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Decision-Driven Analytics: Leveraging Human Intelligence to Unlock the Power of Data
Companies have more data at their fingertips than ever before. Yet, studies show that many executives and organizations fail to extract real value from it.
Challenging the conventional wisdom of data-driven decision-making, marketing professors and behavioral scientists Bart De Langhe and Stefano Puntoni argue that many analytics efforts flounder because data analyses are disconnected from the decisions to be made. In their important book, they offer a new approach they call decision-driven analytics. Counterintuitively, they argue that the key to making good decisions with data is to start by putting data in the background.
Drawing from their own research and teaching, as well as real-world business cases, De Langhe and Puntoni offer four pillars of decision-driven analytics and guide you around common mistakes that have held back many organizations from using data for impact.
In Decision-Driven Analytics, you will learn how to:
+ Avoid common pitfalls in data-driven decision-making;
+ Close the gap between managers and decision-making on one side, and data scientists and data analytics on the other;
+ Enhance the impact of data analytics on business outcomes;
+ Think without data to make better decisions;
+ Prepare for artificial intelligence’s impact on data analytics; and
+ Evaluate the costs and benefits of decision-driven analytics.
A must-read for anyone who wants to harness the power of data for competitive advantage, Decision-Driven Analytics will equip you with the skills and tools you need to more effectively use data for business outcomes and to make better decisions in today’s complex and data-rich world.
Challenging the conventional wisdom of data-driven decision-making, marketing professors and behavioral scientists Bart De Langhe and Stefano Puntoni argue that many analytics efforts flounder because data analyses are disconnected from the decisions to be made. In their important book, they offer a new approach they call decision-driven analytics. Counterintuitively, they argue that the key to making good decisions with data is to start by putting data in the background.
Drawing from their own research and teaching, as well as real-world business cases, De Langhe and Puntoni offer four pillars of decision-driven analytics and guide you around common mistakes that have held back many organizations from using data for impact.
In Decision-Driven Analytics, you will learn how to:
+ Avoid common pitfalls in data-driven decision-making;
+ Close the gap between managers and decision-making on one side, and data scientists and data analytics on the other;
+ Enhance the impact of data analytics on business outcomes;
+ Think without data to make better decisions;
+ Prepare for artificial intelligence’s impact on data analytics; and
+ Evaluate the costs and benefits of decision-driven analytics.
A must-read for anyone who wants to harness the power of data for competitive advantage, Decision-Driven Analytics will equip you with the skills and tools you need to more effectively use data for business outcomes and to make better decisions in today’s complex and data-rich world.
- GenresNonfiction
120 pages, Paperback
Published May 14, 2024
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November 26, 2025In "Decision-Driven Analytics: Leveraging Human Intelligence to Unlock the Power of Data", the authors challenge one of modern business’s most persistent illusions: the belief that more data naturally produces better decisions. They argue that this assumption has quietly reshaped organizations in a way that often backfires. With companies investing heavily in analytics teams, dashboards, machine-learning models, and massive data pipelines, the promise has been that algorithms will outperform flawed human judgment. Yet many organizations, even those drowning in data, feel more confused than ever about what choices to make. This book positions itself as a reminder that data should follow decisions - not lead them - and that real insight emerges when human intelligence and analytical power work together instead of competing for dominance.
The authors begin by examining the two archetypes found in business environments: the 'divers' and the 'runners.' Divers thrive on data, enjoy exploring complex models, and derive satisfaction from technical depth. Runners, meanwhile, are driven by intuition, experience, and market awareness. Businesses need both, yet modern corporate cultures often worship the diver mindset, treating data as the ultimate truth. This imbalance results in companies generating sophisticated analyses that have little relevance to actual decisions. Leaders end up reviewing charts that don’t answer their strategic questions, while analysts chase insights that no one needs. The underlying problem is that the analysis is disconnected from the real-world choices leaders are trying to make.
One reason this happens is the widespread fear of human error. Behavioral science has spent decades highlighting biases and cognitive blind spots, leading many leaders to assume that judgment itself is untrustworthy. At the same time, technological capability has exploded, encouraging the belief that objective data and complex systems can replace intuition. The authors argue that this mindset is not only unrealistic - it’s counterproductive. When people try to remove human judgment from decision-making, they also remove the very ability to define priorities, interpret ambiguity, and understand context. Data does not know what matters. Only humans do.
