The Direct and Indirect Costs of Poor Data Quality

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Imagine that your company runs an online ad for a product. But when your customer gets to your website, this product has actually been discontinued. And from thereon in, every time the customer surfs the web they are now constantly served with banner ads for this non-existent product.

This scenario really happens more often than you think. And it is a perfect example of marketing at its worst: spending your budget to lose customers, ruin your service reputation, and destroy your brand.

We often talk about data quality on this blog, but this time I would like to focus on the results of lack of data quality. In the case above, poor linkages between inventory data and marketing lead to a bad customer experience AND wasted marketing dollars. Much the same thing is true with our specialties of contact data and marketing leads: bad data leads to a wellspring of avoidable costs.

First there are tangible costs. Bad leads and incorrect customer addresses lead to specific, measurable outcomes. Wasting human effort. Throwing away precious marketing capital. Penalties for non-compliance with telephone and email contact laws. Re-shipment costs and inventory losses. According to one Harvard Business Review article, the real costs of poor data quality now exceed $3 trillion per year in the United States alone.

Then there is the part few people pay enough attention to: the indirect costs of poor data quality. In a recent piece for CustomerThink, data management expert Chirag Shivalker points to factors such as sales that never materialize, potential customers who turn away, and the subtle loss of repeat business. Whether it is a misdirected marketing piece, an unwanted pitch on someone’s cell phone, or a poor customer experience, some of the biggest consequences of poor data quality are never quantified – and, perhaps more importantly, never noticed.

Finally there is the fact that data, like your car, is a depreciating asset. Even the most skillfully crafted database will degrade over time. Contact information is particularly vulnerable to decay, with estimates showing that as much as 70% of it goes bad every year. A recent article from insideBIGDATA put the scope of this in very stark terms: each and every hour, over 500 business addresses, 800 phone numbers and 150 companies change – part of a growing ball of data that, per IDC, will swell to over 160 zettabytes (for the uninitiated, a zettabyte is one sextillion, or 10 to the 21st power, bytes). And the bill for validating and cleaning up this data can average $100-200K or more for the average organization. So an ongoing approach is needed to preserve the asset value of this data, as well as prevent the costs and negative consequences of simply letting it age.

A recent data brief from SiriusDecisions breaks down these costs of poor data quality into four key areas: hard costs, productivity costs, hidden costs, and opportunity costs. And it is not like these costs are exactly a surprise: according to Accenture, data inaccuracy and data inconsistency are the leading concerns of more than three-quarters of the executives they surveyed. Their solution? Better use of technology, combined with process improvements such as formal data governance efforts.

The people in our original example above probably had no idea what kind of lost business and negative brand image they were creating through poor data quality. And ironically, I would say let’s keep it that way. Why? Because it is a competitive advantage for people like you and me who pay attention to data quality – or better yet, build processes to automate it. If you are ready to get started, take a look at some of our solutions and let’s talk.

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