READ TIME: 8 MINUTES
What the hell even is strategy?
We talk about data strategies a lot in data leadership circles, but what we don’t talk about enough is good old fashioned business strategy.
If one thing is resoundingly clear from my travels and interactions with data folks it’s this:
👉🏼 They don’t really know what a strategy is.
If that’s you, don’t stress. I got you.
In today’s post, I’m going to do my level best to demystify business strategy for you.
After all, how can you possibly write a data strategy without:
a) understanding what a business strategy is and;
b) knowing your org’s business strategy isn’t a pile of sh*t (clue: most are)
Strategy is one of those things that get talked about a lot but is **actually** understood by very few. Much like nutrition 🤣
So anyway let’s strap in for some strategy! 👇🏻
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Further reading: If you’re looking for 2 well respected titles on the topic of business strategy, these are a great place to start:
Good Strategy Bad Strategy by Richard Rumelt
Excellent for learning how to evaluate strategies by spotting weak signals, vague ambition, and the common patterns that make most strategies ineffective in practice.Playing to Win by A.G. Lafley and Roger L. Martin
A practical, choice-led view of strategy that clearly explains what strategy actually is and how leaders can make deliberate decisions about where to compete and how to win.
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What people usually think a business strategy is
When most data leaders hear the word “strategy”, they typically picture one of the following:
A vision statement about the future
A list of priorities or OKRs
A set of financial targets
A roadmap of initiatives
A slide deck full of aspirational language
A glorified ‘to-do’ list
All of these things might sit around strategy. But none of them are strategy on their own.
This confusion matters, because if you mistake goals, plans, or metrics for strategy, then it become extremely difficult to know how to help deliver on it. This is especially true for data leaders who are expected to “enable” or “support” it via data initiatives.
A simple, working definition of business strategy
Here is the definition we are going to use for the rest of this post:
A business strategy is a set of deliberate choices about where an organisation will compete and how it intends to dominate there.
This framing is heavily inspired by Playing to Win, and I think it is the most useful definition for people who actually have to execute downstream (like data leaders).
There are two important implications baked into it:
Strategy is about choices, not intentions
Strategy is about winning, not just participating
If there are no hard choices to be made, there is no strategy.
If there is no clear idea of how the organisation will win, there is no strategy.
Quick Poll
How do you rate your literacy level around strategy?
What sits inside an actual business strategy
In Playing to Win, Lafley and Martin describe five connected choices. You do not need the jargon, but here is the logic:
A real business strategy answers these questions clearly:
What is our winning aspiration?
Not a vague mission, but what success actually looks like.Where will we play?
Which customers, markets, geographies, products, or segments we are deliberately focusing on.How will we win there?
The specific advantage we believe we can sustain. I.e. Lower cost, faster delivery, better experience, deeper relationships, etc.What capabilities must we be great at?
The few things the organisation must excel at to make that advantage real.What management systems support this?
How decisions get made, how progress is tracked, and how trade-offs are enforced. Obviously data plays a key role here.
You will notice something important here, there is no mention of projects, backlogs, tools, or technologies. Those come waaaaay later.
How to spot a badly written strategy
This is where the book Good Strategy Bad Strategy is particularly useful.
Based on Rumelt’s work, most internal “strategies” fail in very predictable ways. Here are some concrete warning signs.
A strategy is weak if it:
Is written almost entirely as aspirations or values, with no diagnosis of the problem(s) being addressed
Lists many priorities without explaining what is being deprioritised
Avoids making trade-offs explicit, so everything appears equally important
Uses abstract language that could apply to almost any organisation
Cannot be used to say “no” to reasonable sounding requests
That last one should ring very loudly in a data leaders ear. Having a clear strategy tells us what we should (and should not) be working on. Does that request for another dashboard help drive our strategy forward? No? Than you have explicit permission not to do it.
If you cannot use a strategy to make difficult decisions, it is not doing its job. This is also very much true of how we prioritise, accept and reject data projects.
Just chuck it all in there?!
Why this matters so much for data leaders
If you do not understand the business strategy (or one does not exist), you cannot create a meaningful data strategy.
Not because data is special, but because strategy flows in one direction. Business strategy sets direction. Functional strategies exist to support it.
When business strategy is vague or misunderstood, data leaders are left having to decipher clues from fragments around the business. Board slides. Annual goals. Casual comments from executives. This is where misalignment comes from.
Data teams then end up optimising for activity, responsiveness, or local efficiency rather than contributing to the organisation’s chosen and stated ways of winning.
What a data leader should be listening for
When you read or hear a business strategy, your job is not to translate it into a list of technologies, tools or dashboards.
Your job is to listen for:
The explicit choices being made about focus
The advantages the organisation believes it has or wants to build
The constraints and trade-offs leadership is accepting
If you cannot clearly articulate those three things, pause. Do not jump into execution ❌
This is the moment where good data leaders need to ask better questions upstream rather than delivering faster downstream. If things aren’t made clear to you, it possible they’re not clear to anyone. Be a leader and seek that clarity.
Examples of clearly articulated business strategy
There are some solid public examples worth studying, not because they are perfect, but because the choices are visible.
A few worth linking to and reading critically:
Amazon shareholder letters
Bezos consistently articulated where Amazon chose to play and how it intended to win, especially around customer obsession and long-term advantage.Netflix culture and strategy documents
Particularly their writing on focus, trade-offs, and talent density. The choices are explicit, even when uncomfortable.IKEA strategy explanations
Their articulation of cost leadership, scale, and design trade-offs is unusually clear and consistent over time.
When you read these, ask yourself one question: “Could I make a hard prioritisation decision based on this?”
If the answer is yes, you are looking at an actual strategy!
Good times.
Bringing this back to your role
If any of this feels fuzzy in your current role, you are vey much not alone. Very few data leaders are ever taught how to read, interpret, or pressure-test business strategies. Never mind turning that into a coherent data strategy off the back of it.
This is exactly the gap I spend most of my time working through with Heads of Data. Not just writing strategy documents, but building confidence in how to interpret direction, challenge ambiguity, and align data work to what actually matters.
Before you try to fix your data strategy, make sure you truly understand the business one you are meant to be supporting.
📨 Forward this to your resident data strategy person!
💡 If this week’s topic resonated, the Business and Data Strategy Workshop might help.
A focused two-day workshop designed to align data and business priorities. You’ll identify how your data function supports organisational goals and the strategic opportunities that create real value.
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Tristan Burns