Posts in the series:

1. Benefits of Decision Modeling [this post]
2. What is a decision model and what is DMN
3. Decision Modeling: Decision Requirements
4. Decision Modeling: Decision Logic
5. Decision Modeling: Finding and modeling decisions
6. Decision Modeling: Input Data and Knowledge Sources
7. Building decision tables from decision models
8. ML, AI and other forms of Data Analysis and Advanced Analytics

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I’ve been working in digital decision automation management for more than 20 years. The single most effective change in developing decisioning systems has been the adoption of decision modeling. More specifically, the use of the Decision Model and Notation or DMN standard. If you are selling, supporting, installing, or using IBM’s decisioning products (IBM ODM, IBM ADS and IBM DMOE), Decision modeling and DMN matter:

• Decision modeling with DMN is an effective way to design rules and decision services in ODM. Several large IBM ODM customers use this approach.
• IBM ADS, the newer low code decisioning tool in CP4BA, combines a decision model with decision tables. The decision model is based on DMN principles and an understanding of DMN is important for success with ADS.
• IBM DMOE’s latest version is based on DMN and executes decision models defined in DMN. Models built in DMN can be combined with legacy DRL rule applications, but the new tooling is all focused on using decision models and DMN to express your logic.

This blog post is the first in a series designed to introduce decision modeling and the DMN standard, so you can be more effective in your use of IBM’s decisioning tools. In this post I’ll outline the benefits of using decision modeling. In future ones, I’ll dive into some of the specifics. I’m not going to try and cover EVERYTHING and I’ll make some simplifying assumptions as we go but I hope to give you a grasp of the essentials.

In case you are wondering about me, I’ve been an IBM Champion (for Decision Management) since 2015. I’ve written a couple of books on decision modeling, and I’m on the DMN standards committee. Decision Management Solutions is the IBM business partner I founded. We have been using decision modeling and DMN on every business rules development project for more than a decade. We are also an original submitter for the standard. My partner Ryan Trollip founded DecisionAutomation.Org to help promote decision modeling. We’re all in on decision modeling – because it works.

So why use decision modeling?

An Effective Visual Blueprint of Requirements

Building a decision model of your solution requirements creates a visual blueprint.  A simple to read diagram that shows how you intend to solve the problem, what the elements are (data, decisions, knowledge), and how everything fits together. Even the most complex decision-making problems can be broken down into a network of smaller, collaborating sub-decisions and then visualized using a set of related diagrams.

Because the models only use three shapes and two lines, it’s easy for SMEs and business analysts to learn and use. These visual decision models bridge the gap between business/operations SMEs who know how a decision should be made and the IT and analytics teams who need to implement it.

At the end of the day, decision modeling captures your business rules and analytic requirements far more effectively than any other requirements technique.

Increased Speed and Engagement

Decision modeling is quick, much faster than traditional rule writing. Customers tell us that decision modeling gets to a complete definition of what is required 10x as fast as writing requirements or lists of rules. It produces rapid results that are also far more accurate, complete, and consistent.

Because it’s quick and easy to understand, SMEs get engaged in building and reviewing decision models.

Even the most experienced SMEs tell us that building a decision model, thinking logically about their decision-making in this way, teaches them something about it. They get value and the model gets more accurate.

Integrate ML and AI With Your Business Rules

In an era of Machine Learning (ML), Artificial Intelligence (AI), Large Language Models (LLMs) and augmented human intelligence, decision models provide a framework. They capture rules-based decisions, AI-based decisions, ML-based decisions, and human decisions using the same model and the same notation. A robust, holistic picture can be developed quickly and effectively, allowing the right mix of technologies and the right human/machine balance to be delivered. You can build one, integrated set of requirements across them all.

Skills Interchange

Decision modeling allows for skills interchange. Someone who knows how to work with decision models can be an effective participant in a project using any decisioning platform. You can hire people who know decision modeling, even if they have never used the platform you are working on. You still need deep technical experience with the product in use, but much of your team can be effective if they understand decision modeling and how decision models are implemented.

In the next post, I’ll describe a decision model and introduce the best known and most widely used standard, DMN.

If you want more detail, you can get a text book on the approach (written by me and Jan Purchase): Real-World Decision Modeling with DMN 2nd Edition. If you or your company need help with decision modeling, drop me a line james@decisionmanagementsolutions.com and we can schedule a quick call to discuss. And if you’re excited about decision modeling and keen to do more, why not join DecisionAutomation.Org and participate?

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