Along with the customer’s money, financial service providers also deal with vast amounts of data. They use data to assess a customer’s creditworthiness, prevent fraud, analyse the market and so on. Hence data must meet high quality standards.
If the data is reliable, the decisions driven by them can benefit the business and its customers. However, bad-quality data can have disastrous effects. The fall of Lehman Brothers is probably one of the biggest examples of this. 91% of 500 data professionals surveyed agreed that data quality affects their company performance.
Data quality also influences a business’s ability to stay current especially when it’s time for technology and system upgrades. Poor data quality can lead to inconsistencies, duplication, data loss and many more issues. Let’s take a closer look.
Pre-upgrade Data Quality challenges
Many financial institutions still operate on legacy systems with poor quality data. This can make it harder for financial service providers to plan system upgrades. It also compounds duplication and inconsistencies. The key challenges to be addressed are:
Existence of decayed data
Verifying data only at the incoming stage is not enough. Data is known to decay with time. At the time of system upgrades, the system may not recognize the existing record. Instead, it may generate duplicate records with updated information.
Inconsistent formatting
Collecting data from multiple sources often leads to differences in formats. For example, a customer’s mobile number may be saved in a 10-digit format or an 11-digit format. This seems minor but it can cause compatibility issues during system upgrades.
Overcoming Pre-Upgrade Data Quality Challenges
Data preparation is key to smooth system upgrades. It begins with data profiling and cleaning.
Data profiling
Profiling data helps identify data structures and recurring patterns. It also uncovers dependency relationships. Profiling may also help highlight potential issues that can affect system upgradation.
Batch validation
Existing databases must be validated before implementing any upgrades. Regular batch validation can help eliminate decayed data and identify inconsistencies. There are a number of data-cleaning tools that can be used to automate this process. Further, data formats must be standardized across all systems and databases.
Common Data-Quality Challenges Faced During Upgrades
Most system upgrades involve moving data from one system to another. This can cause issues such as:
Unsynchronized data
Poor data quality can make it hard to maintain data integrity. For example, one system may hold customer data from 2015. Another may hold slightly different data from 2017. Bringing these two data sets together could lead to misalignment. This lowers the overall quality of data. Such issues may also arise when systems are upgraded at different times.
Data loss and corruption
Moving data during a system upgrade puts the organization at a high risk of losing data. Sometimes data transfers may be incomplete. For example, certain fields of a table may be locked. In other cases, it may be a human error. A study found that 43% of businesses that suffer from severe data loss never reopen. Tools that are not integrated with the existing systems may also corrupt data.
Overcoming Data-Quality Challenges Faced During Upgrades
Having a good data governance plan can help maintain data quality during system upgrades. There are 2 key factors to be considered.
Multi-stage testing
Validating data at every stage makes it easier to identify outliers. In turn, this makes it easier to address issues in data synchronization. Testing also helps identify data and tool incompatibility issues at an early stage. These may then be fixed to prevent data corruption.
Backup and recovery planning
Automating regular data backups goes a long way to lowering the risk of data loss. This can be implemented for on-premise and cloud-based data. These backups must have version controls. This allows the organization to control the point in time from which they want to recover data.
Post-Upgrade Data-Quality Challenges
As illustrated above, data quality issues can arise even after system upgrades. Some data may be lost in migration. In other cases, duplicates may arise. These issues can lower user trust in the databases. It may also put the business at risk of penalties for non-compliance with data regulations.
Data quality may also suffer if people using the new system users do not know how to access and use the data. In a recent survey, 56% of the respondents cited a lack of documentation as the most difficult challenge for their data operating model.
Maintaining Data Quality After the Upgrade
Monitoring data quality and user training helps maintain data quality after the upgrade.
Automating continuous monitoring
Running post-upgrade data validation checks helps maintain data accuracy. It also ensures that the data stays complete and consistent. Whenever possible, these validation checks should be automated. Automated data monitoring guarantees regular checks. In addition, it helps the organization to detect data discrepancies in real time.
Training and documentation
Human errors are a common cause of data quality issues. For example, if users copy data to create their own repositories, maintaining consistency may become harder. Training can help avoid this. All data users should know how to access and use data. They should also be aware of the organization’s data management practices. Supplementing this with documented policies gives users a reference point and minimizes the risk of error.
Conclusion: Strengthening Your Data Quality During System Upgrades
System upgrades are vital for financial service providers to stay competitive and meet modern demands. However, these upgrades often come with data quality challenges that can disrupt operations, lead to compliance issues, and erode customer trust. Whether it’s dealing with decayed data, synchronization problems, or user errors, addressing these challenges at every stage—before, during, and after an upgrade—is essential.
A robust data governance strategy is key to mitigating risks and ensuring a smooth transition. By investing in comprehensive data profiling, validation, and automated monitoring, businesses can maintain high data integrity and prevent costly disruptions. Moreover, continuous training and documentation empower your team to manage data effectively, reducing the likelihood of human error.
With Melissa’s suite of data quality solutions, businesses can simplify this process. Our tools offer real-time validation, deduplication, and data enrichment to ensure seamless system upgrades and maintain high data integrity. Ultimately, quality data drives better business decisions, helping you provide exceptional customer service and maintain a competitive edge.
Ready to enhance your data quality? Request a demo today and see how Melissa can support your next system upgrade.