Say goodbye to traditional data cleansing methods

Challenges of traditional data cleansing

Data cleansing is the first step in analyzing the data. Traditionally, this is a step that takes the longest of all the others. Being a very critical step that directly affects the accuracy of the findings, data analysts will go through a lot of data trying to ensure there are no inconsistencies, correcting invalid nulls, among others.

Traditional methods are tedious, repetitive, and in most cases leave the data analyst exhausted before they even get to the data analysis part. Here are the challenges of traditional data cleansing:

Error-Prone:

With all the copy-cut-paste, delete rows, add columns across many data sets and tables, it is very difficult for the analyst to keep track of all his actions and still do the analysis within a reasonable time. If they make a mistake while cleansing data, it may be reasonably impossible for them to identify that mistake, and this adds an error to the existing data. the raw data is too complex to alter and understand the full impact on the overall integrity and this leads to analysts overlooking some mistakes as a way of preventing further deterioration of data quality.

Time-consuming:

Forrester estimated that traditional data cleansing processes would take up to 80% of the time consumes for a full data analysis process. Analysts have to do an analysis of the data before they do the actual analysis by going through pages and pages and looking for inconsistencies and this process is time-consuming and can easily lead to human errors. Miscalculations and duplications as a result of exhaustion will sometimes force analysts to start all over in order to get accurate findings.

Difficulty Tracking Changes:

Tracking all the changes that are made is impossible to find even for the most experienced data analysts. This problem is made worse when the analysis is being undertaken by a group of analysts who works as a data analysis team.

Benefits of modern data cleansing:

Data structure visualization:

The critical decisions on the removal of data are made from an overall understanding of the impact on the quality of all data. By being able to visualize all the data, a data analyst can easily scrub some of the data while still maintaining focus on the bigger picture. This ability to focus on some data sets while still monitoring the quality score of the data is important as it ensures accuracy on the final analysis.

Time and Cost-effective:

Data cleansing can now be undertaken fast by one data analyst this saving time and money. Resources allocated to data management can now be directed towards other important activities such as data protection.

Ability to track challenges:

In the case where an independent contractor undertakes the task of data cleansing, the client is able to track all the changes that they made and the impact it had on quality. If a different contractor is engaged, they can easily track how the data got to where it is and easily undertake the cleansing process while still operating above the organization’s quality threshold.

Data Deduplication:

It makes it possible to remove copies related to the same data in order to attain a single copy. This is very critical in data analysis as it increases the overall data quality and the accuracy of the analysis found. This increases data storage capacity by creating more space & increases accuracy in future data usage.

Traditional data cleansing technique is not only time consuming but also increases the chances of negative data quality interference. Modern data analytics tools have simplified the process of data cleansing and their effective analysis of data sets, consistency, and increases the quality of data. With all these benefits it is no wonder many organizations are saying goodbye to traditional data cleansing techniques and adopting modern platforms with AI and ML capabilities.