Table of Contents
- The Symptom: Two Numbers for the Same Metric
- Why It Happens
- How to Measure Data Maturity
- Power BI: From Passive Reporting to Active Steering
- Building Governance That Lasts
- The Signs of « Spreadsheet Level »
- FAQ
- Expert Insight
- Take Action
The Symptom: Two Numbers for the Same Metric
It’s a scene that repeats itself in almost every company, regardless of size: in a leadership meeting, the sales director announces one revenue figure, the CFO announces another for the same period. Nobody is being dishonest — each simply calculated the metric differently, from different sources, using different consolidation rules. That awkward moment, as minor as it seems, is the most reliable symptom of a much broader data governance problem.
This isn’t a tooling problem. A company can have a modern ERP, a sophisticated CRM, and a state-of-the-art Business Intelligence tool, and still produce contradictory numbers — because tool sophistication never compensates for the absence of shared definitions between the teams using them.
Why It Happens
Three causes come up with striking regularity from one organization to the next. The first is source multiplication: the same revenue metric can be calculated from the ERP, the CRM, and a manually maintained consolidation spreadsheet run by finance — each with its own calculation logic and its own refresh schedule. The second is the absence of shared definitions: « revenue » can mean invoiced, collected, or recognized under accounting standards, and nothing guarantees two departments use the same definition without ever having explicitly discussed it. The third cause, often the deepest, is the absence of a clearly identified owner for each data point — when nobody is formally accountable for a metric’s reliability, nobody feels responsible for fixing discrepancies when they appear either.
How to Measure Data Maturity
Measuring a company’s data maturity means going beyond the purely technical question to cover several dimensions: governance (who decides, who validates, who corrects), quality (accuracy, completeness, freshness of data), indicator availability for the people who need them, and real analytical maturity — the ability to use data to decide, not just to produce reports after the fact.
This assessment, like any organizational maturity assessment, should never be reduced to a single score. Two companies can show comparable data maturity on the surface while having radically different priorities — one needs to name data owners first, the other already has that governance but suffers from data quality too poor to be usable. It’s the same principle covered in our complete guide to organizational diagnostics.
Power BI: From Passive Reporting to Active Steering
Many companies confuse adopting a Business Intelligence tool with achieving real analytical maturity. Installing Power BI and connecting a few data sources produces visually compelling dashboards — but if those dashboards just reproduce reports already produced elsewhere, without ever being genuinely consulted to make a decision, the tool stays at the level of passive reporting, not active steering.
The real shift happens when a dashboard becomes the single, undisputed reference point for a decision — when nobody asks « where does this number come from » anymore because the underlying governance already guarantees its reliability. This shift is organizational before it’s technical: it depends on the trust built in the data, not on how sophisticated the visualizations look.
Building Governance That Lasts
Effective data governance rests on principles that are simple to state, harder to sustain over time. Every critical data point needs a clearly identified owner, accountable for its quality and definition. Key metric definitions must be documented and shared, not just implicitly known by the people who’ve been calculating them for years. A process for resolving discrepancies needs to exist — when two numbers diverge, there should be a clear procedure to settle it, not an informal negotiation at every leadership meeting. Finally, this governance needs periodic review as the organization evolves, rather than being frozen in a document written once and forgotten.
The most common trap is treating data governance as a one-off project with an end date, rather than an ongoing practice. A governance document written and never updated quickly goes stale as new systems, new teams, and new metrics appear.
The Signs of « Spreadsheet Level »
- Multiple versions of the same file circulate by email, with names like « final_v2_latest_version »
- A report takes several days to produce because it requires manual consolidation across multiple sources
- Only one person actually understands how the consolidation file works, and nobody else dares touch it
- The same questions about a number’s reliability come up at every leadership meeting, never durably resolved
- A modern BI tool exists, but decisions still get made mostly on intuition or old spreadsheets
FAQ
Do you need a sophisticated BI tool to improve data maturity?
No, governance and shared definitions matter more than the tool; a well-governed spreadsheet often beats a poorly governed sophisticated BI tool.
How long does it take to build effective data governance?
Initial rules can be set in a few weeks; building it sustainably, with genuinely active owners, typically takes several months.
Does an SME really need formalized data governance?
Yes, proportionally to its size — even an SME with few data sources benefits from clear definitions and an identified owner for each key metric.
How do you know if your organization has a data governance problem?
If two people get different numbers for the same metric without knowing why, that’s already a sign there’s a problem to address.
Expert Insight
In my data transformation missions, the question that reveals an organization’s real maturity fastest is never technical — it’s « who’s accountable for this number if it turns out to be wrong? » The silence that follows that question often says more than a full systems audit. Diagnoz® builds this data governance dimension into one of the 20 axes of the Notoriti Transformation Indexâ„¢, precisely because it conditions the reliability of everything that follows — reporting, AI, steering.
Take Action
Assess your organization’s data maturity before adding more reporting tools. Discover Diagnoz®, 7-day free trial.
