Effective data governance and compliance depend on data quality

Delivering global data governance and compliance is crucial for financial institutions. Unfortunately, there are many in this sector that aren’t as rigorous at implementing it as effectively as they should be.

They may be less rigorous in ensuring their data processes comply with regulations such as GDPR, the EU’s Second Payment Services Directive (PSD2), and the Digital Operational Resilience Act (DORA), for example.

This is backed up by research that reveals while a large number of IT decision makers in financial services recognise the importance of data security, at 86%, only half are confident in their organisation’s compliance with existing global regulations.

Inaccurate customer data is a challenge

Inadequate data governance and compliance is often driven by poor-quality customer data, leading to regulatory fines, reputational damage, a negative customer experience and an increased risk of fraud.

The growing vulnerability to data breaches caused by cyberattacks, often powered by AI, is commonly caused by having access to poor quality customer data.

With data breaches costing financial institutions on average $5.56 million in 2025 - a figure that’s 25% higher than the global average across other industries - they are best avoided.

Establish data quality processes

A big step towards best practice global data governance and compliance is ensuring rigorous customer data quality processes are in place both at the customer onboarding stage and within existing customer databases. It’s so important because the quality of customer data influences everything from end-to-end fraud prevention, to undertaking simple ID checks and effective personalisation.

Having accurate customer contact details, such as name, address, email, and phone number, helps to ensure a more reliable verification process. With this data automated ID verification technology can confidently cross-reference the information provided against official databases, or other authoritative sources, without discrepancies that could lead to false positives or negatives.

High quality customer contact data enables improved personalised communications by aiding the delivery of a single customer view (SCV). Using this insight tools such as AI can successfully deliver customer relationship management (CRM) campaigns, and upsell to customers.

Lookup and autocomplete

For best practice data governance and compliance use a lookup or autocomplete service, because these deliver efficiencies in collecting accurate customer data at the onboarding stage. For example, an address lookup tool can provide accurate address data in real time by automatically providing a correctly formatted address as the user begins entering theirs. This way the number of keystrokes required is reduced by up to 81 per cent when entering an address, which speeds up the onboarding process, enhancing the entire experience, making it more likely that an application or purchase will be completed.

Similar tools can accurately capture email addresses, telephone numbers and names at the first point of contact.

Identify and remove duplicate customer data

It’s not uncommon to have data duplication rates of 10 to 30 per cent on customer databases. It usually occurs when errors in contact data collection take place at different touchpoints, when two departments merge their data, or when integrating datasets after a business acquisition. Duplication is costly in terms of time and money, particularly with printed communications. For example, sending the same letter to a customer twice not only wastes money on printing and distribution, but can also damage your reputation as customers lose confidence in how their data is managed.

To prevent duplicate data an advanced fuzzy matching tool works well. It merges and purges complex records to create a ‘single user record’ which delivers an optimum single customer view (SCV) for improved marketing performance.

Data suppression and cleansing

Data suppression, also known as data cleansing, forms an important part of the data cleaning process and the broader data governance and compliance framework, because it helps to identify customers who have moved or are no longer at the address on file.

A key part of this approach requires having access to the National Change of Address (NCOA) database. It highlights those who have moved and provides their new address. It’s available in the UK and US, and some other countries.

Data cleansing services are not only useful to remove inaccurate addresses, but can also include deceased flagging to prevent mail and other communications from being sent to individuals who have passed away - to avoid causing distress to their family and friends.

Adopting suppression strategies therefore enables financial institutions to reduce costs, maintain customer trust, combat fraud, and support best practice data governance.

In summary

Data quality is central to effective data governance and compliance, enabling reliable ID verification, reducing exposure to fraud and regulatory fines, while delivering personalisation, upselling and trust to provide a superior customer experience.

At a time of increasing global regulations, along with sophisticated AI-driven fraud, organisations should implement best practice data quality processes. They are cost effective and typically require only small adjustments to existing procedures.