Your Data Strategy Sucks, and You Don't Actually Need One Anyway

Latest update: 6/5/2026

There is no such thing as a data strategy.

Have you ever experienced the following symptoms:

  • your data team runs around all day long, building dashboards nobody looks at?
  • You still don’t know why finance and sales figures never match?
  • You feel like you’re not making a dent in the organisation’s performance?

It might be time to take a step back and review your data strategy. Or write one for the first time. Or…maybe it’s time to realise that there is no such thing as a data strategy. Let me explain.

First Reason your Data Strategy Sucks: You’re Focusing on Tools First.

If the first word out of your mouth when you talk about your strategy is “we want to integrate everything in [nsert the latest shiny toy here]”, you already failed, badly. If you’re talking about AI at that stage, you’ve failed even harder.

You’re providing a solution to a problem that’s not even defined. A strategy isn’t about choosing Looker over PowerBI, or which LLM will get you the best “insights” (whatever that means). It’s about choosing the right tool for the job. What’s this elusive insight you’re looking for? Why does it matter?

Yes, you’ll need to get into the nitty-gritty and choose your tech stack eventually. And yes, AI tools can do great things for you. But don’t put the cart before the horse, or you’ll end up spending a fortune for nothing. Then your boss will hate you. And you’ll hate yourself. Do you want to hate yourself? Of course not.

Second Reason your Data Strategy Sucks: All You Have is a Wishlist

Ever heard a request like the ones below ?

  • “I want a comprehensive view of every single data point in the company, in real time”
  • “We want to understand our customers better”
  • “We need a dashboard to report on our performances”

All real requests, from real executives, to real data analysts.

It might a good place to start the conversation. But you will need to dig much deeper, and get to something a lot more precises and specific to the business.

PS: did you notice how these requests can apply to any and all organisation? That’s a tell-tale sign of a bad strategy.

Data Infrastructure is Just a Tool. If You Don’t have a Good Strategy, so Are You.

There can never be a separate, distinct “data and AI” strategy. There is only strategy, informed by data and amplified by AI.

If all you’re getting are generic questions like the ones above, your organisation doesn’t have a strategy.

Richard Rumelt, in his classic book “Good Strategy, bad strategy”, explains:

The kernel of a strategy contains […] a diagnosis that defines or explains the nature of the challenge.
A good diagnosis simplifies the often overwhelming complexity of reality by identifying certain aspects of the situation as critical.

When refining requests from the business stakeholder, a data analyst should always ask:

  • “what problem are we trying to solve here?
  • “What are the few key things that will influence the organisation’s performance?”

…and crucially: “what should we actively ignore?“.

If you can’t answer these questions, you don’t really have a strategy. And that’s your real problem: there can be no good data strategy, because there is no strategy at all.

Here’s the secret: data and AI, being just the tools that they are, can only be part of a solid overarching business strategy. There can never be a separate, distinct “data and AI” strategy. There is only strategy, informed by data and amplified by AI.

The Real Key is Courage

Unfortunately, most people aren’t very good at naming the real challenges they face, for two reasons:

  • it’s uncomfortable to acknowledge failures or shortcomings (nobody wants to say that the sales team suck… even if everyone knows)
  • we don’t always know why things go wrong or what needs improving. It takes work. Work is hard.

Courage and transparency is the solution to the first problem. Name the culprits, the things that grind your gears. Respectfully. But don’t sugar-coat it. Clarity is kindness.

Data and hard work is the key to solving the second issue. The secret is asking good questions.

Go deep, face the real challenges. Call data and AI to the rescue when it makes sense.