Building a Data Quality Framework

Improving data quality is not about cleansing spreadsheets in isolation; it begins with aligning data efforts to what the business values most. In securities lending, this means connecting data improvement to outcomes like maximizing revenue, managing counterparty risk, or meeting regulatory obligations. A robust framework provides both the structure and discipline to do this effectively.

1. Start with Business Needs and Critical Data

Every improvement effort should begin by asking: what does the business need from its data? For a securities lending business, priorities may include:

• Revenue growth — accurate trade capture and fee calculations to maximize lending returns.

• Risk control — reliable collateral management and counterparty exposure tracking.

• Regulatory compliance — complete, accurate, and timely reporting to regulators.

These objectives define what “critical data” looks like. Out of hundreds or even thousands of data elements, only some will directly drive revenue, safeguard risk, or ensure compliance. Those are the ones that must be prioritized.

2. Assess the Current State

Before designing improvements, organizations must understand where they stand today. This requires a multi-pronged approach:

• Stakeholder interviews & questionnaires — to surface pain points, strategic goals, and business risks tied to poor data.

• Data profiling & analysis — to measure data completeness, consistency, and accuracy.

• Process and system mapping — to document how data flows through business processes, dependencies, and supporting technical architecture.

Initial diagnostic questions might include:

• How does the business define “high-quality” data?

• What is the operational or strategic impact when data falls short?

• How much tolerance exists for poor data, and where?

• What governance structures are currently in place—or missing?

3. Define What Matters Most

Not all data is created equal. A successful framework draws a clear perimeter around the data that matters most for the business. Firms should:

• Identify master and reference data essential to core processes.

• Rank critical data based on factors such as regulatory requirement, financial value, customer impact, and internal efficiency.

• Focus efforts where value is highest—where better data directly translates to better outcomes.

This approach ensures investment in data quality pays dividends rather than spreading resources too thinly.

4. Establish Roles and Ownership

Sustainable data quality is a team sport. Effective programs draw on contributions from:

• Business stakeholders who articulate priorities and pain points.

• Data stewards who define standards and ensure adherence.

• Subject matter experts (business and technical) who understand process and system dependencies.

• Data quality teams who design controls, perform profiling, and measure improvement.

Clear accountability ensures that data quality is not left to chance—it becomes embedded into how the business operates.