WIREDGOV

The cumulative cost of inaccurate data is 15 per cent to 25 per cent of revenue for most organisations, according to MIT Sloan. This is not surprising because poor quality data wastes resources, undermines everyday operations and communications – particularly personalised communications to citizens.

It causes inefficiencies in both time and money in the creation and delivery of communications that often aren’t relevant or might not even reach the intended user. It frequently leads to data analysts devoting more time trying to sort out data, and source where the issues are, rather than analysing it. In fact, according to recent research, analysts can often spend 60 per cent of their time verifying, correcting and reworking data.

Bad decision making is caused by inaccurate user data. For instance, decision making using poor quality data to inform the future of a service, or the creation of a new one, will be compromised, with negative implications for effective resource allocation. This is a particularly important consideration in the age of AI where these tools are only as good as the data they have access to. If the existing data is incorrect or out of date your AI tool will not add any value. In fact, quite the reverse.

Additionally, inaccurate or duplicate communications sent to citizens based on poor quality data will damage your reputation. They won’t be pleased to see public money being wasted in this way.

Beyond bad decision making and a negative impact on your reputation, having inaccurate user data will most likely mean that you are not know your citizen (KYC) or anti-money laundering (AML) compliant - something which puts your organisation at a greater risk of fraud.

It is why data on users is one of most valuable assets organisations have, particularly as it can deliver an all-important single citizen view (SCV), but only if it’s clean and up to date. This informs personalised communications, and the creation of relevant products and services.

Data decay

According to Gartner, customer contact data deteriorates on average at three per cent a month, and roughly 25 per cent a year, as people move home, divorce or pass away. The public sector is impacted just the same as the private sector by this level of data decay.

Therefore, with data constantly degrading, it is essential to have data cleaning processes in place, not only at the onboarding stage, but to clean held data in batch. All that’s needed is simple, cost-effective changes to the data quality regime.

Making such changes is especially important for the public sector with the new government, as they seek to demonstrate they are driving efficiencies across the board; particularly with their data resources.

Obtain correct data at the user onboarding stage

The best place to start is to source accurate contact data at the user onboarding stage using an address lookup or autocomplete service. It’s these tools that deliver accurate address data in real-time by delivering a properly formatted, correct address when the user starts to input theirs. The onboarding process is speeded up with the number of keystrokes required cut by up to 81 per cent when entering an address, which improves the whole experience, and makes it considerably more likely that the user will complete an application or purchase. The good news is this first point of contact verification can be extended to email, phone and name, so this valuable contact data can also be verified in real-time.

Undertake data deduplication

In the public sector duplicate rates of 10 to 30 per cent on user databases are not uncommon. Such data adds cost in terms of time and money, particularly with printed communications and online outreach campaigns, and it can have a negative impact on the sender’s reputation. An advanced fuzzy matching tool to merge and purge the most challenging records is the answer. It leads to the creation of a ‘single user record’, which helps to deliver a SCV, with the insight from this used to improve communications. Efficiency savings are made because multiple communication efforts will not be delivered to the same person. Furthermore, the likelihood of fraud is reduced with a unified record established for each user.

Data cleansing and suppression

Embarking on data cleansing or suppression activity to highlight those who have moved or are no longer at the address on file is crucial. Along with removing incorrect addresses, these services can include deceased flagging to prevent the delivery of mail and other communications to those who have passed away, which can cause distress to their friends and relatives. Using suppression strategies ensures that those in the public sector save money by not distributing inaccurate messaging, safeguarding their reputations, while enhancing their targeting efforts to overall improve the user experience.

Source a data cleaning platform

Delivering data quality in real-time to support better decision making, improve the user experience and overall generate wider organisational efficiencies, has never been more straightforward. A cost effective, scalable data cleaning software-as-a-service (SaaS) platform that can be accessed in a matter of hours and doesn’t require coding, integration or training can be simply sourced. This technology can cleanse and correct names, addresses, email addresses, and telephone numbers worldwide. Records are matched, ensuring no duplication, and data profiling is provided to help identify issues for further action. A single, intuitive interface offers the opportunity for data standardisation, validation and enrichment, which ensures high-quality contact information across multiple databases. This can be delivered as new data is being gathered and with held data in batch. As well as SaaS, such a platform can be deployed via cloud-based API, connector technology like Microsoft SQL Server, and on-premise.

In summary

Poor quality user data causes a wide range of issues for those in the public sector, from incorrect targeting, which delivers a negative experience and reputational damage, to poor decision making, which causes significant inefficiencies within the organisation. Having inaccurate data can also increase the opportunity for fraudulent activity. Putting ongoing data cleaning processes in place is the way forward to provide a standout user experience, deliver efficiency savings and reduce the opportunity for fraud.

Feel free to check out and download our latest informative guide on WiredGov below:
Reducing the Cost & Risk of Poor Quality Data in the Public Sector

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