I can’t remember the last time my bank sent me a birthday greeting. That’s because they have never sent me a birthday greeting, even though I have banked with them since the 1990’s and they have all my contact details. In fact, they have a vast amount of data on me with a full financial history covering details of all my receipts and payments. Data has been referred to as the new oil for a decade now, and there are no shortage of articles extolling companies to use data to provide personalised customer experiences. So what are the issues that Financial Services (FS) firms face when trying to extract and process this resource?

• Legacy systems and fragmented data ownership. Many banks are the product of acquisitions and mergers and, while the headline savings from combining enterprises are broadcast loudly, the realities of integrating core banking systems are beset with challenges. As a consequence, customer data may still sit in different formats in different systems. In addition, different divisions within the same organisation (mortgage, credit card, online banking, etc) will probably have their own set of customer data and consents in their own systems. This leads to the problem of multiple customer records and data sets. For customer service units, the much-vaunted “single customer view”, if at all possible, usually involves having to open multiple windows on multiple systems.
• Limited connectivity. FS firms have rushed to roll out digital channels and apps but many of these have been standalone efforts that lack seamless integration with core banking systems or operations. As a result, the app data cannot be easily linked to the rest of the customer data.
• Not all data is created equal. There are two broad categories of data – structured data such as transactional data, master data, and static data. These data records sit mostly in various core banking systems and are fairly easy to work with. The majority of data is unstructured, which mainly refers to data in text format (e.g. call report notes, emails, texts) or ‘dark data’. Dark data comprises unstructured data from the Internet, social media, and voice and information from connected devices. Unstructured data is not easy to work with and requires modern data analysis tools, such as machine learning and other types of big data analytics. Vast amounts of unstructured data are created every day and this will only increase as the Internet of Things connects more and more devices.
• Silos, silos, silos. As well as having data trapped in various legacy systems, many financial institutions are unwilling or potentially restricted by regulators as to what and how to share data and results across functional areas. But making data analytics accessible across the wider organization is a critical factor in a successful data strategy – getting a ‘total customer’ view is not possible without access to all available information. A strong and democratic data governance approach is needed to ensure that data and the resulting insights are shared by the most important internal constituencies — marketing, retail, sales, customer service, operations, human resources and finance.
• Lack of management buy-in and no formal data analytics strategy. As with any strategic initiative, data-centricity needs to be driven by business leaders with budgets, funding and support from IT. Equally important is ensuring inter-departmental cooperation and coordination.
• Resource constraints and competing priorities. There is no shortage of projects for technology spend and many established institutions have to support upgrades to core-banking systems, regulatory compliance, PSD2 and digital channels rollouts.
• Permissioning. Can we really do that? Even before GDPR, firms have been subject to a raft of increasingly strident legislation regarding marketing and usage of data. Newer firms have been able to build broad permissioning into their account opening processes and they have all their data in one source. For established firms, customer consents, where they do exist, may be spread across the organization with no complete view of what each customer can be offered.

So it may be a while before I get a birthday message with a personalised gift voucher based on an analysis of my spending history. But without urgent progress, established FS firms will lose out to challengers and organisations that have the ability to use data to create personalisation and emotional engagement.