Data Remediation
Key Takeaways
The Solution
Data classification and cataloging: 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 organization.
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 organizations start improving data governance and data quality?
Organizations can start improving data governance by defining clear roles, implementing data catalogs, 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 prioritize actions. From there, targeted remediation projects - such as data profiling, masking, or governance process improvements - can deliver quick wins while building toward a scalable data strategy.
For a proven approach and implementation roadmap, download the full Thought Leadership.
How does 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 organizations to test, develop, and analyze systems without exposing personally identifiable information (PII).
Masking hides specific data fields (e.g., email addresses), while synthetic data generates entirely artificial datasets that mimic real-world patterns. These approaches reduce regulatory risk and enable safe innovation, especially 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. Organizations 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 down 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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