Process of Data Cleaning with iMarque Solutions

Data cleaning is an iterative process, and it may require multiple rounds of cleaning and validation to achieve high-quality data. It is a fundamental step in data preparation for various applications, including data analysis, machine learning, reporting, and decision-making.

Data Collection

Gather data from various sources, such as databases, spreadsheets, or external systems.

Data Inspection

Review the data to identify potential issues, such as missing values, duplicates, inaccuracies, and inconsistencies.

Data Profiling

Create summary statistics and data profiles to understand the characteristics of the datasets, such as the data types, ranges, and distributions.