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Blink 3 of 8 - The 5 AM Club
by Robin Sharma
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Decision-Driven Analytics summary
Bart de Langhe & Stefano Puntoni
Leveraging Human Intelligence to Unlock the Power of Data
21 mins
Brief summary
Decision-Driven Analytics emphasizes actionable data use by focusing on decisions rather than data accumulation. It guides businesses in leveraging analytics to improve judgment, fostering informed decision-making and driving meaningful organizational change.
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Decision-Driven Analytics
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The drivers of data analysis
The business world has fallen head over heels for big data and machine learning. Everywhere you look, companies are rushing to become "data-driven," convinced that algorithms will eliminate messy human errors and biases. It’s an appealing vision: let the numbers do the talking, and perfect decisions will follow.
But despite all this investment in analytics, many business leaders are discovering their data initiatives aren’t delivering. In one survey, only about a third of chief data officers – the very executives championing these data transformations – believed their own role was well-established and successful. Even the people running the show are skeptical.
So what’s going wrong?
The core problem is surprisingly simple: organizations are focusing on the data itself rather than the decisions they need to make. They’re generating impressive analyses that float untethered from any actual business choice. It’s like building a magnificent bridge that doesn’t connect to either shore.
Two forces are driving this misguided approach. First, behavioral science has spent years highlighting how error-prone human judgment can be. Second, technology has exploded – AI and super-fast computers can process datasets we couldn’t dream of analyzing just a few years ago. Put these together, and the conclusion seems obvious: replace flawed human thinking with objective data analysis.
Now, while many businesses are putting data before humans, some are doing something even worse: preference-driven analytics. This is when executives decide what they want to do first, then send analysts hunting for data to justify that decision. It’s confirmation bias masquerading as rigorous analysis, and it’s rampant across business.
Decision-driven analytics offers a radically different approach by reversing the entire sequence.
You start by identifying the actual decision you need to make. Not vague aspirations, but concrete choices with real alternatives facing your organization. Then you ask specific questions that would genuinely help you choose between those options. What information would actually change your mind? What would make one path clearly superior to another?
Only after clarifying your decision and defining your questions do you start collecting data to answer them.
This isn’t just a subtle shift – it’s about fundamentally reimagining how data serves business. Instead of letting available information dictate your questions, you let necessary decisions guide what data you seek.
In the next sections we’ll see how decision-driven analytics works. We’ll start with the first step: decisions.
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What is Decision-Driven Analytics about?
Decision-Driven Analytics (2024) challenges the traditional approach of data-driven decision-making by proposing that organizations should begin with the decisions they need to make rather than starting with available data. It presents a framework built on four pillars that helps bridge the gap between data analysts and business decision-makers, addressing the common problem of the failure of analytics efforts when data analysis becomes disconnected from actual business decisions. Rather than treating data as the starting point, this approach emphasizes human judgment in determining which questions matter most for organizational impact.
Who should read Decision-Driven Analytics?
- Professionals drowning in data seeking clearer decision-making strategies
- Managers bridging gaps between analytics teams and business goals
- Leaders questioning whether more data actually improves their choices
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