Empower your organization with robust data quality assurance frameworks, ensuring trustworthiness in every data-driven decision.

More and more companies are investing in data-intensive programs – such as Customer Experience Management and Advanced Analytics – as they seek to use data to differentiate and achieve corporate goals such as creating new revenue streams, reducing churn or shutting down unprofitable product lines or channels.

KPIS measure business performance

Traditionally, new programs such as these are linked to Key Performance indicators (KPIs).

For example, companies could measure their Customer Experience team based on the improvement in uptake as a result of their data-driven campaigns. KPIs are intended to measure business performance (and bonuses) against the delivery of business goals.

But, it seems that:
(a)   A metric/kpi is a metric/kpi because someone (or some group) says so.
(b)  Anyone (or any group) can “declare” something to be a metric/kpi.

Malcolm Chisholm, via LinkedIn

KPIs can have unintended consequences.

In many cases, we measure staff on the quantity rather than the quality of their activity.

Contact centre agents may be measured on the number of calls they answer within an hour, rather than the number of queries that they resolve. Our “best” agents may be leaving a trail of disgruntled, repeat callers in their wake – an unintended consequence.

Salespeople may be measured on the amount of new business that they close. Yet, if a large portion of this new business results in bad debt then this new business is undesirable.

Data quality is key

In the new, data-driven world, the quality of data that we capture is arguably as important as the quantity.

Contact centre agents must not only resolve calls efficiently – they must also capture the data necessary to support the ongoing customer experience.

Sales reps must do more than close a sale – they must capture the relevant information to allow the credit department to assess risk and stop bad debt from being taken on.

Studies show that most large companies lose between 10-30% of their profits due to poor-quality data. These costs can be linked to issues such as rework (additional administrative costs), wasted spending (mail shots not reaching intended recipients), regulatory penalties, failed IT investments and many more.

Data governance governs the behaviour we want to see from those of our staff that capture or modify data.  KPIs measure this and need to include data quality measures that drive our staff to capture data correctly the first time.

Our investment in data must look beyond new systems and capabilities at the behaviour of the people responsible for capturing data.

As such, data quality KPIs are necessary to deliver the data we want and need to achieve our goals.

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