Data Scientists & Decision Making

A decision scientist applies scientific principle and methods to gather and analyze data in seeking answers to the questions.
Today’s business environment is volatile, uncertain, complex and ambiguous, business decision making is becoming more as science than art. Though intuition or gut feeling may still play certain role in decision making, it is imperative that data analytics should play more and more critical role in making strategic, operational or even tactical decision. As the decision makers, who are your go-to-advisors and what are the best scenario in making effective decisions?

There are three major pieces in decision making: Defining objectives, dreaming up the alternatives, and forming a point of view upon how well the alternatives are likely to achieve objectives. As decision makers, your go-to advisors would be people with the expertise required to provide the comprehensive view on how well the objectives would work through alternative solutions; or perhaps you may go to a decision professional-the data scientist or analyst, to ensure that the decision process makes best use of the data and expertise. So the point is who are those data scientists, and how could they exactly do to enable more effective decision making?

A decision scientist applies scientific principle and methods to gather and analyze data in seeking answers to the questions. A decision scientist should be a scientist, first, and that implies someone who is seeking answers to questions using scientific methods of inquiry. Although some organizations might be interested in experiments such as pilot programs or "design for experiment" efforts in well-structured operational parts of the organization, most organizations probably need decision researchers or data analysts who, respectively, apply science and research techniques to gathering and analyzing the "right" data with the attributes such as reliability, accuracy, or consistency in the properties of data, apply good statistical methods to analyzing data correctly and providing correct interpretations of the results, and design instruments for conveniently gathering the kind of data that provides the information most needed to make good decisions.

A decision scientist may execute all steps of the scientific method; the first of which is observation resulting in a datum or in data. It refers to the scientific use of data-mining technology, or toolsets, to reduce, interpret, and extract meaning from data in the context of a well-formulated question. The rest of the science is in what is done with the data, such as formulating hypotheses or predictions, designing and conducting experiments, resolving differences between predictions and outcomes of experiments. Particular to decision science is that the outcome of applying the method is a decision recommendation supported by observed and derived data.

Data scientists are the experts who would have developed their expertise by familiarity with available data. The critical weakness, if not the most critical weakness of organizational strategy formulation is the quality and interpretation of input data. In many cases, the data scientist is an individual who provides more information about the transactions within an organization. Transactions such as products or services sales, employee compensation, and procurement inventory focus on dollars and cents. Transactions, however, are more than money received and spent. Transactions consist of additional quantitative as well as qualitative data points that serve as sources for greater understanding. The data scientist would be the expert to provide the needed data for a decision making process. Whether the person was a data scientist or a decision scientist, the focus of the title is scientist and that implies the application of principles of science to the gathering, analyzing, and interpreting of data used as inputs to decisions.

The data scientist, especially with the advancements in social media as well as other digital technologies, brings a point of view that was not easy to have many years ago. And overall speaking, they are the expert who can manage and analyze the data, with the discovery of a new target or mechanism of actions, and create values for decision makers and make contribution to business growth.