Data Cleansing

Data cleansing is the process of analyzing and detecting incorrect or corrupt data and then correcting or removing it from the dataset. 

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Why Do You Need Data Cleansing?

When integrating and unifying customer data, ensuring the final unified dataset is accurate and reliable is critical.

There are a few reasons data cleansing is required. For example, human data entry often results in errors that need to be fixed, such as typos, missing fields, or incorrect data. Also, departments or systems might use different data structures, formats, or terminology to manage the same data types. When bringing that data together for unification and analysis, the data must be cleaned to resolve discrepancies.

What Does the Data Cleansing Process Look Like?

Data cleansing, sometimes referred to as data scrubbing, involves activities such as:

  • Deleting duplicates
  • Modifying or deleting bad data
  • Rectifying incomplete data
  • Validating data formats
  • Identifying and removing erroneous data

Data cleansing operations ensure the final data is of higher quality, providing more accurate, consistent, and trustworthy information to support data-driven decision-making by marketing, sales, customer service, and other departments. It also helps reduce data management costs and ensures that data is accepted for use across the organization.

Data Cleansing vs. Data Transformation

Data cleansing differs from data transformation. Data cleansing involves cleaning existing data in its current format. Data transformation involves converting data from one format to another, which is often required when moving data from one system to another.  

Data Cleansing vs. Data Enrichment

Data cleansing also differs from data enrichment because data enrichment involves augmenting the dataset with additional data from other sources to create a complete data set. For example, a unified customer profile might be augmented by third-party data that adds more customer information.

Data Cleansing Technology

Data cleansing capabilities are often found within systems that unify and analyze data. For example, a customer data platform designed to integrate data from diverse sources to create a unified customer profile includes data cleansing techniques to ensure it creates an accurate customer profile.

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