Data Cleansing and analysis are the first steps in managing the quality of data. Cleansing is the process of correcting and detecting inaccurate data from your datasets. Being a very critical step it directly affects the accuracy of the data.
Data Analyst spends lots of time in data analysis to ensure there are no inconsistencies, fixing errors, correcting invalid nulls among others. All good analysis relies heavily on clean data. Achieving this result of clean data without any negative impact on the data’s integrity is very difficult to achieve using traditional cleansing methods.
Traditional cleaning methods are tedious and repetitive that will make data analysts exhausted before they even get to the data analysis part. This cleansing has now been made bearable by modern analytics such as DQLabs.ai.
Challenges of Traditional data cleansing:
Modern data Cleansing:
1. Data Structures Visualization - Critical decisions such as removal of data are made from an overall understanding of the impact on the quality of data. Visualization of all the data can help analysts to wrangle some of the data while still maintaining focus on the bigger picture.
2. Cost-Effective - Cleansing can now be undertaken by one analyst this saves time and money.
3. Can Track the changes - When the contractor undertakes the tasks of cleansing, clients are able to able to track all the changes they made and the impact they had on data quality.
4. Deduplication - This modern data cleansing is possible to remove the duplicates in order to attain a single golden record. This is a very critical step in data analysis as it increases the overall data quality and the accuracy of the analysis findings.
Traditional cleansing methods are not only time-consuming but also increases the chances of negative data quality. Modern data analytics tools have simplified the process of cleansing and their effective analysis of data sets consistency increases the quality of data. With all these benefits in mind, it is no wonder many organizations are saying goodbye to traditional data cleansing techniques and adopting modern platforms with AI and ML capabilities.