Many businesses find that the data they collect has limited reliability. A recent study by Experian highlighted that 55% of business leaders do not trust their data, ultimately impacting the confidence that business leaders have over the data they collect.
Bad data can have significant business consequences for companies. Research by GlobalTranz found that approximately 77% of companies have the belief that their bottom line is affected by inaccurate and incomplete data, with a further 12% of revenue being believed to be wasted due to poor data quality. Nevertheless, companies that did put a focus on high-quality data saw a revenue increase of 15% to 20%.
So let’s unpack in more detail those reasons why Data Quality is important:
1) Business Decision Making
The usage of a data quality vendor ensures ongoing data quality checks, which ultimately make sure that enterprises have cleaner, safer, and high-quality data. This offers organisations more accurate analytics, clearer insights, and predictive advantages. Overall, leaders can have greater confidence in their business decision making.
2) Helps with scalability
Firms are able to scale more quickly with a strategic and effective data quality model in place. If infrastructure operates automatically, it also becomes a possibility to scale up without the need to increase manpower.
3) Operational Efficiency and Productivity
A data quality tool provides an effective way of removing the need for firms to manually verify large volumes of data. This is often resource heavy, laborious and can lead to duplicate investigations. High quality data overall helps to reduce mistakes with less time needed to manually fix inconsistencies.
4) Regulatory Compliance
Regulators demand high quality, accurate data specifically measured for a vast array of regulations – including BCBS 239, a data-driven regulation in and of itself. Businesses need a technology framework that enables rapid delivery of data quality measurement and remediation to ensure ongoing compliance. It also needs to save time and effort and provide results to senior management that can demonstrate compliance with regulations driven by robust data quality management.
For organisations that have cultural problems around data, how best can they address these?
A lot of people associate owning data or being a data steward with being responsible or held to account in a negative way. As the banking and financial services sector is underpinned by risk management, a common challenge is overcoming people’s fears around taking responsibility for data.
A culture shift needs to happen to ensure that people focus on better delivery, rather than being afraid of responsibility. We asked Kieran Seaward, Head of Sales at Datactics, to comment on this further.
“One of our London-based wealth management clients has gone about this in the best way, because they have a senior leadership team that really understands the criticality of data to the business. They understand it is about empowering those responsible to invest in the quality of data.”
This could mean, for example, being able to say “If the data was 30% better on client information, I would expect to see a 5% increase in revenue or in margin.” Linking the information to business outcomes can help people understand the importance of driving up the quality of information relied upon, and how it can help to drive a long-term revenue strategy.
Many of the businesses being built today that don’t have ‘bad data debt’ are in a great position to benefit from modern techniques from the word go- this starting point is what many banks aspire to replicate. It can be said that if you don’t address cultural issues, there is a risk that the business will end up totally disintermediated and losing what made them great to begin with.
To have further conversations about why Data Quality is important to an organisation, reach out to Kieran Seaward.
