Last year US banks spent $67 billion on new technology but with revenues growing in low single-digits and return on assets below cost of capital, return on these investments have often disappointed. This disconnect clearly demonstrates that there is more to becoming a data business than buying some shiny new tech. Without a data strategy, led from the top and encompassing the whole bank, tech investments run the risk of becoming expensive pet projects, driven by fads and siloed thinking. Which is probably why 85% of them fail!
We’ve all been seduced by the latest technology and have invested in things that don’t really add value or fit into our lives (
minidisk player anyone?!). Businesses can fall into the same trap. Promising new technologies are pitched as perfect solutions for problems that don’t really exist or as
seemingly simple fixes to complex ones. They can deliver innovation and some early wins. Speed is critical and successfully integrating a new technology can short-cut years of development and leapfrog competition. However, for long-term ROI, new approaches must be scalable and repeatable, and this is where many technology investments fail.
Driven by the need for speed, many banks put business unit, product or geography heads in charge of tech investment decisions. This leads to tech solutions designed for specific problems in specific departments. Digitising one customer touchpoint or automating a single process is not repeatable and does not add value to the data or the wider organisation.
Corporate innovation teams are often established to ‘break the mould’ and challenge convention using new technologies. Many banks have essentially tried to recreate fintech start-ups on the fringes of their organisations to do this. But all they have done is replicate the same problems the fintechs face. They deliver narrow aspects of the bank’s functions well, with great customer experience. But they cannot scale.
Scaling even successful projects can be a technical and cultural nightmare. Solutions that worked well with a few thousand queries crumple when faced with tens or even hundreds of thousand needed to work at whole-bank scale. Individual departments resist systems ‘not invented here’ and jealously guard access to their data. Even the most successful tech implemented in this way runs the risk of becoming an expensive white elephant. Meanwhile, tech moves on and another manager seeking to stake their innovation credentials submits a new business case to invest in the latest tech trend.
The whole point of becoming a data business is to become better at turning insights into business outcomes. Technology must capture, analyse and exploit data in ways that quickly lead to measurable progress on reduced or avoided costs. Digitisation should leverage data to create new value either as direct revenue or through enhanced customer experience. An organisation-wide data strategy is the critical first step which creates the framework though which the bank-wide application of technology can be correctly aligned to make this happen. Unless data is shared, orchestrated and exploited across the business, adding trendy apps will not deliver returns on investment or on data as an asset. Any gains from point-solutions are likely to be relatively small and the costs high. Worse, multiple incremental projects add significant cost and fragment data and systems into incompatible silos.
To cut through the demands and competing priorities of different functions and departments across the bank, CEOs need to lead. To be effective, and deliver at speed and scale, most are going to need someone with a foot in both camps. Someone who understands data and how to manage data scientists, but also the business and the wider commercial implications of the data strategy. Time for the Chief Data Analytics Officer to step up…
