The Career Strategy Framework Nobody Taught Data Professionals

I recently finished Playing to Win by A.G. Lafley and Roger Martin. If Rumelt’s Good Strategy Bad Strategy taught me what strategy is and isn’t, this book taught me how to actually build one. It gives you a five-question framework, a cascade of choices that fit together and reinforce each other.

The book was written for companies. Lafley ran Procter & Gamble and used this framework to turn around billion-dollar brands. But the more I read, the more I realised this framework applies just as well to how we manage our careers as data professionals.

Most of us, myself included, don’t really have a career strategy. We have a collection of activities that we hope will add up to one.

Learn this tool. Get that certification. Apply to whatever job looks interesting. Say yes to whatever opportunity pays more. Each activity feels productive in isolation. But without a coherent set of choices connecting them, effort scatters instead of compounding.

This isn’t about mapping out a rigid 10-year plan. Careers are rarely linear, and the best opportunities are often the ones you didn’t see coming. But having a clear set of choices gives you a way to evaluate those opportunities when they show up, rather than saying yes to everything and hoping it adds up.

I also want to be honest about something before we go further. Not everyone has the luxury of choosing right now. If you’re early in your career, or you’ve just been laid off, or you simply need a paycheck, the only strategy that matters in that moment is getting a job that pays the bills. That’s not a failure of strategy. That’s survival, and survival comes first. I didn’t get to choose my first data role strategically either, and most people don’t.

The framework that follows isn’t about pretending you always have perfect freedom. It’s about building awareness to make deliberate choices when you do have room to choose, and to recognise when a constraint is real versus when it’s just a comfortable excuse. Even when you take a job purely because you need it, you can still hold a direction in mind, so that the moment you get some room to manoeuvre, you move toward something rather than drifting. Strategy is less about having unlimited choices today and more about being ready to make them well when they come.

Lafley defines strategy as “a coordinated and integrated set of five choices” that work together to create a sustainable advantage. The keyword is choices. Not plans. Not activities. Choices. And choices, by definition, mean closing off possibilities. Saying yes to one direction means saying no to others. That’s what makes it hard. And that’s what makes it a strategy rather than a wish list.

The Five Questions

The Playing to Win framework asks five questions in order. Each answer constrains and enables the next. Together they form what Lafley calls a reinforcing cascade.

1. What is your winning aspiration?
2. Where will you play?
3. How will you win?
4. What capabilities must be in place?
5. What management systems are required?

It sounds simple. Five questions. But the depth is in how the answers connect. And most of us have never worked through them for our own careers, even though we spend more time on our careers than on almost anything else.

One thing Lafley is clear about: this isn’t a linear exercise where you answer question one, move to question two, and never look back. Strategy is iterative. As you work through the cascade, insights at one level will force you to revisit choices at another. You might define a winning aspiration, choose a playing field, and then realise the capabilities required are ones you can’t realistically build. That pushes you back up to reconsider where to play. The cascade is a loop, not a ladder.

Let me walk through each one the way a data professional might think about it.

What Is Your Winning Aspiration?

A winning aspiration is specific to you. It defines what winning looks like in terms you can actually measure and work toward. The difference matters because vague aspirations lead to vague choices, which lead to scattered activity, which is how you end up ten years into a career, wondering why you’re stuck despite being good at your job.

But there’s a word in this question that most people gloss over: winning. Lafley is explicit that a company must play to win, not merely to participate. Playing to participate, he argues, is self-defeating. It’s the recipe for mediocrity. When you set out merely to participate, you never make the tough choices or significant investments that winning requires.

The same applies to your career. If your aspiration is “have a solid career in data” or “stay employed and grow over time,” you’re playing to participate. You’ll never make the difficult choices that a real strategy demands because your aspiration isn’t demanding enough to require them.

For one data professional, a winning aspiration might sound like: “Lead the data and analytics function at a growth-stage company where I shape business strategy, not just support it.” For another, it might be: “Become the most technically authoritative voice in data infrastructure at my company, the person engineering leadership consults before making any architectural decision.” For a third: “Build a consulting practice where I help mid-market companies stand up their first real data function.”

These are very different aspirations. The first is a leadership path. The second is a deep technical path toward a Principal or Distinguished Engineer role. The third is an entrepreneurial path. None is better than the others. But each one leads to completely different choices in the cascade below. The leadership aspirant needs business fluency and executive communication. The technical aspirant needs depth in systems design and the ability to influence architecture decisions across teams. The consultant needs breadth of domain knowledge and client management skills.

