Producing an analysis is no longer the hardest part of an analyst’s job. The real challenge is turning insight into decisions that align with the organisation’s strategy.
Without a clear, active strategy, decisions about data are mostly left to whoever happens to be making the call. That’s a problem, and the numbers back it up.
Our very own 2026 State of the Data Management Industry report found something worth pausing on: 44% of organisations have a documented data strategy that guides some decisions. Only 11% have a strategy that actively drives decisions across the whole enterprise. Another third have little more than informal ideas, or documents nobody opens.
Why “documented” isn’t the same as “living”
A strategy document usually says things like: prioritise trusted, governed data sources; reduce duplication; invest in assets tied to core business outcomes; treat certain data domains as critical.
None of that becomes real until it shows up in a hundred small choices: which table an analyst queries when three exist, whether a new dashboard gets built from scratch or reuses an existing model, whether a request gets scoped against a stated priority or just built as asked.
If everyday decisions are driven only by local priorities, such as what is fastest, what a stakeholder asks for, or which dataset is most familiar, the strategy remains a document rather than a decision-making tool. When those decisions consistently reflect the organisation’s strategic priorities, the strategy starts to shape how work gets done. That is what a “living” data strategy looks like in practice.
Four things analysts can do to close the gap
The gap between a strategy that exists on paper and one that shapes daily decisions is often seen as a leadership problem. Governance committees, executive sponsorship, and funding models all matter. But strategy also lives or dies at a much more practical level: the everyday decisions analysts make. This is a layer analysts can influence directly, without waiting for change to come from the top down.
- Scope requests against strategic priorities, not just stated requirements. Before building something new, ask: does this align with a documented strategic priority, or is it purely local convenience? If a stakeholder asks for a one-off metric that duplicates an existing governed source, that’s a moment to pause and ask whether the request is really necessary, rather than simply building another version.
- Default to existing, governed assets before building new ones. Before creating a new dashboard, metric, or dataset, check whether a trusted version already exists. Rebuilding the same thing in different places creates duplication and makes it harder for teams to agree on which number to use. This simple check can help reduce unnecessary work and keep analytical outputs aligned across the organisation.
- Make lineage and sourcing visible, not just correct. Governance is not just about compliance. It is about making it easy to understand where data comes from, how it has been transformed, and whether it can be trusted. Analysts can contribute by documenting this information as part of their everyday work. Over time, these small actions help turn governance principles into practical habits rather than leaving them as ideas in a strategy document.
- Tie outputs back to business outcomes explicitly. Instead of “here’s the dashboard you asked for,” try “here’s the dashboard, and here’s the decision or outcome it supports.” This is one of the most effective habits for rebuilding the link between data investment and business outcomes that the report says is eroding. It also makes it easier to demonstrate the value of analytical work and justify future investment.
The bottom-up case for strategy
None of this replaces the need for leadership to govern, fund, and reference the strategy. But only 11% of organisations have a strategy that actively drives enterprise decisions. Waiting for the top-down version to arrive is a reasonable thing to hope for and a risky thing to depend on.
When analysts consistently make the four choices outlined above, they are doing more than producing good individual work. They are helping turn a documented strategy into a living one. When these small decisions are made consistently across teams and over time, they can gradually strengthen the connection between data strategy and enterprise decision-making.
Tian Li
Graduate Data Consultant
