Evidence-Based Decision Making
The practice of evidence-based decision making involves using current information to make empirically supported decisions.
Describe the concept and strategic implications of evidence-based decision making in management (EBMgt)
- Evidence-based protocols have been adopted in fields such as business, education, and law enforcement, demonstrating the usefulness of this approach.
- Evidence-based decision making in management ( EBMgt ) requires that managers and their organizations procure and organize enough empirical and objective data to implement a scientific decision-making process.
- Critics argue that evidence-based approaches do not take ethics into consideration. As a result, managers must take an active role in implementation.
- Overall, EBMgt is a useful tool for managers to generate informed and intelligent perspectives, decisions, and strategies as they lead a company.
- EBMgt: A management practice that emphasizes a rational, objective, and empirical approach to addressing business issues.
Defining Evidence-Based Decision Making
Evidence-based management entails making decisions and creating organizational practices that are informed by analyzing the best available data. The practice of evidence-based decision making in management (often abbreviated as EBMgt) evolved from medicine and emphasizes a rational, objective, and empirical approach to addressing business issues. It is analogous to the scientific method which uses experiments and data collection to advance knowledge.
The EBMgt Collaborative—sponsored by a number of universities and foundations throughout the U.S., U.K., and Canada—is an organization devoted to expanding the practice of EBMgt. The EBMgt Collaborative’s mission statement includes a comprehensive definition of the practice:
Evidence-Based Management (EBMgt) enhances the overall quality of organizational decisions and practices through deliberative use of relevant and best available scientific evidence. EBMgt combines conscientious, judicious use of best evidence with individual expertise; ethics; valid, reliable business and organizational facts; and consideration of impact on stakeholders.
Pros and Cons of EBMgt
Evidence-based protocols have been adopted in non-scientific fields such as business, education, and law enforcement, demonstrating usefulness of this approach. Because the evidence approach examines outcomes, it supports the careful consideration of the relationship between cause and effect. Managers can have more confidence in their choices when they can point to data that supports the likelihood of that choice leading to desired results.
The adoption of EBMgt also creates advantages in how an organization operates. The formal processes of EBMgt require managers and other decision makers to be disciplined and organized in their decision-making process. The degree of structure in collecting and analyzing data helps create a working environment that favors facts over intuition or guess-work.
Critics of EBMgt argue that evidence may not always be complete or appropriately measured; they also argue that analysis is not always neutral or without bias. It is not always possible to agree on what counts as credible evidence; even if data on a certain factor is desirable, it may not exist or be readily available. The idea of objectivity is obscured because data is subject to interpretation, and those with different levels of experience or backgrounds can reach different conclusions about the implication of a given set of findings. Critics also argue that evidence-based approaches do not take ethics into consideration.
Though it has its limitations, EBMgt can be an effective approach to informing the decisions of managers. By acquiring sufficient data that support conclusions, EBMgt can help decision makers distinguish between alternatives and choose the most promising option. It can also help influence others to support a decision once it has been made.
The Value of Analytics in Decision Making
Analytics help decision makers determine risk, weigh outcomes, and quantify costs and benefits associated with decisions.
Recognize the decision-making value of utilizing statistics and analytics to create accurate predictions
- Predictive and descriptive analytics are two methods of using data to inform and evaluate alternatives during decision making. They can also be used to explain performance outcomes.
- Predictive analytics encompass a variety of statistical techniques (such as modeling, machine learning, and data mining) that analyze current and historical facts to make predictions about future events.
- Descriptive analytics focus on developing new insights and understanding of business performance based on data and statistical methods; these analytics are then used to make strategic decisions for the company.
- analytics: The use of skills, technologies, and practices to explore and investigate past performance, gain insight, and drive business decision making.
Analytics refer to the use of skills, technologies, and practices to explore and investigate past performance, gain insight, and drive business decision making. Predictive and descriptive analytics are two methods of using data and statistical methods to assess actual outcomes against target standards and goals. These types of analysis can explain the relationship between factors that influence outcomes; they can also help prioritize improvement and other planning efforts. Companies can use their analytic capabilities to create advantages over competitors and better perform in the marketplace.
Predictive Analytics and Decision Making
Predictive analytics encompass a variety of statistical techniques (such as modeling, machine learning, and data mining) that analyze current and historical facts to make estimates about future events. Models capture relationships among many factors, allowing an assessment of risk or potential associated with a particular set of conditions. This helps to guide decision making for candidate transactions. Data mining draws on large numbers of records to identify patterns that can then be identified as opportunities or risks. For example, by analyzing grades for an entire class of first-year students, academic advisers can predict which students are most likely to struggle in the class.
Predictive analytics help decision makers to predict the outcome(s) of a decision before it is implemented. Using these probabilities, decision makers can calculate the expected value of alternatives once risks and benefits are taken into account. Predictive analytics are particularly useful when there is a high degree of uncertainty. By carefully considering what is not known, decision makers can build confidence in the estimates that inform their choices. Forecasting consumer behavior in response to a new product or marketing initiative are examples of the use of predictive analytics.
