Each year, sales departments lose approximately 550 hours and $32,000 for every sales representative that uses bad prospect data. If a business houses a five-person sales team, the money hemorrhaged in just a single year is enough to purchase two Porsche 911 Carreras, 40,000 boxes of Girl Scout Cookies, or over 150 trips to Disney World.

The magnitude of profits lost to bad data [email required] is detrimental to business growth and unnecessarily demoralizes marketing efforts.

Moreover, bad data funnels in from many fields—wrong phone numbers, outdated physical and email addresses, wrong title or job functions, and misspellings. That information changes rapidly in mimic of the business-world's dynamic nature.

Two commonly-sourced bad data pools exist for marketers. The first is end-user submitted data, or data that business' users input themselves. The second is technology-sourced data, or data that marketers pull together from open availability on the Internet.

Neither source is perfect—the former's accuracy lags with limited utility, and the latter lacks verification methods and access to detailed information. No matter where marketers procure data from, the costs of bad data can be extreme.

Soft Costs of Bad Data

• Employee satisfaction: Bad data can impact employees in different ways. Professionally, sales representatives' salaries are dependent upon closed leads, and bad data can lower profitability. Personally, as employees find less business success, their satisfaction rates decrease, and they can easily become discouraged. Often, that impact on morale is invisible until the effects of better data are realized.
• Time wasted: Marketers spend too much time researching and organizing data that has already grown obsolete. Sales representatives allocate significant hours to sifting through data sources with low yields. In the next hour alone, 41 new businesses will open, 58 business addresses will change, and 11 companies will alter their names.

Hard Costs of Bad Data

• Missed opportunities: If a business is not connecting with the right people or organizations, it's directly missing out on opportunities to find prospects and obtain stronger leads. Lacking the proper automation tools and marketing practices, leads are missed across a variety of mediums—digital, direct, and social, for example. Leads with bad data make marketers' attempt to produce long-term, high-quality business opportunities almost impossible.
  
• Time wasted: The opportunity costs of bad leads are both direct and indirect. Hard dollar costs follow expending time with no profitable return, and hypothetical dollar costs are associated with the time better spent pursuing better prospects. As both a hard and a soft cost, the time wasted on bad data is ultimately the biggest payment that marketers sacrifice when ignoring inadequate sources. Here, time directly translates to money.

Stop Ignoring the Bad-Data Problem

The issues with bad data are largely intuitive—it's why there are so many idioms about it. You're only as strong as your weakest link. A marketer's CMR or database is only as good as the data it contains. A whole is only as valuable as its individual parts, and any component can severely limit a marketing strategy when bad prospect data goes unchecked.

This problem's intrinsic nature is often why marketers end up ignoring bad data. However, marketers need to face the ongoing reality that no organization is immune to bad data. No magical vaccination can eradicate bad data sources, and those inaccurate information wells persist despite marketers' best efforts to fill the financial drains. However, the problems with data sources are adaptable and will continue to change as industry needs and methods fluctuate.

This viruses' solution does not come all at once, but rather in a more consistent management of a company's data sources. Although marketers recognize disparities in their data logs, too often marketers fail to respect bad data's high costs.

Marketers may find updating their data catalogs cumbersome, but the practice is an effective way to expunge bad data. Business would also be smart to dedicate entire teams to more frequent data management, or to invest in strategically chosen sales intelligence solutions.

When evaluating your own data pool, answer the following questions: Do you trust your data? Is data health a known business concern? Do you have processes in place to diligently update your data? If your answer to any of these questions is no, or if you hesitate to answer yes, it's time to change something before your business drowns because of bad data.