07.03.2025 Today’s Insights from Harvard Business Review: The Right Way to Make Data-Driven Decisions
Dear Students,
We often hear ambitious international students like you speak or write about performing “data-driven” analyses and making “data-driven decisions.” And yet, how many of you, as young professionals, really understand what these terms mean?
Candidates (which may include you!) who may be excellent at conducting analyses, even those using complex techniques, may (ironically) not fully appreciate that the value in the huge data sets to which you have access is not actually in the data, but instead in: a) the interpretation of what’s stored within your company’s data warehouses and data lakes, b) the analyses being shared with the appropriate business decision-makers and c) the recommendations emerging from insights extracted from the data. What’s important is “to effectively translate [such analyses] into managerial decisions.”
Some examples of good questions to enable doing so?:
“What are the outcomes that we’re tracking? How do [they] map to the things that we care about? What is the strategy [being used] to know if an effect that they’re saying is causal actually is?”
In other words, the data itself has little to no value, unless deployed to generate business decisions (a fact which may surprise some young people – perhaps even you? – excited by the concept and breadth of “big data.”)
Within the HBR on Strategy podcast below (with transcript included), the contributors convey this message through a wide range of related points (a few of which are highlighted here):
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It’s essential to properly identify the correct business problem being addressed “…before collecting or analyzing data.” Said differently, are we really measuring what matters?
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(Aspiring professionals, like you and your friends, in particular) must be careful to not “over-rely” on the accuracy of the data itself, but rather to “understand the strengths and limitations of the data you have”
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Leaders need to “open up the conversation” about data, rather than simply handing off analysis to Analytics departments
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Analysts (which may include you) need to prepare for both internal and external reactions to analyses emerging from data and consider related strategies for communicating about it with various stakeholders
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Data needs to “be interrogated,” rather than “accepted at face value.” Put differently, Do these results make intuitive, rational sense? New hires, like you and your friends, must be curious and take initiative here
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“Data scientists need to think in a way that is really about supporting the company’s operations and the company’s managers”
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It’s easy to confuse causation with correlation, although they have very different implications. Thus, especially for new grads (which may include you or your peers), understanding statistical relationships is critical
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“Pausing to unpack assumptions” (or “going slow to go fast”) is something every young professional (even you!) ought to become comfortable doing early on
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Be careful to not overgeneralize from smaller data sets, (which can be especially tempting for a sharp, young analyst to do – you know who you are!)
https://hbr.org/podcast/2025/03/the-right-way-to-make-data-driven-decisions?utm_medium=email&utm_source=newsletter_daily&utm_campaign=dailyalert_Active&deliveryName=NL_DailyAlert_20250306
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Your data is only as good as how you interpret it. March 05, 2025. Fueled by the promise of concrete insights, organizations are increasingly prioritizing data in their decision-making processes.
hbr.org
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Given the interdependence of these concepts, and the key relationships between business functions and Analytics, our coaches will make sure to probe, within coaching sessions, for your true understanding of the expressions mentioned in the first paragraph above. Doing so will allow students like you to present yourselves as insightful, sophisticated young professionals as you enter a global company.
Best,
Amy-Louise