Most people concur that your insights and analysis when using data are only as good as the data you are using. Essentially, garbage data in is garbage analysis out. If you want to develop a culture within your organization centered on the use of high-quality data for decision-making, one of the most crucial first steps is data cleaning, also known as data cleansing and data scrubbing.
Correcting or removing inaccurate, corrupted, improperly formatted, duplicate, or incomplete data from a dataset is known as data cleaning. There are numerous opportunities for data to be duplicated or incorrectly labeled when combining multiple data sources. Even though they may appear to be correct, results and algorithms are unreliable if data is incorrect. Because the procedures will differ from dataset to dataset, there is no one definitive way to specify the precise steps in the data cleaning process. But it is essential to create a template for your data cleaning procedure so you can be sure you do it correctly each time.
Data Cleansing Steps & Phases | Data Cleansing Tutorial | Data Science Tutorial
What is data cleansing?
Data maintenance includes data cleansing, also known as data cleaning, which entails locating inaccurate data and correcting it to ensure the right information and format. Processes like removing data fields with missing values, filling in blank fields with missing values, adjusting formatting to maintain consistency, and removing special characters are all part of data cleansing. Data cleansing can be done manually or with the aid of specialized software.
What is data maintenance?
In order to ensure accuracy and accessibility, data maintenance entails auditing, organizing, and correcting the data in management systems and databases. This procedure is crucial because it enables organizations to recognize problems, address them before they become bigger problems, and sometimes even prevent them from happening. The phrase “data maintenance” refers to a broad range of data-related activities. They are:
Data maintenance vs. data cleansing
Although data maintenance and data cleansing are related and share many characteristics, there are also significant differences between the two. Heres a comparison of data maintenance versus data cleansing:
Many aspects of managing your data, including cleansing, are included in data maintenance. While the main goal of data cleansing is to fix problems, the main goal of data maintenance is to avoid problems in the first place. Businesses implement data maintenance policies to keep tabs on databases, limit access to information, and specify management procedures. They develop data cleaning techniques to remove inaccurate data from the company’s database.
The entire data maintenance process is an ongoing effort. The process is continuous even though different organizations might finish each step at different times and intervals. As part of data maintenance, data cleansing typically occurs regularly. Data cleansing may occur once every quarter or just twice a year, depending on the size of the business and the volume of data it manages.
Since each component serves a different purpose, maintaining data requires a number of processes. For instance, data purging entails analyzing and deleting all unnecessary data from the system, as opposed to data monitoring, which involves tracking KPIs that inform an organization about the health of its data. The method for cleaning data is dependent on the organization’s strategy, but it usually entails developing a plan, analyzing the data, correcting it as necessary, and checking it for accuracy.
Customer relationship management (CRM) software is typically used by businesses to manage their data maintenance processes. These programs include data management features that make the process of maintaining data on clients easier, allowing businesses to organize their clients’ data and maintain relationships. Companies can either use separate tools for specific tasks or CRM software to handle every aspect of data maintenance, including data cleansing. Other options include data cleansing software and manual spreadsheet analysis.
The benefit of keeping your data accurate and secure is provided by both data maintenance and data cleansing. Data cleansing makes sure you have accurate, up-to-date information while data maintenance allows you to organize your data management processes. Companies can enhance their business operations by maintaining accurate data and monitoring data management processes.
Tips for maintaining and cleansing your data
Every organization has a unique method for managing data, and your strategy may vary depending on factors like your industry and company size. The following are some general pointers that you can use to develop plans for maintaining and purging your data:
Develop a plan
Depending on the goals of an organization, the process of maintaining data can be changed. Your ability to create a plan for data maintenance and cleansing will be aided by setting up clear goals for managing your data. Your goals will help you develop a framework that will help you choose the KPIs to monitor, define a healthy database, and decide when to complete each step of the data maintenance process.
Standardize your data
By standardizing your data, you can avoid data errors in a number of ways. Decide on a format for your numerical and text data, then set parameters so that only values in that format can be entered into your database spreadsheets. Consider adding, removing, or making certain input fields mandatory if you use data management software to maintain consistency. Additionally, some software lets you forbid users from entering the same information twice.
Conduct employee training
You can improve the efficiency of your data maintenance and cleaning processes by educating employees who have access to data about the company’s data management practices. Think about developing a standard operating procedure (SOP) and storing it somewhere employees can access it when managing, entering, or retrieving data. Allowing employee feedback might make it simpler to spot problems with your current data management system and fix them to increase efficiency.
Establish permissions for users
Most businesses keep private information about their workers, clients, or business operations. Include a permissions feature in the system you use when creating a data maintenance procedure to prevent unauthorized access that might result in data loss or corruption. Think about limiting access to spreadsheets or requiring a password to access data management or CRM software. You might find it easier to reduce errors if you restrict the number of employees who have access to company information.
What is data maintenance?
The process of organizing and curating data in accordance with university needs is known as data maintenance. Data must be properly cared for and maintained in order to continue being accessible and useful for the purposes for which it was originally intended.
What is the difference between data cleansing and data validation?
Data cleansing is the process of correcting inaccurate, incomplete, duplicate, or other incorrect data in a data set. It is also known as data cleaning or data scrubbing. It entails locating data errors and then correcting them by changing, updating, or removing data.
What is the difference between data cleansing and data transformation?
Data validation typically takes place when the data is entered into the database for the first time, as opposed to data cleaning. To ensure that the data is accurate and free of errors, data cleansing can, however, take place at any time after.