By Barley Laing, the UK Managing Director at Melissa
In an open banking and increasingly digital world, large amounts of customer data are being collected by financial institutions.
In the age of AI this provides an opportunity for virtually anyone at any level in financial services to access AI tools and make instant learnings to support better organisation-wide decision making. For example, in delivering insight that aids upselling and the delivery of a standout customer experience.
Data democratisation necessitates organisation-wide cultural change
However, to get to this point requires a sea change in organisational culture that encourages what is commonly called ‘data democratisation’.
This entails empowering everyone within the organisation, such as marketers, customer services teams and beyond – not just the IT and data departments – to have access to customer data, the necessary tools to derive insight from it, and the ability to act on it.
In an AI world where, in many cases, coding is no longer necessary for data analysis, data democratisation is even easier to deliver, and more important.
Data democratisation is proven to drive performance by breaking down information silos, speeding up decision making, and encouraging a culture of innovation that improves financial and operational results. According to Forrester insights-driven organisations with democratised data access are around eight times more likely to report growth of 20 per cent or more, compared to those that allow limited data access to their teams.
Significantly, data democratisation will help to provide a competitive advantage, by enabling financial institutions to react quickly to market changes against new, agile fintech competitors.
Accurate customer data is essential for data democratisation
For data democratisation to work effectively requires the provision of up-to-date customer records to staff. This not only builds company-wide trust in the data, but it enables financial organisations to form an accurate single customer view (SCV) and deliver personalised customer experiences.
Additionally, with AI tools playing an increasingly key role in aiding the data democratisation process, it’s critical to recognise that they can only add value if they have access to quality customer data. Otherwise, AI ‘hallucinations’ can occur which can lead to poor outcomes.
Dedupe databases
It’s not uncommon to have data duplication rates of 10 to 30 per cent on customer databases. This usually occurs when errors in contact data collection take place at different touchpoints, when two departments merge their data, or when amalgamating datasets after a business acquisition.
Data democratisation cannot effectively take place with data duplication on databases.
Duplicate data can be eliminated using an advanced fuzzy matching tool. Such a service can merge and purge the most challenging records and create a ‘single user record’, which delivers an optimum single customer view (SCV) for improved marketing activity, which supports data democratisation.
Adopt data suppression practices
Data suppression or data cleansing is an important part of data cleaning, and therefore supporting the data democratisation process, because these services highlight those customers who have moved or are no longer at the address on file. It’s strongly advisable that the suppression tool sourced has access to the National Change of Address (NCOA) database. Available in the UK and US, and some other countries, it highlights those who have moved, and provides their new address.
In addition to eliminating inaccurate addresses, data cleansing services can also include deceased flagging to prevent mail and other communications from being sent to individuals who have passed away — helping to avoid causing distress to their family and friends.
Undertaking data suppression, along with the implementation of other data quality processes operating as web services, SaaS or on-premise, reduces potential bottlenecks caused by the IT department manually cleaning or verifying data, which hampers effective data democratisation.
Ensure compliance and avoid risk
Another benefit of having quality customer data is that it supports financial institutions in their adherence with data compliance regulations, and therefore helps them to avoid potential fines and reputational damage. This way confidence grows in the data that’s shared across the organisation, with risk minimised.
Furthermore, with quality customer data the opportunity for fraud is diminished, because it thrives where there is a haphazard approach to delivering data accuracy – something bad actors can take advantage of.
Therefore, to be in a position to support data democratisation, while at the same time ensure regulatory compliance and avoid fraud, it’s important to implement processes that aid the delivery of quality customer data in real time.
Consider address lookup or autocomplete
A good place to start is by using an address lookup or autocomplete service at the customer onboarding stage. These improve efficiencies because they automatically deliver in real time a properly formatted, accurate address when the user starts to input theirs, leading to the reduction in the number of keystrokes required by up to 81 per cent when inputting an address. As a result, the onboarding process is speeded up, enhancing the entire customer experience, making it more likely that an application or purchase will be completed. Similar tools can accurately capture email addresses, phone numbers, and names at the initial point of contact.
In summary
It’s time for financial institutions to embrace data democratisation. To effectively deliver it organisation-wide they must ensure they have the right culture embedded throughout, and have the data quality tools in place to make it a success. Doing so will ensure they stay ahead of the competition and deliver long term profitability.
Image provided by Melissa
