You can usually feel it before anyone names it. Meetings run long because people are comparing numbers instead of making decisions.
At some point, someone says, “We probably need a better data strategy,” and the room agrees.
Data now lives in too many places, systems don’t quite line up, and every new initiative seems to add a bit more complexity than clarity. By the time the phrase “data strategy” appears in an email, your marketing, finance and operations teams are usually asking a more fundamental question: where do we start, and how do we ensure the effort actually changes how the business works?
The tricky part is that the problem rarely fits inside a single department. Data challenges might be technical, but their consequences are always business‑wide.
A strategy gets requested, but what’s actually needed is clarity, clarity about where to begin, what to sequence, and what can safely wait.
Prioritisation is uncomfortable, but essential
One of the hardest parts of improving a company’s data strategy is deciding what to put first. Prioritisation.
Every initiative sounds important, every team has a sensible case, and every project promises some kind of value.
On paper, this can look like momentum: plenty of projects, plenty of movement. But actually, foundations are fragile, and the same underlying issues resurface in different forms. Lots of things move a little; nothing moves far enough. Optimising a data strategy is accepting that not everything can advance at once and having the discipline to let some improvements wait without that internal guilt.
And discipline may feel tough in the moment.
The cost of a weak data foundation
- Where does this number come from?
- Why does it differ?
- Which version do we trust?
Taking your data foundation seriously is one of the main strengths of a good strategy. Even if the impact isn’t immediately visible in a dashboard or a KPI, these decisions shape how smoothly everything else can move.
The cost of delay manifests as constant friction that touches every project and every decision.
You do need to connect the dots
You see this a lot. A data strategy gets approved, everyone feels good about it, the slides look neat, and then… nothing really changes.
That usually happens because the strategy lives in one place and the actual work lives somewhere else. One group decides what the organisation should aim for, another group builds the systems, and everyone kind of assumes that people will just start working differently once it’s all there.
But that’s not how it goes. People change what they do when the tools in front of them make their job easier.
That’s where things either click or don’t. When the strategy shows up in the dashboards and processes people use every day, it becomes real. When it doesn’t, it remains a document that made sense at the time and slowly fades from the conversation.
People, processes and technology must move together
Greg Kihlström, writing for Forbes Agency Council (2022), states that transformation initiatives fail when people, processes and technology are not aligned from the beginning. The assumption that a new data hub will automatically improve performance is a familiar one. Yet software on its own does not change how decisions are made.
- People need context and ownership.
- Processes need to translate ambition into concrete steps.
- Technology needs to support the way work actually happens.
When one of these elements advances, the friction increases.
Your data strategy needs to examine all 3 together. The work is about aligning responsibility, routines and infrastructure so they reinforce each other.
What does a strong data foundation brings you?
When a company’s data foundation is strong, the first benefit is stability, and decisions become easier to repeat and explain because people understand where the numbers come from and why they look the way they do.
New ideas can be tested without re‑inventing the basics each time. The business doesn’t just move faster; it moves with more confidence.
New products, channels, markets, and regulations all put pressure on systems that may have evolved in pieces over many years. Without a clear, shared data foundation, every new initiative adds another layer of exceptions and manual work, and the overall strategy quietly becomes overall reactive.
Resilience enables innovation, including the responsible use of AI and advanced analytics, to grow on solid ground and not amplify existing weaknesses.
Optimisation as a continuous, responsible choice
Data strategy optimisation isn’t a one‑off project or a matter of picking the “right” framework and rolling it out. It’s an ongoing practice of making conscious decisions about sequencing, ownership, and readiness. It involves choosing where to focus, when to pause, and how to balance visibility with genuine value.
The businesses that do this well treat data as a shared responsibility that connects leadership, technology, and the teams who use information every day.
- They recognise that a strategy without a proper foundation stays theoretical, and that technology without strategic direction quickly loses purpose.
- They’re willing to say no to certain initiatives because they want the changes they do make to stick.
In that sense, optimisation is about doing the right things at the right time, in a way that the business can realistically absorb.
It’s a series of grounded, responsible choices that gradually transform “we need a better data strategy” from a vague concern into a reality.
