1. Conflicting data
When different parts of the organisation report different numbers for the same outcome, it becomes difficult to make confident decisions.
It’s a Monday morning and you’re in a leadership meeting reviewing your Key Performance Indicators (KPIs), and the finance team are reporting a 12% cost reduction, while operations are reporting an 8% reduction for the same period. Both are confident in their figures, but they are using different definitions and assumptions. The conversation quickly shifts from action to debating which number is ‘correct’.
This is often a sign that there is no shared understanding of the data, and no clear ownership of how your data is defined.
Key problems
Different teams use different definitions for the same metrics, there is no clear ownership of data, and time is spent reconciling numbers rather than acting.
What can you do to resolve this?
- Define standard business metrics.
- Introduce a data governance framework including a shared data catalogue.
- Assign clear data ownership.
This creates a shared understanding of organisational data, allowing teams to trust the numbers they use and focus their time on using the data to make decisions, rather than debating how the figures were calculated.
2. Decisions driven by intuition, not insight
When decisions rely more on instinct than evidence, it can be difficult to align on the right direction.
It’s now Tuesday and you’re setting priorities for the next quarter. Ideas are being discussed based on experience and what people believe is happening across the organisation.
Different perspectives are shared, but there is little reference to data to support or challenge the direction being proposed. This risks decisions being made without clear evidence to support them.
Key problems
Decision-making is based on intuition rather than evidence-based approaches. Data is not readily available to support or challenge assumptions, leadership behaviours do not consistently reinforce data-driven decision-making, and confidence in decisions lowers.
What can you do to resolve this?
- Provide accessible, self-service reporting.
- Ensure data is timely and trusted.
- Align reporting to key business questions.
- Build data literacy across teams.
- Ensure leaders visibly use and promote data-driven decision-making.
By putting these steps in place, intuition is supported and challenged by evidence. With a data strategy focused on increasing data literacy across the organisation, and leadership setting the tone for how data is used, this will encourage employees to rely on data-driven insights to make more confident and consistent decisions.
3. Manual reporting reliant on a single point of failure
When reporting relies heavily on a small number of individuals, it can create hidden risks across the organisation.
Wednesday comes along and you need to rely on a small number of individuals who really understand your data to create your Business As Usual (BAU) reporting. They know how to navigate complex reports, apply manual fixes, and make sure the numbers reflect reality.
It works, but it’s very manual and is wholly reliant on a small group of experts. The reporting is not easily accessible to others and the logic behind it is not widely understood, and when those individuals are unavailable, reporting either slows down or stops altogether.
Key problems
Reporting is manual and time-consuming. Knowledge sits with a small number of individuals, creating a clear single point of failure.
What can you do to resolve this?
- Create a trusted, central data source.
- Automate data cleansing within the data pipeline.
- Standardise reporting outputs.
- Document processes thoroughly.
The key is moving away from individual dependency towards consistent, repeatable processes, where reporting is reliable, accessible and resilient, regardless of who is producing it.
4. Dependency on offline spreadsheets
When centralised reporting isn’t fully trusted, teams often fall back on their own versions of the data.
It’s Thursday and you find yourself in yet another meeting. Your organisation has invested in dashboards, and centralised reporting is available, but in meetings people still refer to their own offline Excel extracts or PowerPoint packs.
Many teams maintain their own trackers or spreadsheets to sense-check the data. Often, this hesitation stems from data quality issues, for example, missing or inconsistent figures that undermine trust in the centralised dashboards.
Key problems
Dashboards are underused and any return on investment is low. Shadow spreadsheets and trackers persist, effort is duplicated across teams, and data quality issues erode trust in the central reporting.
What can you do to resolve this?
- Improve data quality and consistency at source e.g., embedding data quality checks into data pipelines.
- Design dashboards around real user needs.
- Establish clear data governance responsibilities to reduce reliance on duplicated offline reporting.
- Support adoption through training.
The result is a shift towards a trusted, shared view of the organisation, where dashboards become the natural starting point for discussion, and teams no longer feel the need to maintain their own versions of the data offline.
5. Small system changes cause large reporting problems
When small changes have unexpected ripple effects, it often points to deeper issues in how data flows are managed.
By Friday,with deadlines approaching, a small system change has been made; a field is updated, or a process is slightly adjusted. It seems minor at the time, but shortly afterwards, your reporting starts to breakdown. Numbers no longer align, dashboards have missing visuals and teams begin trying to work out what has gone wrong. What should have been a small change becomes a time-consuming investigation.
This is often a sign that data flows are fragile and not well understood across the organisation.
Key problems
Reporting is sensitive to change, data dependencies are not clearly understood, small changes create wider disruption, and time is lost troubleshooting issues.
What can you do to resolve this?
- Design robust data architecture and pipelines.
- Document data flows and dependencies.
- Introduce metadata management.
- Apply structured change processes with governance oversight.
A clear data strategy can bring structure, ownership and visibility to how data flows across the organisation. By embedding standards, documenting dependencies and designing resilient architecture, changes can be made with confidence, reducing disruption, protecting trust in reporting, and allowing teams to focus on using data rather than fixing it.
Time to rethink your data strategy
These signs often show up as everyday frustrations from employees across an organisation, but together, they point to something bigger: a data strategy that is no longer fit for purpose.
The good news is that these challenges are solvable. With the right foundations in place, data can move from being a source of friction to a driver of clarity, efficiency, and better decision-making.
If any of these feel familiar, it may be time to assess your organisation’s data strategy. Get in touch with Sopra Steria's Data and AI Practice to see how we can help you build a stronger, more effective data strategy.