Remember the old adage, “Garbage in, garbage out”?

We can’t expect an algorithm to provide accurate insights if it’s fed bad data. However, all too often, poor data quality is not recognised until the algorithm begins to produce clearly flawed results. The vast majority of businesses are content to live with incomplete, inaccurate, duplicate, or otherwise low-quality data, which manifests itself in a variety of ways: a minor inconvenience here, a sluggish system there, and a reliance on guesswork, workarounds, and manual processes.

However, when it comes to the true impact of bad data, this is only the tip of the iceberg.

The Hidden Cost of Poor-Quality Data

Strong data governance enables businesses to gain control of their data, generate better insights, and improve compliance. It reduces risk and allows them to adapt more quickly to market changes. As a result, the UK government estimates that businesses spend 10-30% of their revenue dealing with data quality issues, which is an inherently fixable problem.

Data quality is another issue that is unlikely to go away anytime soon. According to Gartner, one-third of businesses are heavily investing in data-driven initiatives such as AI, which streamline processes, enable faster decision-making, and provide a competitive advantage. However, poor data quality could seriously stymie these efforts. Poor quality insights – or a lack of trust in those insights – result in lower revenues, lower productivity, inefficient operations, missed business opportunities, and increased exposure to regulatory, security, and compliance risks.

Furthermore, with some industries undergoing several years’ worth of digital transformation in a matter of months during the pandemic, many businesses are now more reliant on data but less capable of handling it.

Raising Data Standards

The path to good data governance begins with a targeted overhaul of data practises to identify outdated methods and ensure data standardisation. A longer-term approach, in the form of a data governance strategy, is also required. When confronted with a large volume of various data types, a comprehensive data governance framework considers context, requirements, and the data’s business value. Furthermore, it establishes clear lines of accountability for maintaining accurate, consistent, and timely data that complies with all applicable regulations.

Everyone’s Responsible for Good Data

Given that the consequences of poor data quality can be felt throughout the enterprise, data quality should be an enterprise-wide priority. As a result, data governance necessitates a cross-functional approach. Senior executives are capable of driving change across business functions, so this should start at the top. Then, educate employees on how the data they touch is used more broadly. Tracking and resolving data quality issues as soon as possible is also important: seeing data quality taken seriously encourages everyone in the organisation to be more accountable.

AI Loves the Jobs You Hate

AI has transformed big data projects, and it can now provide a much-needed boost to data standards reform. Data cleaning bots are used (optimising, de-duping, cataloguing, metadata handling and so on). These tools aid in ensuring quick ROI by drastically reducing the time required to transform unruly data.

Why Quality is a Competitive Discriminator

If you ask any company what digital transformation steps it is prioritising in the coming year, the answer will almost certainly include data-intensive projects such as analytics, cloud, AI, RPA, and ML. The integrity of data will be critical to the success of these initiatives. Whatever stage of digital maturity a company is at, it can take practical steps to improve data quality and, as a result, decision-making. “Good quality data provides better leads, better understanding of customers, and better customer relationships,” says Gartner’s Melody Chien. Quality data is a competitive advantage.”