Fund data quality is the real value behind FE fundinfo Data Feeds

Fund data quality rarely makes headlines and when it’s good it has a way of going unnoticed. It shows up in a Solvency II submission that goes through first time, or a client statement that reconciles without a query. When there’s a problem, however, it’s painfully evident, such as, when a share class identifier is wrong and an entire reconciliation breaks. 

This matters whether you are an insurer meeting a Solvency II deadline, a technology provider redistributing data inside your own platform, or an investment manager whose brand sits behind every touchpoint the data reaches. A data feed is only as valuable as the fund's data quality underneath it. 

This article sets out what poor fund data quality actually costs, what strong fund data quality looks like in practice, and how FE fundinfo Data Feeds are built around it from the point of collection. 

Where poor fund data quality hits hardest 

Fund data quality problems rarely arrive as one dramatic failure. They arrive as small, recurring frictions that compound over time. 

A missing field forces your team to chase the same investment manager for the same template, again. An inconsistent identifier breaks out a reconciliation that used to run cleanly. A late regulatory document pushes your compliance deadline right up against the wire. 

Share classes are where this problem shows up most often, and most expensively. A single fund can carry dozens of share classes, each with its own currency, fee structure, distribution status, and identifier. When that share class-level data is inconsistent or incomplete, the error propagates into the fee calculations, client reporting and regulatory filings built on top of it. 

Inaccurate or incomplete fund data creates direct regulatory exposure. A Solvency II or TPT submission built on unverified holdings data is only as defensible as its weakest input. Poor data quality also undermines the investment analysis built on top of it, since an inaccurate reference data point does not stay isolated. It flows straight into fund selection and client recommendations. 

The moment fund data is redistributed inside a third-party platform or data product; a quality issue in the feed becomes a quality issue that client experiences directly. Poor fund data quality shifts cost and risk onto the teams and clients at least equipped to catch it downstream. 

What strong fund data quality actually looks like 

Strong fund data quality is not simply about volume. Whole-of-market coverage means little if the data underneath it cannot be trusted. 

Reliable fund data rests on four consistent characteristics:. 

1. Accuracy. Every field, down to individual share class level, reflects what the investment manager actually reported, validated against defined formatting and content rules before it reaches you. 

2. Completeness. Mandatory and conditional fields are populated, not left blank or defaulted, so nothing is missing from your calculations or your client's communications. 

3. Timeliness. Data and documents arrive on an agreed, predictable schedule, so your own deadlines are never held hostage by someone else's delay. 

4. Consistency. The same fund, the same share class and the same data point look identical every time they appear, across every template and every delivery. 

Miss any one of these four, and the other three lose much of their value. A dataset that is accurate but late still creates risk. A dataset that is timely but incomplete still forces manual work. 

A fifth characteristic matters just as much, even though it is easy to overlook: auditability. Every value in a feed should be traceable back to its source, with a clear record of when it was collected and validated. Without that trail, fund data quality cannot be evidenced when a regulator, an auditor or a client asks for proof. 

How FE fundinfo Data Feeds build in fund data quality 

Fund data quality has to be engineered at the point of collection, not validated afterwards. 

Every FE fundinfo Data Feed is built on a single, validated golden source database, covering 90% of ECB and FCA-registered fund data. Data is collected directly from investment managers and passed through a four-step validation and control process that combines human expertise with automated checks, covering formatting, mandatory fields and content, down to share class level, before anything reaches you. 

This structure delivers four practical benefits:. 

1. A single point of validation. Rather than each client checking data independently, FE fundinfo applies consistent rules once, at share class level, across the full range of investment managers and funds in scope. 

2. Transparency on data status. Dashboards track coverage and last-submitted dates across your full data scope, so you always know exactly where a fund or share class stands, rather than finding out only when something goes wrong. 

3. Depth as well as breadth. FE fundinfo Data Feeds cover static data, including legal entities, financial instruments and share class-level detail, alongside performance data, corporate events and valuation pricing, aggregated into regulatory templates such as EMT, EET, TPT and EPT. 

4. Scale without added risk. As your fund range grows, whether that means new investment managers, new jurisdictions or new asset classes, the same validation standard applies automatically across all 32 FE fundinfo data packages. Growth does not mean diluted fund data quality. 

This is what sits behind FE fundinfo's position as one of the largest fund document producers in Europe, collecting 1.5 million documents monthly across more than 75 jurisdictions, with ISO 27001 and ISO 9001 certification. 

Rathbones Asset Management, one of the investment groups onboarded onto FE fundinfo Data Feeds, noted that FE fundinfo delivered production "on aggressive timelines" and that its "objectives were delivered on time and to a high standard.". That combination, accuracy delivered reliably against a deadline, is what fund data quality looks like when it is working as intended. 

Fund data quality is the product, not a feature of it 

Fund data quality determines what everything built on top of a data feed is actually worth. Regulatory submissions, client reporting, redistributed analytics and portfolio tools all inherit the quality, or the flaws, of the share class and fund-level data underneath them. 

As fund ranges grow, as regulatory requirements evolve and as more of the investment industry builds products on third-party data, the cost of poor fund data quality only increases. Getting it right at source, at share class level, remains the only way to stop that cost of compounding downstream.

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