Data Quality
Data quality refers to the accuracy, completeness, reliability and timeliness of data that is used in some capacity (such as customer data). High-quality data ensures that a bank’s operations are efficient and effective. Poor-quality data on the other hand negatively impacts analytics and decision-making, potentially leading to errors, financial losses, non-compliance, and reputational damage.
Data integrity, data governance, reliable data
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DQ
Examples
If a bank onboards their customers manually, the odds of having poor quality data is a lot higher. People might misspell their name, forget to fill out a field, or misunderstand which information they should provide. These kinds of errors make it a lot more difficult for the bank to perform Customer Due Diligence and thus puts them at risk of non-compliance. Similarly, data discrepancies between internal systems can lead to an inefficient process and lead to inaccuracies when performing customer analysis.
FAQ
Why is data quality important in banking?
High-quality data is essential for properly calculating risk and staying compliant with relevant regulations.
What factors determine data quality?
Data quality is determined by its accuracy, completeness, consistency, timeliness, and reliability.
How do banks improve data quality?
How do banks improve data quality?
