Data Science vs. Decision Science: What’s the Difference?

Data science Vs decision science: A primer on what makes them unique and what unites them

Data science and decision science are two different yet closely related disciplines. For a company or enterprise with a number of operational areas, certain characteristics overlap between them. This exactly creates confusion among aspiring data scientists and decision scientists. They take the functional areas of either fields for granted only to repent later. Having similar-sounding names and common expertise areas, it is not uncommon that they are mistaken for one another. Decision science and data science are two-data driven fields that have risen to prominence in the past few decades and therefore it is equally important to understand the differences by companies and individuals equally. While data science is all about providing insights, decision science is about putting those insights into an application to achieve better outputs. Let's deep dive into the data science Vs decision science question.

What is Data Science?

Data science essentially involves processing large chunks of data using various mathematical, statistical, and analytical tools and machine learning algorithms. Data scientists interpret data for a better understanding of underlying data from a gigantic number of transactions. The ultimate goal is to provide actionable insights for the decision-makers to choose a course of action. Technically speaking, they write complex algorithms and build statistical models. Data science applications broadly lie in the area of finance, banking, healthcare, e-commerce, education, manufacturing etc.

What is Decision Science?

Decision science is about sketching the optimal strategy to solve the problem at hand with the insights provided. In a way, it involves taking a 360-degree view of the business challenge by taking into account the type of analysis, visualization methods, behavioral understanding, and feasibility of the strategy. Technically speaking a decision scientist applies complex quantitative data-driven methods combined with cognitive science and managerial capabilities. As decision science involves taking course-changing steps, it is mainly applied to public healthcare and policy, law and education, military science, environmental regulation, business, and management.

Key Comparative differences between Data Science and Decision Science

View on Data:

For data scientists, data is a tool for innovation for interpreting and analyzing situations. It helps in building result-oriented solutions thereby leading to adopting data-driven methods. Decision scientists, in contrast, use data as a tool only as a suggestive mechanism for taking decisions. They apply data to design not one but different approaches to a problem. Though data is equally important, the mechanism differs vastly. While data scientists are focused on finding insights, decision scientists use data to reveal those insights.

Purpose:

A data scientist's USP lies in processing structured as well as unstructured data and putting the derived information into an easily understandable format so that it reveals a certain pattern. On the other hand, decision scientists though do not work with big data, but using the insights, they arrive at a principled framework for decision-makers to align with a certain mindset.

Challenges

The challenge lies in complexity. Data scientists have to process large amounts of data and hence have to address the issues related to it such as the development of sourcing, data security protocols etc. For decision scientists, given the techniques they apply are complex, the lack of reliable data and the right data environments stand as hindrances.

For the complimentary space, they hold in the field of analytics they have a great scope for the development of data-driven ecosystems in the future. Futuristic technologies such as automation, augmented reality, robotics, chatbots, virtual assistants, etc are largely going to depend on data science, and decision science will open the gates wide open for automatic decision-making appliances, data empowerment, and such other specialized fields. Clearly, they will be extensions of each other.