Most financial institutions treat regulatory reporting like a tax: necessary, expensive, and to be minimized. That mindset creates a self-fulfilling prophecy. You build reporting processes that barely meet requirements, then wonder why they consume disproportionate resources and deliver no strategic value.
The European Securities and Markets Authority's Data Day 2026, scheduled for 24 November in Paris, will focus on transforming data "from burden to opportunity." The sessions on simplification, financial transaction reporting, and the European Single Access Point (ESAP) reflect what regulators already understand: reporting infrastructure should serve multiple purposes. Yet most institutions keep making the same mistakes that prevent this transformation.
Why These Mistakes Keep Happening
Regulatory reporting has grown through accretion. Each new obligation triggered a new process, often owned by different teams using different systems. You built what you needed to pass the next audit, not what you needed to run the business. Over time, this creates a reporting architecture that's expensive to maintain and impossible to use strategically.
The second problem is ownership. Compliance teams own the obligation but not the underlying data. IT owns the systems but not the business logic. Finance owns some reporting but not all of it. Nobody owns the end-to-end data flow, so nobody can redesign it.
Mistake 1: Building Single-Purpose Reporting Pipelines
You create a new data extraction process for each Regulatory Obligation. Transaction reporting pulls from one system. Fund reporting pulls from another. Each pipeline has its own validation rules, transformation logic, and error handling.
Why it happens: Compliance deadlines don't wait for enterprise architecture discussions. When ESMA announces a new reporting obligation, you need a solution in months. Building on existing infrastructure requires coordination across teams, approvals, and testing. Creating a standalone pipeline is faster.
The consequence: You end up maintaining dozens of fragile, overlapping processes. The same transaction data gets extracted five different ways. When source systems change, every pipeline breaks independently. Your reporting costs scale linearly with regulatory complexity.
The fix: Map your data lineage before building the next pipeline. Document every regulatory report you produce, trace it back to source systems, and identify overlapping extractions. Then build a common reporting layer that serves multiple obligations. This requires upfront investment, but it's the only way to make reporting costs sublinear.
Mistake 2: Treating Data Quality as a Compliance Problem
Your data quality checks happen at reporting time. When you discover incomplete counterparty identifiers or missing transaction timestamps, you scramble to fix them before the submission deadline. Data quality becomes a monthly crisis instead of a continuous capability.
Why it happens: Data quality ownership is unclear. The teams generating transactions don't see regulatory reporting as their problem. Compliance teams lack authority to enforce data standards upstream. So quality checks happen at the end of the process, where they're most expensive to remediate.
The consequence: You spend enormous effort cleaning data for each reporting cycle. Worse, you can't use that data for anything beyond compliance. Strategic analysis requires trusted data, and you've just demonstrated your data isn't trustworthy until it's been through manual remediation.
The fix: Implement data quality rules at the point of capture, not the point of reporting. If you need Legal Entity Identifiers for transaction reporting, validate them when trades are entered. If you need specific timestamps, enforce the format in your trading systems. Move quality checks left in the process, where they prevent problems instead of detecting them.
Mistake 3: Ignoring Regulatory Simplification Initiatives
Regulators announce burden reduction programs. You ignore them because you've already built your processes, and changing them seems riskier than leaving them alone. You miss opportunities to eliminate redundant reporting or adopt streamlined formats.
Why it happens: Compliance teams are risk-averse by nature. If your current process passes audits, changing it introduces execution risk. The benefits of simplification (lower costs, better data) are diffuse and hard to quantify. The risks (missed deadlines, failed validations) are immediate and career-limiting.
The consequence: You continue operating expensive, complex processes even when regulators offer alternatives. Your competitors adopt integrated fund reporting or simplified transaction formats while you maintain legacy approaches. The gap between your reporting costs and industry benchmarks widens.
The fix: Assign someone to monitor regulatory simplification initiatives specifically. ESMA's Data Day 2026 will include sessions on burden reduction, someone from your organization should attend and bring back specific recommendations. Create a business case framework that quantifies both implementation risk and ongoing cost savings. Simplification initiatives often have long implementation windows; use them.
Mistake 4: Maintaining Reporting Logic in Spreadsheets
Your regulatory reports rely on Excel workbooks with years of accumulated formulas, macros, and manual adjustments. The person who built them has changed roles twice. Nobody fully understands the calculation logic, but everyone's afraid to touch it.
Why it happens: Spreadsheets are flexible and familiar. When reporting requirements change, you can modify a formula faster than you can submit a change request to IT. Over time, critical business logic migrates into these workbooks because they're the path of least resistance.
The consequence: Your reporting process is undocumented, unauditable, and fragile. You can't scale it, automate it, or reuse the logic elsewhere. When key personnel leave, they take institutional knowledge with them. Auditors increasingly challenge spreadsheet-based controls under AS 2201.
The fix: Extract calculation logic from spreadsheets into documented, version-controlled code. This doesn't mean abandoning Excel entirely, it means using it for presentation and analysis, not for implementing critical regulatory calculations. Start with your highest-risk reports (those with material financial impact or frequent regulatory scrutiny) and work backward.
Mistake 5: Collecting Data Without Defining Use Cases
You build comprehensive data warehouses to support regulatory reporting. You extract everything because you might need it someday. Storage is cheap, so why not capture all available fields? Then you discover that nobody can find anything, query performance is terrible, and maintenance costs exceed the value delivered.
Why it happens: Uncertainty drives over-collection. You don't know what future regulations will require, so you collect defensively. Technology teams default to capturing complete data sets rather than making business decisions about what matters.
The consequence: You create data swamps instead of data assets. The volume of stored data makes it expensive to search, slow to query, and risky to maintain (especially under data protection regulations). When you do need specific information, finding it in the undifferentiated mass is harder than if you'd been selective from the start.
The fix: Define use cases before building data infrastructure. What decisions will this data support? What reports will it generate? What analysis will it enable? Then collect only what serves those purposes. You can always expand later, but starting with focused requirements produces usable systems faster and cheaper.
Prevention Checklist
Before you build or modify regulatory reporting processes:
- Document which existing pipelines touch the same source data
- Identify opportunities to reuse extraction and transformation logic
- Implement data quality validation at the point of capture, not reporting
- Assign ownership for end-to-end data flows, not just individual reports
- Review current regulatory simplification initiatives that apply to your obligations
- Extract critical calculation logic from spreadsheets into auditable code
- Define specific use cases before expanding data collection
- Calculate total cost of ownership (including maintenance) for new reporting infrastructure
- Identify which reports could serve both regulatory and business intelligence needs
- Establish data governance standards that apply across all reporting obligations
Regulatory reporting won't become a strategic asset by accident. It requires deliberate architecture decisions, cross-functional ownership, and willingness to invest in infrastructure that serves multiple purposes. The alternative is continuing to build single-purpose processes that get more expensive every year while delivering progressively less value.