The point isn’t which aspiration you choose. The point is that you choose one deliberately, because without that choice, every decision below it is unanchored.

Notice what any clear aspiration does. It immediately rules things out. If your aspiration is to lead a data function that shapes strategy, then the next job where you’d be a senior IC writing code all day doesn’t fit, even if it pays more. If your aspiration is to become the most technically authoritative person in your organisation, then the management role that pulls you away from architecture decisions isn’t the right move, even if the title sounds impressive. If your aspiration is to build a consulting practice, then the comfortable full-time role at a large company isn’t your playing field, even if the stability is appealing.

That’s the first function of the cascade. The winning aspiration at the top starts narrowing everything below it.

Where Will You Play?

This is the hardest question for most of us because it requires saying no to things that seem perfectly fine.

Where you play means choosing specific things: which industries, which company stages, which kinds of roles, which types of problems you want to spend your time on. It also means choosing where you will not play. That second part is what makes it strategic rather than aspirational.

Most data professionals answer this question by default rather than by design. They stay in whatever industry their first job happened to be in. They take roles based on title and pay rather than fit with a direction they’ve chosen. There’s nothing wrong with that when you’re starting out or when circumstances demand it. But over time, the absence of a deliberate choice becomes its own choice, and usually not a good one.

When you do make this choice intentionally, the effect is clarifying. Say you decide your playing field is data roles in healthcare, where your domain knowledge compounds over time and your understanding of clinical workflows becomes an advantage that competitors without that background can’t easily match. That single choice shapes what you learn, who you network with, which conferences you attend, and which opportunities you say yes to. Your effort starts compounding in one direction instead of scattering across many.

The specifics will look different for everyone. For one person, the playing field might be defined by industry. For another, by company stage, such as early-stage startups versus large enterprises. For another problem type, like real-time systems or machine learning infrastructure. The dimension that matters most depends on your aspiration. The point isn’t to copy someone else’s playing field. It’s to choose yours deliberately instead of inheriting it by accident.

The where-to-play choice also shapes who you’re competing against. If you position yourself as someone who can do “any data role anywhere,” you’re competing against the entire market. If you become known as the person who builds data functions for early-stage healthtech companies, you’re competing in a much smaller pool where your specific strengths actually matter.

Lafley warns about three dangerous temptations when making where-to-play choices, and all three apply directly to data careers.

The first is failing to choose at all and trying to play everywhere at once. This is the data professional who applies to analytics, data engineering, ML, and management roles simultaneously, across every industry and company stage. Focus is a crucial winning attribute. Without it, you spread yourself so thin that you have no distinct advantage anywhere.

The second is trying to buy your way into a better position. For companies, this means acquisitions. For individuals, it’s the equivalent of thinking another certification or degree will automatically transport you to a better playing field. Sometimes education is the right investment. But it’s not a substitute for making the hard strategic choices about where you actually want to compete. A credential opens doors. It doesn’t pick which one to walk through.

The third is accepting your current position as immutable. “I’m in this industry because that’s where I started.” “I do this type of work because that’s what I know.” It’s tempting to treat your current playing field as fixed, because it makes for a comfortable excuse to stay put. But you usually have more choice than you think. Changing playing fields isn’t easy, but it’s doable, and sometimes it makes all the difference.

The discomfort of this question is real. Choosing where to play means accepting that some perfectly good opportunities aren’t for you. A prestigious role that pulls you away from your chosen direction might be the right move, or it might be a detour dressed up as progress. The framework doesn’t tell you to turn down every opportunity that doesn’t fit a narrow plan. Sometimes a surprising opportunity, even one that relocates you or shifts your focus, is exactly the right call. The point is to evaluate it against where you’re trying to go, rather than saying yes simply because it’s prestigious or pays well. A clear sense of your playing field is what lets you tell the difference.

How Will You Win?

Given where you’ve chosen to play, what’s your specific theory of advantage? Why would someone hire you over every other qualified candidate on this playing field?

This is where most career advice falls apart. “Be great at your job” is not a how-to-win. “Network more” is not a how-to-win. Those are generic activities that every competent professional does. Your how-to-win needs to be specific enough to differentiate you from the other people playing on the same field.

Lafley frames the how-to-win question around two fundamental approaches, and I think they apply to careers just as well as they apply to companies: cost leadership or differentiation.

