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.
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.
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