Measuring What Matters: KPIs for Data Governance in Reinsurance
Reinsurance is built on the assumption that things will go wrong—and that the math will be right when they do. But when your numbers are stitched together from conflicting systems, late bordereaux, and ambiguous treaty logic, governance becomes more than a policy. It becomes the only thing standing between financial solvency and a quiet accounting error that snowballs into regulatory fallout.
So, how do you know your data governance program is actually working? You measure it. And if you work in reinsurance, what you measure needs to be different.
The Purpose of KPIs: More Than Checkboxes
Data governance is often sold like insurance itself—an abstract promise of protection. But promises don't audit. Numbers do. That’s where KPIs—Key Performance Indicators—come in. They're not about bureaucracy. They're about clarity.
A governance policy is a map. A KPI tells you whether you're still on the road—or in a ditch. They show you where the friction lives: in stewardship gaps, late data feeds, lineage black holes, and duplicate claims coded as unique events.
Yet, most KPI lists feel generic. Accuracy, completeness, timeliness. Fine in theory. But reinsurance is a different animal, with different risks, different stakeholders, and different blind spots.
Why Reinsurance Needs Different KPIs
Imagine trying to paint a portrait in a hall of mirrors. That’s what data looks like in reinsurance—fragmented, duplicated, refracted across ceding companies, brokers, and legacy systems. Unlike direct insurers, reinsurers don’t control the data they rely on. They inherit it, reconcile it, and often have to guess at its intent.
Let’s break this difference down:
1. Multi-Party Data Dependency
Reinsurers depend on cedents for premium, claim, and exposure data. But each cedent has their own formats, controls, and timelines. Data isn’t consistent. It’s negotiated.
A KPI like “Data Accuracy Rate” isn’t about internal hygiene. It’s a measure of how well your partners understand your expectations.
2. Retroactivity as a Feature
In reinsurance, data doesn’t just arrive late—it arrives backdated. A loss reserved in 2023 might reopen in 2025 with a completely different set of attachments. That isn’t an error. It’s business as usual.
Your “Timeliness Index” has to account for change latency, not just delivery windows. Otherwise, you'll be chasing ghosts in every quarterly reconciliation.
3. Treaty Complexity
One claim can relate to multiple treaties. One treaty can reference multiple years, layers, and programs. That creates ambiguity in aggregation. A simple net loss figure might rest on a dozen conditional filters.
Your “Data Lineage Coverage” KPI should reflect whether analysts can trace every transformation rule across every treaty system—not just whether the data moved from A to B.
4. Actuarial Dependency on Detail
Actuaries don’t estimate. They extrapolate from noise. And if that noise is polluted with incorrect claim dates, missing closure status, or misclassified exposures, your capital models become elaborate fiction.
This makes “Data Completeness” more than a footnote. It’s a gating item for model validity.
5. Regulatory Granularity
Solvency II, IFRS 17, ORSA, SEC—they all require consistent, traceable, justifiable data. It’s not enough to report the right number. You must prove where it came from, who touched it, and when.
“Audit Readiness” isn't a compliance metric. It's a proxy for survival.
The Five KPI Categories That Matter
Let’s strip away the theory and get to what works. The following KPI categories are grounded in real reinsurance operations—modeled after live programs with measurable outcomes.
1. Data Quality KPIs
These are the oxygen of governance. When they’re low, every downstream metric is poisoned.
a. Data Accuracy Rate
This measures the percentage of records that contain correct, validated values.
In one reinsurer, over 3% of ceded claim records listed an invalid ICD code or a missing injury description. The result? Inflated severity assumptions in actuarial models and underreserved IBNR projections.
Aim for 98%+ accuracy in high-impact fields: loss date, reserve amount, claim type, and policy number.
b. Completeness Score
If you’ve ever received a bordereaux file with 200 missing exposure amounts, you know what this is.
