Continuous decisioning is when a single execution of a decision (i.e. rules, optimization, etc.) is not sufficient to conclude the outcome. You need to continuously and dynamically create new decisions that are applicable for new situations and apply the decisions until the final outcome of the case is determined.

Your business rules to today's state of the case might be valid, but when the state of the case changes, your decisions based on the previous rules may be stale, and you will need to learn more about the new state and new business rules related to the new state of the case. But how can you understand the rules from the new state of the case?

Generally, business rules are modeled by domain experts and operations teams in decision automation scenarios. But what if you see a behaviour in your operational system from your customers, clients, etc. but you don't know the business rules? This is where the need for business rule mining arises to extract, analyse, and understand business rules from customer or market data

Business rule mining is the ability of a system allowing you to extract rules from data. In this process, you connect the data FlexRule Designer and it will analyze the data and extract the rules from it.

Business rule mining

The overall process of business rule mining for continuous decisioning approach.

The business rule mining enables business analysts, or operation people take the historical data from operational database and systems, provide them to the business rule mining module and FlexRule Designer processes the data, automatically apply multiple different Machine Learning algorithms, and represents a readable form of rules such as below:

if (Outlook == 'sunny') and (Humidity <= 70) then Play = 'yes'
if (Outlook == 'sunny') and (Humidity > 70) then Play = 'no'
if (Outlook == 'overcast') then Play = 'yes'
if (Outlook == 'rain') and (Windy == false) then Play = 'yes'
if (Outlook == 'rain') and (Windy == true) then Play = 'no'

Ready to Use – Simulate, Debug, Deploy and Explain

FlexRule Designer will take it one step further and prepare and model a Decision Table and Fact Concept related to those rules automatically and will add them to the project.

Decision tablefact concept

Now you can start debugging and simulation activities, play with the rules by feeding relevant values for inputs (conditions), and retrieve the results (outputs):

business rule mining - golf example

As you see in the below screenshots, after rules execution is completed, the result is set to “Yes”.

business rule mining - parameters window result

One of the benefits of this approach is because the decision is now driven by rules rather than a particular Machine Learning trained model, you have the full visibility and explainability on why a specific decision is made:

Event viewer to show explainability

Continuous Decisioning

The kind of business decision where a continuous change of state of a case over time does not allow the final decision outcome to be conclusively determined in a single cycle/iteration of decision execution is called continuous decisioning.

Depending on the frequency of the change of the case's state, there are different approaches that can be utilized to implement continuous decisioning scenarios:

  • A long-running decision allows the state changes to be tracked and responded to over a span of time in multiple connected execution sessions.
  • Decision Graph's state management ability can handle how to respond to the new changes of the case within itself rather than an extra process management or other code-driven approach.
  • Integrated Rule Mining and CICD to extract new knowledge from cases and situations and create new decisions for the future

Where the frequency of the change is not (nearly) realtime – business rules mining gives you the ability to update the decision based on newly discovered rules for the future state of the case.

Business rule mining is a very important part of the continuous decisioning process. The business rule mining module enables you to utilize a Machine Learning with a couple of clicks without having the knowledge of a data scientist and extract the business rules from your data.

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By building this process into any continuous integration such as a CICD pipeline and deliver a continuously decisioning platform by looking at the latest dataset in systems, extract the business rules, build the decision model, test, debug, approve and go to production. Here is how the continuous decisioning cycle is:

continuous decisioning cycle using business rule mining techniques

With this approach, you can keep monitoring and measuring the Decisions KPI constantly and improve them as needed in an iterative and incremental manner.

Last updated March 25th, 2024 at 10:26 am Published September 3rd, 2020 at 11:10 am