Last week, Lloyds Banking Group announced that it was destroying 6,240 jobs… to create 8,240 new jobs to support its digital transformation programme. Even more surprising, Lloyds said that 75 per cent of the new roles would be filled by existing staff. In a world where we increasingly hear about the data skills gap, it seems incredible that Lloyds could source such high-demand skills from the existing workforce, currently shaped to service legacy models of banking. Or is it?
This focus on leveraging more value from business data is not exactly a new priority for the banks, but the way they go about it is facing significant change. 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. Meanwhile, initiatives such as open banking are spurring incumbent financial institutions to think about the new world of competition they will face once consumers are using one interface to access all the financial products they have. 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.
The past ten years have shaken up the retail banking scene. From the financial collapse to the advent of apps, both consumers’ demands and banks’ offerings are vastly different than they were back in 2008. Matching the personal feel of regional branches with the technological capabilities to answer the growing digital demands of modern consumers. Being able to offer at once the immediacy of digital with the human touch will separate the banks of the future from those destined for extinction. It’s worth noting that the jobs destined for redefinition at Lloyds aren’t branch-based but form a part of the behind-the-scenes operations. Building customer loyalty is key in an increasingly competitive and globalised retail banking sector. As big tech try to take over the payments game, as with Amazon and Apple pay services, banks are prioritising building in the tech that will allow them to keep control of the customer relationship.
The large banks have both strengths and challenges with their own IT departments and legacy systems. Their huge customer databases and long-established reputations give them great influence. Yet some are still relying on a siloed business model that doesn’t allow the greatest value to be drawn from their data. Where these banks have merged and reformed over the years, there may be numerous profiles of one customer in the systems at any one time. As banks innovate and adapt, they often create a hybrid IT environment in which systems can’t or won’t ‘talk’ to each other. This leads to missed opportunities and the stilted customer journey that can’t identify the Joe Bloggs looking for a mortgage with the Joe Bloggs who just bought a house.
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.
But introducing the technologies that will enable the value of data to be harnessed is only one half of the answer. Financial institutions are also facing the challenge of operational structures which are siloed in much the same way as their data. When data science skills are brought into these enterprises, not enough time is spent exploring how the new team will interact with existing functions to drive innovation and growth. A fundamental cultural shift is often required, bringing data awareness into the job description of all employees in the organisation. Look at JP Morgan Chase for example. Recently the organisation announced that it is putting hundreds of new investment and asset managers – roles you wouldn’t necessarily associate with data science – through mandatory coding lessons. What’s more, those that are already part of the bank are supposedly going to be ‘upskilled’ – a fundamental example of how data science is making its way into the mainstream.
Within many organisations, inside and outside of the banking sector, there are hidden ‘data heroes’ driving for and implementing this change. Acting as 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. But they can only do so much alone. Bold moves like this recent decision from Lloyds shows that corporate action is starting to follow intention. The aspiration to fill new digitally-focused positions with 75 per cent from the existing workforce shows that organisations do have the scope to find data aware skills amongst current employees. The key is to acknowledge the skills which already lay dormant as interests and providing the training to unlock its value to the business.
As new regulations like open banking and the switch guarantee ensure customers have more power to vote with their feet, banks need to be thinking not just digital-first, but experience-first. The human touch might still be valuable, but banks will struggle to deliver this without ‘smart’ technologies such as customer data platforms to support them. If a high-quality hybrid of digital and in-branch customer experience is to become the norm, banks need to ensure their workforce is a hybrid of skills. The de-skilling of data science 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.
Ian Matthews, Data Evangelist, NGDATA
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