THE DIFFERENCE BETWEEN DECISION SCIENCE
AND DATA SCIENCE
The difference between Data Science and Decision Science is not always clear.
Data Science is a term increasingly used to identify a broad and interdisciplinary field. However, Data Science should not be confused with Decision Science.
A Data Scientist is a professional figure specialized in the in-depth study of data, after these data have been collected, processed, and structured by a data engineer.
For the Decision Scientist, on the other hand, data represents a tool to support decisions, in particular those that solve common business problems.
Data Science is an interdisciplinary field where valuable information is extracted from data through the use of algorithms, techniques, and scientific methods.
The main purpose of Data Science is to obtain specific information from data for application in the interest of other sectors.
Decision Science, on the other hand, is the collection of quantitative techniques used to support decision-making. These techniques include:
- decision analysis
- risk analysis
- cost-benefit and cost-effectiveness analyses
- constrained optimization
- simulation modeling
- operations research
- management control
Therefore, the purpose of Decision Science is to use data-driven insights to make decisions.
It turns out that the data are equally important to both, but the mechanisms to treat the data are quite different.
Data Science finds applications in various sectors such as retail, consumer packaged goods, entertainment, media, healthcare, telecommunications, finance, travel, manufacturing, and agriculture.
Decision Science, however, pertains more to theoretical areas of business and management, law and education, environment, regulation, military science, public health, and politics.
In addition, both areas have specific characteristics that help to highlight the differences. Data cleansing, security issues, and development procurement difficulties are critical challenges that Data Science has always faced. Instead, Decision Science addresses the difficulties associated with the lack of reliable data, the difficulties caused by complex data environments, and the complexity of applied techniques.
Decision Scientists must have knowledge in mathematics, finance, and analysis to make the right decisions beginning with the data.
What are the future trends that indicate the further developments of Data Science and Decision Science?
More than likely, Data Science will increasingly follow its path toward automation and the extensive use of chatbots and virtual assistants with a widespread use of augmented reality and the robotization of industries. Decision Science will continue to push us toward automated decision-making and data empowerment, achieving vital importance and broad applicability in different sectors that favor the growing demand for specialized professionals.
In conclusion, although entrepreneurs often rely solely on data science as a solution to business problems, it alone is not enough. Our experience suggests that the best approach lies somewhere between Data Science and Decision Science, with data as the foundational unit of a more complicated decision-making process.