Quality Counts: A Checklist for Enabling High-Quality Data

In capital markets, high-quality data is foundational to driving great results. Yet as data volumes grow and operating models become more complex, achieving trusted, reliable data is increasingly difficult. Despite significant investment in data modernization, many global banks and sell-side institutions still struggle with data quality challenges that can undermine decision-making, introduce risk, and limit operational efficiency.

If you're responsible for improving data quality at your firm, you're not alone. For example, in Deloitte’s 2024 Banking & Capital Markets Survey, 81% of respondents cited data quality as a top challenge.¹

This checklist is designed to provide a practical framework for strengthening data quality. It covers steps for setting up a modern foundation and using the technology and processed needed to keep high-quality data available at scale.

Understanding the Challenges

Before diving into solutions, it's important to recognize some of the common obstacles that often hold firms back:

1. Organizational Complexity: For global banks and sell-side institutions, data often flows through a complex and intricate web of systems and processes, typically with fragmented ownership across functions, regions, and legal entities, which may lead to unclear accountability and eroding data confidence.
2. Fragmented and Legacy Systems: Disconnected or outdated systems accumulated through growth, internal technology builds, and M&A activity can introduce conflicting records and complicate integration, transparency, and data quality controls at scale.
3. Inconsistent and Unstructured Data: Critical information often resides in unstructured formats like PDFs and emails, requiring manual extraction that can introduce inconsistencies, delays, and human errors.

With these challenges in mind, here's a checklist to help serve as an actionable tool to enable meaningful improvement.

Assess Your Data Ecosystem

You can't improve what you don't fully understand. When looking to establish or expand a data quality initiative, it’s essential to develop a clear view of your current data ecosystem – where data originates, how it flows through your organization, and which teams and processes rely on it for critical decisions and operations. A comprehensive assessment across data sources, business lines, functions, and regions helps establish a foundation for meaningful improvement.

1. Develop clear mappings of your key data sources and uses across functions such as treasury, risk, and operations
2. Run initial data quality assessments to identify gaps and establish quality baselines
3. Evaluate data quality concerns in the context of business or compliance impact to prioritize efforts strategically
4. Consider engaging external solution providers for specialized expertise, guidance, and enabling technology

Adopt a Platform Mindset and Approach

Data quality is a critical component of a broader data management strategy. By aligning modern, scalable platforms with disciplined quality frameworks, firms can move beyond reactive issue remediation toward proactive, ongoing data stewardship. A platform mindset enables consistency, transparency, and control across the data lifecycle, while reducing fragmentation and manual intervention.

Key data management pillars to support high-quality data include:

1. Data collection and ingestion: Unify data from various sources and formats
2. Data cleansing and normalization: Standardize formats, identifiers, and classifications to ensure comparability
3. Data quality and governance: Embed data accuracy, completeness, and assign accountability
4. Data analysis and reporting: Leverage high-quality data for actionable insights
5. Data platform and solution: Institutionalize these practices with a scalable data platform

Embed Data Quality Frameworks and Align Technology

With a modern data foundation in place, you’ll need to define clear data quality standards and implement the technology needed to measure, monitor, and sustain those standards at scale. Aligning frameworks and platforms ensures data quality expectations are consistently applied and operationalized across teams, systems, and regions.

1. Establish data quality standards aligned to key dimensions such as accuracy, completeness, consistency, timeliness, validity, and uniqueness
2. Consider modern data platforms that support automated measurement, rule enforcement, and continuous improvement across these dimensions
3. Prioritize ease of use with tools that enable users to define and manage data quality rules without necessarily having to produce complex code

By combining clear quality standards with modern technology tools, you create a systematic, scalable approach to catching and preventing data quality issues before they impact your business.

Strengthen Data Governance, Lineage, and Cataloging

Data quality standards and rules are a necessary starting point, but they aren’t always sufficient just on their own. Sustaining high-quality data over time requires clear ownership, end-to-end transparency into data flows, and efficient discoverability to build organizational trust in your data.

1. Elevate the role of data governance and move toward a strategic framework that integrates a culture of stewardship, clear policies, ownership, and awareness programs to better enable data quality at scale
2. Implement modern data lineage capabilities that capture where data originates, how it is transformed, and how it’s consumed
3. Deploy a centralized data catalog with comprehensive metadata and enable teams to efficiently discover data and understand its definition and context

Streamline and Empower

Data quality shouldn't be bottlenecked by technical resources. Equip your team with self-service options, clear visibility, and AI-assisted tools that better embed data quality into operations. Interactive dashboards and reporting can provide continuous insight into data quality trends and issues, enabling faster prioritization, consistent standards, and measurable improvement.

1. Gain transparency into where data quality breaks down to prioritize fixes, track progress, and reinforce standards
2. Enable self-service tools with the option to define, configure, and tailor data quality rules and validation checks
3. Prioritize low-code or no-code capabilities to reduce dependency on often scarce technical resources
4. Explore AI-powered assistance and copilots to accelerate rule creation using natural language and broaden participation in data quality initiatives

Get Ready to Transform Data Quality

Data quality transformation doesn't happen overnight, but with a systematic, platform-driven approach, firms can position themselves for measurable and sustained progress. By committing to quality, aligning business and technical teams, and partnering with the right technology providers, firms can better embed data quality into operations.