Clean Data Reduces Costs

Nearly every business that uses data suffers from common  data quality issues. That’s just a fact.  The most common problems with business data are inaccurate data, outdated contacts, incomplete records, and duplicates. Clean and accurate data is critical for finding new customers and responding to current customers’ needs.

Research has demonstrated that poor data quality costs businesses at least 30% of their revenue. According to Gartner, “The average financial impact of poor data quality on organizations is $15 million  per year.” This can add up to enormous losses with some estimates saying it costs the US economy over $3 trillion annually. And since every year 25-30% of data  becomes inaccurate ignoring the problem makes it progressively worse—it’s critical to have a data cleansing strategy for your business.

Issues with your data makes it innacurate and can hurt your business

Clean data increases overall productivity and allows  for the highest quality communications efforts. Benefits include:

  • FEWER ERRORS MEAN BETTER RESULTS AND HAPPIER CLIENTS.
  • REMOVAL OF ERRORS WHEN USING MULTIPLE DATA SOURCES.
  • MONITORING ERRORS AND BETTER REPORTING TO SEE WHERE ERRORS ARE COMING FROM, MAKING IT EASIER TO FIX INCORRECT OR CORRUPT DATA FOR FUTURE APPLICATIONS.
  • PROPER DATA CLEANING TOOLS WILL MAKE FOR MORE EFFICIENT COMMUNICATIONS AND MARKETING EFFORTS.
There are multiple steps int he data cleansing process

Data cleansing includes fixing structural errors (typos, nicknames, etc.), removing duplicates and outdated entries, handling missing data through removal or enhancement, and validation of existing data. The availability of a  correct, complete, validated and duplicate free address data is important to your communication and marketing efforts. Regular and rigorous data cleansing ensures your message is reaching the right people with minimal waste and unnecessary duplication.

It is critical for businesses to embrace the importance of data cleansing. Businesses are dependent on their data and even though data cleansing can be seen as time consuming and repetitive, it is a process that needs to be a priority.  

 

 

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