Signup
Signup

LIVE WEBINAR: See AI move from chatbot to governed agent for the close. Register Now →

See defensible AI built for finance + a sneak peek at the next-gen platform.  Watch Now →

How To Build an AI-Ready Finance Function. Read the Guide →

Login
Book a Demo

Data Cleaning

Data cleaning removes errors, duplicates, and inconsistencies to ensure datasets are accurate, complete, and reliable for analysis.
Back to Glossary

What Is Data Cleaning?

Data cleaning, also known as data cleansing, data scrubbing or data sanitization is the systematic process of identifying, correcting or removing inaccurate, incomplete, inconsistent, duplicate, irrelevant, or erroneous data within a dataset or database. This vital step ensures that the data is accurate, complete, and reliable for analysis. Common tasks in data cleaning include removing duplicates, filling in missing values, correcting errors, and standardizing formats. By addressing these issues, organizations can improve the quality of their data, leading to more informed decision-making and insights. Data cleaning often complements data profiling, working hand-in-hand to prepare data for analysis and maximize its value for business intelligence.

See also
Previous
Data Blending

Data blending merges datasets from multiple sources for unified analysis, enabling deeper insights and better data-driven decisions.
Next
Data Completeness

Data completeness ensures all required information is present in a dataset, enabling accurate analysis, reliable insights, and better business decisions.