The cost leadership equivalent in a career is: I do the same work as everyone else, but faster and cheaper. I’m the most efficient pipeline builder, the quickest dashboard creator, and the person who ships reliable work with less overhead. This strategy works, but AI is eating it alive. If your advantage is speed and execution efficiency, you’re competing against tools that are getting faster every month.

The differentiation equivalent is: I do work that others can’t easily replicate. Not because I’m faster, but because I bring a combination of skills and judgment that’s rare on this playing field. Maybe it’s the combination of deep technical capability with genuine business fluency. Maybe it’s domain expertise in a specific industry paired with the ability to translate between technical teams and executives. Maybe it’s the ability to diagnose an organisational data problem before writing a single line of code, something I wrote about in my earlier article on Rumelt’s framework.

One thing Lafley stresses is that where-to-play and how-to-win choices must be considered together. They aren’t independent. A strong where-to-play choice is only valuable if it’s supported by a credible how-to-win. And no, how-to-win doesn't work equally well on every playing field. The combination creates the advantage, not either choice in isolation.

Your how-to-win should be something you can articulate in two sentences. If you can’t, you probably haven’t made the choice yet. And if you haven’t made the choice, you’re competing on the default dimension, which, for most data professionals, is technical execution, which is the dimension being commoditised fastest.

There’s also an important encouragement embedded in the book: if you can’t find a credible how-to-win choice given your current situation, you can create one. Just because an obvious path to winning doesn’t exist today doesn’t mean it’s impossible to build one. And if, after a genuine effort, you still can’t find a way to win on your current playing field, that’s a signal to change where you play rather than accepting mediocrity.

What Capabilities Must Be in Place?

Given your winning aspiration, your playing field, and your theory of how to win, what do you specifically need to be able to do?

This is not a list of everything that would be nice to have. It’s the three to five capabilities without which your strategy fails. Everything else is secondary.

Lafley borrows a concept from Michael Porter here that’s worth understanding: the “system of reinforcing activities.” Sustainable competitive advantage rarely comes from any single capability. It comes from a set of capabilities that fit together and reinforce each other, making each one stronger than it would be alone. The system as a whole is more powerful than any individual component.

Applied to a career, this means your advantage isn’t any one skill. It’s the combination. Deep technical ability alone doesn’t win. Business fluency alone doesn’t win. Domain expertise alone doesn’t win. But all three together, reinforcing each other, create something that’s genuinely hard to replicate. Someone might match your technical skills. Someone else might have a better understanding of your business literacy. But the person who has both, plus domain expertise, plus the communication skills to connect them? That combination is rare, and it’s the system of reinforcing capabilities that creates the advantage.

Most data professionals get this backwards. They start with capabilities (I should learn Spark, I should get a cloud certification, I should learn a new language) without first establishing what those capabilities are in service of. That’s building the bottom of the cascade without the top. It’s like a company investing in manufacturing capacity before deciding what product to sell or who to sell it to.

It’s tempting to start with “what am I good at?” and build a strategy from there. Lafley warns against this. The things you’re currently good at may be irrelevant to the playing field you want to compete on. Instead, start with your aspiration, decide where to play and how to win, and then ask what capabilities that specific combination requires. Some will be things you already have. Others will be gaps you need to close.

If your how-to-win is differentiation through the combination of technical depth and business judgment, then the required capabilities are clear: business and financial literacy so you can operate in executive conversations, strategic problem framing so you can diagnose before prescribing, domain expertise in your chosen industry so your judgment has real substance, and communication skills so your insights actually influence decisions.

If your how-to-win is being the deepest technical authority in your area, the required capabilities look different: mastery of systems design and architecture patterns, the ability to evaluate and adopt emerging technologies before they’re mainstream, and the communication skills to influence technical direction across teams without positional authority.

The specific capabilities depend on your cascade. But the discipline is the same regardless of path: identify the three to five capabilities that directly support your how-to-win, and focus there.

Once you’ve identified them, Lafley suggests testing them against three questions. First, feasibility: can you realistically build this capability set? If not, reconsider your where-to-play and how-to-win. Second, distinctiveness: is this combination different from what your competitors have? If everyone on your playing field has the same capabilities, there’s no advantage. Third, defensibility: can this combination be easily copied? If someone can replicate your capability set quickly, the advantage won’t last.

These three tests are useful for a career because they force honesty. “I’ll differentiate through technical skills” might be feasible, but is it distinctive when every other candidate on your playing field also has strong technical skills? And is it defensible when AI tools are making those skills more accessible every month?

