What is data cleaning and why it is essential?
Data cleaning is the procedure of preparing data for business analysis by eliminating or modifying data which is inappropriate, duplicated, incomplete or improperly planned. This data is certainly not essential or helpful in any way, especially when it comes to analyzing the data of the company as it might delay the process or can lead to inaccurate results. There is a number of tactics essential for data cleaning that depends on how it is hoard along with the solutions being required.
Data cleaning is not only about eliminating information in order to create space for new information or data, but it is all about finding a technique to exploit an accuracy of data set without necessarily deleting data or information.
For organizations that work day and night, data cleaning includes several actions more than eliminating data, for instance, syntax errors, correcting errors (empty fields, identifying duplicate data points and missing codes), fixing spelling and standardizing data sets. Data cleaning is measured an introductory element of the basics of data science as it has an essential role in the uncovering reliable answers and analytical process. Most essentially, the aim of data cleaning is to create sets of data which are uniform and standardized to let business data analytics and intelligence tools to simply admittance and locate the right data for each query.
Advantages of data cleaning
Here are some important key advantages that come out as a result of the data cleaning process:
- The capability to plot the diverse functions and where it is coming from the data and what your data is intended to do.
- It eliminates chief mistakes and contradictions which are predictable when numerous sources of data are getting drawn into one set of data.
- Fewer mistakes mean satisfied customers and less frustrated employees.
- Using techniques and tools to clean up data will make every employee work more efficiently as they will be able to get what they required from the data.
The main and essential step towards starting a Data Cleaning project is to ask yourself what is your expectations and goals from the project and look into the entire scenario.
There are basically six essential steps in the data cleaning process that are as follows:
- Monitor mistakes or errors
- Standardize your processes
- Validate Accuracy
- Scrub from duplicate data
- Analyze
- Communicate with the team
How can you use data cleaning?
In spite of the type of data visualizations and analysis you required, data cleaning is one of the essential steps to make you sure that the solutions you create are correct. While gathering data from numerous streams and with labor-intensive input from users, data might have mistakes, have gaps or be incorrectly inputted. Data cleaning assist in ensuring that the information constantly counterparts the exact fields and makes it easy for business intelligence tools to interrelate with sets of data to locate information more accurately.
