The Cost of Bad Data

Cost of Bad Data

Cost of Bad DataBad data is data that is not perfect – it could be incorrect data, data with missing chunks or data that is delayed. Most businesses do not realize it yet, but bad data should be ranking as one of their biggest problems.

It costs ten times as much to complete a unit of simple work with bad data than with perfect data. All it takes is an error rate of just one percent to double the cost of operations. Gartner estimated way back in 2007 that one out of every two data warehouse projects would face either limited acceptance or outright failure because they do not address data quality issues in a proactive manner. Today, with the prominence of data, the rate would be much higher.

The cost of bad data happens both directly and indirectly.

Poor data hygiene, which manifests as duplicate entries, incomplete, erroneous, irrelevant or outdated information, directly leads to additional costs for the business. For instance, duplicate entries in a mailing list would shoot up the marketing budget considerably, often more than the investment required to clean up the data. Cleaning up the data involves additional work, with even a very low overall error rate of 3% adding almost 30% of non-value-added costs to the operating expenses. A case in point: a billing error takes considerable amount of work to rectify, and, at the end of the day, it adds no value to the process. Worse, underbilling due to bad data may result in direct loss of revenue, and overbilling may result in loss of credibility and trust.

The indirect cost of bad data, which manifests as loss of trust and lost opportunities, is huge and worse than the direct cost of bad data.

In today’s hyper-competitive world, marketers have no option but to engage with customers on a personal or customized basis. The success of these interactions depends on data accuracy, or the marketer's ability to decipher what the customer wants or prefers by analyzing the data on hand. Bad data could result in the marketer making a wrong move, approaching the customer in a channel different from what he prefers, reaching out to a prospect at an inappropriate time – leading to loss of reputation. Much worse is the loss of credibility that comes when the marketer says the wrong thing or fails to honor a commitment due to bad data.

Equally devastating is lost opportunities. Bad data may put blind spots in the path of the marketer as they seek out newer customers and engage with existing customers in a better way. Many companies today swear by big data and invest a considerable sum in big data analytics. The success of big data endeavors depends wholly on accurate data. Bad data can distort the results, not just rendering the investment pointless, but also having spill-over effects when the business makes wrong decisions and investments based on the flawed assumptions.

Some manifestations of flawed big data analytics include mailing to people who have no interest, clients remaining unaware of a new service that may interest them, wrong advice given by the customer support help desk, launch of a new product under the flawed assumption that there is demand, and much more. Lost productivity, lost revenue and lost clients would be the least of a company’s worries in that state of affairs. It could undermine the very existence of the business itself.

The impact of bad data amplifies ten-fold and manifests as the 1-10-100 rule: if the cost to fix a data error at the time of entry is $1, the cost to fix it an hour after it's been entered escalates to $10. Fix it later, and the cost becomes $100.

One way to improve data is through system integrations. ReadyTalk integrates directly with Eloqua, Marketo, Salesforce and other platforms to automatically move your webinar and conferencing data between platforms. By eliminating manual processes, the quality of the data is greatly improved. Learn more about ReadyTalk integrations.

 

Reference:

  1. http://blogs.hbr.org/2012/08/make-the-case-for-better-qua/
  2. http://www.melissadata.com/dqt/1-10-100-rule.pdf
  3. http://economia.icaew.com/opinion/july-2013/the-unseen-cost-of-bad-data
  4. http://www.ciozone.com/index.php/Editorial-Research/What-To-Do-About-Bad-Data/The-Cost-of-Bad-Data.html
  5. http://www.nten.org/articles/2013/the-cost-of-bad-data

 

 

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