Data Science vs. Decision Science: Basic Descriptions

In Data Science, a variety of advanced technologies like data mining, statistics, predictive analytics, AI, and machine learning are used in conjunction to deliver solutions for business problems.

In Decision Science, analyzed data is “interpreted” to arrive at business decisions that meet specific objectives.

So while Data Science involves collecting and analyzing business data, Decision Science involves the process of making decisions through interpretation of data. However, the “interpretation of data” is generally conducted by applying mathematical models and algorithms.

Decision Science, a relatively new field of study, has gained momentum in global businesses over the past 10 years. Since the main objective of Decision Science is to understand the underlying business problem in depth, it requires a good understanding of the data.

Publications about Data Science generally fail to explain the differences between Data Science and Decision Science. Predictive analytics, a subfield of Data Science, is often confused with Decision Science, because it involves the prediction of future events.

Data Science and Decision Science can be combined to solve business problems. For example, if a company wants to analyze its customer database to find out what products are selling the best, it can combine Data Science and Decision Science to get the best results.

In this case, the data scientist may be involved in extracting insights from huge piles of data, while the decision scientist will review the insights to solve a business problem. Decision scientists with exceptional understanding of business goals, can apply Data Science skills to define and solve business problems.

Data Science vs. Decision Science: The Dissimilarities

The concept of Data Science is closely related to Decision Science, as both deal with making decisions based on available data. However, there are some significant differences between these two fields.

Data Science focuses more on the analysis of large sets of data. Role of Data Science in strategy and decision-making process explains how Data Science is shaping the data-driven business world.

On the other hand, Decision Science focuses more on the application of mathematical models and algorithms to make better decisions. In addition, Data Science also deals with the collection of data, whereas Decision Science deals with the interpretation of the data.

In this article about Data Science vs Decision Science, the author explains that data scientists often analyze and interpret data with the mission of improving existing products, services, or processes. Data quality, statistical discipline, and perfect measurements guide their practice. Business problems come afterward. They apply a “statistician’s lens” to everything they do. The author says that decision scientists view data analysis as means to making better business decisions. So, decision scientists often analyze data in relation to the business problem or problems they are seeking to solve. Decision scientists make insights “actionable.”

Why Decision Science Matters describes this practice as “machine-assistance” to business decision making, which was traditionally conducted by human brains. The best characteristic of Decision Science is its ability to define every solution in economic terms – clearly outlining the risks versus the rewards of a decision. This method enables human decision-makers to “separate the bias and pitfalls often introduced by emotion and ego.”

Some of the fundamental differences between Data Science and Decision Science may be summed up as:

• Data Science is about the collection of data, while Decision Science is about the interpretation of the data. Decision Science also, in a sense, involves the collection of data, but it does not involve the collection of large sets of data.
• As mentioned in the previous point, Data Science deals with the analysis of large sets of raw data, while Decision Science deals with the analysis of small sets of data.
• Decision Science deals with the application of mathematical models, while Data Science deals with the application of Data Science techniques.

The author of A Beginner’s Guide to Data Science and Decision Science makes these valuable observations:

• Data Science is applied across verticals like banking, finance, manufacturing, e-commerce, education, and so on. Decision Science is usually applied to business, policy making, healthcare, and military problems. Data Science works with big data while Decision Science relies on small data.
• Data is equally important to both data scientists and decision scientists. However, their approach to data analysis is quite different. Data scientists use data analysis to uncover insights to improve products and processes, while decision scientists use data analysis to aid decision-making based on those insights.
• Data scientists are technology nerds – applying mathematics, statistics, and advanced data technologies to uncover insights; decision scientists are business wizards – exceptionally knowledgeable about both business and technical issues.
• Data scientists create the data framework for feeding machines, whereas decision scientists provide the framework for human decisions.

According to K.V. Rao, founder and CEO of sales forecasting software company Aviso:

• “Decision Science aptly encapsulates how computers are helping to systematically identify risks and rewards pertinent to making a business decision.”

Data Scientist vs. Decision Scientist: Contrasting Roles

The most common role of a data scientist is to analyze business data using statistical methods. The data scientist uses analytical methods to discover hidden patterns in the raw data, which are used to predict future events. The most important qualification of a data scientist is their ability to communicate the results of their analysis to others.

The decision scientist, on the other hand, is eager to find insights from the available data – as they relate to the problem at-hand. For decision scientists, the business problem comes first. Data analysis follows and is usually dependent on the question that a particular business problem is raising. The decision scientist takes a 360-degrees-view of the business challenge.

A data scientist needs to have a broad range of skills. These include programing languages like R and Python, knowledge of tools like Hadoop, Apache Spark, SAS, Tableau, Excel, superior analytical skills, statistical methods like linear Regression, K-Means Clustering, or Random Forest. According to the Bureau of Labor Statistics, Data Science is a hot field with an ever-growing demand for qualified people.

This insightful article from Vital Flux can help an individual decide whether to learn Data Science or Decision Science.

Decision Science for Data Scientists

In an SDG webinar, the speaker provides an excellent introduction to Decision Science that is particularly useful for data scientists. This webinar offers a basic understanding of Decision Science and explains how Data Science and Decision Science can be used together to make important business decisions.

According to a Forbes author:

“The modern decision-making process, (strewn with varied) information sources and technologies can be as complex as any of the business decisions they need to make.”

Turning to automated decision-making tools in an age of advanced technologies can be risky, so it’s better to treat decision making as a scientific discipline. When decision making is approached as science, the classic steps of arriving at a decision, doing the research, creating a hypothesis, testing, monitoring results, and then repeating the steps amounts to Decision Science.

In CSIRO’s Data 61Business Unit (Australia), data analytics projects are augmented with Decision Science. Some of the typical projects undertaken here include bio-security risk and surveillance, transport analytics, genomic selection in plants, natural hazards, financial risks, and more.

Going Beyond the Sciences: Decision Intelligence

A Towards Data Science article reveals that decision intelligence helps to make good decisions related to system implementation. While training AI systems, decision intelligence can guide a data scientist to determine whether “a particular piece of data should be included in a training dataset, or if another piece of data should be excluded.” These kinds of decisions are critical for the performance of the training model. Thus, this article further helps explain that while data scientists can solve one piece of the puzzle by building data-driven training models, social scientists are required to solve the other piece of the puzzle – making good decisions with data, a role that data scientists cannot fulfill. 

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