The financial industry has always focused on data quality, driven by the need for compliance with regulations such as Basel III, MiFID II, and Solvency II. However, as the industry evolves, organizations recognize that improving data quality is not just a compliance requirement but a key driver of business value. According to Gartner, 86% of financial organizations believe that improving data quality is essential for achieving their business objectives.
Impact of Poor Data Quality
Poor data quality can significantly impact a financial institution’s bottom line.
- A study by the International Association of Financial Executives Institutes (IAFEI) found that poor data quality can cost financial institutions an average of $3.1 million per year in lost revenue.
- In addition, a separate study by IBM found that financial services firms spend an average of $1.3 billion per year on compliance related to poor data quality.
- The same study also found that financial services firms estimate that poor data quality costs them an additional $2.2 billion per year in lost revenue opportunities. The cost of poor data quality in terms of lost revenue is almost double the amount spent on compliance.
Addressing the challenges of Poor Data Quality
To address these challenges, financial organizations are turning to modern, automated data quality solutions that can help them improve data quality and drive business value.
While effective for compliance, traditional data governance tools, such as Collibra and Informatica, can be cumbersome and time-consuming. Additionally, these legacy tools may need help to model complex business rules, making it difficult for organizations to leverage their data for business insights and growth fully.
One solution that is gaining traction in the financial industry is the use of self-service analytics tools, such as data lakes and data catalogs, that can help organizations improve data accessibility and governance while also providing a more streamlined way to manage data quality. These tools can also be integrated with machine learning and data integration solutions like Ab Initio to improve data quality further and unlock business value.
In addition to implementing the right tools and technologies, financial organizations must also focus on building a data-driven culture. This includes investing in training and development programs for data literacy and engaging business users in the data quality process. By fostering a culture of data-driven decision-making and encouraging employee participation, organizations can ensure that data quality becomes an integral part of their overall business strategy.
Conclusion
The financial industry has traditionally focused on data quality for regulatory compliance, but the industry is recognizing the need for improving data quality to unlock business value through accurate insights, operational efficiency, and growth. Legacy data governance tools must be more robust to achieve agility and effective data quality. Organizations need to invest in fit-for-purpose data quality processes and tools focused on the business value of data quality with a focus on improving data quality through business engagement, modeling business rules, and integrating with specific data governance and data integration tools. With data quality costing financial institutions an average of $3.1 million per year in lost revenue, the financial industry must invest in modern, automated data quality solutions to achieve a competitive advantage.