The discipline of the cascade is focusing your limited development time on the capabilities that directly support your how-to-win choice and letting go of everything else. That’s hard because learning new technical things feels satisfying and productive. But satisfying and strategic are not the same thing.

What Management Systems Are Required?

This question sounds corporate, but for an individual it translates to: what are the personal systems and habits that support, measure, and reinforce the choices above?

If your strategy requires building business literacy, what’s the system? Reading financial statements quarterly, taking a finance course on a specific schedule, and writing about what you learn to force comprehension.

If your strategy requires building a professional network in a specific domain, what’s the system? Publishing on a consistent cadence to attract the right people, attending specific conferences where your target contacts gather, and reaching out to a set number of new connections per month.

If your strategy requires developing strategic judgment, what’s the system? A daily practice of precommitting to predictions before meetings and evaluating them afterwards. Something I described in an earlier piece, drawing on Rumelt’s framework for practising judgment.

The system’s question is where strategy becomes real or dies. Most people can articulate a winning aspiration and pick a playing field. Far fewer build the daily and weekly systems that actually develop the capabilities their strategy requires. Without systems, the strategy is just a set of good intentions that gets overwhelmed by the demands of the current job and the distractions of whatever’s trending on LinkedIn this week.

Why the Cascade Matters More Than Any Single Choice

The power of this framework isn’t in any individual question. It’s in how the answers reinforce each other.

When your winning aspiration aligns with where you play, which aligns with how you win, which aligns with your capabilities, which aligns with your systems, each choice amplifies the others. Your learning compounds because it’s focused. Your networking compounds because it’s targeted. Your public writing compounds because it’s building a specific reputation, not a generic presence. Your career moves compound because each one builds on the previous in a coherent direction.

Most data professionals I know have strong answers to one or two of these questions, but haven’t connected them into a cascade. They know what they want to be good at (capabilities), but haven’t decided where to play. They know what kind of role they want (winning aspiration), but haven’t defined how they’ll win that role over every other qualified candidate. They have great daily habits (systems), but those habits aren’t aligned with a strategic direction.

And remember, the cascade works in both directions. If you discover that your capabilities can’t support your how-to-win, you go back and adjust. If your where-to-play doesn’t offer a credible path to winning, you change the playing field. The choices inform each other. A strategy isn’t something you set once and execute mechanically. It’s a set of choices you continuously refine as you learn more about yourself, your market, and your competition.

The career-to-do-list approach fills your time. The cascade approach focuses on it. And in a market being reshaped by AI, where the value of generic skills is compressing, and the value of focused, strategic positioning is increasing, focus is the difference between drifting and navigating.

Where This Leaves Us

I’m not going to pretend I had this framework figured out years ago. I didn’t. For most of my career, I was running the to-do list like everyone else. Learn the next tool, take the next opportunity, hope it adds up to something. And for a good while, especially early on, I didn’t have much choice in the matter. That’s the reality for most of us at the start.

What changed was reading strategy seriously and realising that the frameworks I was learning for business apply just as directly to a career. A career is a small business. You have a product (your skills and judgment), customers (employers and clients), competitors (other professionals), and a market (the job landscape). Running it without a strategy is like running a company without one. You might do fine for a while on talent and hard work alone. But you’ll never perform as well as you would with deliberate, interlocking choices guiding every decision, made as soon as you have the room to make them.

The five questions aren’t hard to answer. They’re hard to commit to. Because committing means closing doors, saying no to good things in service of better things, and accepting that you can’t be everything to everyone.

But that’s what strategy is. Not a plan to do more. A framework for choosing what matters and letting go of what doesn’t.

One last thought from Lafley that I keep coming back to: there is no perfect strategy. The goal isn’t to find the one right answer. The goal is to find a distinctive set of choices that work for you, that reinforce each other, and that create an advantage on the playing field you’ve chosen. You can always refine them later. But you can’t refine what you haven’t committed to.

If you’re a data professional feeling the pressure of a market that’s shifting fast, where AI is compressing the value of execution and elevating the value of judgment, where the old rules of “be technically excellent and you’ll be fine” are quietly breaking down, I’d encourage you to sit down with these five questions and answer them honestly. Not for the industry. Not for data professionals in general. For you, specifically, and for where you actually are right now.

What does winning look like for you? Where will you play? How will you win there? What capabilities do you need? What systems will you build to develop them?

The answers won’t come in an afternoon. And you may not be able to act on all of them today. But the act of asking is what separates a career strategy from a career to-do list.