Operationalizing Data Quality in Securities Lending

On an agency lending desk, traders, operations, risk teams, and regulators all rely on data behaving exactly as expected. But rules and frameworks alone aren’t enough. To realize the benefits of strong data quality, firms need to deploy it into daily operations — managing rules, monitoring issues, and embedding accountability. Done right, this shifts the business from reactive firefighting to proactive assurance.

Managing Data Quality Rules: Preventing Errors at the Source

Every securities lending trade begins with rules — and so should data quality. A well-defined set of data quality rules ensures that common errors are prevented before they spread across systems.

Examples include:

• No trade can be booked without a valid counterparty LEI.

• Collateral eligibility must comply with the agreed schedule.

• SFTR reporting fields must be populated consistently across submissions.

Capturing rules as metadata makes them transparent, reviewable, and testable. When linked to quality dimensions (accuracy, completeness, timeliness), rules can be validated against live market data and subject-matter expertise.

Crucially, rules need to evolve. As new business processes are added (e.g., non-cash collateral programs) or regulations shift, rules must be reviewed and refreshed. Embedding them directly into system design ensures that traders, operations, and compliance teams don’t just detect errors — they prevent them.

Measuring and Monitoring Data Quality: Building Transparency

Measurement is what separates theory from reality. In securities lending, continuous monitoring of quality metrics provides visibility and accountability across the lifecycle:

• Error levels: Booking errors in the front office, reconciliation breaks in the middle office, or SFTR submission failures in the back office.

• Trends: Whether settlement breaks are declining month-on-month.

• Incidents: Time to resolution, escalation rates, and backlog of unresolved issues.

• ROI: Reduction in settlement fails, regulatory penalties, or dispute-related costs.

For example, monitoring collateral mismatches over time can show whether changes to allocation rules are reducing risk. Or tracking reporting exceptions can highlight where ETL processes need tighter controls.

By combining profiling, historical analysis, and ongoing alerts, firms build a living view of their data quality — one that both operational teams and senior management can act upon.

Incident Management: Bringing Discipline to Data Breaks

In IT, health and safety, or risk management, incidents are managed with rigor. Data deserves the same treatment.

A structured incident management process in securities lending means:

1. Standardized vocabulary: A “trade capture error,” a “collateral mismatch,” and a “payment break” are logged consistently.

2. Assignment and escalation: Issues are routed to the right experts — asset servicing for corporate actions, regulatory ops for SFTR breaches. High-impact breaks escalate quickly under pre-agreed service levels.

3. Root cause analysis: Analysts trace issues across systems, assessing whether they stemmed from training gaps, upstream feeds, or flawed mappings.

4. Remediation choices: Fix the record, improve the process, enhance system validation, or — if cost outweighs value — document the error but prevent recurrence.

With clear workflows, firms can spot recurring patterns (e.g., a specific counterparty’s feed causing repeated breaks) and fix root causes instead of patching symptoms.

Data Quality SLAs: Defining Expectations

A Data Quality Service Level Agreement (SLA) formalizes accountability across teams. Like any SLA, it defines what “good” looks like, how breaches are escalated, and who is responsible for remediation.

Key components include:

• Scope: Which datasets (trade capture, collateral, reporting) are covered.

• Metrics: Dimensions monitored, thresholds, and tolerance levels.

• Notification & Escalation: Who is alerted and within what timeframe.

• Remediation: Expected resolution times, ownership, and corrective actions.

• Reporting: Regular reviews, trend analysis, and transparency on performance.

With SLAs in place, data stewards and operations teams have a clear mandate — aligning their objectives with the business’s need for reliable, compliant, and timely data.

Data Quality Reporting: Making Results Actionable

Assessment and monitoring only matter if insights are communicated. That’s where data quality scorecards and reporting come in.

Effective reporting delivers:

• Scorecards: High-level views of quality against thresholds.

• Trends: Progress in reducing errors or settlement fails.

• SLA metrics: Performance of operational teams against agreed timelines.

• Issue status: Open, resolved, or escalated breaks.

• Governance checks: Whether teams are adhering to policies and ownership rules.

• Impact measurement: Evidence of reduced disputes, improved settlement rates, or avoided penalties.

By presenting results in a structured, visual way, data quality becomes tangible to traders, operations, compliance, and executives alike — building transparency and reinforcing trust.

From Firefighting to Assurance

Deploying data quality operations transforms the role of data in securities lending. Instead of reacting to daily breaks, firms embed rules, monitoring, incident management, SLAs, and reporting into the business fabric.

The result?

• Lower risk: Fewer settlement fails and regulatory breaches.

• Higher efficiency: Less time wasted on reconciliations and rework.

• Stronger trust: Clients and regulators see data they can rely on.

• Business value: Teams focus on lending decisions and client service, not fixing avoidable errors.

In the fast-paced world of securities finance, operationalized data quality is more than a safeguard — it’s a competitive advantage.

Please note that this article was written with the DAMA-CDMP course material as a backbone relating it to the securities lending market.