Data Remediation
Key takeaways
The solution
Data classification and cataloguing: Data profiling and record keeping of critical data elements, and data elements that are present in inappropriate environments are vital to the successful operation of the organisation.
Pragmatic controls: Controls that can be quickly and easily implemented.
Purge and archive: Depending on regulation, policy and the appetite for risk, sensitive data must be removed from all environments.
Remediation: data masking and synthetic data: Data archiving and purging may not be acceptable in some scenarios because it derails some parts of the business. In this case, remediation using data masking or substituting production data for synthetic data is a potential mitigation.
MDM adoption: Full-blown MDM adoption can seem to be an intimidating and long-term prospect, but incrementally adopting MDM practice and tooling creates benefits quickly.
FAQ: Data Remediation
How can organisations begin improving data governance and data quality?
Organisations can begin improving data governance by defining clear roles, implementing data catalogues, and introducing practical controls such as data lineage tracking and access policies. These foundational steps create visibility and accountability across data assets.
An initial maturity assessment helps identify gaps and prioritise actions. From there, targeted remediation projects – such as data profiling, masking, or governance process improvements – can deliver quick wins while building towards a scalable data strategy.
For a proven approach and implementation roadmap, download the full Thought Leadership document.
How do data masking and synthetic data improve compliance and security?
Data masking and synthetic data protect sensitive information by replacing or obscuring real data while preserving usability. This allows organisations to test, develop, and analyse systems without exposing personally identifiable information (PII).
Masking conceals specific data fields (e.g. email addresses), while synthetic data generates entirely artificial datasets that replicate real-world patterns. These approaches reduce regulatory risk and enable safe innovation, particularly in AI and machine learning use cases.
Learn when to use each approach and how to implement them effectively in the full Thought Leadership paper.
What are the risks of poor data governance in financial services?
Poor data governance creates significant risks, including regulatory non-compliance, data breaches, and unreliable analytics. Organisations may unknowingly store or process sensitive data unlawfully, leading to financial penalties and reputational damage.
Additional impacts include duplicated datasets, inconsistent data versions, and reduced access to data for analytics. These issues slow innovation, increase operational costs, and limit business agility.
To understand how to mitigate these risks with a structured approach, download the full data remediation guide.
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