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Today we are tackling one of the most misunderstood and abused phrases in our industry: data strategy.

Every man and his dog on LinkedIn loves telling you what data strategy is not. But very few come forward to explain what it actually is. Most data strategy explanations descend into a wash of buzzwords and architecture diagrams that look like they belong in a NASA control room.

So today, we’re gonna fix that.

(Check out this earlier post on the difference between strategy and tactics)

Put simply, a data strategy is: “the plan for how your organisation will use data to create business value”.

Nothing crazy. Nothing obscure or complex. Just a clear set of choices that help you influence revenue, reduce costs, manage risk or improve customer outcomes - powered by data.

If you get this right, your data team becomes a strategic partner that helps shape business performance and overarching strategy.

Get it wrong and you’ll likely spend the next two years building platforms no one needed and trying to justify why there is no ROI for all of your extremely expensive data toys.

Sadly, most data strategies fall apart because they are execution details, basically a list everything the team could do, instead of making the few focused choices that help the business win.

Let us look at the real impacts when your so called strategy is actually just a Christmas wish list of toys you want from Santa.

Here are the problems you create for yourself and the business when you misinterpret data strategy:

☹️ Impact 1: You build everything except what the business needed
A long list of projects or tools is not a strategy. It’s execution detail that sits way downstream from the actual strategic layer. What you plan to build as part of your strategy is actually the last piece of the puzzle - not the first.

☹️ Impact 2: You confuse activity with progress
Your people stay very busy, but nothing shifts the dial. We love demonstrating tangible output, and strategy can often feel like something that’s quite intangible. But beware, this is a trap.

☹️ Impact 3: You burn goodwill with the exec team
Expectations rise, results do not. At some point the CFO asks where the impact is. No one enjoys this conversation and it is one of the key reasons why Chief Data Officers are famously the shortest tenured C-suites. ROI matters to execs.

☹️ Impact 4: You stretch your team too thin
Without clear choices, everything becomes equal priority. Morale sinks, quality drops and you become a ticket machine instead of a strategic function, led by the needs of others rather than the org’s strategy.

☹️ Impact 5: No one knows what success looks like
If you cannot measure and demonstrate success, the business assumes failure. Organisational strategic goals need KPIs and measurement. Data teams play a crucial role in this. We hold a mirror up to the business that lets it know how it’s tracking against it’s strategy.

While these challenges are many, fear not. They are fixable.

A good data strategy does one thing: it creates clarity. It makes it painfully obvious what you will do, why you will do it and how it helps the business win.

Here is the simple way to build one.

🛠️ 1. Start with the business goals
What specific outcomes is the overall organisation trying to influence? Growth, efficiency, risk, customer retention? If your data strategy does not anchor to the relevant strategic pillar, it is already lost. Your org strategy is 100% your starting point, so get clear on it!

🛠️ 2. Identify the data required to influence those goals
Strip it back to basics. What information do we need to understand, track or predict in order to help the business succeed? This is a commercial thinking exercise so put your CEO hats on for this.

🛠️ 3. Define the few capabilities that actually matter
What skills, roles, tools and processes are essential for the value you want to unlock? It’s often phrased as People, Process and Technology. The key thing to remember her is the technology is the LAST piece of the puzzle.

🛠️ 4. Decide your priorities and trade offs
Strategy is choice. You cannot do everything. What are you intentionally saying yes to, and what are you saying no to? This is where teams grow up and start performing. Do not do things that the business has not asked you to prioritise via its strategy. Aka - if it is not in the biz strategy it is not on the to-do list! That’s the fast lane to critical failure.

🛠️ 5. Create an operating model that makes the work flow
Who owns what? How are decisions made? How does data move from source to insight to action? What is the mechanism for partnering with the various business domains and ensuring the right data is a part of the critical conversations?

🛠️ 6. Establish how you will measure success
If your strategy is anchored to business goals, your success measures need to be anchored there too. What are it’s strategic goals and what KPIs or metrics are needed to understand how the business is progressing towards those goals? Decide which KPIs or business metrics you expect to influence and how you will track that influence over time. This is your proof that the choices you made in the strategy were the right ones. If you cannot show movement in a metric the organisation cares about, the strategy needs rewriting.

And there you have it.

A data strategy. Not just a slide deck. Not a glorified shopping list. A set of deliberate choices that position your team as a strategic partner instead of a cost centre.

💡 Oh, and don’t forget to tell people what your strategy is!!

Buy-in and alignment are crucial!

Tristan Burns

Find me on LinkedIn or Book a 1:1 call

Last week I asked you: What is the biggest reason data teams jump into solution mode too early?

Here’s how you responded:

You overwhelming voted stakeholders asking for outputs rather than out comes.