Decision scientists are professionals who use data analysis to drive better decisions and solve business challenges. They consider data as a tool to provide insights that can inform decision-making processes. Decision theory is a branch of applied probability theory and analytic philosophy that is concerned with the theory of making decisions based on assigning probabilities to different factors and assigning numerical consequences to the outcomes.
There are some key differences between data science and decision science. Data scientists focus on finding insights and relationships through statistical analysis. On the other hand, decision scientists are looking to find insights that are relevant to the specific decision that needs to be made. Decision scientists prioritize the business problem or decision at hand, and the analysis they conduct is dependent on that specific question. They might analyze age groups to focus on, determine the most optimal way to spend a budget, or measure a non-traditional media mix. For decision scientists, the business problem comes first and the analysis follows.
Data scientists aim to understand, interpret, and analyze data with the goal of building better products. They prioritize data quality, statistical rigor, and measurement perfection. Data scientists think about data in terms of patterns, processing, algorithms, and statistics. They conduct deep analysis and experimental statistics to find causal relationships. They are obsessed with finding insights and learnings from big data that are relevant to their product or focus area.
Decision science is a collection of quantitative techniques used to inform decision-making at both the individual and population levels. It encompasses decision analysis, risk analysis, cost-benefit and cost-effectiveness analysis, constrained optimization, simulation modeling, and behavioral decision theory. Decision science incorporates elements from fields such as operations research, microeconomics, statistical inference, management control, cognitive and social psychology, and computer science. By focusing on decisions as the unit of analysis, decision science provides a unique framework for understanding public health problems and improving policies to address them.
The main difference between decision science and other research approaches is that decision science is concerned with making optimal choices based on available information, rather than focusing solely on producing new knowledge. Decision science aims to clarify the scientific issues and value judgments underlying decisions and identify any tradeoffs that might accompany a particular action or inaction.
Decision scientists use a variety of tools and methods, including models for decision-making under uncertainty, experimental and descriptive studies of decision-making behavior, economic analysis of competitive and strategic decisions, approaches for facilitating decision-making by groups, and mathematical modeling techniques.
Decision science has applications in various fields such as business and management, law and education, environmental regulation, military science, public health, and public policy. It is used to inform policies and practices that improve population health by integrating scientific evidence with consideration of individual and societal values.
In summary, decision scientists are professionals who use data analysis to drive better decisions and solve business challenges. They differ from data scientists in that they prioritize the business problem or decision at hand and conduct analysis based on that specific question. Decision science is a collection of quantitative techniques used to inform decision-making and considers decisions as the unit of analysis. It incorporates elements from various fields and has applications in numerous domains.