Have you ever noticed how client briefs sometimes seem more interested in sounding clever than being clear? More often than not, a brief basically turns out to be a riddle wrapped in buzzwords. They say they want “insights,” or “a powerful model,” or sometimes they throw in the big one — “business transformation.” which of course sounds impressive; but the thing is, most of the time, they don’t actually know what any of that means. That’s why decoding the client brief is step one. Because before you jump into data lakes and algorithms, you need to figure out what they’re actually asking for — not what they say they want, but what they mean. Understanding this detail unlocks everything else: what data to use, how to model it, and how to make sure the final solution is something that actually works in the real world.
Start With the Why—What’s the Client Actually Trying to Do?
A client doesn’t wake up one morning and think, “I want a regression model.” What they do wake up thinking is, “Why are my sales tanking?” or “How do I stop customers from ghosting me?” That’s the real business problem, and it’s buried deep in that client brief under layers of jargon and buzzwords. So, what’s your job as the data scientist? You’re a translator. You take vague goals and turn them into specific, solvable problems. This is where RFPs come in clutch. As experts at RFPHub would attest, they actually give you structure. We’re talking scope, budget, timeline, and what the client expects at the finish line. In other words, they help you connect the business side of things to the technical details you’re about to do. Without that link, you risk building something very smart that no one needs or understands. But with a solid RFP, you’ve got a blueprint — one that tells you where the guardrails are and how not to crash the project halfway through. With the structure an RFP gives you, it’s way easier to ask the right questions — what’s the real goal here, and how can data actually help make the right call?
When Data Plays Hide and Seek, Nobody Wins
Even the fanciest AI model can’t save a project if the data is a train wreck — or worse, missing, which is why right after understanding what the client wants, you need to figure out what kind of data is even on the table. Is it coming from the client’s own systems — like sales logs, website clicks, customer support calls — or are you going to have to dig into third-party APIs or public datasets? Sometimes the brief just says, “Data available upon request,” which is more often than not, code for “we have no idea what we’re working with yet.” So, this stage is like being a data detective. You figure out where the data lives, what condition it’s in, how you can get access, and whether it even fits the problem you’re trying to solve. In case the data you need isn’t there or doesn’t fit the problem, it’s time to switch things up or push back on what’s expected. This is a big moment because projects usually either take off because of it or slow down.
Problem Formulation—Name It Before You Tame It
Once you know what the client wants and you’ve scoped out the data, the next question is: What kind of problem are we solving here? And this part matters more than most people realize, because you can’t build a good solution if you don’t even know what kind of question you’re answering. Is it a supervised problem, where you’ve got labeled data to teach your model what to do — like predicting who’s going to churn based on past patterns? Or is it unsupervised, where you’re looking for hidden patterns — like grouping customers into types when nobody’s labeled them for you? Maybe it’s about recommending the right product, forecasting demand, or spotting fraud. Either way, this step is where you frame the problem in a way that makes it solvable — technically and logically.
The Science is Great, But the Translation is Power
Machine learning and predictive models are amazing — there’s no doubt about it, but the thing is, none of it matters if the data scientist can’t first decode what the client actually wants. Because at the end of the day, while you’re building models, you’re also building solutions to real problems. That’s why reading between the lines of a client brief, asking the right questions, hunting for good data, and framing the problem properly isn’t just busywork—it’s the work. It’s what makes the difference between a clever experiment and a solution that actually changes something — for the business, for the customer, maybe even for the world.
