Data cleaning and data entry are common tasks in all companies, especially used in the administration department. Incorrect and inconsistent data management during the input process can cause errors and hinder other related tasks. Cleaning information can therefore be an essential routine that optimises productivity. It is the process of verifying data or checking for errors for correctness, consistency and functionality of input data. The quality of the data significantly affects the accuracy of the result. Inaccurate, unusable or corrupt information will be corrected or deleted. Or at worst, it causes many problems caused by incorrect results, which can cause losses and damage operations.
- Increased productivity: This benefits everyone as the information is sorted logically, which can help people get the extracted information they need.
- Reduced employee exhaustion
- Reduced time-consumption: The time for seeking data decreases
- Improved quality of decision-making: By utilizing potential and up-to-date information, you can build a brighter path that can lead you to better and more effective decisions in all matters.
Can-Not-Be-Easier Data Cleaning Steps
Monitor and scheme errors / mistake scanning
The company should have performance records of every data usage. It is essential to observe and recognize how effective data is in advancing each project. Based on this, the user can evaluate and decide which information is necessary for his work. In addition, they can take immediate action to control corrupt, redundant or incorrect data.
Design working flow with data in the standard
Each department can decide for itself what kind of information or entries it usually needs for its task and project. From now on, they can start gathering potential information and discarding irrelevant information. Thus, it makes the work environment cleaner and more logical. This type of data sequencing process works effectively with repetitive administrative tasks. Staff can avoid unnecessary data entry errors and save a lot of time.
Be wise with duplicate data during data cleaning process
Not all duplicate data is bad; not every duplicate is useful. Scanning and identifying duplicates with each copy can help you save time later when analyzing data. You can add more awareness to your data work behaviour whenever you think you need to copy data.
After data cleaning: Accuracy confirmation, validation, and reporting
After cleaning the database, you should ensure that the database is valid and accurate. You can use a range of data tools such as AI, SQL, Excel VBA, etc. to check existing data is correct, well-functioning and up-to-date. You can make a report to your team involved with the cleaned database that the data is in order and ready for use.
You should ensure that everyone contributes to maintaining the cleanliness of the database. The team saves more time by cleaning messy databases over and over again. Instead, everyone can spend time more productively on other important tasks. They can also focus more on the customer: ensure the highest quality services, search for more potential customers and reach more target customers through segmentation.