As a highly-regulated industry, banking has traditionally focused on locking data down and making it secure. But a new breed of digital-first consumers is now challenging financial services organisations to innovate. In the face of fintech start-ups, incumbent banks are racing to complete digital transformation programme to secure future business from these customers. But the real transformation is being driven by those with the mindset to unlock the value of data by sharing it between siloed teams.

This fundamental shift in strategy has led to the emergence of the ‘business data manager’. Often taken on as a secondary role with no conventional job description, these are the people who have the know-how to look at data with an analytical eye and identify the prospects, customers and potential products you never knew were out there. These are your organisation’s hidden, unsung data heroes.   

And despite what their title might suggest, if they even have it as a title, a business data manager is not an in-house data scientist. Still a specialised skill, data science tends to be outsourced and will only reach its true potential when built on your organisations existing analytics capabilities. So rather than focusing on analytics and building models, data managers have traditionally been unofficially allocated the responsibility because they understand how to get value from data in a way to improve the day-to-day operation of the business. By collaborating with IT, analytics and business intelligence teams they can put into practice the insights gained. But the fact remains that business-critical insights can only be driven by clean, reliable data. As the saying goes, garbage in means garbage out.

Since it is often an additional responsibility, and needs secondary stakeholders, the time dedicated to preparing data for use in analytics can waste a huge amount of resources. This not only slows down the process of getting insights and actioning them but means a revenue-generating exercise is instead seen as a cost centre for the business. And in an increasingly competitive sector, some more traditional companies are hesitant to innovate.

In an ideal world, as with many things, data capture and consumption would be seamlessly automated and complete without human intervention. As futuristic as that may sound, many banks are realising that, by harnessing the latest developments in automation and machine learning, they can take out a lot of the repetitive manual work that goes along with formatting a spreadsheet, for example. This is allowing them to catch up with the requirements of new data-sharing initiatives like open banking, as well as customer demands for banking apps and up-to-the-minute account information. For the banks, that frees up key resources to understand insights and explore new opportunities. By driving for and implementing this transition, business data managers can be indispensable, increasing the velocity and resource efficiency of spotting new opportunities and enabling the adoption of cutting edge advancements such as machine learning and artificial intelligence.

In general, the de-skilling of the process means employees from across business functions are able to add value to an organisation. By pooling their expertise, a project to leverage data might then generate a new product, or identify a different demographic of customers, which might even lead to a reimagined marketing strategy.

By automating the generation of insights that might once have taken weeks to develop, a professional with a few spare hours and a bit of initiative can now develop new strategies to help drive business growth. In a world of increasing competition and shrinking profit margins, it is vital that enterprises invest in technologies that quicken and simplify the process of finding new customers and identifying their product and service needs.  The organisations driving value from investments in digital transformation, big data, and analytics will be the ones with the business domain experts engaged with the data.

The change to internal processes for development opportunities is just the beginning. By giving a wider set of employees data science skills, a whole business can be revolutionised.  The true benefit of business data managers is the effect their work has on the customer experience. In a world where bank’s offers are already highly optimised, this could be the answer to securing long term loyal customers.