4 Best Practices for Data Quality Management

Does your organization struggle with poor data quality? If so, you are not alone. According to a leading data quality expert, 91 percent of organizations are troubled by data errors.

The Big Data phenomenon has taken a toll on many businesses that have not matched their infrastructure to the increased flow of data. Many businesses are unwilling to update their systems because cleansing and integrating data can be a tedious, costly and- let’s face it- scary process.

Unfortunately, ignoring the problem will not make it go away. In fact, data is growing at such a fast pace that waiting will simply make it harder and costlier to clean up the systems. Estimates project that by 2020, there will be 50 times the data that exists today.

banner_tracking_smallSo, how does data become inaccurate in the first place? According to a recent study, these are some of the leading causes:

►  Manual date entry: Manual data entry is by far the #1 cause of data inaccuracy
Outdated, disparate database systems: 66 percent of organizations lack a coherent, centralized approach.
Manual data management: Manual data management techniques are used by 53 percent of organizations.
Duplicate records due to minor discrepancies: 30 percent of organizations rank duplicate data among the top three causes for their inaccurate data.
Outdated data: Each month, approximately two percent of contact data becomes outdated. That equates to almost 25 percent of the database annually.

Fortunately, there are some common-sense approaches that can be implemented while your business plans for a longer-term data quality solution.

Here are 4 basic steps to improve data quality:

  1. Verify data at the time it is entered
    It is much more cost-effective to verify data at the time it is entered- whether it comes from an invoice, a customer profile, a purchase order, or other form. Experts estimate that it costs $1 to verify data while it is being entered, but will cost $100 if it goes unchecked.
  2. Regularly check databases for duplicate entries
    It is important that databases are checked at regular intervals to ensure that duplicate records haven’t been created. When duplicates are found, the information should be integrated as quickly as possible.
  3. Expand search functionality
    Basic search functionality within database systems is usually poor, requiring an exact match to find an existing record. That limitation is often how duplicates are created. Minor variations, such as name abbreviations or transposed letters or numbers can exclude the target record. With expanded search functionality, records with similar names or numbers can be included for a more comprehensive results list.
  4. Validate customer information
    Contact information changes regularly. So, it’s important to verify records as often as possible. Efforts can include directly reaching out to the consumer, verifying existing information the next time the individual makes contact with your business, or watching outbound communication efforts for signs that customer information may not be accurate- such as bounced e-mail marketing or returned mailers.

Big Data is here to stay. The sooner your business develops a cohesive plan, the better. With the four steps listed above, you can begin to gain insight into your data’s current quality and implement a strategy to validate, monitor and cleanse your data, all while planning for a more comprehensive solution.

Advanced Data Spectrum has over 22 years of experience helping businesses capture and manage their data. Contact us for more information on how we can help your business.

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