Poor data quality is more than just an inconvenience---it' russia phone number s a costly issue. According to The Data Warehouse Institute (TDWI), businesses lose approximately $600 billion annually due to dirty data. Outdated addresses, incorrectly formatted emails, and incomplete phone numbers are just a few examples of how bad data can infiltrate databases.
The consequences of unclean data are significant: it hinders sales efforts, disrupts customer relations, causes compliance risks, and can even damage your company's reputation. Ultimately, poor data quality not only affects operations but can also tarnish your brand's credibility and reduce overall efficiency across the business.
The solution to combat dirty data is straightforward---start with a comprehensive initial cleanup of your dataset. Once you've refreshed your data, it's crucial to set a recurring schedule for re-cleaning, such as every 3, 6, or 12 months.
By doing so, businesses can maintain high-quality data that supports greater operational efficiency. A clean dataset ensures smooth business processes, improves decision-making, and enhances customer interactions. Regular data maintenance optimizes marketing efforts and boosts overall performance, helping businesses meet their goals without unnecessary obstacles.
Data Cleansing May Involve:
Address Standardization (Correcting & Validating Address/City/State/Zip)
National Change-of-Address
Contact Name Corrections
Company Name Corrections
Phone Number Fixes - Adding Area Codes, Removing Extra Characters
Identifying Deceased Persons
Suppressing 'Do-Not-Contact' Individuals or Households
Removing or Merging Duplicate Records
Enhancing With Missing Elements Like Phone or Email or Sic Code