Putting data governance at the heart of a customer redress scheme

Many remediation projects require historical scenarios to be reconstructed, which becomes extremely difficult with incomplete or inaccurate historical data. 

Strong data governance reduces the likelihood of issues requiring remediation in the first place, by ensuring data quality, integrity, and compliance with regulations.

For example, incomplete or outdated customer information makes it challenging to identify and contact all affected customers, particularly those who have moved or changed contact details. 

Systematic data monitoring and controls help identify potential issues early, allowing for more proactive remediation before problems escalate.

Poor data quality makes it difficult to accurately determine how many customers are affected and to what extent, potentially leading to under or over-estimation of the scope of remediation. 

Well-documented data lineage and metadata help accurately define the scope of affected customers and products.

Data gaps or inconsistencies can obscure the true root causes of issues, making it difficult to develop effective remediation strategies.

Data governance frameworks provide visibility into data flows and dependencies, making it easier to trace issues to their source.

Without clear governance establishing defined roles and responsibilities, effort may be duplicated and resources focused ineffectively.

Clear data ownership and stewardship structures establish accountability for remediation actions and decisions.

Inaccurate data can lead to incorrect compensation calculations, resulting in further customer detriment or unnecessary costs to the business. 

Established data quality rules and validation processes ensure remediation calculations and customer communications are accurate.

Without reliable data, it becomes challenging to demonstrate to regulators that remediation has been complete and effective. This may result in confidence in regulatory reporting being undermined, potentially leading to additional scrutiny or penalties. 

Governance documentation provides essential evidence for regulators that remediation efforts are comprehensive and systematic.

Remediation projects can be large in scale, with cases ranging from the simple to complex exceptions, and data may need to be modified over the project duration. 

Governance change control processes help manage remediation-related data modifications while preventing unintended consequences.

Poor data quality makes it difficult to accurately measure the effectiveness of remediation efforts and identify any customers who may have been missed. 

Established data metrics and dashboards enable effective tracking of remediation progress and outcomes.

When data is of poor quality significant resources must be diverted to cleansing and validation, rather than remediation activities. Additional validation and cleansing activities can typically extend project timelines. 

Governance frameworks ensure that resources are used effectively and that remediation solutions are durable by embedding controls that prevent recurrence of similar issues.

Once a remediation project ends, its learning and experience can easily be lost over time. 

Documentation requirements preserve institutional knowledge about the remediation, which is valuable for future reference and regulatory inquiries.