Hidden costs of poor data quality

Tackling data quality saves money and reduces risk

High quality data is essential for policy and decision making and underpins your organisation’s strategic outcomes. Poor quality data, including data that is inaccurate, incomplete, or out of date, is data that is not fit for purpose. Poor quality data increases risk and can cost you time and money. This article looks at the costs and risks associated with poor quality data.

Poor quality data costs the government, businesses, and society every single day. The DAMA Data Management Body of Knowledge states that estimates differ, but experts think that organisations spend between 10-30% of revenue on handling data quality issues. However, there are also direct and indirect costs associated with poor quality data that are more than just monetary.

The direct costs of poor quality data are often seen in short-term operational issues. We may send staff to the wrong place, pay the wrong amount, fail to provide services correctly, or spend extra time on verification or re-entering data.

Indirect costs caused by poor data quality can include the costs of poor or wrong decisions or the costs of reputational damage. These often have strategic costs or risks that can have a longer-term, negative impact in the future.

There are costs involved in improving data quality, such as training, monitoring, IT and planning costs. However, the benefits of high quality data will always outweigh the costs of poor data quality.

Indirect costs can be harder to measure. You may not immediately realise the true cost of poor data quality. It may be a longer-term consequence, such as a damaged reputation. Poor data quality can weaken evidence, create mistrust, and lead to poor decision making. This in turn can lead to poor outcomes for society.