Descriptive Analytics and Decision Making
Descriptive analytics answer the questions, “What happened and why did it happen?” This approach seeks to understand past performances by using historical data to analyze the reasons behind past success or failure. Understanding cause and effect can help refine business and operational strategies. Most management reporting—such as sales, marketing, operations, and finance—uses this type of analysis. Descriptive analytics are used in quality management techniques and other methods of statistical process control.
Analytics in the Modern Business World
Descriptive and predictive analytics have increased greatly in popularity due to advances in computing technology, techniques for data analysis, and mathematical modeling. Desktop tools can easily create reports and summaries of analytic results that help decision makers readily understand the findings and their implications. These tools create tables, charts, and graphs to present the data visually, which can help to clearly communicate the meaning of the data.
Making Decisions Under Conditions of Risk and Uncertainty
Conditions of risk and uncertainty frame most decisions rendered by management.
Outline the various risks that influence the decision-making process
- Uncertainty and risk are not the same thing. Whereas uncertainty deals with possible outcomes that are unknown, risk is a certain type of uncertainty that involves the real possibility of loss. Risks can be more comprehensively accounted for than uncertainty.
- Decision -making under conditions of risk should seek to identify, quantify, and absorb risk whenever possible.
- The quantity of risk is equal to the sum of the probabilities of a risky outcome (or various outcomes) multiplied by the anticipated loss as a result of the outcome.
- A firm’s ability to absorb, transfer, and manage risk will often define management ‘s risk appetite; once risks are identified and quantified, decisions may be made as to what extent risky outcomes may be tolerated.
- force majeure: An unavoidable catastrophe, especially one that prevents someone from fulfilling a legal obligation; an unforeseeable act of nature.
- hedge: A contract or arrangement reducing one’s exposure to risk.
Uncertainty is a state of having limited knowledge of current conditions or future outcomes. It is a major component of risk, which involves the likelihood and scale of negative consequences. Managers often deal with uncertainty in their work; to minimize the risk that their decisions will lead to undesired outcomes, they must develop the skills and judgment necessary for reducing this uncertainty. Managing uncertainty and risk also involves mitigating or even removing things that inhibit effective decision-making or adversely effect performance.
One cause of uncertainty is proximity: things that are about to happen are easier to estimate than those further out in the future. One approach to dealing with uncertainty is to put off decisions until data become more accessible and reliable. Of course, delaying some decisions can bring its own set of risks, especially when the potential negative consequences of waiting are great.
Managing uncertainty in decision-making relies on identifying, quantifying, and analyzing the factors that can affect outcomes. This enables managers to identify likely risks and their potential impact. Types of risk include:
- Strategic risks: These are risks that arise from the investments an organization makes to pursue its mission and objectives. They are often associated with competition and can include macroeconomic risks (the alignment of buyers and sellers consistent with the principles of supply and demand), transaction risks (the operational risks from merger and acquisition activity, divestitures, or partnerships), and investor relations risk (the risks associated with communicating effectively or ineffectively with the investment community).
- Financial risks: These relate to potential economic losses that can result from poor allocation of resources, changes in interest rates, shifts in tax policy, increases or decreases in the price of commodities, or fluctuations in the value of currency.
- Operational risks: These risks can arise due to choices about design and use of processes to create and deliver goods and services. They can include production errors, substandard raw materials, and technology malfunctions.
- Legal risks: These risks stem from the threat of litigation or ambiguity in applicable laws and regulations (including whether they are likely to change); these threats create uncertainty in the steps an organization should take to address its obligations to customers, employees, suppliers, stockholders, communities, and governments.
- Other risks: Risks are very commonly associated with force majeure, or events beyond the control of the organization. These can include weather disasters, floods, earthquakes, and war or other hostilities.
Once management has identified the appropriate risk category that may impact a certain decision, it may go about quantifying these risks. In other words, management will ascertain the costs incurred if a risky outcome were to happen. This can be mathematically daunting for many types of risk, especially financial risk. Generally speaking, however, risk is equal to the sum of the probabilities of a risky outcome (or various outcomes) multiplied by the anticipated loss as a result of the outcome. This is similar to performing a sensitivity analysis if the universe of outcomes is known.
The ability of a firm to absorb, transfer, and manage risk is critical in management’s decision-making process when risky outcomes are involved. This will often define management’s risk appetite and help to determine, once risks are identified and quantified, whether risky outcomes may be tolerated. For example, many financial risks can be absorbed or transferred through the use of a hedge, while legal risks might be mitigated through unique contract language. If managers believe that the firm is suited to absorb potential losses in the event the negative outcome occurs, they will have a larger appetite for risk given their capabilities to manage it.