{"id":1921,"date":"2026-04-01T16:59:50","date_gmt":"2026-04-01T16:59:50","guid":{"rendered":"https:\/\/datadecoded.com\/ldn\/?p=1921"},"modified":"2026-04-01T16:59:50","modified_gmt":"2026-04-01T16:59:50","slug":"how-to-prevent-damaging-ai-hallucinations","status":"publish","type":"post","link":"https:\/\/datadecoded.com\/ldn\/news\/how-to-prevent-damaging-ai-hallucinations\/","title":{"rendered":"How to prevent damaging AI hallucinations"},"content":{"rendered":"

News article by Barley Laing, the UK Managing Director at Melissa<\/a><\/em><\/p>\n

Fast evolving AI is driving real time business success. In particular, in enhancing data-driven insight which is having a positive impact on sales and productivity, while improving the customer experience.<\/p>\n

However, in the rush to adopt AI many organisations haven\u2019t taken into consideration how having poor quality data on customers could lead to gibberish, or worse, biased and inaccurate outcomes.<\/p>\n

These AI induced \u2018hallucinations\u2019 can generate poor results.\u00a0To provide an example, poor personalisation could be delivered by AI having access to incorrect customer data, such as an incorrect name or address, which would have a negative impact on sales and the customer experience.<\/p>\n

Data decay is a key challenge<\/strong><\/p>\n

A significant factor impacting on the effective implementation of AI is data decay. Customer contact data lacking regular intervention degrades at 25 per cent a year, as people move home, die and get divorced. Also, 20 per cent of addresses entered online have errors; these include spelling mistakes, wrong house numbers and incorrect postcodes.<\/p>\n

To prevent the scourge of inaccurate contact data it\u2019s important to have verification processes in place at the point of data capture, and when cleaning held data in batch. This involves simple and cost-effective improvements to the data quality process.<\/p>\n

Address autocomplete \/ lookup for data accuracy <\/strong><\/p>\n

Using an address lookup<\/a> or autocomplete service is a valuable piece of technology to use at the customer onboarding stage. These deliver accurate address data in real time during customer onboarding by returning properly formatted, correct addresses as the consumer starts to enter theirs.<\/p>\n

This way the number of keystrokes required is reduced by up to 81 per cent when typing an address, resulting in the entire onboarding process being speeded up, which reduces the probability of a purchase not being completed.<\/p>\n

This first point of contact verification approach can also be applied to email and phone, enabling these valuable contact data channels to be verified in real time.<\/p>\n

Geocoding for a fast and standout delivery experience <\/strong><\/p>\n

Once you are confident you have the correct address you can ensure accurate delivery and improve the customer experience via geocoding<\/a>, because it helps to provide a consistently accurate delivery service. It does this by taking a verified postal address and enriching it by appending rooftop latitude and longitude location coordinates. This highly accurate location information speeds up the delivery process, reduces shipping costs, and prevents expensive, in monetary and customer experience terms, \u2018return to sender\u2019 scenarios.<\/p>\n

Eliminate duplicate records<\/strong><\/p>\n

With duplicate rates on customer databases of 10 to 30 per cent not uncommon, data duplication is a significant issue for many organisations. It often occurs when two departments merge their data, integrating datasets after a business acquisition, and when errors in contact data collection take place at different touchpoints. Data duplication can confuse AI applications, and add cost in time and money, particularly with printed communications. Also, the resulting duplicate messaging has the potential to damage the sender\u2019s reputation in the eyes of the recipients.<\/p>\n

Access to an advanced fuzzy matching tool to deduplicate data can help. It can merge and purge the most challenging records to create a \u2018single user record\u2019 which delivers an optimum single customer view (SCV) that AI can make learnings from.<\/p>\n

This way multiple outreach efforts will not be made to the same person, maximising efficiency and reducing costs. Furthermore, the potential for fraud is reduced because a unified record will be established for each customer.<\/p>\n

Carry out data cleansing<\/strong><\/p>\n

Data cleansing<\/a> – also known as data suppression – highlights people on a database who have moved or are no longer at the address on file. It\u2019s a vital element of the data cleaning process, and consequently in supporting efforts with AI.<\/p>\n

In addition to removing incorrect addresses, these services can include deceased flagging to prevent mail and other communications from being sent to people who have passed away, which can cause distress to their friends and relatives. Overall, suppression strategies enable organisations to save money, protect their reputations, avoid fraud and support their AI efforts.<\/p>\n

Delivering data quality is straightforward <\/strong><\/p>\n

Ensuring data quality in real time to support AI and wider business efficiencies has never been easier. A scalable data cleaning software-as-a-service (SaaS) platform can be deployed within hours, and as a stand alone platform requires no coding, integration, or lengthy training. This technology can cleanse and correct names, addresses, email addresses, and telephone numbers worldwide using official data sources, including those from government agencies, credit agencies, and utility companies. It can do so as new data is being collected, and with held data in batch on-premise. Such a platform is available not only as a SaaS, but also as a cloud-based API and through connector technologies such as Microsoft Dynamics and Salesforce.<\/p>\n

In summary<\/strong><\/p>\n

AI has the ability to provide organisations with a competitive edge, but this is dependent on the quality of data fed into the AI models. Inaccurate data increases the risk of AI \u2018hallucinations\u2019, resulting in unreliable predictions and consequently poor outcomes. Therefore, to maximise the success of your AI efforts apply best practice data quality procedures. Doing so will boost your sales and improve the customer experience.<\/p>\n

Explore Melissa\u2019s range of award-winning global data quality, address and identity verification services on stand D30 at Data Decoded LDN, 22-23rd<\/sup> April 2026.<\/p>\n","protected":false},"excerpt":{"rendered":"

News article by Barley Laing, the UK Managing Director at Melissa Fast evolving AI is driving…<\/p>\n","protected":false},"author":2,"featured_media":1923,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1921","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"acf":[],"yoast_head":"\nHow to prevent damaging AI hallucinations - Data Decoded LDN<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datadecoded.com\/ldn\/news\/how-to-prevent-damaging-ai-hallucinations\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to prevent damaging AI 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