Ask three different teams how much new business came into the platform last quarter and you may get three different answers.
Sales may report one figure, Finance another and Operations may have a third. Often, none of those figures are wrong. The difference usually comes down to how the data is being interpreted, calculated, or classified.
Anyone who has worked around platform reporting for long enough will have come across this situation at some point.
As platforms grow, acquisitions take place, reporting requirements increase, and new systems are introduced, reporting processes often evolve independently. Teams create their own extracts, dashboards, and ways of measuring performance, all with the best of intentions, but what’s really happening is data siloes are being created. Over time, that can make it more and more difficult to establish a single, trusted view of the business.
In this article, we explore why data inconsistencies emerge, why they often become most visible during transformation programmes, platform change, and acquisitions, and what firms can do to build greater confidence in the information used to drive decisions.
The real challenge lies in understanding how quality is being proven and having a framework which provides the right checks and balances without getting in the way of progress.
Same data but different answers?
Most firms already have the data they need. The difficulty often lies in creating a consistent view across multiple systems, teams, and reporting processes.
Most reporting landscapes are the product of years of change. New requirements emerge, systems change, businesses grow, and teams build reports to answer the questions in front of them. Additional calculations, definitions, and workarounds are introduced along the way, often to solve a legitimate business need.
None of these changes are unusual. The difficulty comes when they start to build on top of one another over time.
One team may include a particular transaction type within a report. Another may exclude it. A transfer may be treated one way in operational reporting and another in sales reporting. A figure generated on Monday may differ from one generated on Friday because the underlying data has changed.
Eventually, the conversation shifts from understanding performance to understanding why the numbers don’t match.
Why flows create so many challenges
Flow reporting is a good example. Many firms use flows as a key measure of growth, yet the definition of a flow is not always as clear as it first appears.
Transfers between platforms, adviser migrations, in-flight transactions, reversals, and assets moving between business units can all affect how flows are reported. Depending on how those events are classified, two reports looking at the same period can produce very different results.
Different firms handle these situations differently. Sometimes different teams within the same firm do too. The result is that flow reporting can quickly become one of the most debated and least consistent data sets within a business.
Given that flows are often used to assess growth, adviser performance, and commercial success, even small differences in treatment can materially change the story the data appears to tell.
The impact goes beyond reporting
Most data issues don’t surface through client-facing information. Valuations, account balances, and transaction records are usually supported by established controls and well-defined processes.
Where things become more complicated is within management reporting. Information is extracted, transformed, combined with data from other systems, and used to produce metrics that support business decisions.
At each stage, additional logic, assumptions, and interpretations can be introduced. The underlying data may be accurate, yet different reports can still produce different answers.
For leadership teams, that creates a difficult position. Multiple reports appear to answer the same question, yet arrive at different conclusions. Confidence starts to weaken, not necessarily because the data is wrong, but because it becomes difficult to explain how a figure was produced, what assumptions sit behind it, or why it differs from another version of the same metric.
And when confidence in the data falls, confidence in the decisions built upon that data often follows.
Technology isn’t a fix-all-solution
When reporting inconsistencies start to emerge, attention often turns to technology. Firms invest in new reporting platforms, dashboards, or data tools, hoping greater visibility will solve the problem.
It’s also common for the issue to be viewed as an IT problem. After all, if different reports are producing different answers, it can feel like something in the system must be wrong.
In reality, the technology is often doing exactly what it has been asked to do. The difficulty usually sits in the rules, definitions, ownership, and reporting logic that sit around it.
A new reporting tool won’t decide how flows should be defined. It won’t determine how transfers are treated, which metrics should be used to measure performance, or who owns key reporting definitions across the organisation.
Those decisions need to be made before the technology can deliver meaningful value. Otherwise, firms risk creating a more sophisticated version of the same problem.
AI is a good example. It’s often presented as a solution to reporting and data challenges, but if the underlying information isn’t consistent or trusted, AI will simply work with the same issues faster.
The quality of the outputs will always depend on the quality of the inputs. If different teams are working from different definitions, applying different reporting logic, or relying on conflicting data sources, AI isn’t going to resolve those inconsistencies. In some cases, it can make them harder to spot by producing insights and recommendations that appear credible but are built on the same underlying issues.
The technology can help uncover patterns, identify trends, and support decision-making, but it can’t resolve discrepancies around definitions, data ownership, or how key metrics are calculated.
When the data doesn’t line up
By the time conflicting figures appear in a report or board discussion, the underlying issue has often existed for some time.
The more important question is whether the organisation can clearly explain how those numbers were produced, which assumptions sit behind them, and where ownership sits when discrepancies arise.
This is often where data confidence is won or lost. Not because every figure needs to be perfect, but because people need to understand where information comes from, how it is calculated, and whether it can be trusted.
For organisations reviewing their reporting landscape, a few questions are worth considering:
- Can key business figures be traced back to a clear source?
- Are reporting definitions applied consistently across teams?
- How are transfers, in-flight transactions, and reversals treated?
- Do different departments produce different versions of the same metric?
- Is there a shared understanding of how critical business reports are generated?
- Can users access trusted data without creating their own workarounds?
The answers can provide valuable insight into the consistency, reliability, and transparency of reporting across the firm.
Building data confidence
As wealth management firms continue to evolve through platform change, acquisitions, outsourcing, and transformation programmes, knowing where information comes from, how it’s calculated, and why it can be trusted is becoming just as important as the information itself.
AheadMG works with wealth management firms to bring greater clarity to complex delivery environments, helping them understand how information flows across platforms, processes, and teams.
Book a call to discuss how reporting, governance, and data visibility can be strengthened across your firm.
