Which of the following is not a characteristic of a management decision problem?

Data-driven decision making

Traditionally, decisions were made by business managers or corporate executives using their intuitive understanding of the situation at hand. However, intuitive decision-making has several drawbacks. For example, a gut-feel approach makes it hard to justify decisions after the fact and bases enterprise decision-making on the experience and accumulated knowledge of individuals, who can be vulnerable to cognitive biases that lead them to make bad decisions. That's why businesses today typically take more systematic and data-driven approaches to the decision-making process. This allows managers and executives to use techniques such as cost-benefit analysis and predictive modeling to justify their decisions. It also enables lines of business to build process automation protocols that can be applied to new situations as they arise, removing the need for each one to be handled as a unique decision-making event.

If designed properly, a systematic decision-making process reduces the possibility that the biases and blind spots of individuals will result in sub-optimal decisions. On the other hand, data isn't infallible, which makes observing the business impact of decisions a crucial step in case things go in the wrong direction. The potential for humans to choose the wrong data also highlights the need for monitoring the analytics and decision-making stages, as opposed to blindly going where the data is pointing.

Challenges in the decision-making process

Balancing data-driven and intuitive approaches to decision-making is a difficult proposition. Managers and executives may be skeptical about relying on data that goes against their intuition in making decisions or feel that their experience and knowledge is being discounted or ignored completely. As a result, they may push back against the findings of BI and analytics tools during the decision-making process.

Getting everyone on board with business decisions can also be a challenge, particularly if the decision-making process isn't transparent and decisions aren't explained well to affected parties in an organization. That calls for the development of a plan for communicating about decisions internally, plus a change management strategy to deal with the effects of decisions on business operations.

Decision-making models can also be used to avoid these various challenges by creating a structured, transparent process.

What is a decision-making model?

A decision-making model is a system or process which individuals can follow or imitate to ensure they make the best choice among various options. A model makes the decision-making process easier by providing guidelines to help businesses reach a beneficial conclusion.

Decision models also make the decision-making process visible and easily communicable for everyone involved, including all managers, stakeholders and employees. They can be used for a wide variety of purposes across departments, businesses and industries, but they are especially useful when selecting software vendors or new tools, choosing new courses of action or when implementing changes that effect large amounts of people.

Types of decision-making models

Common types of decision-making models include:

Rational models. Rational decision-making is the most popular type of model. It is logical and sequential and focuses on listing as many alternative courses of action as possible. Once all options have been laid out, they can be evaluated to determine which is best. These models often include pros and cons for each choice, with the options listed in the order of their importance.

A rational decision-making model typically includes the following steps:

1. Identify the problem or opportunity.
2. Establish and weigh decision criteria.
3. Collect and organize all related information.
4. Analyze the situation.
5. Develop a variety of options.
6. Assess all options and assign a value to each one.
7. Decide which option is best.
8. Implement the decision.
9. Evaluate the decision.

Intuitive models. These decision-making models focus on there being no real logic or reason to the decision-making process. Instead, the process is dictated by an inner knowledge -- or intuition -- about what the right option is. However, intuitive models are not solely based on gut feelings. They also look at pattern recognition, similarity recognition and the importance or prominence of the option.

Recognition primed models. These models are a combination of rational and intuitive decision-making. Its defining element is that the decision maker only considers one option instead of weighing all of them.

The recognition primed decision-making process involves:

1. Identifying the problem, including all its characteristics, problem cues, expectations and business goals.
2. Thinking through the plan and performing a mental simulation to see if it works and what modifications might be needed.
3. If the plan seems satisfactory, then the final decision is made, and the plan is implemented.

In recognition primed models, alternative courses of action are only considered if the original plan does not produce the intended results. The success rate of this model correlates to an individual's experience and expertise.

Creative models. In this decision-making model, users collect information and insights about the problem and create some initial ideas for solutions. Then, the decision maker enters an incubation period where they do not actively think about the options. Instead, they allow their unconscious to take over the process and eventually lead them to a realization and answer which they can then test and finalize.

When to use decision-making models

Even when rules and procedures are set up to make business decision-making more systematic, there can still be room for intuition on the part of decision-makers. For example, after gathering data about different alternatives, more than one might seem similarly advantageous, or management might find itself lacking certain information needed to make a decision with full confidence. This is a good use case for incorporating an intuitive decision-making model into the process.

On the other hand, decisions that happen frequently and have clear optimal outcomes benefit from a structured, rational decision-making models. This approach to business problem-solving uses clearly prescribed steps and, usually, data analytics software to evaluate the available options and arrive at a decision. 

Sometimes involving more people in the decision-making process can pay off. This is known as participatory decision-making; in the business world, it involves managers seeking input and feedback on decisions from the workers they oversee. The participatory approach has the potential advantage of generating many ideas for solving a business problem; it also helps to engage employees.

Decision management

Decision management -- also known as enterprise decision management (EDM) or business decision management (BDM) -- is a process or set of processes that aims to improve the decision-making process by using all available information to increase the precision, consistency and agility of decisions. The processes also focuses on making good choices by taking known risks and time constraints into consideration. 

Decision models and Decision support systems (DSS) are key elements of decision management. Decision management processes also use business rules, business intelligence (BI), continuous improvement, artificial intelligence (AI) and predictive analytics to access the capabilities of big data and meet the needs of modern day user expectations and operational requirements. 

Decision management systems treat decisions as reusable assets and introduce technology at decision points to automate the decision-making process.  Decisions may be fully automated, or they may be presented as possible choices for a human to select.  

Increasingly, organizations who deal with financial services, banking and insurance are integrating decision-making software into their business process systems as well as their customer-facing applications. This approach is especially useful for high-volume decision-making because automating such decisions can enable more efficient, information-based and consistent responses to events.