Whenever someone shows me their organisation's strategic plan, there's something I always find myself scanning for. I'm not hunting for flaws, but after reading so many of these documents over the years, I’ve noticed the same pattern keeps turning up. Somewhere on the page sits a priority worded something like 'Improve our use of data', 'Build data capability' or 'Become more data-informed'.
And every single time, one question always crosses my mind: why does data get treated as a finish line? Why does it earn its own dedicated box on the page?
Don't get me wrong, I'm genuinely in favour of a deliberate plan to lift how people use data and evidence in their work. But giving it a standalone spot in a strategic plan misses something important, in my view. Data and evidence aren't meant to be a destination sitting off to the side. They're meant to run through everything else an organisation does. The moment you carve data out as its own priority, it ends up in a silo of its own, and it risks looking like just one more thing to manage: another project, another skill to develop, another meeting on the calendar, another initiative competing for attention, often looking completely unrelated to the other priorities around it.
None of that is inherently a bad thing. Plenty of it matters. But it only has value if it improves the everyday decisions people make and if it becomes woven into how the organisation naturally operates, not tacked on as an afterthought.
The point was never the data itself
I've never viewed data as the outcome. The real goal is always better decisions and stronger next steps. Data isn't the finish line and it's not meant to stand alone. It's simply a powerful tool for getting somewhere that matters more.
Over recent years, I've noticed myself talking less about "data" on its own and more about how information, decisions and impact connect to each other. Data hasn’t become less important to me, but I’ve seen too many organisations collect more and more information without their outcomes improving. Better outcomes come from using multiple data sources to make sound decisions, and then continuing to check in afterwards to see whether those decisions actually delivered what was hoped for.
That's a meaningfully different lens to view data through. It's also what's pulled my attention towards the gap between what I call pre-decision data, the information we use to make a decision, and post-decision data, the information that tells us whether it worked.
Pre-decision data is only half the job
Most of us are pretty confident with the pre-decision data: gathering information, spotting trends, and using evidence to back a decision before we make it.
But a decision isn't the end. Once it's locked in, new questions show up: are we following through? Is it working? What side effects are appearing? Is it creating the impact we wanted? Answering that needs good post-decision data too.
This is where things usually fall down. We put effort into justifying a decision up front, then rarely check on it with the same rigour afterwards, measuring activity instead of impact, outputs instead of outcomes and completion instead of whether it worked. Data often gets used to reach a decision, but not to stay the course and measure the impact. That's exactly why it bothers me when data sits boxed off as its own line in a strategy document.
Data should run through all priorities, not sit beside them
If a strategic plan had a standalone priority called ‘improve communication,' it would feel a bit off. Communication isn't something you tick off before the real work starts. It's how the real work happens. The same goes for collaboration, curiosity, feedback and learning. None of these are destinations. They're capabilities that shape every other priority.
Data deserves the same treatment. Instead of sitting beside your other priorities, it should run through all of them, pointing you towards where attention is needed, helping you choose the smartest option, showing whether change is happening as intended, and telling you whether it’s making an impact..
When you look closely, the thing at the center of all this is the decision, with data playing a different role on either side of it. Pre-decision, it sharpens judgement; post-decision, it sharpens learning.
What it looks like when an organisation gets this
One of the clearest signs an organisation has built a strong data culture is that people stop saying "we need to use data" at all. Instead, the conversation shifts towards asking better questions, thinking carefully about what information matters most for a decision, having a clear approach to how decisions get made, and leaders engaging with all of this as part of leading, not as a compliance exercise.
That's the mindset you see once people understand that data was never meant to be a destination. It's the companion that sits alongside every meaningful decision an organisation makes, from how data is visualised in the first place, right through to how a decision is reviewed once it's live.
A different question worth asking
So instead of asking, 'How do we get better at using data?' Maybe the more useful question is 'How do we make sure evidence sits inside every decision before it's made as well as long after?'
For me, that's where strategy comes to life and it's a true sign of a data-informed culture. It's not the destination. It's (cue Miley Cyrus).
FAQ
Why do organisations struggle more with post-decision data than pre-decision data?
Most organisations are comfortable justifying decisions upfront but rarely apply the same rigor afterwards. They tend to measure activity instead of impact and outputs instead of outcomes.
Does you offer speaking, consulting or training?
Absolutely - I offer keynote speaking, consulting, workshop and training services for organisations looking to build a data-informed culture.
What does "data doesn't belong in a silo" actually mean?
It means data shouldn't be managed as its own isolated initiative, project, or strategic priority. Instead, it should be built into how every decision is made and reviewed. So it disappears as a separate "thing" and becomes part of normal practice.
What's wrong with having "improve our use of data" as a priority?
It turns data into a side project instead of part of everydaydecisions, you can hit the initiative's goals and still change nothing about how decisions get made.
What makes your take on data different from typical data strategy advice?
Most data strategy content tends to focus more heavily only on the pre-decision data, collecting more or better data, improving dashboards, and reviewing reporting. I focus on both pre-decision and post-decision data so organisations can see whether their decisions delivered the impact they were hoping for.
