22/07/2022 Continuing Covid-19 uncertainty and more recent geopolitical unrest in Europe may not seem the perfect time for the financial services sector to take stock of its data strategy. However, with data now acknowledged as a critical ingredient for future growth, analysing data maturity levels can provide essential signposts for financial services leaders looking to reach their data maturity potential.
In Mazars’ first global study on data, the financial services sector has emerged as one of the most confident sectors in terms of data maturity. Yet, many leaders fail to meet best practices that underpin data maturity. Jim Dolan, a Partner at Mazars, digital business expert and veteran of the banking sector, unpicks the study findings to help financial services leaders take stock of any maturity gaps to help leverage data investments more effectively and drive growth.
82% of data leaders in the financial services sector rate themselves more data mature than their competitors. Is this an accurate reflection, and what can they do to avoid overconfidence or even complacency?
This confidence may reflect that data maturity is more of a priority for the financial services sector than other industries. Plus, other industries may not have the spending capacity that the financial services sector has to devote to reaching data maturity. There’s also a question of perception. Do financial services leaders perceive they are more data mature than they actually are? For example, 69% also say their data is completely protected, yet we know that achieving 100% data security is impossible. This would suggest that leaders could be overconfident in their data maturity journey, which poses risks. Perception and reality are very different.
How data is gathered and how secure you keep it is about consistently aiming for best practice. Good data hygiene provides the foundations for obtaining accurate customer information that allows organisations to reach the level of data maturity required to drive actual growth. To avoid falling into overconfidence or complacency traps, financial services leaders need to ensure they are committed to best practice by having complete oversight of their data strategy at all times. Reaching full data maturity is a long and complex journey that requires aligning the data culture with the organisation’s capabilities and ambitions.
Data maturity is of little value if data is not secure. So what can financial services organisations do to improve resilience and protect data?
It’s often a question of approach. Are you just compliant enough to not lose your licence and are happy to work with regulators on improvement plans, or are you more proactive? By asking yourself some simple yet essential questions, you can determine how secure data is. For example, how quickly would you know if you had a data breach? Would you know how the breach happened, which parts of the organisation were affected and what data was taken? Finally, how confident are you in your ability to answer those questions within 24 hours? As well as internal analysis, external tools such as ethical hacking will demonstrate just how secure your network is against attack and enable you to plug the gaps. With cyber-attacks on the rise, insurance is becoming much harder to obtain, which puts new emphasis on cyber incidence response tools to help improve resilience. Plus, while insurance against ransomware attacks is reasonably easy to get, you will either be in regulatory breach or deteriorate your market value if you don’t react in the right way or within the right timeframe. Being ahead of the game is essential.
Why is Big Data key to future growth for the financial services sector, particularly banks, and what is a common mistake when setting a strategy?
A permanent shift is underway. Customers expect instant, convenient and frictionless access to the services they want. Digitalisation and big data have not just become a gateway to a limited suite of products but an entire ecosystem. In terms of product development decisions, you need accurate data on how people are using or not using your products, why they click on your website and what attracts them so you can develop financial solutions more accurately. So instead of offering yet another life insurance policy or loan, predictive analytics and machine learning can be employed to detect household spending patterns that allow you to deliver more relevant and customised products. So the right data gives the ability to move from guessing what customers may want to driving actions automatically. This is where the value of big data is in the future. It’s about gathering the right data in the right way to gain a more accurate understanding of customer needs and behaviours.
A common mistake is to put in a shiny new Enterprise Resource Planning (ERP) system but then populate it with existing data. It’s a bit like pouring dirty oil into a brand new engine. Like oil, data feeds every part of the business, including risk systems and compliance. In effect, you have a top of the range ERP system that will work at a deeper organisational level, but it’s running on contaminated fuel. This in itself is not a great strategy, but then add in the fact that data algorithms and machine learning lie at the heart of future growth for the financial services industry, and it reflects a lack of foresight. The two need to be integrated much better so that data quality is given equal priority to the technology it feeds.
What steps should banks take to get their data maturity strategy to the next level, and how can they navigate the risks?
The financial services sector has reached a decisive moment where it risks being overtaken in the race for digital transformation. While it does not help that financial services operate in a sector where there is little or no tolerance for inaccuracy or downtime, banking executives need to assess and face up to the state of their current technology. There’s no sense in pretending systems are serviceable because the prospect or expense of change is too daunting. In that approach lays redundancy as competitors and challenger organisations move ahead. There will come the point where banks will need to stop relying on application programming Interfaces (APIs) or microservices to provide a solution and do something fundamental with legacy systems.
In terms of strategic steps, given the breadth of expectations on the sector, it will be difficult for banks to be all things to all customers, so they will need to pick a place in the market and pitch their data strategy accordingly. Will they become more like a marketplace like Amazon or a provider of services and products like independent sellers? At the same time, banks need to remain trustworthy partners that treat data securely; otherwise, customers will not share their information. Going back to the engine oil analogy, unless you’ve got robust data governance fundamentally embedded in your organisation, your data will get dirty again very quickly. There also needs to be a cultural shift at a grassroots level that gives clear ownership of customer data across the organisation to protect and maintain its value. As we’ve seen in recent times, data leaks can be financially catastrophic. Also, while artificial intelligence (AI) used for machine learning may appear to be an indispensable solution, the process makes tracing how a decision is made much more difficult. So best practices in data mapping, ownership, security, and governance will help provide firm foundations for improving data maturity, drive growth and positively impact an organisation's performance.