About a-team Marketing Services
The knowledge platform for the financial technology industry
The knowledge platform for the financial technology industry

A-Team Insight Blogs

Data Management Experts Discuss the Dilemmas of Data Quality

Subscribe to our newsletter

Data quality has become an imperative for financial institutions as they face increasing regulation and look to data for business benefits and opportunities – but it is not always easy to achieve and requires significant investment in time and resources.

For many institutions, a definition of data quality is based on some or all of the data characteristics set out in regulation BCBS 239 and including accuracy and integrity, completeness and timeliness. Defining data quality can be a good start to improvement projects, but how good should data quality be, how can it be measured and demonstrated, and how can data quality be geared to different business processes?

These are just some of the issues that will be discussed during a panel session on data quality at next week’s A-Team Group Data Management Summit in London.

Fiona Grierson, enterprise data strategy manager at Clydesdale Bank and a member of the panel, has been developing data quality at the bank for about three years. The bank defines data quality as data that is complete, appropriate and accurate, and uses the Enterprise Data Management Council’s Data Management Maturity Model to score data quality and drive improvement. It also has a data management framework for projects to ensure they are implemented using best practice around data quality.

Grierson explains: “We look at the business case for particular strategies and consider the data quality requirement. For example, we look at regulations and the extent of their data quality requirements and at customer initiatives and their need for data quality to ensure seamless customer service.”

Grierson will be joined on the data quality panel by practitioners including Jon Deighton, head of global efficiency and strategy for UK data management at BNP Paribas Securities Services; James Longstaff, vice president, chief data office, at Deutsche Bank; and Neville Homer, head of RWA reference data, regulatory reporting, at RBS.

To find out more about:

  • Regulations driving data quality
  • Approaches to improvement
  • Data quality metrics
  • Technology solutions
  • Practitioner experience

Register for next week’s A-Team Group Data Management Summit in London.

Subscribe to our newsletter

Related content

WEBINAR

Recorded Webinar: End-to-End Lineage for Financial Services: The Missing Link for Both Compliance and AI Readiness

The importance of complete robust end-to-end data lineage in financial services and capital markets cannot be overstated. Without the ability to trace and verify data across its lifecycle, many critical workflows – from trade reconciliation to risk management – cannot be executed effectively. At the top of the list is regulatory compliance. Regulators demand a...

BLOG

A-Team Group Announces Winners of the 2025 RegTech Insight Awards (USA)

A-Team Group is delighted to announce the winners of the 2025 RegTech Insight Awards USA, recognising the leading providers of RegTech solutions, and consultancy services for capital markets across North America. Spanning more than 30 categories, the 2025 awards programme recognised excellence across a wide range of regulatory compliance solutions and services. A-Team Group also presented...

EVENT

AI in Data Management Summit New York City

Following the success of the 15th Data Management Summit NYC, A-Team Group are excited to announce our new event: AI in Data Management Summit NYC!

GUIDE

Regulatory Data Handbook 2025 – Thirteenth Edition

Welcome to the thirteenth edition of A-Team Group’s Regulatory Data Handbook, a unique and practical guide to capital markets regulation, regulatory change, and the data and data management requirements of compliance across Europe, the UK, US and Asia-Pacific. This year’s edition lands at a moment of accelerating regulatory divergence and intensifying data focused supervision. Inside,...