Creating Agility and Operational Efficiency with Decision Modeling

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Decision modeling is designed to bridge the gap between business processes and the key decision-making technologies needed to transform traditional processes with digital decisioning, which typically includes both business rules management systems (BRMS) and predictive analytics. Here we’re using the term “predictive analytics” in its broadest sense, including artificial intelligence and machine learning technologies.

Decision modeling delivers the agility and operational efficiency required for a real-time customer experience while assuring the highest levels of consistency, compliance and risk management.

Why Model Decisions?

Today’s customer expectations, whether internal or external customers, are driving the need for truly digital decisioning. To achieve digital decisioning, transactional decisions, such as risk management decisions, fraud management decisions, marketing and sales decisions, and other decisions that impact a customer’s experience must be understood, modeled and digitized.

Decision modeling provides a framework that teams across an organization can use. It works for business analysts, business leaders, IT professionals and analytic teams. Decisions are also easily tied to the performance measures and business goals of an initiative. This makes it easier to focus teams where they will have the highest impact and to measure results.

Explained by the process director at a tax administration agency, “Decision modeling enables us to model our business by dividing it into concrete parts that are understandable to business people without being too detailed. It also helps us not to lose sight of the overall picture of the process while delving deep into the details of business rules.”

Gaps in Typical Approaches

Organizations use a variety of techniques to describe the requirements for an information system or related initiative. These techniques increasingly involve business analysts describing requirements in terms of business process models. Experience shows, however, that when process modeling techniques are applied to describe decision-making, the resulting process models are overly complex. Decision-making modeled as a business process is messy and hard to maintain over time. In addition, exceptions and other details can quickly overwhelm process models. Sorting these out during development creates both delays and additional costs. By identifying and modeling decisions separately from the business process, business processes are kept cleaner and it is easier to make changes. By modeling the decisions specifically, a clear and concise definition of decision-making requirements can be developed, providing clarity and context.

Business Rules & Predictive Analytics

Successful business rules and predictive analytic initiatives begin by focusing on the decision-making involved. Business rules analysis can seem never-ending, with teams trying to capture all the rules in a business area. The result is often a “big bucket of rules” that are poorly coordinated and hard to manage. Instead, by understanding which decisions will be made, when and to what purpose, it is now easy to tell when business rules analysis is complete. Beginning with a higher-level concept of a decision provides:

• An overarching principle to impose business-oriented structure and reduce the complexity.

• The separation of a declarative definition of the rules from the sequence-oriented business process – improving both.

• A structure that can be expanded in a series of iterations, allowing progress to be made in a more agile and less waterfall approach.

Specifying a graphical decision requirements model that provides a repeatable, scalable approach to scoping and managing decision-making requirements, making it easier to:

• Draw the automation boundaries.

• Re-use, evolve, and manage rules beyond the first business rules project.

• Consolidate business rules across multiple implementations and platforms.

• Assign ownership, governance and sources appropriately.

For predictive analytics-oriented initiatives, a clear business objective is critical to success. Specifying this objective in terms of the decision-making to be improved by the analytic is one of the most effective ways to do this. Business analysts have the tools and techniques of decision requirements modeling to identify and describe the decisions for which analytics will be required. Then how the data requirements support these decisions, and where these decisions fit, is clarified and the use of analytics focused more precisely.

It is essential to first define the decision-making required and only then focus on details like the specific business rules or predictive analytic models involved. Specifying a decision model provides a repeatable, scalable approach to scoping and managing decision-making requirements for both business rules and analytic efforts. According to one head of analytics at a North American insurance company, “This is the critical path to monetizing advanced models.”

Shared Framework and Implementation Mechanism

Many business analysts know that decisions, and decision-making, need to be a “first class” part of the requirements for a system. Systems that assume the user will do all the decision-making fail to deliver real-time responses (humans struggle to respond in real-time), fail to deliver self-service or support automated channels (no human available to respond) and fail front-line staff because instead of empowering them with suitable actions to take it will require them to escalate to supervisors.

Enterprise Architects meanwhile are chartered with fitting business rules and analytic technologies like data mining and predictive analytics into their enterprise architecture. Decisions are both the shared framework and the technical mechanism to easily implement these technologies. Decision modeling is a powerful technique for business analysis and for enterprise architecture. A decision requirements model allows the accurate specification of decision requirements. Decision modeling is also central to Decision Management, a proven framework that ties business rules and analytics to business objectives.

By focusing on the decisions that matter to an organization, Decision Management accelerates adoption of a Business Rules Management System (BRMS) or Predictive Analytic Workbench, focuses where you will have the highest impact, ensures business ownership of the business rules, and delivers agility and continuous improvement of the decisions across the company.

One lead business analyst for a North American insurance company shared that, “What used to be one week of requirements work was done in a few hours with decision management.”

Suitable Decisions for Decision Modeling

Organizations make several types of decisions:

• Strategic Decisions ― organizations make infrequent but large impact strategic decisions. Much analysis is done before the decision is made and the implications for a business can be dramatic.

• Tactical Decisions ― regular tactical decisions involving management and control are also made. There is generally still time and energy for significant analysis but there is time pressure too, a need for consistency and the opportunity to learn what works.

• Operational Decisions ― every organization makes large numbers of operational decisions about individual transactions or customers. Time pressure is often extreme and these decisions must generally be embedded into operational systems and processes.

While Decision Modeling can be used for any decision, it takes time and energy to build, so most organizations will only do so when a number of things are true of the decision:

1. There is clear value to defining the decision because it will be made multiple times (often many, many times) and it has sufficient complexity to warrant modeling.

2. They are non-trivial decisions because many policies or regulations apply, there is a wide range of options to select from or lots of data to consider. If the way a decision is made must change often, if it is very dynamic, or if there is a mix of drivers combined with a more modest pace of change then that will also make the decision non-trivial. Almost any decision that involves an assessment of fraud, risk, customer opportunity or similar through the analysis of historical data is non-trivial.

3. The value of the decision must be measurable and should be definable in advance. You should be able to identify the KPIs and metrics that will be improved by a better decision or weakened by a poor one.

Getting Started with Decision Modeling

Decision modeling is easy to adopt whether your project or initiative is kicking off or underway. Now is the time to optimize and streamline your business, building in agility and gaining operational efficiency. To learn more about creating agility and operational efficiency with decision modeling, visit


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