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Decision Management in 2024: 4-Step Implementation & Benefits

Good decision making is critical for all organizations. However, according to a recent Gartner survey, 65% of business decisions made are more complex, in terms of the stakeholders and options involved, than they were two years ago.1

Given that nearly 70%2 of CEOs ignore insights provided by data analysis and computational models because they contradict their intuition, the increasing complexity of business decisions requires a more systematic approach to decision-making processes in an organization.

As part of broader business process automation initiatives, decision management is an important practice for organizations that want to improve their data-driven decision-making. In this article, we’ll explore what decision management is, when you should use it, the steps to implement it, and its benefits.

What is decision management?

Decision management, also called enterprise decision management or business decision management, is a business practice that combines advanced analytics and business rules to automate decision-making across the enterprise. 

Decision management is a subset of decision intelligence, which aims to improve both automated and manual decisions in an organization.

When to use decision management?

Business decision management focuses primarily on operational decisions, which are made daily, as opposed to strategic or tactical decisions, which are more complex and concern longer-term objectives.

Operational decisions include relatively simple choices, such as what type of paper to use for printing, or more complex ones, such as whether a transaction is fraudulent. These types of decisions are suitable for automation as they:

  • Are high in volume: Businesses make hundreds, if not thousands, of operational decisions every day.
  • Have low variability: Expected outcomes of operational decisions are limited to a relatively narrow range.
  • Are digitizable: They can be digitally modeled as business rules as their underlying business logic is relatively straightforward.

While operational decisions may not have a major impact on the overall direction of a company, they can still have a significant impact on its bottom line. According to a McKinsey survey, a typical Fortune 500 company wastes more than 500,000 days per year making ineffective decisions.3

What are the 4 steps of decision management?

Decision management involves:

1. Decision identification

Decision identification, also called decision discovery, is the process of recognizing operational decision points within an organization and analyzing the factors that go into making each decision.

For instance, for a hiring process, this involves:

  • Questions addressed by the decision, such as “What is the minimum number of years of experience an applicant should have to be considered for the position?”.
  • Data used in decision-making, such as an applicant’s resume or recruitment exam results.
  • Stakeholders that are affected by the decision and that have the authority to make the decision.

Businesses can use the process visualization feature of process mining tools to identify and analyze decision points in their business processes. In a complete business process automation solution, decision management and process mining features are included along with tools to automate the processes and decisions.

2. Decision modeling

After identifying and analyzing the decisions with all relevant data, businesses should model their operational decisions to make them ready for automation. Decision modeling is creating a model that captures the decision-making logic of a process to simulate different decision scenarios.

Decision models are often created using:

  • Business rules that govern specific decisions and explain what to do when certain conditions are met. These are often expressed with conditional logic statements such as “If-Then,” “When-Then,” etc.
  • Decision trees that show the sequence of decisions and the associated outcomes. Decision trees can be used to determine the optimal decision path, identify potential risks, and to assess the impact of changes to the decision logic.
A simple decision tree for a hiring process
Figure 1. A simple decision tree for a hiring process. Source: Toptal

Business rules management systems (BRMS) allow organizations to define, store, manage, and optimize their business rules and create decision trees to model their decisions without coding.

3. Decision automation

Modeled operational decisions are then automated using automation tools. This can include both simple tasks, such as routing a customer call to the appropriate department, and complex decision trees, such as deciding whether to approve a loan. Automation can involve technologies such as:

Automating complex business processes requires a combination of these tools, and end-to-end business automation solutions can help implement them all.

4. Decision monitoring and improvement

Decision monitoring is the process of tracking and assessing how well an automated operational decision is performing over time. This can be done by:

  • Comparing the decision outcomes to the decision objectives
  • Measuring the decision-making process against success metrics
  • Simulating other decision models for the same process to compare the outcomes

Decision improvement is then the process of making changes to the way decisions are made, in order to improve their effectiveness. This might involve changing the decision criteria, introducing new methods for data gathering and analysis, or revising the decision-making process itself.

What are the benefits of decision management?

The main benefits of decision management include:

  • Improving the consistency of decision-making and helping organizations ensure that day-to-day operational decisions are aligned with overall business strategy,
  • Reducing human errors through automation,
  • Reducing complexity by managing business logic separately from enterprise applications,
  • Saving time, increasing efficiency, and reducing delays in processes,
  • Increasing business agility by providing an overview of the logic of enterprise decisions and allowing them to be changed as needed.

If you have questions about decision management and want to learn about solutions to get started, feel free to reach out:

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Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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