industry news SME profile Thursday 10 Mar 2022 @ 15:56 Four steps to spring clean public sector databases
Four steps to spring clean public sector databases:
Better decision making, user experience and waste prevention
By Barley Laing, the UK Managing Director at Melissa
The challenging last two years have seen many in the public sector understandably focus on providing core services to the public. This has meant a large number have neglected important ‘back office’ functions, such as ensuring the cleanliness of their databases.
It’s only by maintaining clean databases of those that use their services that public bodies can avoid wasting time and precious budget on inaccurate communications, and continue to provide a standout service to the public.
Accurate data on users also enables the public sector to obtain valuable insight, such as a single customer view (SCV). This can be used for better targeting, including personalisation with communications. In fact, best practice decision making is based on high quality, reliable user data, because the insight that it helps to provide makes it possible to create informed decisions; for example, on the future of a service, or the creation of a new one.
Data decays rapidly
Unfortunately, 91% of organisations have common data quality issues, with user contact data degrading at 25% a year without regular intervention. Also, 20% of addresses entered online contain errors, such as spelling mistakes, wrong house numbers and incorrect postcodes.
The good news is that incorrect contact data, such as name, address, email or telephone number, can be easily fixed, often with simple and cost-effective changes as part of their data quality regime.
Four steps to spring clean databases:
- A standout option as part of the cleaning process is an address autocomplete or lookup service. Using such a tool it’s possible to gather accurate address data in real time at the onboarding stage - vital in an age when many people are completing contact forms on small mobile screens where they are more likely to make errors. These services also enable those in the public sector to deliver a standout service by reducing the number of keystrokes required—by up to 81 per cent—when typing an address. This speeds up the onboarding process, reducing the probability of the user not completing an application or making a purchase.
- The issue of duplicate data can be a problem. It’s commonly caused by mistakes in contact data collection at different touchpoints, and when two departments merge their data. It’s costly in terms of time and money when communicating with the public, particularly via direct mail, with the wasted printing and mailing costs. It can also adversely impact on reputation – especially when people receive more than one of the same printed communication. They will see this as a misuse of public money, at what is a challenging time for public sector finances. To prevent this, source an advanced fuzzy matching tool to deduplicate data. By using such a service and merging and purging the most difficult records you save money with communications, improve the user experience and effectively deduce a SCV.
- Data suppression strategies help those in the public sector highlight those who have moved - commonly called goneaways - or are otherwise no longer at the address on file. In addition, suppression services that include deceased flagging ensure mail and other communications are not sent to those who have passed away, upsetting their friends and relatives. Using these suppression strategies is not only the ethical thing to do, but helps those in the public sector to save money and protect their reputation.
- Once public bodies have undertaken the basics to ensure accurate customer data, artificial intelligence (AI) can add even greater value to the data they hold. For example, a type of machine learning called semantic technology can readily deliver high value, in-depth intelligence on users of your services. Semantic technology, or semtech, associates words with meanings and recognises the relationships between them. It works by delivering powerful real time connections between records, combining the missing pieces of data to support an informed decision about the content of a communication to a user.
If those in the public sector have not done so already, improving their practices with data – to ensure it’s clean and accurate - should be one of the first things that they turn their attention to as the health crisis abates. Making a concerted effort to spring clean their databases will make sure they deliver stand out, cost effective communications to the public, and ensure they have access to the appropriate insight to improve their services. To do this they must capture accurate data at the onboarding stage, particularly address data; undertake deduplication of records; adopt suppression strategies; and investigate possible AI technologies to improve data enrichment and learnings from the data.
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