Decision science is often compared to similar fields that apply analytics, algorithms, machine learning and artificial intelligence to business.
“While data science is perhaps the most broadly used term, ‘decision science’ seems like the more fitting description of how machines are assisting business leaders in solving problems that have traditionally relied on human judgment, intuition and experience,” according to K.V. Rao, founder and CEO of sales forecasting software company Aviso, in TechCrunch. “It may not be the sexiest phrase in the world — I’ve never seen it in any marketing materials — but ‘decision science’ aptly encapsulates how computers are helping to systematically identify risks and rewards pertinent to making a business decision.”
Decision science can be confused with other analytics- and data-based fields, but decision science is a separate area. The following sections take a deeper look at decision science to provide some perspective on this emerging field.
What Is Decision Science?
Dhiraj Rajaram, founder and CEO of Indian data analytics company Mu Sigma, provided definitions for data-based disciplines in Analytics Magazine.
• Data engineering applies technology to help collect, store, process, transform and structure data to enable it to be used for decision support.
• Data science applies math and technology to solve business problems. This involves analysis, visualization and algorithmic/mathematical computations to extract insights in response to clearly defined business problems, questions and hypotheses using clearly identified data elements. The field integrates and builds on data engineering by adding the discipline of math.
• Decision science is the interdisciplinary application of business, math, technology, design thinking and behavioral sciences. “It facilitates the design thinking paradigm: Taking business problems that start off as a hunch or as mysteries to becoming heuristic, rules and judgment based, to becoming algorithm as one starts to see patterns, to becoming codified and tool-ified in parts before being operationalized in systems,” Rajaram says. It enables data-driven insights to help organizations make better decisions. The decision science field integrates and builds upon data sciences by adding business context, design thinking and behavioral sciences.
Decision science incorporates an economic framework that is “a consistent, rational and objective system to ‘price’ each possible outcome, taking into account risks and rewards,” Rao adds. “It is simply a better way to make decisions.”
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Jobs in Decision Science
Rajaram says that “decision scientists are truly rare — much more rare than data scientists.” This is because decision scientists “artfully blend business, math, technology and behavioral science” and are “both precise and good with communication.” This diverse skill set allows decision scientists to help businesses make informed decisions.
Similar to related positions like data analysts and data scientists, the Bureau of Labor Statistics does not collect salary data and make employment outlook projections for decision scientists. Decision scientists are classified as operations research analysts or statisticians; both positions earn a median annual wage of more than $78,000 and are projected to grow by at least 30 percent by 2024. According to PayScale, data analysts earn an average salary of $57,261, and data scientists in information technology earn $91,000.
Decision scientists are employed across several fields, such as advertising and marketing, human resources, insurance and supply chain management and logistics. These professionals can be found in virtually any type of business.
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