Data cleansing plays a key role in data management. Also referred to as data cleaning or data scrubbing, data cleansing is the process of fixing incorrect, incomplete, or duplicate data in a data set. Data cleansing improves data quality and can provide more accurate, consistent and reliable information for any organization.
Though many businesses prefer to utilize outsourced data cleansing solutions from experienced data quality analysts and engineers or other data management professionals, businesses may also perform the data cleansing process in-house. Professional data cleansing services provided by reliable companies can remove bad data from the database in an efficient manner.
Importance of Data Cleansing Services
Clean data will save time and money and make any organization more efficient. Error-free data can –
- Prevent document loss
- Provide faster access to data
- Ensure better data security
- Keep data safe and organized
- Increase productivity
Data cleansing can fix different types of errors such as –
- typographical errors
- wrong numerical entries
- structural errors in data sets
- duplicate or irrelevant data
4-step Data Cleansing Process
While the data cleaning techniques used in organizations may vary according to the types of data the company handles, basic steps are –
Data inspection and monitoring errors
The first step is to inspect the data and audit information to evaluate its quality and identify issues that need to be fixed. This step involves data profiling, during which the analyst documents the connection between data elements, monitors data quality and gathers statistics on data sets to find errors, discrepancies and other issues related to the data set. Clear notes are kept of the errors to identify and fix incorrect or corrupt data.
Identify duplicates and remove irrelevant data
The next step is to identify duplicate data and any unnecessary information. Duplicate observations may occur during data collection. Filter out data or observations that aren’t relevant and repeated. Data cleansing tools can analyze raw data in bulk, automate the process and merge the needed records. This step helps to make data analysis more efficient, minimize distraction and thus create a more manageable dataset. Make sure to scrub your data on a regular basis and eliminate duplicate leads as they come in.
Fix structural errors and clean data
Structural errors include things like misspellings, incongruent naming conventions, improper capitalization, incorrect word use, strange naming conventions, and more. Such mistakes can cause mislabeled categories or inconsistent data, and duplicates. Correct those errors and then clean other data issues. The data cleaning process can be done using different techniques based on the project’s nature and data type. However, the final goal is removal or correction of data.
Check data quality
Once the cleaning step is done, run data health checks to verify data cleanliness. Check data quality to ensure that the final data makes sense and meets the current standards. Ensure that your data is regularly structured and sufficiently clean for custom needs. Check whether data points are corresponding and make sure nothing is missing or inaccurate. Automated data health checks using machine learning and AI tools can be used to verify that your data is valid. It can save time and cost, compared to manual efforts.
Data Cleansing – Best Practices
- Choose a data cleaning process that’s right for your data
- Use advanced data cleansing tools, based on your data type and volume
- Scrub data regularly and keep track of errors
- Develop effective data quality strategies
- Validate accuracy with proper data quality control
- Try filling or appending the missing information
- Train your staff on the importance of maintaining clean data
Busy with your core business works? Consider approaching one of the business process outsourcing companies that provides advanced data cleansing solutions. Secure your data and make it error-free.
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