The hidden costs of bad data

Gartner estimates that poor data quality costs organisations on average $12.9 million every year. A straw poll of wealth managers conducted by DCI found costs charged to the P&L due to bad data (for client compensation, regulatory fines, etc.) range from 54bps to 111bps of turnover, with an average of 80bps. And with data volumes expected to almost double by 2025, data quality issues are only set to grow.

That’s bad enough. But the hidden costs of poor data mean the situation is far worse than the headline figures suggest.

Bad data has Rumsfeld-style opacity

Problem is, most wealth managers have little awareness of whether their data is up to scratch or not, with many organisations suffering from a “Rumsfeld-style” ignorance about its quality.

Firms will likely have some visibility into certain data problem areas and what that is costing them: the known knowns in Donald Rumsfeld parlance. These may show up in the headcount needed to remediate errors and regulatory penalties that stem from data mistakes.

Wealth managers may also have known unknowns, where they recognise problems exist, but not where or what they are.

The biggest problems though lie with the unknown unknowns – those issues where firms aren’t even aware something is awry. Mistakes may only show up days, months, even years later. Tracking back to the source and rectifying the original error is time consuming, costly and difficult. Sometimes nigh-on impossible.

A lack of metrics and methods to monitor data integrity, and measure the true enterprise-wide impact of poor quality/value of good quality data leave organisations further in the dark.

Why you can’t afford bad data

This matters. Arguably more than any other element in a wealth manager’s operations.

Firms’ ability to achieve their business objectives and build a competitive advantage increasingly depends on accurate, trusted data. It is central to compliance and governance, reducing operational risks and costs, gaining insights on customers, developing stronger client relationships and boosting margins.

Bad data though is pervasive and can manifest anywhere. It can take the form of gaps in client information or fee structures set up wrong. Duplication from siloed data. Inconsistencies, bad sequences, logical failures, variant errors. Data feed inaccuracies.

And that bad data – be it inaccurate, incomplete, inconsistent, outdated or poorly defined – produces significant direct and indirect costs.

There are the downstream mistakes that result, plus the expense (in time, headcount, systems, regulatory fines and client compensation) of finding, fixing and dealing with those data quality issues.

The implicit costs often dwarf the explicit ones though, and can be more far-reaching.

Indirect cost damage

Data problems tend to have a delayed reaction – showing up in client reporting for example, when they are too late to rectify. A one-time mistake, while embarrassing, may be explained away. Persistent errors breed customer distrust and dissatisfaction, with serious relationship consequences.

Poor quality data heightens regulatory risk too. Regulatory reporting errors can lead to censure and fines, with knock-on reputational damage and the prospect of further regulatory investigation.

Data deficiencies often affect management information as well. Since firms base operating and strategic decisions on their MI, bad data can have profound implications for a wealth manager’s activities and success.

Decision making will be compromised. Innovation may be hindered and critical business initiatives undermined, including moves to automate, digitise and adopt AI solutions. Organisations become incapable of accurately assessing their own effectiveness and determining whether money and resources are being deployed optimally.

Bad data solution

With so much at stake, effective data quality management that prevents errors and allows issues that do emerge to be resolved quickly should be embedded into your organisation. As our white paper The hidden cost of bad data explains, that takes automation.

Bottom line: poor quality data exacts a heavy toll on wealth management firms, one that has a huge and ongoing impact on their competitive standing and operational efficiency, while clean data and accurate client reports can confer significant cost and competitive advantages – issues we’ll explore further in our next blog.