---
title: Cleaning
url: https://documentation.cluedin.net/govern/cleaning
content_type: landing
section: Govern & improve
---

# Cleaning

Data cleaning helps you identify and correct any issues in your data: normalize value representations, transform capitalization, modify values, and more. After you've cleaned your data, you can use it to gain valuable insights that help you make more informed decisions.

In this section, you will learn how to fix data quality issues through the use of clean projects. In addition, you will learn how to proactively prevent the same data quality issues by generating rules based on your actions in the clean project.

The following diagram shows the basic steps of cleaning the data in CluedIn.

![Diagram: four steps to clean data in CluedIn - 1 identify data to be cleaned, 2 create a clean project choosing data and properties, 3 clean the data by transforming, normalizing and fixing values, 4 process cleaned data to send values to golden records](https://documentation.cluedin.net/assets/images/preparation/clean/clean-1.gif)

This section covers the following areas:

- [Creating a clean project](https://documentation.cluedin.net/govern/cleaning/create-clean-project.md) – discover different options that you can use to create a clean project.

- [Managing a clean project](https://documentation.cluedin.net/govern/cleaning/manage-clean-project.md) – learn how to work with a clean project, fix data issues, and prevent the same issues from happening again.

- [Reference information about clean projects](https://documentation.cluedin.net/govern/cleaning/clean-reference.md) – find information about clean project statuses and get an understanding of the clean project workflow.

## In this section

- [Create a clean project](https://documentation.cluedin.net/govern/cleaning/create-clean-project.md) (how-to): A clean project is where you specify the data to be cleaned, retrieve it from CluedIn, perform the cleaning process, and then send the cleaned values back to golden records.
- [Manage a clean project](https://documentation.cluedin.net/govern/cleaning/manage-clean-project.md) (how-to): After creating a clean project, you can begin the cleaning process to get a more ready-to-use set of data.
- [Clean project reference](https://documentation.cluedin.net/govern/cleaning/clean-reference.md) (reference): In this article, you will find reference information to help you understand the statuses of clean projects and the process of working with them.
- [Clean application reference](https://documentation.cluedin.net/govern/cleaning/clean-application-reference.md) (reference): In this article, you will find reference information about common transformation functions in the clean application.
- [Processing logic in clean projects](https://documentation.cluedin.net/govern/cleaning/processing-logic-in-clean-projects.md) (concept): In this article, you will learn about the underlying mechanisms involved in processing clues generated by the clean project.
- [Tutorial: clean data](https://documentation.cluedin.net/govern/cleaning/tutorial-clean-data.md) (tutorial): Cleaning the data in CluedIn involves finding the data that needs to be cleaned, creating a clean project, and modifying the data in the clean application.
- [How to fix address data](https://documentation.cluedin.net/govern/cleaning/how-to-fix-address-data.md) (how-to): In this article, you will learn how to fix address data using various methods:
- [How to standardize dates](https://documentation.cluedin.net/govern/cleaning/how-to-standardize-dates.md) (how-to): In this article, you will learn how to standardize dates using two different methods: by applying data part rules and by configuring data type during mapping.

Up: [Govern & improve](https://documentation.cluedin.net/govern.md)