Excel, Databases, Tables, are the applications that store a large number of data. They have extensive data items and retrieving a single file from thousands of files is really very challenging. To organizes this data properly and increase accessibility users perform Data Cleansing. It is the technique that ensures that data present is correct, usable, consistent and free from errors. It groups the same type of data files, arrange them in an organized manner so that it can be fetched fast without any error.
Data imported from various sources excel, CSV, Databases has the same content but different format. Data cleansing includes removing the file duplicates and merge similar data from multiple groups. The main aim of Data cleansing is that the data is uniform, clean, qualitative and usable by the organization. There are some Data Cleansing tools available like Winpure Clean & Match for Data Cleansing automated process but in this article we will tell you about best practices to store your data in an easy and clean way so that you don't need any software. These simple steps will help to achieve this.
Data Cleansing Best Practices
Standardize the Process: User must adopt a standard way of data storing, as the contact details of a person name, first name, last name, email address are in their respective columns. It prevents the to duplicate data items. Moreover, there should be a set of rules from entry to checkpoints in which the data is to be stored, managed and export.
Continue Check for Errors: When the data is imported from Excel, CSV, and Databases it may contain error and if a single file gets combines with rows and data sets there is a risk of data corruption. So, monitor the data source and identify which source results in more errors and attacks, change the settings and disable that source from exporting. When the source is known the errors could be fixed at a faster rate.
Normalize Data: This is also an essential need to save similar items in same data format like if excel uses Mr. as initial for name and SQL uses mr. for the name, then both the data can be merged and set to Mr. only. In the same way, adopt a standard method to normalize the data types and save them as required.
Check for Duplicates: Data duplication is the major cause of high storage, the same information is displayed in two or more formats. It could be considered like users save contact numbers with different names on different devices. Misspelling, missing values are also the cause of data duplication. Remove the data duplicates and update the file accordingly
Check the Data Accuracy: It is the most important factor to achieve complete data cleansing. Make sure the data that is entered after removing duplicates, normalization and standardization is accurate. This could be achieved while merging the data after looking from the records. The format and extension of files are accurate according to the standardized method.
Remove Blank Spaces and Characters: When setting the format of an application, the data entered adapts to its customizations. We often observe long gaps between two words, as a part of data cleansing, user identifies such entries and remove them, during data entry random characters also get listed, so remove these characters before anything goes wrong.
Handle Row and Column values: Data stored in Excel sheets and Database tables is represented in tabular format, entries are written in rows and columns, with data cleansing one can handle the missing values in a row or cell. They can merge the row or remove the entire row whichever suits the best.
Validate and verify the data: Validate data for migration with other tools using the same type of data like Outlook mail contacts to Apple Mail. This includes credit rankings, key contacts, employee size or other parameters used by the organization.
Analyzing all these data sets and preparing the data file after these parameters, will give a consistent, healthy data that is easily accessible across multiple formats and let professionals to use it without any problem.