Data Cleansing with AI/ML

We all know about the new-age data analytics organisations the core function of which is to thoroughly analyse different types of data flowing in from a variety of sources. But what we don’t know is that firms end up spending only about 20% of their total work time in data analysis. What do they do the remaining 80% of the time? Well, they involuntarily spend it on cleaning all the “dirty data” and prepping it up for deep analysis.

Now practices like these inevitably lower man productivity in any company, not to mention increase costs of labour and result in time wastage too. This is why a large number of business enterprises, especially the data analytic firms, are now considering better and more efficient options for data cleansing, namely AI or ML-driven data-cleaning techniques.

What Exactly is Data Cleansing?

Before we proceed any further, let’s clarify this first. The data recorded and stored in an organisation is usually never error-free. It’s always cluttered and filled with anomalies. The reason: data entries are done by humans in these enterprises and humans are bound to make errors.

However, the problem arises and comes to the fore when these large amounts of discrepancy-laden data start seeping down to the basic level, affecting the entire data analysis process. As a result, analysts need to spend huge amounts of time, effort, and money to clean up all this “bad data” before they can get on to analysing it for application in specific streams.

How Can ML and AI Data Cleanser Help?

AI stands for Artificial Intelligence and its subset Machine Learning (ML) both have crept into various business industries rather rapidly. Their potential scope in effectively carrying out data-cleaning processes in organisations is now gradually being explored.

Now, data cleaning would require two important steps:

• Identifying errors and inaccuracies in data
• Fixing data discrepancies for better analysis

If machine algorithms can be trained to carry out these two steps efficiently, it’s a win-win situation for data enterprises because they can save up huge bucks and manpower invested into these tasks.

The AI/ML-driven software can be used to first identify the exact error and the various multiple sources where it exists. The machine algorithm then needs to work on replacing the inaccurate data values with correct and updated data, while also ensuring consistency in pattern across all the various sources of data present.

Besides this, the machines would also need to be trained to auto-detect and auto-update data as and when needed to ensure that there is no bad data existing in the system at any point in time and that your customers always have access to refreshed or updated data.

To Sum Up

The very reason for the existence of bad data is human error, which is a natural phenomenon considering that human mind isn’t perfect. It’s only a machine that can be nearly perfect in whatever it does (depending on how well it’s been trained). If data cleaning with AI is implemented right, it can help solve a huge data problem.