The field of decision science focuses on making data-informed decisions. Decision science helps to analyze the impact of a decision on the business. The best decisions are often made with a combination of data and precise business questions. The more precise the questions, the more precise the data requirements will be.
Harvard’s Center for Health Decision Science (CHDS) explains that this unique science is a “collection of quantitative techniques” applied to decision-making at both the individual and population levels. It includes “decision analysis, risk analysis, cost-benefit and cost-effectiveness analysis, constrained optimization, simulation modeling, and behavioral decision theory.” Further, “decision science provides a unique framework for understanding public health problems.”
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However, decision science is not just applied to public health but also pricing decisions such as the optimal price for a product or service; product decisions such as measuring profitability vs. customer satisfaction; marketing decisions such as allocating budget across different marketing activities like public relations, advertising, or sales promotion; and finally, HR decisions such as hiring or firing decisions or performance evaluations.
When the right type and volume of data are used to make any of the above decisions, the decisions are far more likely to be accurate and effective.
In businesses, different types of decisions are made daily. As decisions have direct impacts on business performance, they come with inherent risks as well as payoffs. Every time a business decision is made, the risks and potential benefits are quantified and measured. The process of making informed business decisions through a combination of quantitative data analysis, data visualization, and deep modeling techniques is known as decision science.
So, to put it in one sentence, decision science “is the process of analyzing the impact of a decision on a business.” The two primary components of decision science are data and a set of tools, which may be both qualitative and quantitative. Qualitative tools include content analytics or data visualization tools. In contrast, quantitative tools include statistical or machine learning (ML) solutions – for example, linear regression may be used to study the impact of advertising budget on sales growth. The data for each business case helps answer business questions, and the set of tools helps analyze the data for making informed decisions.
In a typical scenario, a business analyst may use sales data to predict the total number of customers likely to buy a product. If high-quality and high-volume data are available for this exercise, then this type of analytics can help in making multiple future decisions.
Data quality plays a critical role in decision science, without which the decisions will neither be reliable nor accurate. Another related requirement for effective decision science is a precise business question to narrow down the exact data sets.
An infographic from KDNuggets.com explains how decision science differs from data science.
While data science is an interdisciplinary field designed to extract insights from data, decision science involves the use of both qualitative and quantitative techniques to analyze data and insights for better business decisions. Though data is equally important for both the sciences, the approaches to data analysis and applied mechanisms are quite different.
In some business cases, the decision may involve identifying the customer adoption rate for a product or measuring the impact of change in a government policy on your business. In those cases, a scenario analysis may be used to compare two or more probable “outcomes” so that the most suitable decision is taken based on the result of the comparison. These outcomes may include a scenario describing what is most likely to happen, a scenario describing what is least likely to happen, and a third scenario describing the extreme that could happen.
Use of Statistics in Determining Outcomes in Business Analytics
A “statistically significant” result indicates whether a particular result is likely valid. This type of analysis can be applied to both qualitative and quantitative data. A good example of qualitative analysis is a survey to gauge customer sentiment. The results of this survey will help identify whether the customers are satisfied or dissatisfied with your business.
The statistically significant result will create a confidence interval around the survey results. The confidence interval represents the statistical significance of the survey results and can be applied to any survey question.
Data-Driven Decision Making: Benefits of Decision Science
Business decisions that are based on data are more likely to be successful than decisions made without data. This is especially true for large decisions that will have a significant impact on the future of the business.
The typical benefits of data-driven decision-making are increased certainty around outcomes, increased chances of outcomes matching your expectations, and enhanced understanding of customers. As you gain a better understanding of customers and competitors through data-informed decisions, the odds of making wrong decisions are substantially reduced.
Here are some major benefits of using decision science in an organization:
- It helps businesses make unbiased, data-informed decisions.
- When used with decision support systems, decision science can enable enhanced interpretations and effective decisions promptly.
- It can offer a competitive edge in a business environment requiring intelligent data interpretations.
- It helps senior management identify uncertainties, value outcomes, and other issues involved in business decisions.
- Decision science often helps compare available alternatives and zero in on the optimal solution.
The Decision Science Role
In decision science, the analyst takes a “360 view” of the business challenge. By combining different types of data analysis, data visualizations, and behavioral understanding of customers, the decision scientist can make specific, data-informed decisions.
The average decision scientist works with various data sources, insights, and highly specific business questions to make business decisions. So, the decision scientist must be a superior data analyst and be skilled in business. The decision scientist analyzes insights as they relate to specific business problems at hand.
Decision science is frequently used in the military, business, government, law and education, public health, and public policy. CHDS uses decision analytics to create policies designed to improve population studies through “systematic integration of scientific evidence” to measure the value of outcomes such as mortality rates, quality of life, and costs.
In the future, data science will progress toward more automation and further evolution of AI-enabled platforms, including augmented reality, robotization of industry processes, and reinforcement learning. In sharp contrast, decision science will move toward automated decision-making and data empowerment. The rising importance of decision science in industries will lead to increasing demand for specialists.
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