5 Common Mistakes in Establishing Data Governance
Establishing a data control framework is critical for managing critical data elements in compliance with regulatory requirements and ensuring the accuracy and integrity of data.
However, financial institutions often make some common mistakes when setting up a data control framework. Let's explore some of these mistakes and how to avoid them:
Inadequate Data Governance
Without a proper framework, it's challenging to establish effective data governance. Institutions must have a clear understanding of their data landscape, their challenges and establish standard operating procedures adjusted to their size, businesses and risks.
Insufficient Investment in Technology
A data control framework requires the right technology tools and systems to manage data effectively. Institutions should invest in the right technology infrastructure, including data quality checks, data lineage, data catalogs, and data security.
Overlooking Data Lineage
Data lineage is the history of data from its creation to its use. It's critical to have a clear understanding of data lineage to ensure the accuracy and reliability of critical data elements. Institutions should establish a robust data lineage process to trace data back to its source and ensure that the data remains accurate and reliable.
Inadequate data quality controls
Data quality is essential for making informed business decisions. Without proper data quality controls, the data control framework is ineffective. Institutions should establish a comprehensive data quality control process, including data profiling, data cleansing, and data enrichment.
Insufficient stakeholder engagement
Establishing a data control framework requires management buy-in and coordination across departments and teams within the institution. The data control framework's success is dependent on the integration and coordination of these teams in establishing the right and proportionate processes and controls.
Institutions should ensure that all stakeholders are aligned with the framework's objectives and are involved in its development and ongoing maintenance.
Conclusion
Institutions must avoid these common mistakes to establish a robust data control framework.
Beyond regulatory requirements, a comprehensive data control framework is essential for ensuring the accuracy and reliability of critical data elements and make informed decisions.
If you need help, you can contact us:
Christine Laut
SAFE AI NOW
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