Best Practices To Keep Your Data Clean

Data cleaning projects are common at Essentity. However, data is an important part of an organization’s journey; we frequently discover that data cleansing and transfer tasks are not given the attention or resources they deserve.

Importance of Data Cleaning

We believe it is essential to define what we imply by “clean data.” Clean data is defined as accurate, full, constant, and unique in broader terms.


The criteria for keeping data correct and up to date can vary depending on the nature of the data. Information about companies, for example, is more constant than information about people.


From full datasets, staff can make more solid predictions and get a more broad overview.


There should be consistency between related data in the system whenever possible. Data entry conventions for record identities and fields make reporting and database segmentation simpler.


Reports can be misinterpreted if there are too many redundant records. On the programming side, it can also deliver false and unclear information to people who require it. On the other hand, users can trust the monitored data if duplicates are kept to a minimum or eliminated from the system.

How to Clean Data

Arrange a Data Discovery Session

Use this time to go beyond your company’s data, identify risks, and look for general issues and patterns (duplicates, incorrect data, data sitting in the wrong fields, fields used incorrectly, unused fields, etc.). This is also a good time to talk about post-cleaning governance activities and ensure that everyone in the organization understands the data vision.

Begin with the Basics

Start with a small portion of your data to clean. This will assist you in identifying a consistent approach to the process, establish a more exact view of the time required, and uncover any concerns sooner.

Extraction of Information from a Database

For each data model, transfer the necessary raw data from your system.

Create a Backup

Always keep a backup of your ‘raw data’ document and use this copy (renamed ‘Cleaned Data’) as the document to clean the data with. The idea is to keep a duplicate of your original raw data so that you can refer to it if necessary.

Set Time Limits for Yourself

At the start of a project, figure out how much time you’ll need to perform data tasks. The data project will take less time if the data is cleaner. If the data is more disorganized, you’ll need more time and may need to enlist the support of those with greater organizational knowledge to sort through it.

Assign responsibilities

Everyone needs to play a part. We’ve seen the most successful initiatives always have a data champion, someone assigned to quality assurance, policymakers, and organizational context holders.

Encryption and File format

When transferring data, pay close attention to the file type and encoding. For example, CSV files are a standard file format for huge datasets (they take up little space), and UTF-8 is a secure encoding language that works across multiple operating systems and languages.

Make a Note of Everything that Changes

Data cleaning always entails various jobs (sometimes more than expected), and it isn’t easy to keep track of what’s been changed. However, keeping a record (a basic spreadsheet or google doc will help) is a fantastic way to maintain a record of the history of recent changes, and it’s especially valuable when numerous people are working on data cleansing rather than just one.

Labelling of Files

When transferring your data, make sure each file is correctly labelled. Add the data model, the export date, and the label ‘Raw Data’ to indicate that the file contains the raw data before it was cleaned.

Importing Data

Put your data back into your system once you’ve completed a cleanse of all your data.

The Advantages of a Good Data Cleaning Method

In the twenty-first century, data is one of the most valuable commodities. 

But, unfortunately, one of the major difficulties for organizations today is verifying that data is reliable and useful.

Let’s move on to some of the advantages of effective data cleansing processes.

It helps you make better decisions

Clean, high-quality data can help with analytics and business intelligence. As a result, it may be possible to make better decisions and get better results. One of the most important advantages of having a robust data cleansing process is that it reduces the amount of data that needs to be cleansed.

It hastens the acquisition of new customers

By assuring that their data is of excellent quality, businesses can sharply improve their client acquisition efforts.

An efficient data cleansing strategy can help you achieve this. A corporation might, for example, be significantly more efficient at attracting new consumers and even retargeting previous ones by cleansing and assuring data accuracy. CRM (Customer Relationship Management) software is built on this guiding philosophy.

It conserves precious resources

Getting rid of duplicate and erroneous data from databases can help a company save money. Both storage space and processing time are included in these resources. Duplicate and erroneous data can quickly drain an organization’s resources, especially if it is heavily data-driven. Cleaning and scrubbing data after it’s been acquired can take a long time and cost a lot of money.

It increases output

Clean data allows employees to make the most of their working hours. However, employees may spend a large amount of time cleaning data and re-analyzing it if the data is of poor quality. Furthermore, as the data is of inferior quality, employees may make inaccurate conclusions. At best, this can result in major inefficiencies, and at worst, it can result in disastrous mistakes.


Clean data will allow you to move the relevant organizations, connections, and grants to the new system and build the proper links between these data elements. It’s also crucial to have clean data in areas that are utilized to generate functional logic.

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