Customers, are the life blood of any company. With customers comes information, and no matter the size or the industry, every organization has some sort of data repository that houses contact details. This information is critical to operations and allows businesses to market and serve their customers and prospects appropriately.

The difficulty of maintaining data is almost as prevalent as businesses having contact information in the first place. Because of the fluidity of customer relationships, businesses have a hard time keeping contact data clean and up to date.
 
Most organizations work to keep data clean, but many people view inaccurate data as a standard part of doing business. While that is true to a certain extent, data can be maintained and cleaned to enable more efficient and cost effective business practices if organizations put certain processes in place.
 
It is important to realize how much contact data affects each business and, therefore, to ensure its accuracy. While this seems like such a simple piece of information to maintain, errors in collecting the data frequently occur and negatively impact the organization. Analysts should understand common data quality errors and develop a strategy around fixing those errors.
 
Common Data Quality Errors
Data quality errors occur for many different reasons and can vary by business. While it is important for each organization to review its own processes to identify which errors are prevalent, there are certain errors that are common.
 
First, contact data may be incomplete or just inaccurate. At the root of these problems is human error. At some point, data is manually entered by a person, and that information may or may not be verified. When free-form typing information, staff members can easily leave a field blank or fail to realize they have been provided incomplete information.
 
Next, outdated information plagues systems. As mentioned, contact data is very fluid and changes frequently as individuals move, change jobs or organizations go through mergers and acquisitions. Information can become outdated quickly if processes are not in place to update or verify that information on a regular basis.
 
Finally, duplicate accounts are prevalent within many organizations. Customers may mistakenly create new accounts online or may neglect to update old information when contact details change. Additionally, a call center operator may mistype information and create a duplicate record. This spreads account history across multiple areas and leaves organizations with an incomplete customer view.
 
Developing a Strategy
The first step in any data strategy development process is analyzing internal data to find out where errors occur and what types are most prevalent. Once those are identified, an appropriate strategy can be determined.
 
Here are some common practices to consider when developing a strategy:

  1. Staff training. Training is essential to any data strategy. No matter what processes an organization puts in place, any new practice will not work without buy-in from staff. Train staff members on the importance of data quality and let them know how inaccurate information affects the business. Additionally, incentivize staff to collect information accurately and create measurable metrics.
  2. Manual cleansing. Reviewing information in a database or row by row in an Excel spreadsheet is common for many businesses. While this is certainly an option for smaller databases, it lends itself to human error. While a certain amount of manual checking and cleaning is important, businesses should limit how much they rely on manual processes because they are frequently inaccurate and are time-consuming to complete.
  3. Automated verification. Contact information that already exists in a database can be cleansed by software in house or by a third-party provider. These tools can standardize information, eliminating misspellings, and append missing details without requiring the company to contact the customer or prospect.
  4. Duplicate removal. Duplicates can divide account histories and waste valuable resources. Consolidating records can be done automatically with software or through a manual process. 
  5. Point-of-capture validation. The only way to make sure contact information is completely exact and up to date is to ensure its accuracy as it enters a database. Point-of-capture validation corrects information as it is entered and prompts the user for missing details, like a street directional.

Dirty data is a problem that every organization faces, and it affects a variety of departments. Review data to find common errors and what departments are affected by inaccuracies. Then identify solutions that will solve those issues and develop a data quality strategy.
Any and all of these practices can be used in conjunction with one another to help form a data cleansing strategy. It is important to realize that there is no one-size-fits-all solution. Every business has its own data quality needs, and data should be completely reviewed before putting new practices in place.

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