What is decision management?

Decision management finds its place within analytics—the discipline that uses business logic as well as data mathematics to help provide insights that make better decisions.

Analytics can be one of four types:

• Descriptive: Tells you about something that has occurred, but not what you can do about it.
• Diagnostic: Where you can explore the reasons why something occurred.
• Predictive: This data models the likelihood that an event may happen in the future.
• Prescriptive analytics: Combines all of the above but takes an extra step to make recommendations on how to respond or you can automate responses.

Decision management forms a part of prescriptive analytics. It helps organizations make operational decisions and trigger a response where needed as well. Decision management becomes a discipline which has sets of tools and techniques that enable businesses to make better decisions resulting in improved outcomes.

Areas of decision management

The goal of decision management is to enhance business operations intelligence by ensuring quick, consistent, and accurate fact-based decisions. The quality of structured operational decisions, no matter how complex, should be constantly improving. There are five areas that affect decision management:

• Data and analytics: Data is accessed and processed with the help of descriptive, diagnostic, and predictive techniques. You need strong data quality as a basis for accurate decision making, and the outcomes of those decisions affect the data as well.
• Business process management: Managing human tasks and the sequence of business process automation and task management. The information from staff helps to make better decisions, and their roles are enhanced as a result.
• Operations research: Optimizing and managing various goals based on standards and priorities that can be modeled. Decision management analyzes operations and suggests improvements that can be made.
• Business rules management: Automating business rules and managing them based on inputs provided by subject matter experts.
• Robotics: Using software to imitate human behavior in the automation of actions and related interactions with software systems.

Decision management results in efficiency and productivity, two critical factors for successful business operations. As a concept, decision management can be used in a wide number of industries, functions, and areas of business. There are so many businesses that make scores of operational decisions on a daily basis. The quality of these decisions has a direct impact on the effectiveness of the company. All decisions are impacted by data, regulations, market dynamics, and decision management—and therefore becomes a necessity.

There are a range of ways that decision management interacts and influences a variety of sectors.

In the financial sector:

• Mortgage approval
• Insurance and loan disbursal
• Financial trading
• Leasing and billing

In the corporate world:

Supply chain management

• Product configuration
• Risk management
• Cross-selling
• Logistics

In the public sector:

• Permit approval
• Subsidy determination
• Welfare
• Taxes

Benefits of decision management

For businesses considering decision management, there are a number of benefits.

Better utilization of time

Regardless of the model of the decision management support system, research shows that it reduces the decision time cycle. Employee productivity is the immediate benefit from the time saved.

Better efficacy

The effectiveness of decisions made with decision management is still debated because the quality of these decisions is hard to measure. Research has largely taken up the approach of examining soft measures like a perceived decision quality instead of objective measures. Those who advocate the creation of data warehouses are of the strong opinion that better and larger scale analyses can definitely enhance decision-making.

Better interpersonal communication

Decision management systems open the door for better communication and collaboration among all decision makers. Set rules ensure that all decision makers are on a single platform, sharing facts and any assumptions made. Data-driven rule sets analyze and provide decision-makers with the best version of the possible outcome, encouraging fact-based decision-making. Better access to data always enhances the quality and clarity of decisions.

Cost reduction

An outcome from good decision management rule sets is saving costs in labor (which comes from good decision-making, lowered infrastructure, and technological costs).

Better learnings

In the long term, a by-product of decision management is that it encourages learning. There is more openness to new concepts, fact-based understanding of businesses, and the overall decision-making environment. Decision management can also come in handy to train new employees—an advantage yet explored in full.

Increased organizational control

With decision-making rule sets, a lot of transactional data is made available for constant performance checks and ad hoc enquiries by business heads. This gives management a better look at how business operations work. Managers find this to be a useful aspect of decision-making. There is a financial benefit to highly-detailed data, and this gradually becomes evident.

Disadvantages of decision management

As with any system, decision management systems can have a few disadvantages.

Information overload

Considering the amount of data that goes through the system (and the fact that a problem is analyzed from multiple aspects), there are chances of information overload. With too many variables available on hand, the decision maker may be faced with a dilemma. Streamlined rule sets can help.

Over-dependence

When decision making is completely computer based, it can lead to over-dependence. While it does free up man hours for better use of skills, it also increases dependency on computer-based decision making. Individuals can be less inclined to think independently and come to rely on computers to think for them.

Subjectivity

One of the important aspects of decision making is the number of alternatives that are offered based on objectivity. Subjectivity then tends to take a backseat, and this can affect decision-making and impact businesses. Things that cannot be measured cannot be factored.

Overemphasis on decision making

Not all issues an organization is faced with needs the power of decision management. An emphasis has to be placed on utilizing decision making capabilities for relevant issues.

Types of Decision Support Systems for Decision Making

Decision support systems are classified into two types:

1. Model-Based Decision Support Systems: These stand independent of any corporate information system. They work on the basis of strong theory or models and come with an excellent interface for easy interactivity.
2. Data-Based Decision Support Systems: These set-ups collect large amounts of data from a variety of sources, store it in warehouses, and analyze it. The warehouse stores historical data and also comes with some reporting and query tools.

In data-based decision support systems there are two main techniques that are employed:

1. Online Analytical Processing (OLAP): Based on queries, this provides quick answers to some complex business needs. Managers and analysts can actively interact and examine data from multiple viewpoints.
2. Data Mining: By finding patterns and rules in existing data, useful decision making information can be extracted to help in trend and consumer behavior patterns.

There is no doubt that the most significant benefit of decision management is the overall improved management of revenue and profitability thanks to better decisions. While the investment in the system may be higher, the return on investment is undoubtedly quick. Taking away guesswork when it comes to decision-making ensures that it is a sensible business solution.