This is where decision-driven analytics flips the usual process. Instead of beginning with available information and hunting for patterns, leaders must first articulate the decision they are facing. This sounds simple, but the authors show that most leadership teams are surprisingly vague about the choices in front of them. Without a clear decision on the table, any analysis becomes performance rather than progress. Identifying decision alternatives forces leaders to confront what is actually on the table: What can we do? What can’t we do? What options fall within our control, budget, and risk tolerance? Which choices could truly influence outcomes? Only after defining that landscape can data begin to play a meaningful role.
The authors also emphasize how easily 'bounded awareness' limits creativity. Teams typically only think of the alternatives that fit within familiar habits and internal perspectives. Breakthroughs often come from bringing in different departments or outside voices who reveal overlooked possibilities. The example of a car audio team learning from the engine department is one such case: sound quality is shaped not only by speakers but by engine noise, something they wouldn’t have considered on their own. The authors argue that decision-making becomes more intelligent when leaders deliberately widen the frame before narrowing it down again.
Once the decision is clear, the next step is crafting the right questions. Many organizations, however, pose questions that are far too broad or strategic to be answered by data. Asking analysts 'How can we improve revenue?' or 'How do we retain customers?' forces them into guesswork. Instead, questions must be sharpened until they directly compare decision alternatives. A refined question might be: 'Which customer segment would yield the highest profitability if offered a retention incentive?' This reframing creates a clear analytical target. It also ensures that data teams aren’t tasked with answering questions that require managerial judgment rather than statistical evidence.
The distinction between factual and counterfactual questions is another pillar of the book. Factual questions look at what is likely to happen based on existing patterns. Counterfactual questions, however, examine what would happen under different choices or interventions. The Obama 2012 campaign famously embraced this distinction by focusing not on who supported the candidate, but on who could be persuaded. Understanding how actions change outcomes is far more valuable than predicting what will happen on its own. Leaders who fail to ask counterfactual questions risk wasting resources on analyses that never inform real decisions.
When the authors turn to data itself, they highlight how fragile it can be. Simple errors - like a single spreadsheet typo - can derail massive operations. More importantly, misinterpreting data often causes bigger problems than the data itself. The Meta advertising example illustrates how correlations can be mistaken for causal effects. Targeting customers who were already predisposed to buy does not prove that the targeting algorithm itself generated the observed gains. The authors explain that more data does not solve these issues; it often makes them worse by giving a false sense of certainty. The right data, not the most data, is what matters in decision-driven analytics.
A major theme throughout the book is learning to embrace uncertainty. Leaders often crave precise, definitive numbers, believing that specificity conveys authority. But precise answers are frequently misleading. A valuation of Twitter that ranges between $3 billion and $100 billion is not a sign of weak analysis - it’s a sign of honesty. Ranges capture the uncertain nature of real-world systems far better than single-point estimates. The authors urge decision-makers to stop demanding artificially crisp answers and instead use uncertainty as a tool for improving questions and uncovering hidden assumptions.
Even after assembling decisions, questions, data, and answers, organizations still face a final constraint: limited resources. Not every decision deserves analytical effort. Some choices are trivial and do not justify any data collection. Others involve questions that may be interesting but irrelevant to the outcome. And in many cases, the cost of gathering or modeling the necessary data far outweighs the value of obtaining the answer. The authors emphasize that prioritization - deciding which decisions deserve investment - is the ultimate test of strategic clarity.
In its closing message, "Decision-Driven Analytics: Leveraging Human Intelligence to Unlock the Power of Data" argues that businesses must reorient themselves toward purposeful decision-making. Data is not meant to replace human intuition but to strengthen it. Leaders must define their decisions, shape their questions deliberately, seek only the information that truly matters, and welcome uncertainty as a realistic reflection of the world. The power of data is unlocked not by accumulating more of it, but by giving it a clear purpose. In the end, the authors remind us that the quality of decisions - not the quantity of data - is what determines a business’s success.
The authors begin by examining the two archetypes found in business environments: the 'divers' and the 'runners.' Divers thrive on data, enjoy exploring complex models, and derive satisfaction from technical depth. Runners, meanwhile, are driven by intuition, experience, and market awareness. Businesses need both, yet modern corporate cultures often worship the diver mindset, treating data as the ultimate truth. This imbalance results in companies generating sophisticated analyses that have little relevance to actual decisions. Leaders end up reviewing charts that don’t answer their strategic questions, while analysts chase insights that no one needs. The underlying problem is that the analysis is disconnected from the real-world choices leaders are trying to make.
