According to Gartner, by 2022, organizations that rigorously track data quality levels via metrics will generate 60% more improvement and benefits from their data quality improvement efforts.

Here are some interesting data quality statistics from ZoomInfo:

  • 67% of businesses rely on CRM data to segment and target customers and 94% of businesses suspect that their customer and prospect data is inaccurate

  • Data decays at an average rate of 2% per month, which means that 25-30% of your organization’s data goes bad each year

  • It costs $1 to verify a record as it’s entered, $10 to scrub and cleanse it later, and $100 if nothing is done

  • Bad data costs US businesses more than $611 billion each year

No matter the type of data, data quality is important. When you’re analyzing organizational data to make strategic decisions, it is best to start with a thorough data cleansing process. So, how can you get started with data cleansing? What makes it such an overwhelming task? How can data and analytics leaders ensure better data quality?.

This e-book provides a reliable starting framework to understand the various benefits of data cleansing and the best practices that must be followed during the process.

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