By Debashis Rana –
When one thinks of financial services operations, their customer-focused financial products, ability to provide comprehensive account management, and ability to provide stewardship and advice to their clients comes to mind first.
But what’s the common thread between all of these elements? The data that is moved through them, informs them, and is generated by them.
This is why it is more important than ever for financial services organizations to understand the role of data quality and how to bolster it across their application stack. When it is done right, financial services organizations can enjoy more accurate analysis and informed decision-making, better adhere to regulations that require accurate and reliable data reporting1, and reduce operational and financial risks by providing a clear and comprehensive understanding of the business landscape.
So how can financial services organizations harness their data to take their business to the next level?
Let’s dive in.
Common Data Quality Challenges in Financial Services
While every financial services business is unique, there are several common hurdles that can prevent organizations from being able to achieve optimal data quality.
These challenges include:
- Data entry errors: Mistakes made during manual data entry processes.
- System integration issues: Data inconsistencies resulting from incompatible systems or data transfers.
- Missing information: Omissions or gaps in data fields that impair accurate analysis.
- Inadequate data capture: Failure to collect all relevant data points during the data collection process.
- Data discrepancies: Contradictory or conflicting information across different sources or systems.
- Lack of data standardization: Varied formats, units, and structures hinder data integration and comparability.
- Repetitive entries: Multiple occurrences of the same data, leading to redundancy and confusion.
- Data replication: Data duplication due to system errors or improper data handling processes.
- Stale information: Data that is no longer relevant or reflective of the current business environment.
- Delayed updates: Inadequate processes for updating data in a timely manner, resulting in outdated records.
- Enterprise data out of context: Data that is acceptable within the bounds of an application but not at an enterprise level.
- Cross-functional data out of context: Data that is sufficient for a given function but lacking when shared with another function.
4 Strategies for Enhancing Data Quality Standards
So how does a financial services organization begin to address this long punch list of data quality issues and begin to more fully leverage the benefits of their data?
Here are four key steps and how they contribute to a robust data quality framework:
1. Establish clear data governance policies and procedures.
Establishing clear data governance policies and procedures2 is crucial in financial services operations to ensure data quality. This includes assigning roles and responsibilities for data management, implementing guidelines for data collection and storage, and conducting regular monitoring and audits to assess data quality. By doing so, organizations can maintain data integrity and make informed decisions based on reliable and accurate information.
2. Conduct comprehensive data profiling and cleansing.
This step involves utilizing data profiling tools to identify and rectify errors, inconsistencies, and missing data by analyzing data patterns, outliers, and anomalies.
However, this isn’t a one-time action; organizations should establish data maintenance schedules and apply data quality controls—supported by automated data validation—to help ensure that data remains accurate and up to date.
3. Enhance data integration and interoperability.
Another key step involves implementing standardized data formats and protocols to ensure seamless data integration across the enterprise.
Whether it’s an industry-standard protocol and data exchange format or one developed in-house, organizations can increase the efficiency of data sharing by ensuring data is properly formatted, organized, and refreshed. This work can be taken to the next level by establishing procedures to regularly validate data interfaces and APIs for their performance and security, which can help to maintain the integrity and confidentiality of the information being shared.
4. Prioritize data security and privacy measures.
Finally, financial services organizations should implement strong access controls, such as the use of:
- Role-based access controls (RBAC) to restrict data access based on user roles and responsibilities.
- Encryption to safeguard sensitive financial data during storage and transmission.
Additionally, given increased awareness of data security and privacy, financial organizations should stay updated on data privacy laws (such as GDPR or CCPA) to ensure they remain in compliance. This can be done by conducting regular assessments to help identify and address potential privacy risks, ensure the protection of customer data, and maintain customer trust in the organization's data handling practices.
Take the next step to achieve data quality excellence.
Ultimately, it’s important to remember that enforcing data quality is not a one-time task, but rather an ongoing process that requires continuous monitoring, evaluation, and improvement.
To do so, financial services organizations must allocate the necessary resources and effort to establish the foundational tools, procedures, and guidance—and prioritize their continued implementation. This will give financial services organizations the ability not only to maintain high data quality standards but also to better maintain their competitive edge in the crowded financial services industry.
Want to read more about how to sharpen your business’s competitive edge? Then make sure to download our comprehensive guide, 5 Steps To Take Your Financial Services Business To the Next Level, here:
1. TechRepublic (2022, Sept 9) "How banks can benefit from implementing data governance" Retrieved from https://www.techrepublic.com/article/data-governance-in-banking/
2. Finextra (2023, Apr 20) "The importance of high-quality data for risk decisions" Retrieved from https://www.finextra.com/blogposting/24083/the-importance-of-high-quality-data-for-risk-decisions