Data is one of the most worthy assets of any organization. However, raw data is often incorrect & incomplete which results in inaccurate analysis and decision making. In order to ensure the reliability & data accuracy, two critical processes are performed including data cleansing & data validation.

What is Data Cleansing?

Data cleansing also known as data scrubbing includes finding out & rectifying errors in datasets. This process guarantees that data is accurate, complete and consistent for decision making.

Key Steps in Data Cleansing:

• Removing Duplicates: Duplicate records can skew analysis and lead to redundancy.
• Taking care of missing data: Filling in missing values using methods like interpolation, mean/mode imputation or predictive modelling.
• Removing Inaccuracies: Fixing & detecting incorrect data entries.
• Getting rid of irrelevant data: Filtering out irrelevant & unnecessary information that doesn’t contribute to analysis.

Data Validation

Data Validation is the process of ensuring that data meets the specific quality and format standards before being used for analysis. This helps reducing errors & inconsistencies in datasets.

Data Cleansing & Validation- Quick view At Its Benefits

• Data Accuracy: Minimizes errors and data is reliable.
• Improved Decision making: High Quality data leads to precise analytics and insights.
• Operational Efficiency: Clean data curtails the processing time & storage costs.
• Adherence to Regulations: Most industries boasts of having strict data quality regulations that require accurate and validated data.

On a Final Note:

Data validation & cleaning are essential processes to sustain data integrity and ensure productive decision making. Organizations that invest in robust data quality practices can gain better insights, improve efficiency and maintain a competitive advantage in todays’ world driven by data.