One reason this happens is the widespread fear of human error. Behavioral science has spent decades highlighting biases and cognitive blind spots, leading many leaders to assume that judgment itself is untrustworthy. At the same time, technological capability has exploded, encouraging the belief that objective data and complex systems can replace intuition. The authors argue that this mindset is not only unrealistic - it’s counterproductive. When people try to remove human judgment from decision-making, they also remove the very ability to define priorities, interpret ambiguity, and understand context. Data does not know what matters. Only humans do.
This is where decision-driven analytics flips the usual process. Instead of beginning with available information and hunting for patterns, leaders must first articulate the decision they are facing. This sounds simple, but the authors show that most leadership teams are surprisingly vague about the choices in front of them. Without a clear decision on the table, any analysis becomes performance rather than progress. Identifying decision alternatives forces leaders to confront what is actually on the table: What can we do? What can’t we do? What options fall within our control, budget, and risk tolerance? Which choices could truly influence outcomes? Only after defining that landscape can data begin to play a meaningful role.
The authors also emphasize how easily 'bounded awareness' limits creativity. Teams typically only think of the alternatives that fit within familiar habits and internal perspectives. Breakthroughs often come from bringing in different departments or outside voices who reveal overlooked possibilities. The example of a car audio team learning from the engine department is one such case: sound quality is shaped not only by speakers but by engine noise, something they wouldn’t have considered on their own. The authors argue that decision-making becomes more intelligent when leaders deliberately widen the frame before narrowing it down again.
Once the decision is clear, the next step is crafting the right questions. Many organizations, however, pose questions that are far too broad or strategic to be answered by data. Asking analysts 'How can we improve revenue?' or 'How do we retain customers?' forces them into guesswork. Instead, questions must be sharpened until they directly compare decision alternatives. A refined question might be: 'Which customer segment would yield the highest profitability if offered a retention incentive?' This reframing creates a clear analytical target. It also ensures that data teams aren’t tasked with answering questions that require managerial judgment rather than statistical evidence.
The distinction between factual and counterfactual questions is another pillar of the book. Factual questions look at what is likely to happen based on existing patterns. Counterfactual questions, however, examine what would happen under different choices or interventions. The Obama 2012 campaign famously embraced this distinction by focusing not on who supported the candidate, but on who could be persuaded. Understanding how actions change outcomes is far more valuable than predicting what will happen on its own. Leaders who fail to ask counterfactual questions risk wasting resources on analyses that never inform real decisions.
When the authors turn to data itself, they highlight how fragile it can be. Simple errors - like a single spreadsheet typo - can derail massive operations. More importantly, misinterpreting data often causes bigger problems than the data itself. The Meta advertising example illustrates how correlations can be mistaken for causal effects. Targeting customers who were already predisposed to buy does not prove that the targeting algorithm itself generated the observed gains. The authors explain that more data does not solve these issues; it often makes them worse by giving a false sense of certainty. The right data, not the most data, is what matters in decision-driven analytics.
A major theme throughout the book is learning to embrace uncertainty. Leaders often crave precise, definitive numbers, believing that specificity conveys authority. But precise answers are frequently misleading. A valuation of Twitter that ranges between $3 billion and $100 billion is not a sign of weak analysis - it’s a sign of honesty. Ranges capture the uncertain nature of real-world systems far better than single-point estimates. The authors urge decision-makers to stop demanding artificially crisp answers and instead use uncertainty as a tool for improving questions and uncovering hidden assumptions.
Even after assembling decisions, questions, data, and answers, organizations still face a final constraint: limited resources. Not every decision deserves analytical effort. Some choices are trivial and do not justify any data collection. Others involve questions that may be interesting but irrelevant to the outcome. And in many cases, the cost of gathering or modeling the necessary data far outweighs the value of obtaining the answer. The authors emphasize that prioritization - deciding which decisions deserve investment - is the ultimate test of strategic clarity.
