{{announcement.body}}
{{announcement.title}}

Dirty Data a Persistent Hurdle for B2B Companies

DZone 's Guide to

Dirty Data a Persistent Hurdle for B2B Companies

Clean data is a must have, but what if you're data is less than accurate? An industry executive discusses the problems that could arise.

· Big Data Zone ·
Free Resource

Reports have shown that 10 to 25 percent of B2B marketing database contacts contain critical errors. Inaccurate or dirty data can cause expensive marketing activities to go to waste. Some examples consist of data inaccuracies, duplication, or data including those who are no longer with the respective company. This negative effect can ripple throughout a company, potentially causing double digit losses in overall revenues.

One of the more staggering statistics is that 33 percent of marketers feel that they can rely on CRM data to make decisions and 27 percent of business leaders aren’t sure how much of their data is accurate. This is a recipe for disaster, especially if there is no oversight in updating and maintaining customer data and your sales team may also accrue lost selling time if they don’t obtain the correct information.

Therefore, starting with clean data is important, but ongoing data maintenance needs to be the top priority for marketers if they want to properly target their audience. With stats from over 100 million contact records over 15-plus years, MountainTop Data has observed that marketing data goes bad at a rate of 39% per year. The longer this bad data sits in the system, the more likely that it’s going to cost enterprises in the long run.

However, there is always a percentage of dirty data in even the cleanest CRM with an acceptable level of inaccuracies. What percentage you can be at before you go over the dirty data tipping point varies, but if your key fields are accurate 90 percent or more of the time then it will suffice. In fact, if you were to get your data 99.9 percent accurate, you’d be wasting money on accuracy. 

Additional examples of issues created by poor data include:

  • Sending a message to the wrong target audience.
  • Poor email marketing traction or even blacklisting.
  • Inaccurate identification of customer segments due to inaccurate firmographics.
  • With CRM and marketing system costs frequently based on the number of records, removing bad data can lower these costs. The 1-10-100 rule, created by George Labovitz and Yu Sang Chang, and cited by data scientists, says it cost $1 to verify a record initially, $10 to clean it later and $100 to do nothing.

    Overall, the best way to clean marketing data to rectify any shortcomings is to focus on removing duplicates, invalid contacts and assuring you have complete and accurate information for the fields that are critical to both the company’s targeting (company size, industry, geography, titles, etc.), and outreach (email, phone number, address).

    Nevertheless, if you don’t have the expertise in-house to clean and maintain company data, don’t waste marketers’ time having them work on data cleaning themselves. There are many companies that specialize data cleaning and maintenance.  You could say dirty data is expensive or clean data pays for itself, but it’s obvious that the accuracy of your marketing data will give you a healthier bottom line.

    Topics:
    dirty data ,big data ,data integration ,clean data

    Opinions expressed by DZone contributors are their own.

    {{ parent.title || parent.header.title}}

    {{ parent.tldr }}

    {{ parent.urlSource.name }}