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4 Steps to Eliminating Human Error in Big Data

Apr 1, 2013   |   2 min read

Knowledge Center  ❯   Blog

human error data entryCompanies are increasingly depending on Big Data to communicate with consumers and provide business intelligence, which delivers truly positive customer experiences. While most organizations have a strategy in place to monitor and correct the quality of data, 94 percent suspect their customer and prospect data might be inaccurate in some way, according to the Experian QAS study “Data Quality and the Customer Experience.”

In the study, Experian QAS found that 65 percent of organizations cite human error as the main cause of data problems. To err is human, but when it comes to Big Data, to err is to lose new business and customer loyalty.

If you suspect your organization’s multi-channel marketing efforts are being negatively impacted by poor data quality, check out these four steps to eliminating human error as outlined by Experian QAS:

  1. Identify Data Entry Points – Before they’re able to correct human errors, organizations must understand how information enters their systems and through what means. Does your organization collect consumer data at point of sale? Or do you strictly capture consumer data from online forms? Does your sales team input consumer data? Or are consumers responsible for entering their own data? Consider all channels and data entry points to create a full data workflow. Then, prioritize projects based on high-volume channels or excessive data-quality errors.
  2. Train Staff – Many organizations still ask staff to manually enter consumer information, which means staff education can go a long way toward improving data quality. As a marketer, you experience data quality challenges first-hand, so you understand just how vital it is for consumer information to be entered correctly. Sales or data entry staff, however, may not see the big deal if the same consumer is listed under two aliases in your directories. Explain the importance of accurate data to staff and educate them about how information is used throughout the business.
  3. Utilize Automated Verification Processes – Organizations can implement software solutions in various channels to help prevent inaccurate information from making its way into their directories. Decide which data is most important to the business and evaluate and prioritize available solutions, such as email validation APIs.
  4. Clean Data Over Time – In an ideal world, verifying data as it enters your databases would be enough to maintain accurate lists; however, frequent changes of address and other information require repeated list hygiene. Regular cleansing allows organizations to review information and ensure their data is managed to the expected level of quality.

However, eliminating human error can only take your email list so far. If you are ready to take your email list to the next level, download our free guide to learn how today.

Photo Credit: Fuschia Foot

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