4 Steps you need for a Successful Decision Intelligence Project.
In the Decision Intelligence series, here is the next discussion on how to get started with the decision intelligence project.
Before you start your project, choosing the right decision that truly impacts your key business objectives and the right approach is crucial for your project’s success.
The previous blog of this series talks about the decision intelligence platform. You can check it here Decision Intelligence Platform.
According to Gartner, almost 33% of organizations will use Decision Intelligence by this year. While we know Decision Intelligence was among the top technology trends of 2023. Here is what Gartner says about Decision Intelligence.
Decision intelligence (DI) is a practical discipline to improve decision-making by explicitly understanding and engineering how decisions are made, and outcomes are evaluated, managed, and improved via feedback.
DI makes the entire decision-making process more straightforward for the stakeholders. It clearly shows what happens before, during, and after an AI or human or combined decision is made, improving the transparency of the process. Thereby, DI can help enterprises improve the speed and quality of business decisions. You can now set a business goal and map it down to the decisions that impact the business objectives and intelligently manage it for better impact.
How to do it? Decision Intelligence Platform is the Key. Let’s find out how to get started on a Decision Intelligence Project. Here are the four steps you must follow.
Step 1: Frame Business Decision
Framing the business decisions allows you to understand the nature of each decision unit. The critical part of Decision Intelligence is the business decision. In this step, you will identify and understand how your organization makes the decision and map down its crucial parts. While it might sound similar to decision management, DI has more to it. The Decision Management Suite (DMS) is the advanced Business Rules Management System (BRMS). The DMS has BRMS and the ability to integrate AI/ML.
“Decision Management” carries a lot about rules by its natural progression from rules management. Decision Intelligence” as the successor of the DMS, has more to it. At its core, it must model business decisions.
Step 2: The Intelligence
In this stage, you decide which technology to use for each decision unit of your decision. As business decisions are complex, you may need to integrate different technologies for different sub-decisions of the business decision to automate and manage them. Alternatively, you can leverage an open, unified platform with an integrated approach that covers your decision intelligence requirements from the technology aspect.
A decision unit in your model can have any of these.
- Machine Learning model
- Rule driven
- Computational calculation
- Data aggregation and filtering
- Statistical model and algorithm
- Optimization such as leaner programming, constraint programming, etc.
At the end of this stage, you will have a decision graph that brings all decision units together as a visual representation and an executable decision model that can integrate all decision units into a unified model.
Step 3: Orchestrate
In the Orchestrate stage, you connect the data sources and shape the data. In the model, your orchestration logic should
- Collect the necessary information from the respective data source, i.e., database, systems, web services, etc.
- Transform them into shapes and forms that the decision model (decision graph) needs.
This is context-building for decision execution. It is a critical stage of decision automation. Many tools and platforms do not allow you to build the context in their platform; therefore, it becomes the responsibility of the decision consumer to pass the correct information.
This can increase the complexity of the process, especially when
- The data is not exposed to the customer.
- It requires internal dependencies on other parts of the organization.
- The data is extensive.
- It is evolving and needs the consumer to change, which causes the dependence of the consumer on decision internals.
It is crucial to encapsulate the whole data context inside the decision, so the consumer does not need to create the context, and this simplifies the process while reducing the time to decisions. After the data is collected and transformed, the context is ready, and the orchestration model calls to the decision for execution by passing the context.
Step 4: Final
After execution, you now have to address the decision outcomes. DI enables you to integrate the outcome of the decisions into the enterprise ecosystem. Integrating the decision’s outcomes may require dealing with legacy portals without any API or data interaction layer. In these cases, you can either
- Use a human workflow to assign tasks and delegate the outcome integration to domain experts and other people in the organization.
- Automate the outcome integration using Decision Robotics.
Takeaway
Decision intelligence enables enterprises to approach business decisions more precisely and make high-quality business decisions quickly. By following the above steps, you can identify the right technology for each decision unit of your business decision and make your project succeed.
One of the significant characteristics of a comprehensive and efficient Decision Intelligence platform is its ability to accommodate different technologies under one roof. So, you can automate any decision of your organization catering to various backgrounds and teams to make impactful decisions even in complex business environments.
For a comprehensive Decision Intelligence Platform, Check out FlexRule X-The Next-Gen Decision Management and Automation Platform
