Let me give a quick overview of what Decision Sciences is and why it is increasingly important, including some pretty staggering findings on how poor, ill-informed decisions destroy business value.

In the broadest sense, Decision Sciences is an established interdisciplinary approach to research that covers business, public policy, healthcare, non-profit organizations, and beyond. In more recent years, however, different “flavours” of Decision Science have emerged in different industries and functional areas within business. The key common thread across the board, however, is a clear and explicit focus on generating and applying business knowledge to better support decision-making. Simply, it is the science of making choices.

According to Harvard, Decision Science is “a collection of quantitative techniques used to inform decision-making at the individual and population levels. It includes decision analysis, risk analysis, cost-benefit and cost-effectiveness analysis, constrained optimization, simulation modelling, and behavioral decision theory, as well as parts of operations research, microeconomics, statistical inference, management control, cognitive and social psychology, and computer science.” ¹ There is even a Decision Science Institute dedicated to the study of managerial decision-making with the following vision: “a scholarly professional association that creates, develops, fosters and disseminates knowledge to improve managerial decisions.”²

In more recent years, however, “big data” and analytics, machine learning, and artificial intelligence have been increasingly integrated into decision-making processes. Decision Science (also known as Decision Intelligence) has emerged as a specialized extension of the field of data science, re-focused on developing analytical methods and expected value frameworks — i.e. the use a probabilities to determine outcomes — to help close the “intelligence gap” and make better decisions. These advancements have happened against the backdrop of an increased understanding that decision-making should be a scientific process, grounded in data (not driven by intuition) because evidence-based decisions are more likely to lead to positive outcomes. As such, a Decision Scientist is responsible for making insights more usable through increasingly predictive and prescriptive insights, not only diagnostic summaries of what happened in the past. To do this successfully, they must shift focus from the science of inputs (data and analysis) to the science of outputs (recommendations and decisions).³

What does this mean exactly? At the core, it is about the necessity for “interpretation” of analyzed data.⁴ Decision-making happens through interpretation; looking at the possible outcomes, comparing alternatives, and choosing the best option. I will be attempting to unpack and expand upon this idea in more detail in future posts on this site (and shed some light in this post). In the meantime, as suggested by Erik Larson in this Forbes article, it is important to consider that decision-making is both “the most important and most poorly managed business activity”. These two numbers tell the story:⁵

• Decisions drive 95% of company performance
• Decision-makers fail to use best practices 98% of the time

A McKinsey survey revealed that for a typical Fortune 500 company poor decision-making resulted in approximately 530,000 wasted days of managerial time each year. Furthermore, this inefficiency cost these enterprises around $250 million in annual wages. Consequently, the average employee spent 47 days each year dealing with the repercussions of ineffective decisions, which included rework, delayed product launches, and missed business opportunities.⁶ Ugh!

Furthermore, according to a 2017 Gartner report, 85% of big data projects fail⁷, while VentureBeat in 2019 highlighted that a staggering 87% of data science projects never reach production.⁸ Furthermore, Gartner’s 2019 prediction indicated that through 2022, only 20% of analytic insights are expected to deliver business outcomes.⁹ All the above statistics underscore the significant challenges faced by organizations in harnessing the full potential of data science initiatives, and the fall-out for effective data-driven decision-making.

Against this backdrop, the motivating spirit of the Decision Scientist is perhaps best captured in this quote from Cassie Kozyrkov, former Chief Decision Scientist at Google:

”I just want the world to be so much better at decision making. We’re going to embarrass ourselves in 500 years time. We’re going to look at all the things we thought were okay, and we’re going to be amazed that we ever allowed ourselves to do this as a species, like the way we look at bloodletting and leeches now. And I’m just so excited that we’re on the precipice of change, accountability, and actually taking decision making as a skill.”
— Cassie Kozyrkov, former Chief Decision Scientist at Google

In the writings on this site, I will mostly talk about applied Decision Sciences in the context of Marketing, where I think there is a great and necessary opportunity to further formalize and establish the field of Decision Sciences as a specialized extension of data science more broadly. Marketing is a very data rich function. The goals and objectives of Marketing organizations are unique, as are the systems, tools, and processes (and the underlying data collected) that must be optimized to generate incremental value from spend decisions. This can only be accomplished through improved data-driven decision-making.

1. What is Decision Science? ↩︎
2. About DSI – Decision Sciences Institute ↩︎
3. Since Data Scientists Don’t Do Business Decisions — Businesses Need Decision Scientists ↩︎
4. Data Science vs. Decision Science: A New Era Dawns ↩︎
5. Can Market Researchers Save The Day By Becoming Decision Scientists? ↩︎
6. Three keys to faster, better decisions ↩︎
7. Why Big Data Science & Data Analytics Projects Fail ↩︎
8. Why do 87% of data science projects never make it into production ↩︎
9. CIO: Transforming analytics into business impact ↩︎