Diseased data costing super millions: QMV

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Data errors are costing the superannuation industry, and its customers, millions of dollars every year with poor data spreading like a disease through the system, says Mark Vaughan, managing director of QMV.

“As many superannuation funds have discovered to their detriment, poor data quality isn’t just a technical problem. It is a customer service problem, a compliance problem and a financial problem,” he says.

Mr Vaughan says the exponential harm that the proliferation of data errors can cause can aptly be characterised as the ‘disease effect’.

“If data quality issues are not detected and remedied soon after occurring, there is a tendency for the error to spread to contaminate or infect other data, jumping across to other systems if not quarantined and corrected.

“The financial harm of poor data typically follows a variation of the 1-10-100 rule – used to describe how failure to take notice of one cost escalates the loss in terms of dollars – QMV’s experience suggests it is, in fact, a 1-5-50 rule.

“Identifying the error after one day means the remediation cost is minimal. While the errant data requires correction, the error can be quarantined to minimise any external visibility and further impact.

“Identifying the error after one month results in a five-fold increase in the cost to remediate. The error may have filtered into several monthly processes (such as fees and premiums) and some investors may have left or transferred products (such as from super to pension), resulting in an escalation of the remedial costs.

“Identifying the error after one year results in a fifty-fold increase in the cost to remediate. The error will likely have filtered into several annual processes (such as member statements, ATO and APRA reporting); and may now be a breach which requires compensation and additional reporting to various external stakeholders.”

Mr Vaughan says the costs of diseased data aren’t merely financial.

“The damage that poor data quality can have on customer service and an organisation’s reputation is profound. As we have seen in recent years, there is nothing more likely to break the trust that Australians place in financial services institutions than ‘stuffing up’ their hard-earned savings.

“The impact of poor data quality also extends to the compliance obligations of financial institutions, both generally and specifically. On one hand, poor data quality can cause a specific breach to laws, fund rules or policies that need to be complied with. Even worse, systemic data quality issues can lead to major breaches and regulatory intervention.”

In terms of financial, reputational and non-compliance costs, it is clear that time makes things worse. And problems with data quality are amplified when funds migrate or upgrade from one system to another, Mr Vaughan says.

“While data quality should always represent the first phase of a migration, it is too often left to the business to deal with as a ‘post migration’ activity. The blinkered focus on project deadlines often sees data quality efforts de-scoped, with identified data errors reclassified downward from ‘severity 1′ to ‘severity 2′ until they are overlooked.

“Then, 18 months post migration, funds are cornered into a costly and painful major data remediation program because during the migration, errors have been de-prioritised or ignored.”

The focus on data quality for superannuation funds should be on prevention, detection and correction, Mr Vaughan says.

“For most executives, data quality is viewed as an expense rather than as an investment. But without understanding the return on investment that data quality can deliver, this leads to a reactive approach where data quality spending is heavily geared toward correction.

“The key is to break the endless cycle of manually fixing defective data, by transitioning from a reactive approach heavily focused on correction to one that finds the appropriate balance between all three classifications. In addition, corrective controls are more targeted, helping to mitigate the spread of the ‘disease effect’.

“Of course, each error is different and there is no one-size- fits-all approach for measuring the associated cost. But whether it’s 1-10-100 or 1-3-10 or 1-50-1000, the key theme is clear: the financial and reputational costs of data errors spread like a disease.

“While prevention is better than cure and remains the optimum solution, early detection is a critical tool that can significantly reduce the cost and impact of data errors on superannuation funds and trustees and, perhaps most importantly, their members.”