The real obstacle to business decision-making
The meeting has only been under way for a few minutes, but there are already three different versions of the same reality on the table. The sales team presents the revenue figure extracted from the CRM, the finance team opens an Excel file updated the previous evening, while the operations manager shows a dashboard with yet another value.
Everyone has worked carefully, and no one appears to have made an obvious mistake. Yet before the discussion can move on to customers, margins or forecasts, it comes to a halt over a much more basic question: which figure is correct?
This is where doubts about data begin. The numbers are available, but how far can they be trusted?
Two departments may reach different results, or it may be unclear where a figure comes from or when it was last updated. Before the information can be used to make a decision, someone first has to determine which version is reliable.
Instead of discussing what to do, people end up spending their time comparing figures, retracing individual steps and checking information that should have been trustworthy from the start.
The problem is not a lack of data
In recent years, companies have multiplied the tools they use to record activities, customers, orders, costs, inventory and performance.
Each business function has its own specialist applications, reports and spreadsheets designed to meet specific requirements. This wealth of information can become a limitation when the different sources are not connected or exchange data without clearly defined rules.
The same order may be recorded as won in the CRM, posted in the ERP at a later date and classified as fulfilled by the logistics system only after shipment.
When departments apply different timelines and criteria, the data is not necessarily wrong. It may simply describe different stages of the same process. The conflict arises when these differences remain implicit and every figure is presented as definitive.
In this scenario, Excel often becomes a workaround. It makes it possible to combine data, correct exceptions and quickly produce a useful view.
Over time, however, copies multiply, formulas change and knowledge of the process remains in the hands of a small number of people. The file created to deal with an urgent need becomes a parallel data source that is difficult to control and even harder to share.
When the same KPI tells different stories
Even two departments working with the same data can reach different conclusions. The problem often lies in how the data is interpreted. What exactly counts as an active customer? When is a sale included in revenue? Does the margin also include logistics costs?
If these definitions are not shared, technically accurate data is not enough. Marketing, sales and finance may continue to interpret the same KPI differently.
Data quality also depends on context. A figure may be correct within its source system but unsuitable for a particular analysis. To trust it, people need to know where it comes from, what it means, when it was updated and who is responsible for it.
| Situation | What happens | Impact on decisions |
| Systems are not integrated | Data and updates do not match | Before making a decision, people must determine which source is reliable |
| Parallel Excel files | Different versions, formulas and corrections circulate | Time is spent checking and reconciling figures |
| KPIs are defined differently | The same indicator is interpreted in different ways | Different departments reach different conclusions from the same data |
| Responsibilities are unclear | It is unclear who should verify and update the data | Errors are discovered late and may be repeated |
The hidden cost of mistrust
When data is not considered reliable, the organisation creates additional checks. People request confirmation by email, export new tables, repeat calculations and organise meetings to align different versions.
This cost is difficult to see because it does not appear in any report. But time spent checking and rechecking figures is time that cannot be used to analyse them and make decisions.
In the long term, decision-making also suffers. If every number can be called into question, a manager may postpone a decision or rely more heavily on personal experience than on the available data.
Experience remains essential, particularly when working under uncertain conditions. But choosing to rely on judgement is very different from having to do so because the available data does not provide sufficient confidence.
A single source of truth is more than one place
A single source of truth is often understood as a central repository where all company information is collected. But storing data in one place is not enough.
The people using it must also know where it comes from, when it is updated and which rules are used to interpret it.
Achieving this requires system integration, but it also requires people to agree on shared rules. The organisation needs to understand where each piece of information originates, which stages it passes through and where it can be modified.
The same applies to KPIs. If “active customer” means one thing to the sales team and something else to finance, even the most advanced platform will continue to produce different figures.
Data Governance turns data into a shared asset
Data Governance establishes clear rules around data: who is responsible for it, who can modify it, how it is verified and which standards it must meet.
This does not necessarily mean starting with a company-wide project. An organisation can begin with a process where the difficulties are already apparent, such as sales, profitability, inventory or customer service, and trace the journey of the relevant data.
Which systems does it pass through? Where is it entered or modified manually? Which KPIs generate the most frequent doubts or disagreements? Who decides which definition should be used?
Questions like these help identify where the problem actually originates, before making changes to dashboards, reports or analytics tools.
Where to start rebuilding trust
Trust in data does not come from having more information or more advanced tools. It comes from knowing that when a figure appears in a report or dashboard, it has the same meaning for everyone looking at it and can be traced back to a specific source.
A useful starting point is to examine the areas where doubts, manual checks and comparisons between departments occur most frequently:
- Identify the decisions that require the most manual checks or comparisons between different sources.
- Map where the data comes from, who manages it, how it is modified and how frequently it is updated.
- Agree on common definitions for the most important KPIs, including any exceptions.
- Integrate systems according to operational priorities, preventing every new requirement from generating yet another parallel file.
- Monitor whether data, definitions and rules remain consistent over time.
Technology can then perform its role more effectively. System integration, data models and Business Intelligence tools can automate many of the tasks that currently require manual exports, checks and updates.
But they need a clear foundation: which information is required, where it comes from and who is responsible for it.
If part of the working day in your company is still spent determining which figure is correct, the right place to start may be the processes that produce those figures.
At Focus Informatica, we approach system integration, Business Intelligence and Data Governance by starting with the systems and processes already in place within the organisation.
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Frequently asked questions
It means ensuring that people and systems refer to consistent, up-to-date and shared data. It does not necessarily mean storing everything in a single repository. What also matters is knowing where the data comes from, how it is processed and what meaning is assigned to it.
No, but they are closely connected. Data quality describes how accurate, complete, consistent, timely and fit for purpose data is. Data Governance establishes the roles, rules and processes through which the organisation maintains that quality and ensures that data is used responsibly.
No. Integration reduces manual steps, duplicates and misalignment, but systems—and the people using them—must first agree on what the data means. If the CRM and ERP use different criteria to define a customer or an order, connecting them will not automatically solve the problem.
A dashboard can become the shared point of reference for reviewing specific data and KPIs. Its reliability, however, depends on what sits behind it: accurate sources, clear calculation rules, defined update cycles and precise responsibilities.