In its closing message, "Decision-Driven Analytics: Leveraging Human Intelligence to Unlock the Power of Data" argues that businesses must reorient themselves toward purposeful decision-making. Data is not meant to replace human intuition but to strengthen it. Leaders must define their decisions, shape their questions deliberately, seek only the information that truly matters, and welcome uncertainty as a realistic reflection of the world. The power of data is unlocked not by accumulating more of it, but by giving it a clear purpose. In the end, the authors remind us that the quality of decisions - not the quantity of data - is what determines a business’s success.
March 15, 2025
Good starting material of theory
This was an insightful for those that are not well versed in the world of analytics OR believe that data teams should be driving business solutions.
The book Introduces the premise that well formed questions and clear direction should drive analytics which is important and it's something as a data consultant I have practiced parts of for years.
I gave this 4 star because I felt like it could have delved deeper on how to do what is proposed. How to arrive at the decision and key questions. It's touched on, but I left wanting more.
I loved the last formula for determining what should be done. Scoring rubrics are critical to getting the right work done.
I will recommend this to many!
This was an insightful for those that are not well versed in the world of analytics OR believe that data teams should be driving business solutions.
The book Introduces the premise that well formed questions and clear direction should drive analytics which is important and it's something as a data consultant I have practiced parts of for years.
I gave this 4 star because I felt like it could have delved deeper on how to do what is proposed. How to arrive at the decision and key questions. It's touched on, but I left wanting more.
I loved the last formula for determining what should be done. Scoring rubrics are critical to getting the right work done.
I will recommend this to many!
May 29, 2024
A must read for anyone working with or in an analytics team.
The authors clearly understand and perfectly outline the problems an analytics team suffers on a daily basis regarding the relationship and requests from stakeholders.
From meetings without a real purpose or a decision in mind just to "discuss numbers", or even the consideration of decisions that aren't feasible while not thinking outside the box, to the confusion of factual and counterfactual questions, which I believe is one of the most common mistakes we face everyday.
It's a short and light read filled with diagnostics, definitions, examples, suggestions that will improve decision making in any organization.
The authors clearly understand and perfectly outline the problems an analytics team suffers on a daily basis regarding the relationship and requests from stakeholders.
From meetings without a real purpose or a decision in mind just to "discuss numbers", or even the consideration of decisions that aren't feasible while not thinking outside the box, to the confusion of factual and counterfactual questions, which I believe is one of the most common mistakes we face everyday.
It's a short and light read filled with diagnostics, definitions, examples, suggestions that will improve decision making in any organization.
November 27, 2025
The authors of this book argue against the idea that decisions should solely rely on data, introducing the concept of decision-driven analytics. They emphasize that data becomes truly valuable when it is used to support key decisions that have already been recognized as significant. Through timely and relatable examples, the authors effectively convey essential concepts in an engaging and easy-to-understand manner. More importantly, the book encourages readers to think critically about the motivations behind their decisions, a perspective that is becoming increasingly important in our complex world.
Found it to be a really great and catchy read.
Found it to be a really great and catchy read.
September 29, 2025
A novel approach to data analytics, putting decisions firmly at the center of the analytics process and working back to the data. The framework aims to align and improve the quality of decision-making, collaboration, and foster an organizational culture that is action-oriented. The four core principles are: 1. Decisions 2. Questions 3. Data 4. Answers
November 12, 2024
This book is not for you if you have ever worked with Database design or run analytics. Instead, it feels like a book for people who know nothing about data (databases)—pretty common-sense information, in my opinion.
February 21, 2025
Helpful! Will refer back to it
March 1, 2025
Some great points in this book that have helped enhance how I get to the root of decision-making when building analytic products.
July 3, 2025
good read
Simple look at data and how we should or could apply it. Gives different perspectives on angles to view decisions from.
Simple look at data and how we should or could apply it. Gives different perspectives on angles to view decisions from.
August 16, 2025
One day read. It’s okay, some examples are hot, but still average read. Idea is Answer = Reasoning (whyimportant) x Decision (willanalyticshelp) x Data (atwhatcost)
This entire review has been hidden because of spoilers.
December 1, 2025
Questions Shape Insights - [more to come]
June 19, 2024
Excellent, easy-to-read, relevant book. Highly recommended for business leaders and data scientists, even though it’s not a technical book. I recently wrote a newspaper column reviewing the book and adding in my own thoughts about it. You can read it here:
https://thecitizen.com/2024/06/18/foc...
https://thecitizen.com/2024/06/18/foc...
Displaying 1 - 12 of 12 reviews