Brexit is not the Greatest Risk Facing the City, It's Technology and Data
- Ian Murrin, Founder and CEO at Digiterre
- 15.04.2021 06:30 pm data
Financial institutions in Europe are falling behind in the technological arms race. In twenty years of working with financial institutions of all kinds, we have noticed that the most successful firms recognised early on that technology and data would be game changers in the digital age and acted accordingly. On the buyside, investment managers with the strongest growth relative to their peers all invested early in institutional class technology and data management in the areas of trading, valuation, risk and operations. On the sell side, a striking geographic disparity has emerged with European banks under-investing in technology compared to their US peers. US banks have transformed into technology companies, in some cases employing more developers than Google. They continue to invest heavily in data and technology engineering and adopt workable technologies early on. For example, using the cloud earlier in the cycle compared to European banks or investing in more speculative technology such as blockchain.
The greatest obstacles to digital transformation aren’t technical; they’re cultural. Often the cultural roadblocks start at the top of the organisation. Technology is not well represented at board level in European banks. Recent board reshuffles favour bankers and deal makers not technologists or engineers. So the board is not qualified to draw inspiration from Big Tech or run their businesses with an engineering and technology mindset – they are grounded in traditional financial strategy and product skills which are no longer sufficient, on their own, for success in the digital world. In fact, recurring calls for M&A activity in European banking, which these deal makers are predisposed to heed, would result in short term financial gains but risk creating more unintegrated organisational and data silos, and incompatible technology cultures, in the long term. This could spell the death of digital transformation for many banks in Europe.
Board members who did not rise up through the ranks understanding technology have little or no experience of what great looks like – precisely at a time when digital transformation is so crucial to success. They may be too willing to accept mediocre, sub-par technology delivery. This is because they don’t appreciate what is actually
possible when the right type and size of technology team is assembled to solve the problems at hand and they don’t understand how to manage innovation effectively.
Data is one of the greatest drivers of both efficiency and innovation, but crucial aspects of data management are not well understood at board level, for example Agile methodologies or the data maturity model. The former is a powerful methodology that emphasises the use of small, focused teams, working in a pragmatic, iterative way, and partnering with those closest to a problem, to deliver useful outputs as they go. Rather than wait until the end to produce something which may not be the right solution, the methodology enables businesses to work in a nimble, efficient way to resolve day-to-day pain points while also identifying and delivering on longer-term strategic goals.
The data maturity model, on the other hand, charts the stages of data utilisation within an organisation. It begins with an initial “no data” stage where there is limited known or useful data for an organisation to gain information-based insights. However, in the digital age, as soon as a business starts exploring its data, it accelerates into the “big data” stage where there is a deluge of data. At this point, businesses need advanced data wrangling, which encompasses data selection, ingestion, validation, visualisation and schema creation. We find that up to 90% of a data scientist’s time can be spent in data wrangling, yet organisations often under-invest in it. Businesses get less value and output from their data management, despite spending huge sums on data scientists, if they fail to take this in to account. The subsequent “quality data” stage is when a business can perform effective data analytics and modelling, create and easily interpret standardised sets of data, and take decisions based on explainable data. It’s followed by the “predictive” stage when organisations can conduct predictive analysis and retroactive analysis. The data maturity model culminates in the final “strategic” stage when data is fully embedded in all business processes and no decisions are made without forward looking analytic data. There are very few businesses that have reached this stage, especially in the West.
In fact, Europe and the West is not leading the way in financial technology at all – China is. For example, contrast the fate of RBS’ Bo digital bank in the UK to Ant Financial’s end-to-end financial lifecycle management in China. Even Russia’s once monolithic state savings bank Sberbank, which is now the second largest bank in Europe after HSBC, is on the cusp of leap-frogging the rest of the European banking sector.
Recent European regulatory change, including GDPR and MiFID II, forced the microscope on data strategies and provided an opportunity to gain a competitive advantage through data. However, the opportunity was largely squandered with some organisations treating it only as a mandate to comply with regulation rather than a spring board for true digital revolution.
The structural changes now occurring due to Brexit present a second chance to get technology and data management right. European finance leaders should conduct a root and branch review of their operations from a data perspective – and ensure Agile methodologies, data maturity models and advanced data management are built into their DNA for generations to come.