Track the percentage of required fields populated in inbound data feeds. Prioritize treaty ID, cedent code, limit and attachment points, and loss description.
c. Timeliness Index
Measure the percentage of datasets received or refreshed within a defined window. Include an “impact weighting” to flag how late submissions affect reserve calculations or reporting cutoffs.
Set SLAs. Enforce them.
d. Data Duplication Rate
Duplicates often result in duplicate payments. One reinsurer discovered it had paid over $1.2 million in double-processed losses—thanks to duplicated FNOL entries with slightly varied spellings.
Monitor duplicates per 1,000 records. Anything over 2% is an urgent red flag.
e. Issue Resolution SLA
Time to closure matters. This KPI tracks how long it takes to fix data issues once they’re raised.
Use triage levels—high-impact issues resolved within 3 days, medium within 10, low within 30. Anything longer is a backlog disguised as a governance program.
2. Adoption KPIs
A data governance program that nobody uses is worse than no program at all. It creates the illusion of order while rot spreads underneath. Adoption KPIs don’t measure systems. They measure human behavior.
a. Policy Adherence Rate
Measure how many business units comply with data governance policies (e.g., naming conventions, approval processes, classification rules).
In one case, an underwriting team refused to use the new glossary, preferring “tribal knowledge.” It took a single override to misclassify a portfolio’s risk band—triggering a dispute over a $50M catastrophe layer.
Set a minimum adherence threshold—90% or higher. Then report it monthly, publicly.
b. Steward Engagement Rate
This tracks the percentage of data stewards who are actively performing governance activities: reviewing issues, contributing to definitions, closing out tasks.
Don’t be fooled by titles on an org chart. If stewards aren’t logging in, contributing, or responding, you have placeholders, not partners.
c. Training Completion Rate
Training isn’t a compliance box. It’s a cultural wedge.
If a data steward hasn’t completed training, they shouldn’t touch the data. Period. Aim for 100% in all assigned roles. Automate nudges and escalate gaps to managers.
d. Tool Usage Metrics
Track login frequency, glossary searches, lineage visualizations accessed, and definitions updated.
A good tool is like a gym membership. If it’s never used, it’s a waste. If it’s used but misunderstood, it’s dangerous.
Highlight power users. Promote them. Encourage their peers to follow suit.
e. Business Glossary Contribution
If your glossary has 11 terms and 7 of them are synonyms for “premium,” you're not doing governance. You’re doing semantics.
Track new term contributions, revisions, and approvals per domain. Make glossary completeness a performance objective for stewards.
3. Metadata & Lineage KPIs
Metadata and lineage aren't luxuries. They're defense mechanisms against misinformation, misattribution, and audit failure.
a. % of Critical Data Cataloged
Define what’s critical: regulatory data, treaty data, claims and reserves, reinsurance settlements. Track what portion is cataloged—field descriptions, owners, sensitivity tags.
If it’s in a board report or a regulatory filing, it better be cataloged.
b. Data Lineage Coverage
Trace data from source system to reporting endpoint. Not just where it flows, but how it changes—transformations, joins, aggregations, filters.
One reinsurer uncovered a manual Excel model adjusting reported reserves—untraceable, untested, and unreported. That’s lineage failure.
Set milestones. Require full lineage for top 20 reporting elements in the first 6 months.
c. Glossary-Term Usage
It’s not enough to define terms. Are they actually being used?
Track whether glossary terms appear in report metadata, transformation scripts, or user queries. A growing trend indicates adoption. A flatline indicates shelfware.
4. Compliance & Risk KPIs
Reinsurers aren’t just fighting against bad data. They're fighting regulatory pressure, disclosure requirements, and audit trails that can span a decade.
a. Data Privacy Compliance Score
Track what percentage of PII and sensitive financial data has valid access controls, encryption, retention policies, and consent flags.
Reinsurers often process data from dozens of jurisdictions. One GDPR oversight can trigger multi-million-dollar penalties. You don’t want to learn that through litigation.
b. Audit Readiness Index
Monitor how many open audit findings relate to data, how many have been resolved, and how many are overdue.
