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All you need to know about Data Cleansing.

  /  Anti-Money Laundering   /  All you need to know about Data Cleansing.
Anti-Money Laundering, Compliance

All you need to know about Data Cleansing.

 
One of the main challenges of cyber crimes, frauds, compliance and regulatory breaches that occur is data theft. Data protection is essential for every business and organisation not only for compliance with the law, but also for good reputation and to prevent consequent losses. Last year when I attended the RegTech expo I got the opportunity to learn more about data cleansing. When I first heard the term I was a bit confused as most of us believe that data stored and processed by organisations are clean. Well even though that definitely is the initial goal of every data protection team, as time goes by and the number of users/clients increase and our data lakes become swamps instead.
 
There is a need to create a clear structure to clean data to ensure that whenever compliance teams use data analytics tools, they get accurate results. The only way artificial intelligence would work is if data is clean and perfect. There is so much more importance given to invest in newer technology and softwares rather than on perfecting user data. Technology is not a hurdle anymore, but data management is. If data is not modified, erased and updated, then there will be a lot of inconsistencies (and irrelevant data) in search results while conducting due diligence related tasks. A very recent example of data breach and cyber crime due to data not being updated and cleaned is the Disney Plus case. It was also reported that due to the hack, users had to face customer service issues later on.
 
The one solution to most of these problems is data cleansing. And how do we do that? Here’s a quick guide to cleansing data.
  1. Detect corrupt data

One of the most basic ways to get started with data cleansing is by monitoring errors in the system and observe the trend of corrupt data by checking their source. This would help streamline the process.

2. Standardisation

This step would really help get rid of the unnecessary entries in the system. Standardising the point of entry is very crucial to manage data as having several data points could make data processing more complex.

3. Digitise validation

Validating data is important to consider once you cleanse the existing data and streamline it. Digitising the data validation process would help save a lot of time and costs when compared to manually doing the same.

4. Identify replicas

Identifying replicas helps get rid of unnecessary copies and will make life easier for data analysts and processors. It cleans a major chunk of the data and gets rid of irrelevant information stored in the system.

5. Analysis

Data Analysis is the best way to check the quality of your data. This step not only estimates what kind of data should be stored and what should be erased, it also helps determine the health of the data collected by your organisation.

Every organisation should include data cleansing as a part of their processing policy. Automating most of these steps could help save a lot of time, money and unnecessary data during compliance procedures.
 

Please note that all opinions made on this blog should be treated as a guide and not legal advice.

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