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The cost of bad data: have you done the math?

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Bad data is bad business. Indeed, organisations believe poor data quality to be responsible for an average of $15m each year in lost earnings, according to research by Gartner. Here we reveal the cost of bad data and why it might be time to clean up your act.
Cost of bad data maths

Before we examine the cost of bad data, it’s worth looking at the scale of the problem. The International Data Corporation (IDC) predicts that the amount of data in the world will grow from 33 ZB in 2019 to 175ZB by 2025. It presents both a daunting challenge and a limitless opportunity for marketers and data-driven organisations.

Businesses that most effectively manage and optimise data can provide superior services to customers, improve decision-making, drive greater efficiency and achieve assured compliance with regulators.

“The old adage ‘garbage in, garbage out’ has never been more true or more important for businesses. Good quality data means better business decisions, better marketing and more profitable relationships.”

Jon Cano-Lopez, CEO, REaD Group

But nobody said it would be easy. There exists a variety of ongoing and unfolding challenges posed by Big Data, including:

  • The evolving regulatory climate which requires organisations to ensure they source and utilise data in a legally compliant way.
  • The practicalities of handling the sheer volume and complexity of data across the business in an integrated way.
  • The difficulties of pulling meaningful insights from the vast amount of data collected and acting upon them.
  • Adapting to the speed of technological change – such as machine learning, AI and unforeseen technological innovations.
  • The proliferation of ‘bad data’ which is inaccurate, outdated or irrelevant.

Bad data = lost customers

cost of bad data lost customersThe growth of bad data represents a major impediment for organisations trying to maximise the strategic benefits that data can provide, while also posing a compliance risk.

Indeed, research by Royal Mail Data Services revealed that organisations believe inaccurate customer data costs them, on average, six per cent of their annual revenues. Perhaps more worryingly, over a third were not sure how much it costs them.

Losses caused by bad data can occur for a number of reasons, including lost time spent chasing phantom customers (such as duplicate contacts or redundant email accounts), or misinformed decision-making. A recent data management study by Dun & Bradstreet found that 19% of businesses had lost a customer by using inaccurate or incomplete information – a loss exacerbated in industries where customers have a high lifetime value.

If bad data undermines our ability to address the big challenges posed by Big Data – and the potential benefit it provides – then it’s ‘clean data’ which has a fundamental role to play in helping us meet the challenge, grasp the opportunity and safeguard ourselves against tightening data protection regulations.

Clean data can be defined as: accurate, up-to-date, uncorrupted and relevant data which has been appropriately sourced. In today’s data-driven age, it’s the optimal fuel that companies depend on.

Are you addicted to bad data? Get clean and stay clean! Check out our in-depth report with data and insight specialists, REaD Group, and discover what organisations must do to establish a mature data culture and make dependable data becomes the norm.

Bad data impacts all businesses. But some more than others…

The more complex the data needs of an organisation, the more difficult it becomes to keep all data sets clean, and the greater the threat bad data poses.

So, while clean data is vital for businesses of all sizes, larger organisations with complex data requirements are those that stand to gain the most through the combination of more effective marketing campaigns, more efficient spend, and lowered compliance risks and associated brand damage.

Examples of bad data in action:

  • Wrongly using contact data for deceased contacts or gone aways – which can damage your brand and waste resources.
  • Holding on to personal data longer than is necessary for the purpose it was processed – falling foul of GDPR Article 5 (e).
  • Collecting multiple duplicate data sets – skewing decision-making, corrupting the insights provided by a machine learning algorithm, or wasting resources.

According to Dun & Bradstreet, 42% of companies said they struggled with inaccurate data while 43% had seen ‘some’ data-led projects fail. These findings seem to suggest that companies with bad data are more likely to experience project failure.

The business benefits of clean data

cost of bad data cleaningMarketing: A direct marketing campaign using high quality data reaches the right target contact with relevant offers. This drives up sales leads and campaign ROI.

Sales: A sales representative can reliably contact current customers. With access to complete and accurate data, they can ensure no-one is forgotten or missed.

Compliance: Avoidance of penalties and the related brand damage. Complying with the GDPR and other global data protection regulations require companies to maintain clean data.

Better insights and decisions: Accurate data will deliver more accurate insights and enable better business decisions.

Protect your brand: Applying data quality best practice will help to protect your organisation from brand and reputational damage. For example, it will stop you from contacting deceased individuals.

In today’s data-driven age, clean data is no longer a preference for companies wanting to improve their ROI: it’s increasingly a business fundamental.

Download our in-depth report in association with data and insight specialists, REaD Group: Are you addicted to bad data? Get clean and stay clean.


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Image by Gerd Altmann from Pixabay

Mel Dixon
Author: Mel Dixon
Editor at Global Marketing Alliance

Editor and freelance writer wading his way through the world of data. One step at a time. Interested in data driven marketing insights supported by... data.

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