Create a red/amber/green heatmap for visibility. Use it as a stick, not a carrot. Delay is risk.
c. Regulatory SLA Adherence
Measure your on-time rate for reports filed with regulatory bodies (e.g., Solvency II QRTs, IFRS 17 disclosures, ORSA submissions).
Build automated trackers. Missed deadlines aren’t just procedural—they shake investor and regulator confidence.
5. Program Management KPIs
Behind every governance program is a PMO—tracking progress, flagging delays, and chasing decisions. These KPIs keep the whole machine accountable.
a. % of Milestones Delivered On-Time
If you say the policy rollout will be done by Q2 and it’s still dragging in Q4, nobody will believe your data—or your leadership.
Track every major milestone. Report the on-time percentage quarterly. Use burndown charts. Be transparent.
b. Stakeholder Satisfaction
Survey users quarterly. Ask about clarity, usability, responsiveness, and business impact. If satisfaction dips, don’t argue. Investigate.
Treat feedback like a data quality issue—diagnose, address, improve.
c. Governance Council Attendance
If your council isn’t showing up, your program isn’t a priority. Track attendance rates. Publish them.
Public visibility shames apathy—and rewards commitment.
d. Change Request Volume
This metric tells you how alive your program is. Too few changes? You’re being ignored. Too many? You're unstable.
Categorize changes: operational, policy, strategic. Monitor trends.
Real-World Stories: Governance on the Edge
At a mid-sized reinsurer in Zurich, a single misclassification of exposure type on a facultative treaty caused a cascading reporting error. The downstream impact distorted modeled capital adequacy by 2.3%, triggering additional buffer capital under Swiss Solvency Test. All from a dropdown menu error.
In another case, a reinsurer’s QRTs were rejected twice by the regulator. Cause? A malformed CSV uploaded by hand because the data pipeline wasn’t trusted. The CFO had to personally apologize. Trust doesn’t scale when built on workaround files.
These aren’t hypotheticals. They’re common. And the absence of effective KPIs is the thread that runs through them.
How to Get Started Without Getting Lost
Governance programs often collapse under their own weight. Too many metrics. Too much tooling. Too little clarity.
You don’t need to track everything. You need to track what matters. Start with five KPIs:
One from Data Quality (e.g., Accuracy Rate)
One from Adoption (e.g., Steward Engagement Rate)
One from Lineage (e.g., % of Critical Data Cataloged)
One from Compliance (e.g., Audit Readiness Index)
One from Program Health (e.g., Milestones Delivered On-Time)
Assign owners. Set thresholds. Visualize results. Discuss them—every month.
This isn’t a suggestion. It’s a blueprint. The programs that succeed aren’t the ones with the best frameworks. They’re the ones that measure early, course-correct often, and build trust through visibility.
What Success Feels Like
You’ll know your KPIs are working when data quality issues are surfaced and resolved before they reach Finance. When regulators stop raising the same questions in every review. When actuaries stop rewriting datasets in Excel. When business users search the glossary and find an answer, not a dead end.
More importantly, you’ll know your program has traction when KPIs become part of the conversation—not the background noise.
A CFO asking about policy adherence isn’t meddling. He’s aligned.
A data steward flagging low lineage coverage isn’t nitpicking. She’s engaged.
A PM escalating overdue milestones isn’t overreacting. She’s protecting momentum.
These moments aren’t metrics. They’re signals. And they only emerge when KPIs stop being vanity dashboards and start becoming operational tools.
You Govern What You Measure
Reinsurance is built on trust—between cedents, brokers, underwriters, regulators, and investors. But trust isn’t an act of faith. It’s a consequence of evidence.
KPIs are that evidence.
Without them, data governance is a theatre of process. With them, it becomes a system of truth.
And in a business defined by what might go wrong, that truth might be the only certainty you have.