Poor Data Governance is Leading to Poor Compliance

"Dirty data, begone..." Martin Williams, Head of Infostretch UK, has some practical steps financial institutions can take to improve their approach to data governance. 

Good data governance should be a no-brainer. It is supposed to help financial services companies get a handle on their data, improve compliance and adapt more readily to change. And, as anyone in risk and compliance will testify, adapting to regulatory change is a near constant state of affairs, whether relating to cyber, data privacy, the use of fintech, compliance with international regulations or the many specific compliance requirements of individual finance sectors.  

Despite “change being the only constant” in the financial services regulatory landscape, adapting to changes too often involves repetitive, manual tasks carried out using outdated systems. This isn’t just a time suck: the UK government estimates that businesses spend 10-30% of revenue handling issues relating to data quality. This is a staggering amount for something that’s inherently fixable.

The financial hit is not the only knock-on effect of poor data stewardship. Data governance that’s outdated or not fit for purpose also increases exposure to regulatory, security and operational risks. For a sector that is among the most digitally mature, that has trailblazed digital adoption through the pandemic, poor data governance could well become the Achilles heel of its digital transformation ambitions, especially given the upward trend in the adoption of AI, ML and analytical solutions – all heavily data-reliant initiatives.

Dr Henrike Mueller (FCA), Leo Gosland (FCA), Mohammed Gharbawi (Bank of England) and Olover Thew (Bank of England), highlighted in their OECD.AI joint blog that AI-driven analytics within the UK financial services sector presents new challenges in terms of complexity and large datasets that set it apart from previous technologies. While AI “offers huge potential for efficiency gains, it can also amplify the risks and consequences of those decisions” and the topic of governance is central “because it is crucial to addressing many data and model-related challenges.”

As financial institutions increasingly use data – including large, disparate datasets – for an expanding amount of decision-making, the need for data literacy and analytical now permeates into all areas of a financial organisation. Yet, many financial institutions are still falling into the trap of seeing data governance as an IT issue for the data team to deal with. This is understandable when you consider the challenges in harnessing data at scale – few would argue that transforming data from different states and locations into a usable single source of truth is anything but the domain of data specialists. However, considering the upsides and opportunities that improved data quality confers should more than justify its consideration as an organisation-wide priority.

Data governance has far-reaching consequences. Poorly handled data is not just impacting compliance, it’s preventing business agility. Furthermore, by improving the quality of data and how it’s used, access and controlled, financial companied are better able to trust the decisions they make based on that data. There are several practical steps financial institutions can take to improve their approach to data governance.

1: Make Everyone Accountable for Good Data

Poor data governance may sound a little theoretical, but it impacts the whole organisation, whether as reduced revenue, decreased productivity, operational inefficiency or missed business opportunities. A cross-functional approach to improving data handling is the most effective because it makes clear that data is everyone’s priority. This starts with senior level buy-in capable of delivering change across business units. Next, educate employees to understand how the data they touch is used. Finally, highlight easy actions employees can take to better gather and understand data.

2: Dirty Data, Begone

A data makeover? No, not the latest well-being fad, but an initiative that finally cleans up the many types of disparate data lurking across financial companies’ systems. This kind of data overhaul ensures data becomes standardised, deduplicated and, crucially, gives more accurate outputs.

3: Embed Standards into Data Stewardship

Improving data quality is an essential first step, but maintaining best practices requires strategy, a governance framework and clear lines of accountability for maintaining accurate, consistent, standardised and timely data that conforms to the evolving regulatory landscape.

4: Leverage AI beyond Simple Automation

AI has been transformational in improving compliance – the burgeoning RegTech sector is stand-out proof of the value of digitising and automating data-heavy, repeatable compliance tasks. The next challenge will be to go beyond discrete automation tasks to automating whole chains of tasks and transforming entire processes. Gartner recently identified hyperautomation among its top trends for 2022, predicting it could lower operational costs by 30%

5: Track and Resolve Compliance Issues Fast

Even when your systems are primed to spot compliance risks fast, in a rapidly-evolving regulatory landscape (national and international), issues can creep in. From a tech standpoint, an active approach to digital assurance should counter these vulnerabilities and increase agility across the whole data lifecycle, but in human terms, there’s a lot that financial companies can do. This starts with educating employees to question and flag data. Demonstrating that data governance is a priority will encourage everyone to take it seriously.

Conclusion

Financial institutions have undergone unprecedented digitisation in the past two years, assimilating huge volumes of data in a relatively short space of time. Harnessing that data so that it’s usable, accessible and compliant has left many struggling. And while data transformation is a core component to many digital transformation roadmaps, they don’t need to wait before they start implementing immediate, practical steps. Data governance most immediately improves compliance helping to better secure data, eliminate bottlenecks in data processing, streamline data-intensive compliance tasks, but it also underpins all business decision-making. Good data governance enables financial institutions to make better decision at every level with greater confidence.

By Martin Williams, Head of Infostretch UK.

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