Guest blog: Want effective AI in 2024? Have clean data as a New Year’s resolution
Artificial intelligence (AI) is evolving at a rapid rate. It’s been changing the way we work and adding value in ways that were unimaginable only a year ago.
With many of those in technology looking to ramp up their AI efforts in 2024, it’s important to recognise that AI tools are only as good as the data they have access to.
AI will not add any value where the data is incorrect or out of date, in fact quite the opposite. Poor data fed into an AI model will lead to poor results.
For example, if someone living in a low crime rate part of a city applies for home insurance, and the postcode the insurer has on their system for them is in an area of the city with a higher rate of crime, this will impact on the premium calculated by AI. A higher than expected quote because of incorrect data may encourage the customer to source a quote elsewhere.
Data decay is an issue
A big problem for all organisations is that data decays quickly - something that has a huge impact on the effective implementation of AI. The causes of this include people moving home, death and divorce. It’s the main reason why user contact data lacking regular intervention degrades by as much as 25 per cent a year. Additionally, 20 per cent of addresses inputted online contain errors; these include spelling mistakes, wrong house numbers, and incorrect postcodes, mainly due to the typing of contact information on small mobile screens. Such factors are why 91 per cent of organisations have common data quality problems.
Having verification processes in place at the point of data capture, and when cleaning held data in batch, avoids the scourge of incorrect data. The good news is the delivery of such practices usually involves simple and cost-effective changes to the data quality process.
Use address autocomplete
A valuable piece of technology to use at the customer onboarding stage is an address autocomplete or lookup service. It delivers accurate address data in real-time when onboarding new customers by providing a properly formatted, correct address when they start to input theirs. It also cuts the number of keystrokes required, by up to 81 per cent, when entering an address. This accelerates the onboarding process and diminishes the probability of the user not completing an application to access a service.
This approach to first point of contact verification can be extended to email and phone, so that these valuable contact data channels can also be verified in real-time.
Deduplicate data
Data duplication is a significant issue, with the average database containing 8-10 per cent duplicate records. This occurs for various reasons, for example when two departments merge their data and mistakes in contact data collection occur at different touchpoints. The duplication of data not only has the potential to confuse an AI application, but it adds cost in terms of time and money, particularly with printed communications, and it negatively impacts on the sender’s reputation.
Using an advanced fuzzy matching tool is the best approach to merge and purge the most challenging records to create a ‘single user record’ and obtain an optimum single customer view (SCV) that AI can make learnings from. Also, organising contact data in this way will maximise efficiency and reduce costs, because multiple outreach efforts will not be made to the same person. A further benefit is that the potential for fraud is lessened because a unified record will be established for each customer.
Commence data suppression / cleaning
Data suppression, or cleaning, using the appropriate technology that highlights people who have moved or are no longer at the address on file, is a crucial part of the data cleansing process, and therefore in supporting efforts with AI. Along with removing incorrect addresses, these services can include deceased flagging to stop the distribution of mail and other communications to those who have passed away, which can cause distress to their friends and relatives. By applying suppression strategies organisations can save money, protect their reputations, avoid fraud and support their AI efforts.
Obtain a SaaS data cleaning platform
Evolving technology means it’s never been easier or more cost-effective to deliver data quality in real-time to support AI and wider business efficiencies. It’s possible to source a scalable data cleaning software-as-a-service (SaaS) platform that doesn’t require coding, integration, or training. This technology cleanses and corrects names, addresses, email addresses, and telephone numbers worldwide. Also, records are matched in real-time, ensuring no duplication, and data profiling is provided to help identify issues for further action. A single, intuitive interface provides the opportunity for data standardisation, validation, and enrichment, resulting in high-quality contact information across multiple databases. It can deliver this with held data in batch and as new data is being gathered. Also, such a platform can be accessed on-premise, if required.
The quality of data you have access to has a direct correlation on the success of your AI models. If the quality of data is good AI can provide your business with a competitive edge. If it’s poor quality it will lead to unreliable predictions and poor decisions. It’s why implementing best practice data quality procedures must be a New Year’s resolution for those serious about harnessing the power of AI in 2024.
This guest blog was written by Barley Laing, UK Managing Director at Melissa. To learn more about Melissa, please visit their LinkedIn and Twitter page.
Digital Devices updates
Sign-up to get the latest updates and opportunities from our Digital Devices programme.
Authors
Barley Laing
Barley Laing joined Melissa and set up the UK office in 2014 during an exciting expansion phase of the California headquartered company.