Data entry is a common task in any company, used especially in the administration department. During the entry process, incorrect and inconsistent data administration can cause faults and may harm other further related tasks. Thus, data cleaning or data cleansing are essential routines that can optimize productivity. Besides, by adding a method of data cleaning, people can avoid time-consuming problems identifying and fixing later. Data cleaning is the process of checking, or mistake-proofing for the correctness, consistency, and well function of entry data. Data quality contributes significantly to the precision of the result. Inaccurate, unusable, or corruptible data will be fixed or deleted. Or worst, it will cause many troubles of false result deliverable that can cause loss and harm the operation.

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Advantages of data cleaning that anyone should know:

  • Generate positive results in improving productivity – Everyone can get benefit from this since the data is sorted in a logical way which can help people to get the needed extracted data.
  • Reduce the employees’ exhaustion when they are dealing with such a mess in the database.
  • Reduce waste in time-consuming, unnecessary movement, and activities of employees to seek data. Thus, the company can maintain its budget.
  • Improve the quality of the decision-making process – by utilizing the potential and up-to-date data can build up a brighter path that can bring you to better and efficient decisions on any issues.
  • Ensure the compliance with data security and GDPR which can eliminate or decrease chances of data-related legislation compliance risks

Everyone and every business can get these benefits through can-not-be-easier data cleaning’s steps:

Monitor and scheme errors – mistake scanning as the first step of data cleaning

The company should have performance records for every set of data usage. It is essential to observe and identify how effective data contribute to each project. From this, the user can give judgment and decide which data is necessary for the task they are working with. Furthermore, they can act immediately to monitor the corrupt, unnecessary, or incorrect data.

Design working flow with data in the standard:

Each department can decide what kind of data or entry that they usually need for their task and project. From this, they can start to gather potential data and throw away unrelated data. Thus, it makes the working environment cleaner and more logical. This type of sequencing data working process will work effectively with repetitive administrative tasks. The staff can avoid unneeded data entry errors and save time a lot.

Be wise with duplicate data during data cleaning process

Not every duplicate data is bad; not every duplicate is useful. Scanning and identifying duplicates to decide with each duplication can help you save time later when you do data analyzing then. You can add a habit to your data working behavior as more conscious whenever you think you need to duplicate any data

after data cleaning: Accuracy confirmation, validation, and reporting

After cleaning the database, you should make sure the database is valid and accurate. You can use a bunch of data tools such as AI, SQL, Excel VBA, etc. to verify the correctness, well function, and up to date of existing data. You can make a report to your team, who is involved with the cleaned database, that the data is healthy and ready to use.

AFTER DATA CLEANING: Communication for the future with effective data usage

You should ensure that everyone will contribute to maintaining the cleanness of the database. The team will save more time when keep over and over again, cleaning messy databases. Instead, everyone can spend time more productive in other essential tasks. As well, they can have more focus on the customer: ensure the best quality services, seek more potential clients, and reach more target customers in the segmentation.

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