Decision Science & Data Science – Differences, Examples

Decision science and Data Science are two data-driven fields that have grown in prominence over the past few years. Data scientists use data to arrive at the truth by coming up with conclusions or predictions about things like customer behavior and assess suitability of those conclusions / predictions, while decision scientists combine data with other information sources to make decisions and assess suitability of those decisions for enterprise-wide adoption. The difference between data science and decision science is important for business owners to understand in clear manner in order to leverage the best of both worlds to achieve desired business outcomes. In this post, you will learn about the concepts of data science & decision science and what’s the difference between data science and decision science. Those venturing out to learn data science must understand whether they want to learn data science or decision science or both. The following are some of the key questions in relation to understanding the concepts related to data science and decision science.

• What is data science & decision science?
• When do we need data and decision science as part of the analytics strategy?
• Are there specialized courses for decision science?
• What are some good websites for decision sciences?

What is Decision Science & Data Science?

Decision Science is the pursuit and application of knowledge derived from the data in order to understand the effectiveness of the decisions to achieve the desired outcomes based on systematic methodology driven by evidences. It is an interdisciplinary field that draws on applied probability, psychology, philosophy, economics, operations research, machine learning, statistical decision theory, forecasting, and cognitive psychology.

The goal of the decision science is to establish whether a decision and related actions impacting business processes can be adopted across the enterprise by bringing appropriate change management. In other words, whether a decision and related actions can be established as a truth in the organization and adopted appropriately by bringing change to one or more business processes. The following represents some of the key aspects or steps of decision science:

• Hypothesize and take decision; As a result, have the organization take appropriate actions in the business process.
• Track / monitor the decision impact by measuring output of actions (leading KPIs)
• Report the business impact outcomes from the decisions
• Decide based on the evidences whether the decisions and related action can be adopted as part of one or more business processes in the organization

The following represent three key aspects of decision science:

• Normative analysis: Normative analysis is a critical tool for decision science. By evaluating options against predetermined criteria or alternatives, it can help to identify what should be the best possible course of action. It is based on the idea that there is a correct or the best course of action in any given situation, and, that this can be determined through reasoning and logic derived from the information or insights which get derived from the data. Normative analysis can be used to compare different alternative action rising out of one or more decision options, assess the likely outcomes of different courses of action, and evaluate the risks and benefits of different choices. The normative analysis takes into account not only the likely costs and benefits of each solution option but also the values and preferences of those involved. This makes it an essential tool for making decisions that are both effective and ethically defensible. In some cases, normative analysis can be used to resolve conflicts between different stakeholders. For example, if two parties cannot agree on a course of action, they can each conduct a normative analysis and compare the results. This process can help to find a compromise that is acceptable to both parties. Normative analysis aligns with classical decision making model and can be used for programmed decisions.
  
  Normative analysis can be used to assess a wide range of choices, from personal decisions like what job to accept, to public policy choices like whether to invest in early childhood education. In each case, normative analysis can help identify the option that is most likely to lead to the desired result. It is an essential part of decision science, and it can help to ensure that decisions are made in a sound and systematic way.
  
  In recent years, there has been growing interest in using insights derived from the data to inform decisions, and decision science has emerged as a new field of study. Data science plays a key role in normative analysis by providing data-driven insights that can help to inform decision-making. However, data alone is not enough to make sound decisions; Normative analysis can provide the ethical and logical framework needed to ensure that decisions are made in the best interests of all parties involved. By taking into account the values and principles that ought to guide decision-making, Normative analysis can help to ensure that data-driven decisions are made responsibly and in line with the greater good.
  
• Descriptive research: Descriptive research is a type of research that is used to gather information about a population and provide insights into how people make decisions. It is often used in decision science. It is typically used to collect data about the characteristics of a group, such as age, gender, income level, etc. It can be used to study both small and large populations. It is used to study how cognitive, emotional, social, and institutional factors affect judgment and choice. Descriptive research can be conducted through surveys, interviews, focus groups, or observation. It is an important tool for understanding human behavior and can help to improve decision-making in many different fields.
  
  One of the most important aspects of descriptive research is data collection. This data can then be analyzed to help understand the phenomenon being studied. In some cases, descriptive research can be used to identify trends or patterns. For example, descriptive research could be used to study the performance of a new product on the market.
  
• Prescriptive intervention: Prescriptive interventions are designed to improve decision-making in a variety of domains, from personal finance to military strategy. Prescriptive interventions typically make use of mathematical models to identify and understand the impact of different choices. In many cases, prescriptive interventions can be used to automate decision-making, or at least provide recommendations that are tailored to the individual’s needs. Data science plays a very important role in recommending prescriptive interventions. Prescriptive interventions are an important part of decision science, and can often help to improve the efficiency and effectiveness of decision making. For example, one common prescriptive intervention is to provide people with information about the likely consequences of their choices. This type of intervention can help people to make better decisions by taking into account all of the potential outcomes of their choices.

