Data quality – Why it matters and how financial services firms can best achieve it
Too many financial services organisations are still failing to implement effective data quality and risk management policies today. Part of the problem is that they typically adopt too much of a reactive approach. Their overriding focus and priority is on validating and cleansing the data that comes into their organisation before they subsequently distribute it more widely. They are motivated by a desire to prevent downstream systems receiving erroneous data. That’s important, of course – but by focusing on ad-hoc incident resolution in this way, organisations almost inevitably struggle to identify and address recurring data quality problems in a structural manner.
To remedy this problem, firms need to be able to more continuously carry out analysis, targeted at understanding their data quality and reporting on it over time. Very few organisations across the industry are doing this today and that’s a significant issue. However, much data cleansing an organisation does, after all, if it fails to track what was done in the past, it will not know how often specific data items contained gaps, completeness or accuracy issues, nor appreciate where those issues occur most frequently.
Focusing their data quality efforts exclusively on day-to-day data cleansing is also likely to result in organisations struggling to understand how frequently data quality mistakes are made, or how often quick bulk validations replace more thorough analysis. For many, their focus on day-to-day data cleansing disguises the fact that they don’t have a clear understanding of data quality, let alone how to measure it or to put in place a more overarching data quality policy.
When firefighting comes at the expense of properly understanding underlying quality drivers that’s a serious issue. After all, in an industry where regulation on due process and fit-for-purpose data has grown increasingly prescriptive, the risks of failing to implement a data quality policy and data risk management processes can be far-reaching.
To tackle this effectively, organisations need to put in place a data quality framework. Indeed, the latest regulations and guidelines across the financial services sector from Solvency II to FRTB increasingly require them to establish and implement this.
That means identifying what the critical data elements are, what the risks and likely errors or gaps in that data are, and what controls and flows are in place. Very few organisations have implemented such a framework so far. They may have previously put stringent IT controls in place, but these have typically focused more on processes than data quality itself.
By using a data quality framework, firms can sketch out a policy that establishes a clear definition of data quality and what the objectives of the approach are.
