by offering effective rule-based solutions.

Business Rules and Other Concerns

Not everything business rules address, however, is so easily cast into a pure-process perspective, nor should it be.  Consider these areas:

  • Requirements.  Analysts and developers face several key challenges.  One is ensuring that models are robust and complete.  Simply, without business rules, diagrams are just diagrams.  Another example is user stories.  It has been said that user stories represent merely the tip of the iceberg with respect to requirements.  What is the other 90%?  A significant percentage of the missing content is business rules.
  • Communication.  Information Technology is central to running every business, so it is simply unacceptable that communication gaps still exist between the business side and IT.  How can these gaps be closed in a manner that offers full service to the complexity and richness of what business people know about their business, and to the vocabulary they use to talk about it?  The content gap can be closed with business rules.
  • Knowledge Retention.  In traditional companies, retirement of key subject matter experts (SMEs) is a hard reality.  In newer companies, the turn-over rate of workers is unprecedented.  How can any organization capture core knowledge so it is not lost when workers walk out the door?  Business rules enable the instantiation of the knowledge from all staff.

Business Rules and Knowledge

In thinking about all these areas that business rules address, it is hard to escape the insight that business rules are a pure form of explicit knowledge.  They shape business behavior and guide decisions — independently of, but in cooperation with, procedures and platforms.

Machine learning (ML) and Artificial Intelligence (AI) are all the rage, and many IT professionals ask if business rules still matter.  Will ML and AI make business rules obsolete?  If not, where is the dividing line between them?

This is the beginning of a wild and exciting ride for how machines will address knowledge.  In certain respects, however, the shape of things to come is already clear.  The answer is “No, ML and AI will notmake business rules obsolete,” for many reasons.

Currently, ML capabilities are purely statistical.  In fact, that is the very reason for the unbelievable progress AI has made over the last decade — the realization that many problems do notrequire symbolic representation such as words.  A bot driving a car does not need to explain why it did not hit a pedestrian — it just needs not to hit them.  A bot listening to voice commands does not need to explain why it ‘knows’ with whom it is communicating — it just needs to be sure it is the right person.  A bot playing a game of chess does not need to explain its moves — it just needs to win.

All of this is stating the reason, the bottom line, for business rules — they always tell you why something is done or not done.  Whenever humans are in the loop — and there are explicit laws, obligations, and rules to worry about— one will always need to know the reason “why.”  That is what business rules are ultimately about — the reason “why.”

Conclusion

There are many opportunities to examine the ideas behind business rules and their role in each industry.  World-class practitioners have achieved much success in their businesses with business rules.  However, all the success starts with understanding the fact that business rules exist and they exist for a purpose: to explain the reasons for business actions, thereby supporting business success. This article is an excerpt from the Foreword of Business Rules: Management and Execution https://www.amazon.com/dp/0986321486 by Ronald G. Ross.  A version of this article appeared at http://www.brcommunity.com/a2019/c012.htmlBusiness Rules Journal Vol. 20, No. 11, (Nov. 2019)

Share on linkedin
LinkedIn
Share on facebook
Facebook
Share on twitter
Twitter

Contact us

  • This field is for validation purposes and should be left unchanged.

Request a free consultation
with a DMU Expert

  • This field is for validation purposes and should be left unchanged.

Subscribe To DMU

Be the first to hear about articles, tips, and opportunities for improving your data management career.