Which scope should be used? Which period should be considered? Why does the figure presented by Finance differ from the one presented by Sales? Which source should be trusted? Why has last month’s result changed since the previous meeting?
These questions are legitimate. But when they return in every meeting, they often reveal a deeper issue: the organisation does not share the same definitions of its key indicators and does not have sufficient confidence in its data.
The problem is not always a lack of data
Organisations rarely suffer from a lack of data. They usually produce a great deal of it.
Information is spread across ERP systems, CRMs, financial tools, business applications, spreadsheets and reporting environments. Each team has part of the picture and builds its own reports to answer its own questions.
The difficulty appears when this information needs to be brought together to support a collective decision.
Sales may follow opportunities and orders. Finance may rely on invoices and payments. Operations may analyse deliveries or inventory. Each view can be useful, but they do not always describe the same reality in the same way.
The executive team is then left to arbitrate between several figures that all claim to represent the same situation.
A trusted indicator must be understood
The reliability of an indicator does not depend only on whether it has been calculated correctly.
A figure can be mathematically accurate and still be difficult to use if its context is unclear:
- What exactly does it measure?
- Which period does it cover?
- Which data has been excluded?
- Where does it come from?
- Which transformations have been applied?
- Who is responsible for its definition?
Without clear answers, the discussion quickly moves away from the strategic topic and towards the construction of the number itself.
Instead of deciding how to improve commercial performance, executives discuss the definition of revenue. Instead of arbitrating an investment, they try to understand why two reports produce different results.
Trust is built over time
Trust in data cannot simply be declared. It has to be built.
It starts with shared definitions. An active customer, an open order or an operating margin must have a meaning that can be understood and used by several departments.
It also depends on traceability. Teams need to understand where an indicator came from, how it was calculated and when the underlying data was loaded.
Finally, it depends on quality. Data must be checked, documented and monitored over time. Complete data that is no longer current can be just as problematic as recent data that is inconsistent.
The role of data governance
Data governance is sometimes seen as a documentation or compliance topic. In reality, it directly affects the quality of management decisions.
It helps clarify definitions, responsibilities, quality rules, data origins and transformations.
Governance should not create unnecessary bureaucracy. Its purpose should be to reduce avoidable discussions and help teams work from the same understanding.
What this changes for executives
An executive team should not have to reconstruct the reliability of an indicator during every meeting.
Its role is to decide, prioritise and provide direction. This requires information that is clear enough to support a discussion about the underlying issue.
The goal is not simply to produce more dashboards. It is to build an environment in which indicators are understood, documented and reliable enough to support action.
This is where a data platform such as beVault can help structure data, preserve its history and improve documentation and traceability. It does not replace operational systems or BI tools. It provides a governed foundation for them.
Conclusion
When figures are constantly debated in executive meetings, the problem does not always come from the people or the visualisation tools.
It may reveal a more fundamental difficulty: the organisation does not yet share a common definition of its data and indicators.
An organisation does not make better decisions simply because it has more data. It makes better decisions when it knows which data to trust and why.