Listen to the audio transcript here:
More money, more problems: part 1
In our latest blog series, we look at the real headaches that come with a growing institutional asset base, and the reporting burdens that scale alongside it. In this article, we focus on one of the most common and persistent pain points: data issues and the question of who is responsible for solving them.
Understanding the many faces of data issues
It’s the catch-all phrase that reporting teams know too well: ‘It’s a data issue’.In reality, it covers a wide range of problems, from small delays to major system failures. These can include a missing transaction, a misaligned holdings file or even market-driven events such as currency fluctuations caused by political decisions. But while the phrase is generic, the reality is not. Data issues come in every shape and size, and the sheer variety is what makes them so tough to pin down and fix.
Here are just a few real-life tangles we’ve seen:
- Conflicting sources
A manager runs both alternative investments and long-only strategies within the same portfolio but across two different systems. Both systems send data independently to the reporting platform and the Enterprise Data Management (EDM) system. Because the EDM system runs its calculations on a scheduled batch process, if data from one system is delayed, the summary reports become inaccurate. This causes inconsistencies in reports shared with clients. - Rounding errors in top 10 lists
A report shows the top 10 sectors and geographies. Upstream processes round percentage weights to two decimal places before selecting the top items. Occasionally, more than 10 entries share the same rounded value, causing the report to display extra rows. This pushes other content out of place and results in an unprofessional appearance. - Missing translations
A firm translates its reports from English to French to meet regulatory requirements. While careful to ensure all terms are correctly translated, the system lacks a translation for an unusual asset class, such as lean hog futures. This causes the report to fail when that asset appears. - Unprocessed stock splits
A widely held stock undergoes a split, but this event is not properly handled in the EDM system. The result is a sudden and incorrect spike or drop in reported performance, affecting all holders and damaging credibility.
Each of these is a ‘data issue’. Each has a completely different root cause, yet they all land on the reporting team’s plate to sort out.
The hidden burden on reporting teams
It’s no wonder reporting teams feel stuck in a Bermuda triangle between clients, technology, and internal ops. As industry expert Simon Swords from Fundipedia puts it: The only real fix is to get the structure right and empower the right people to tackle the problems at the root. He argues for a Head of Data that reports to the COO — close enough to the business to see what’s happening, senior enough to drive action, and well-supported by tech (but not buried in IT).What if you can’t rebuild the org chart?
Most firms are not in a position to rip up their org chart overnight. What practical steps can they take in the meantime?
Six principles for improving data quality
Here are some proven steps reporting teams can implement today to reduce risk and increase confidence:- Use independent validation tools Do not assume upstream data is correct. Equip your team with tools that independently verify data before it is used.
- Automate checks across the workflow Manual checks are error-prone and slow. Automate validations before, during and after report generation to catch problems early.
- Choose configurable tools New problems pop up all the time. The reporting team should be able to add or tweak checks themselves — waiting for IT defeats the point.
- Implement tiered alerts Differentiate critical errors such as missing data that should block a report from minor issues such as formatting. This prevents alert fatigue and makes prioritisation easier.
- Use intelligent, comparative checks Some errors only appear when compared to historical data. For example, a sudden spike in performance might signal an unprocessed stock split.
- Track and analyse recurring problems Good tooling should collect evidence of recurring issues. If you can show where the delays come from, you’re in a stronger position to fix the source.
Better data means better business outcomes
In a perfect world, these problems would be fixed upstream and the reporting team could sleep at night. In reality, the reporting team is the final line of defence, and they need the right tools to protect the firm’s credibility.When you invest in technology that catches errors before your clients do, you protect more than your reports. You protect your brand, save time and effort and let your team focus on higher-value work. It’s ROI you can measure in client trust.
In a market where great service sets managers apart, addressing data problems head-on is not just a technical fix, it’s a strategic advantage.
More money, more problems: Part 2
In this follow-up, we explore why your underlying data infrastructure matters so much — and how modern solutions like cloud data warehouses can reduce risk, cut costs and give your reporting teams a clearer path forward.
FAQs: Solving data issues in institutional reporting
- What are common ‘data issues’ in reporting? Data issues cover any problems with the data used in reporting, from late files to incorrect or missing information caused by system errors or market events.
- Why are data issues so common in institutional reporting? Data comes from multiple systems and teams. Different formats, processes and timelines mean errors can easily slip through.
- Why do data issues cause such challenges? Even small errors can lead to incorrect reports, missed deadlines or regulatory risks. Reporting teams are often responsible for fixing these issues even if they originate elsewhere.
- Who should be responsible for data quality? Ideally, a dedicated head of data with both strategic and technical authority reporting to the COO should own data quality and resolution.
- What if there is no head of data? Reporting teams can still improve quality by implementing their own checks, validations and alerts to catch issues early.
- What validation tools do reporting teams need? Independent tools that verify data accuracy rather than relying solely on upstream data owners.
- How does automation improve data quality? Automated checks reduce manual errors, save time and help identify problems at multiple stages of the reporting process.
- Why use tiered alerts? Because not all issues are equally urgent. Tiered alerts help teams focus on critical problems without being overwhelmed.
- How does tracking recurring issues help? It provides evidence to support process improvements or investments in better technology.
- How does Kurtosys assist with data issues? Our platform offers automated validation, configurable workflows and real-time reporting to help teams catch errors early and deliver with confidence, thus protecting your reports and your brand.
