For organizations to thrive in the digital age, they need dynamic processes to implement thousands of business decisions they make day in and day out. Whether it be for sales commissions, claims verifications, benefits administration, or something else, a primary feature for executing processes effectively are business rules.
A business rules engine is what helps to manage business rules without interacting with code. Thus, business end-users can update business rules without relying on assistance from the IT team.
As business rules set the conditional statements to manage organizational processes, business rules engines are the application side that outputs whether the results are true or false. With business rules engines in play, organizations can increase their ability to respond to market fluctuations at any given time.
In this article, we’ll expand upon data-transfer models used by business rules engines:
Forward-chaining is also described as data-driven reasoning. The reasoning executes from known data and moves forward with it. In addition, inference rules are applied consistently until the objective is achieved. Also, with forward-chaining, all rules are set in a predefined order and you can specify by terms, including:
As each step finishes, another is added until the entire “chain” is completed. The best scenario for forward-chaining is when data has been collected, and you want to better understand and use the information. For instance, you can use forward-chaining in task analysis.
Another term to describe backward-chaining is goal-driven reasoning. The point of execution begins with a list of objectives. However, unlike forward-chaining, backward-chaining works backwards–as the name implies. The premise is to find paths that enable goal completion. Also, this data-transfer model will search for the most appropriate rule that aligns adequately with the desired outcome. Imagine coming up with a hypothesis, and looking for supporting evidence–this scenario is similar to the concept of backward-chaining.
Deterministic engines based on domain-specific languages
A deterministic rules engine based on domain-specific languages will always produce the same output for each input. Further, it always passes through a similar state sequence. If inputs are the same, you will get the same results. A deterministic rules engine is one of the most practical data-transfer models since it is also the most consistent. Once you enter the input, the deterministic rules engine transitions to the start state. Then, it’s current state will predict its next state. Every move to the next state is predetermined.
Fuzzy logic was created in the 1960s by Dr. Zadeh of the University of California. It came from the “fuzzy set theory.” It’s called “fuzzy” because instead of producing the traditional “true or false” statements (1 and 0), it provides “degrees of truth. Thus, this data-transfer model focuses on approximations instead of pure accuracy. To facilitate fuzzy logic, it would help to work with a wide range of values.
You can apply fuzzy logic to complex business models that can produce multiple outcomes. Fuzzy logic is concerned with the “truth” in between extreme truths of 1 and 0. For example, you might use fuzzy logic and determine your organization is 0.85 solvent.
Use fuzzy logic when you are working with a wide variety of inputs. It also helps when you want a model that replicates human reasoning more closely than the other models listed above.
Using a business rules engine makes it easier for your organization to retain flexibility when company processes evolve or markets change. Since the rules are isolated from the code, business-users can make modifications when needed. Therefore, you can validate your processes and data based on business-specific requirements and end-user expectations.