• By : Byaak Digital
  • 2020-10-08 09:49:39
blog 1

Steps for Data Cleaning & its Significance

Let’s start with what is data cleaning; Data cleaning is the process of detecting the inaccurate, imprecise, irrelevant data and then, replacing the data with the modified one or deleting the errors.
Since you are working with data to compute and improve your program, have you ever added data cleaning to your practice?

Let’s get started with some reviews on data cleansing: The data quality is important either the data is collected from telematics or any other sensors. Data cleaning is the process of detaching the inaccurate data, inconsistency, and errors and replacing them with modified one or removing the errors. It is also the process of assuring that your data is correct and prevent the error from happening again.

What makes the data cleaning massive task? As we all know a lots of data cleaning can be done by software which must be monitored and the inconsistency of the data should be recorded. This is the reason which creating a protocol for data cleaning is essential.

Benefits of Data Cleaning                           

  • It improves the quality of data and processing it, can prevent the errors from happening.
  • It enables for more accuracy and precise analytics that helps in the overall decision making process.
  • It helps people to easily find what they are searching from the data which makes the work efficient.
  • The major errors and inaccuracy can be detected and detach from the data. It ensures that your data is correct, consistent and usable to make decision.

Steps for Data Cleansing

To get accurate and consistent data and meet your goals on how your swift data can satisfy you, you must first focus on how will you evaluate and implement data cleansing successfully. The main reference for data cleaning is to focus on your top metrics. What is the major goal of your company and what are you expecting to achieve from it? What are the members of your company wants to achieve. Gather the interested members, involve them and start bringing out the idea is a good way to get started.

Let’s start with some of the basic steps for the process of cleansing the data:

  • Monitor the errors:

                          Obverse the whole data carefully and keep a record of where the most of the errors are coming from; this will help to detect the errors, inaccurate and imprecise data and fix them. This is the most essential factor if you are intermixing other solutions with your active management software so that errors don’t block the work and efforts of all other department.

  • Standardize your data:

                         When you have large volume of data points, it’s both time consuming and expensive to handle the scale and complexness of the data quality. So, it’s important to standardize your data and to handle such large scale of the data, the point of entry is must. By implementing this process, the risk of the duplication of data will reduce.

  • Validate your data accuracy:

                        Validating your data accuracy will help to reduce the cost of manual coding, the cost of data processing, saves time and also reduce the risk of human error. Research, verify and find the tools to clean your data in real-time. Many tools even use AI or machine learning to test the accuracy of data.

  • Scrub the duplicate data:

                       Duplicated data also increases the inconsistencies between data sets and reduce the quality of data. Duplicated data also increases the storage needs. Better identify the duplicates which will save your time when analyzing the data and automate the process for you.

  • Analyze: 

                     After completing the process of standardizing, validating and scrubbing the duplicated data, use the third party sources to include it. The third party sources will directly get the data from first party sites, then analyze the data to provide complete information for business intelligence and analytics. 

  • Communicate with the team:

                    Communicate with the team about the standardized cleaning process. Reduce the introduction of errors that comes from manual processes. Send the information to the targeted customers will help to meet the requirements of large customers in short time. Make sure you get your team involve in this. 

  • Get Your ROI from Data

               Consistency and accuracy of the data are two underlying jobs that are to be handled while doing the task of managing data. These steps make it easier to create a protocol. Once you are done with data cleaning, you can move to other task as now your data is accurate and consistent.