Analytics methods/techniques and tools such as dashboards (Excel, Qliksense, Tableau, etc) can prove to be very helpful with decision sciences. Leveraging insights (extracted from the data) for decision-making can be useful because it helps make data-driven decisions thereby help achieve desired business outcomes. The analytics techniques range from sharpening statistical intuition to quantitative decision analysis. The following are some of the research areas in the field of decision sciences:

• Decision making under uncertainty
• Individual and group decision making
• Optimized decision making using Machine learning & AI

The following are some of the examples of areas where decision science drives the decision-making process:

• Medical decision making: The following can be different decisions which can be assessed using decision sciences. Accordingly, you can choose to use different tools including different machine learning models to make these decisions. Decision scientists and data scientists can evaluate the effectiveness of different models in relation to different decisions.
  • Whether a patient is suffering from a disease including severity of the disease.
  • Whether a patient is suffering from which of the diseases
  • Which treatment option will be best out of different options
• Legal decision making:
  • Which party should the legal decision favor
  • Which all cases should be taken up on priority basis for judgement
• Risk management:
  • Whether an entity including persons or organizations are eligible to get a loan. Decisions to provide loan to ineligible persons pose a risk related to financial losses.
• Marketing
  • Which customers should be chosen for a particular marketing campaign
  • What marketing campaign should be most favorable for a particular class of customer
  • Understanding the effects of inter-temporal choice on purchasing decisions)
• Business, in general; For example, identifying unrecognized conflicts of interest.

Data science is a wide field that leverages evidences & knowledge extracted from the data to validate hypotheses regarding the present and future events. It involves activities such as data gathering, data consolidation, data analysis, data visualization to help businesses understand their customers better. Data science helps extract insights from the data which can then be used to make decisions. Data scientists use statistical techniques and machine learning to analyze large volumes of data in order to discover patterns, draw conclusions, or predict future outcomes based on data. Learn more about data science in this post – What is Data Science? Concepts & Examples

While Data science is used to extract insights from the data after performing data preparation activities and validate hypotheses related to present and future events, decision science helps make the decision based on the insights and also validate hypotheses regarding decisions with an aim to solve business problems. Decision science integrates analytical and behavioral approaches to decision-making. Decision science depends on data science to extract insights from the data. The decision science field integrates and builds upon data sciences by adding business context, design thinking, and behavioral sciences.

Data scientists look at the data to extract insights and build models of high accuracy without caring much overall impact on business outcomes. However, decision scientists see insights extracted from the data as a tool to make decisions. For decision scientists, solving business problems by taking most appropriate decisions that maximize the value is of paramount importance. Unlike data scientists, decision scientists need to have both business acumen and analytical ability.

The must-have skills for a decision scientist include hypotheses formulation and validation related to decisions based on data analysis, data visualization, and data mining. In order to be a successful decision scientist, it is also important to have strong communication skills so you can effectively communicate findings from data analysis with others in the company. Decision science allows organizations to make well-informed decisions using data analytics which helps businesses become more efficient and effective.

When do we go for data and decision sciences as part of analytics strategy?

In a complex business environment, where improved decision-making could impact the business in a positive manner, it will be good to have both data science and decision science teams. When there is a need to drive actions based on insights in order to achieve desired outcomes, decision science is the way to go. Data science has lot of applications which aid in decision making. For example, classifying a document in a particular category is an application of data science. However, if the classification results in different decisions, it is an application of decision science. Decision scientists work with data scientists to help managers make better decisions resulting in positive business outcomes.

As a matter of fact, it will be good to have all of the following teams as part of a solid analytics strategy:

• Data engineering team: Helps with data processing
• Data visualization team: Helps brings KPIs on dashboard which can aid in making decisions.
• Data sciences team: Helps with extracting insights in relation to business problems. Insights can be related to present or future events.
• Decision sciences team: Helps with better and optimal decision making

Decision scientists use data to make decisions, while data scientists gather data and perform analysis in order to come up with conclusions or predictions about things like customer behavior. Data scientists rely on large volumes of data that they analyze using statistical techniques and machine learning, which is not always available.

Decision scientists are rarer than data scientists as they blend business, math, technology, and behavioral science and are “both precise and good with communication.”

Are there specialized courses for Decision Sciences?

Here are some links which could take you to specialized courses for decision sciences:

• Decision Science (BS) from CMU: Decision science major from CMU covers the following topics:
  • Normative analysis, creating formal models of choice
  • Descriptive research, studying how cognitive, emotional, social, and institutional factors affect judgment and choice
  • Prescriptive interventions, seeking to improve judgment and decision making.
• Decision Sciences from IIM Bengaluru: Decision sciences course is offered as a specialization course under the Doctor of Philosophy (Ph.D.) program from IIM Bengaluru. The course covers lectures on operation research (OR) methods, statistical methods including Bayesian and multivariate statistics, time-series forecasting, etc.
• Decision sciences from INSEAD
• Decision science-related courses from Coursera: There are several courses from Coursera which are related to data-driven decision making. Some of them are the following:
  • Effective problem solving and decision making
  • Ask questions to make data-driven decisions
  • Mathematical thinking in computer science

What are some good websites for Decision Sciences?

Here are some good websites for decision sciences:

• Decision science journal

Here is a great video from chief decision scientist of Google, Dr. Cassie Kozyrkov