How poor data is holding your organisation back - and how to fix it

We discovered that 84 per cent of organisations struggle with inaccurate or duplicate data in recent research we carried out and published in the state of enterprise data quality 2025 report.

Our study highlighted numerous data-related challenges organisations face that cause data quality issues. The biggest data problem they encountered was having outdated contact information, cited by 32 per cent of respondents. In second place was compliance with evolving data regulations, raised by 25 per cent of interviewees, with incomplete customer profiles third at 23 per cent. Each of these issues are amplified when records are scattered across systems without real-time synchronisation.

Additional data-related challenges included identity fraud or impersonation which was cited by 17 per cent of respondents, duplicate records by 15 per cent, and difficulty in integrating multiple data sources was raised by 12 per cent. Taken together, these lower-scoring areas can carry a greater cost than more visible pain points. Also, fraud losses tend to spike in isolation, while manual deduplication efforts often accumulate without warning or budget allocation. 

Targeting the root causes of data problems and turning ad hoc firefighting into a continuous, rules-driven process is the way forward. To achieve this necessitates leveraging technology that supports data quality.

Implement address verification processes

A good starting point is to use technology that delivers address verification. This will ensure contact data accuracy on an ongoing basis. Only clean, verified addresses enable the fast, frictionless shopping experience consumers expect. Also, clean customer data can be effectively analysed to gain vital customer insight that can be used to keep them happy, with personalised communications and offers. In addition, on the ID side, it's much easier to match and verify identities across multiple sources with up-to-date and standardised customer addresses. This means verifying the accuracy and legitimacy of an individual's address should be the first step in any identity-related process, with discrepancies between claimed and official records serving as red flags for fraud or compliance risk.

Address lookup

Gathering accurate customer data should begin at the onboarding stage with an address lookup or autocomplete service. Tools such as these provide correct address data in real-time by delivering a properly formatted, correct address when the user starts to enter theirs. A significant advantage of using such a service is the number of keystrokes required is cut by up to 81 per cent when entering an address, speeding up the onboarding process, enhancing the entire experience, making it much more likely that an application or purchase will be completed. Additionally, similar tools can accurately capture email addresses, phone numbers, and names at the first point of contact.

Deduplicate data

Organisations that lack data quality initiatives often see duplicate rates of 10–30 per cent on their customer databases. This is costly in terms of time and money, particularly with duplicate printed communications, but also with other marketing activity such as online outreach campaigns. Furthermore, with customers receiving two or more of the same communication there's reputational damage to the sender. To effectively deduplicate databases it's important to obtain an advanced fuzzy matching tool that can merge and purge the most challenging records. This enables organisations to create a 'single user record' and source an optimum single customer view (SCV). Such insight is vital in improving customer communications.

Data cleansing

Conduct data cleansing or suppression activity to identify individuals who have moved or are no longer at the listed address. As well as removing incorrect addresses these services often include deceased flagging to prevent the delivery of mail and other communications to those who are deceased, which can cause distress to their family and friends. By avoiding inaccurate communications, suppression strategies reduce costs, protect organisational reputation, and improve targeting to enhance the customer experience.

SaaS data quality platform

Delivering data quality in real-time to support wider organisational efficiencies and provide a better customer experience has never been easier. A scalable data cleaning SaaS platform can be deployed within hours and requires no coding, integration, or training. This technology is able to cleanse and correct names, addresses, email addresses and telephone numbers worldwide from official data sources - government agency, credit agency, and utility companies. It can do so as new data is being collected and with held data in batch. Such a platform is not only available as a SaaS, but can also be accessed as a cloud-based API, via connector technology like Microsoft SQL Server, or on-premise.

In summary

Data quality has evolved from an IT afterthought into a core strategic business priority. Organisations that adopt real-time, automated data verification are better equipped to reduce risk and prevent fraud, speed up customer onboarding, and drive long-term growth.