Data cleansing is one of the most important practices that can go a long way for any enterprise. It ideally refers to removing any wrong or incorrect entries in the data to ensure coherence of the existing data at any given time. It basically helps in ensuring that the data a company relies on remains error free by a uniform and periodic scrutiny of it. It is a dedicated task that requires constant management to ensure highest standards possible. Data cleansing is a holistic task that can range from removing typing errors to altering previously entered data based on new findings.
 
In several cases, it also includes removing information that might now be redundant or of no use to the company. The biggest advantage of data cleansing is that it helps in ensuring availability of standard consistent data for reference at any given point so that there are no errors. This can be done through one of the several ways available or by combining them.
 
Data cleansing is applicable to data at any point or pertaining to any specific function of a company. Hence, production processes, statistical data, generic information all come under preview of data cleansing to ensure their coherence. Of the several ways available to do this, one of the most common methods for the same is regular analysis of data. Checking available information for their validity by personnel who have been trained or have sufficient background information to do the same is one of the most common ways to do that. Larger enterprises handling much more complicated data can rely on data cleansing tools which help in maintaining data quality.
 
However, when talking about data cleansing, it is also important to talk about data governance. As the name suggests, it refers to management of data to ensure overall quality and integrity of data. Both terms are interrelated to a certain extent as data governance is the first step towards ensuring quality of data, which is also, the ultimate goal of data cleansing. However, the terms differ in their points of application as data governance pays a lot of importance to ensuring that only correct and high quality data is entered into the system.
 
Overall, data governance and cleansing are two of the most important functions towards data management that any enterprise must invest in to ensure an error free working.

Further Reading –

Blog – Ensuring Data Quality for Purchased Parts & Products

White Paper – Improve Enterprise Data Quality through Master Data Management

Case Study – Large Wood Products Company leverages Verdantis for ERP consolidation across 25 plants

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Jessica Banks

Jessica is one of the most passionate marketing professionals in Verdantis. She is a strong proponent of master data management solutions for large enterprises. For her, Data drives Performance.

Latest posts by Jessica Banks (see all)

• Data cleansing- what it does for your business - February 5, 2015
• Tips and strategies for enterprise data management processes - September 12, 2014
• Master Data Governance is Re-shaping the Enterprises - August 22, 2014