Enough is Enough: Let's Fix Data. It's Holding Us Back, and We Deserve Better.
- Maria Alice Maia
- Jul 1, 2024
- 3 min read
Updated: Jul 14
For years, I've sat in boardrooms, launched ventures, and scaled businesses – from Itaú and Ambev to Stone and Alura, and my own NaHora.com. I've seen firsthand the sheer power of data when it's done right, and the absolute chaos when it's done… well, like kindergarten. Or worse, when it’s "tech-for-tech's-sake" – brilliant minds building complex solutions that never quite hit the mark for the business.
This isn't just a frustration; it's a gaping hole in productivity, a drain on resources, and a massive missed opportunity for growth. And I’m here to fix it. Why? Because this knowledge isn't mine to keep. My purpose, forged through decades of executive experience, entrepreneurial grit, and deep academic rigor from places like FGV, Berkeley, HEC Paris, and HarvardX, is to give back. To share what I've learned, what I've built, and what truly works.
The "Doing Data Wrong" Epidemic: A Consulting Firm's Costly Blunder
Let's talk about a common scenario, one I’ve seen repeatedly in consulting firms. Imagine a firm that prides itself on "data-driven" recommendations. Their internal tech team builds impressive dashboards, tracks hundreds of KPIs, and generates elaborate reports. But are they truly delivering impact? Often, no. This is a classic case of "Tech-for-Tech's-Sake" where the data infrastructure is complex, visually appealing, yet fundamentally fails to answer the critical business questions or drive actionable change.
The common pitfall? They might be measuring everything, but understanding nothing about causation. They'll tell a client, "Departments with higher employee engagement scores have 15% lower turnover rates." Sounds insightful, right? But is higher engagement causing lower turnover, or are both effects of something else entirely – like a particularly inspiring new leader, or a recent positive market shift that makes everyone happier?
The Wrong Way: "Correlation is Causation" (The Kindergarten Approach)
In our consulting firm example, the "wrong way" would be simply presenting correlation as a definitive driver. "Implement an engagement program, and your turnover will drop by 15%!" The tech team proudly delivers a dashboard showing the correlation, and the managers confidently present it, believing they have solved the problem. But without understanding the underlying causal links, the recommended program might be a huge expense with no real impact, or even negative unforeseen consequences. It’s data-based, but not value-driven. It's "kindergarten data" because it stops at basic observation without digging into the why.
The Right Way: Unlocking True Causal Understanding
The "right way" demands more than just correlation. It requires a deep dive into causal inference. It's about meticulously designing studies, even with observational data, to isolate the true impact of a particular intervention. This means applying methodologies like instrumental variables, regression discontinuity, or difference-in-differences – techniques that move beyond superficial correlations to reveal what actually drives outcomes.
For the consulting firm, this would mean:
Tech Professionals: Instead of just reporting correlations, you'd be tasked with identifying potential confounding factors. You'd explore quasi-experimental designs, or leverage advanced statistical techniques to control for unobserved variables, ensuring that when you say "X causes Y," you have a robust argument. You'd actively seek to understand the business question behind the data request and propose the right analytical approach, not just the easiest. This might involve setting up pilot programs with control groups, or analyzing historical data in a way that simulates an experiment.
Managers: You need to demand more than pretty dashboards. Ask: "Can we confidently say this intervention caused this outcome, or is it just associated with it?" "What are the alternative explanations?" "How robust is this finding?" You should push your tech teams to move beyond descriptive analytics to prescriptive, causally-sound recommendations. Focus on the ROI of the data initiative itself. Is the proposed solution truly going to move the needle, or just look good on a slide?
By adopting this approach, the consulting firm moves from simply reporting what is to scientifically informing what will be if a specific action is taken. This isn't just about reducing turnover; it's about making data-driven investments that genuinely yield results, increasing productivity, and decreasing losses in the long run.
The era of "guesswork data" is over. We have the tools, the research, and the potential to build truly intelligent organizations. My journey has shown me that bridging the gap between cutting-edge research and pragmatic business application is where the magic happens. I'm committed to sharing these insights, no holds barred.
Join me as we dive deeper into practical, research-backed insights on unlocking real data value and fixing broken data practices. This isn't just a newsletter; it's a movement to elevate how we all use data for true societal and business benefit.
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