Data Cleansing Tools: What You Need to Know?

By |2022-10-25T05:11:12+00:00January 30th, 2022|

Data cleansing has made the reliance on data information manageable by maintaining data quality and keeping integrity a top priority for businesses.

However, if data quality issues are not identified and validated at an early stage, it can lead to operational inefficiencies, financial losses, and missed opportunities. It is essential to have a well-planned data cleaning and preparation strategy to avoid such losses.

The data cleaning process can be complex if you have different data sets coming from disparate sources. Having an efficient data cleansing strategy maintains data integrity during a data cleansing project. This article highlights the following:

What is Data Cleansing, and Why is it Important?

Data cleansing, also known as data scrubbing or cleaning, is the first step in data preparation. It involves identifying errors in a dataset and correcting them to ensure only high-quality and clean data is transferred to the target systems.

When information comes from multiple sources, such as in a data warehouse, the need for cleansing data increases as the sources might have redundant, incompatible, or dirty data formats. For instance, many organizations collect data directly from customers through surveys and forms.

Data processing and data cleansing may be essential to sort the data into a single format. In this case, lead cleansing software and Salesforce data cleansing tools can be beneficial for general information cleansing and sorting.

Another relevant example could be data cleansing and profiling in data analysis, which could help an analyst find meaningful patterns in clean, validated data to support business decisions.

Moreover, given the increasing reliance on information systems and technology for deriving strategic business insights, poor data quality increases an organization’s exposure to risk.

Hence, to remain competitive in today’s dynamic business environment, it is essential to eliminate data inconsistencies. Therefore, enterprises must employ a rigorous data cleansing process to ensure that their data assets are accurate and complete.

Difference between Data Scrubbing and Data Transformation

Businesses generate and receive large volumes of data from every business function. This data is stored in separate information systems in a variety of formats.

To create a central data repository and aid data retrieval and analysis, organizations use various information systems, including data warehouses or databases, for storing data. Data transformation and data cleansing in ETL are two techniques that help prepare this enterprise data for integration, reporting, and analyses.

Data cleansing helps ensure data accuracy so that only high-quality data is made available for analysis and decision-making. For instance, a business receives data from various sources, such as a CRM data or an ERP system, containing duplicated records or incorrect information. Nevertheless, the question now becomes how to do data cleansing?

An effective data cleansing or data scrubbing tool would help identify these inconsistencies and rectify them. The cleansed data will be converted into a suitable format and loaded into a data warehouse or target database.

On the other hand, data transformation involves converting raw data according to the format and structural requirements of the target database. Depending on the data integration scenario, the data transformation process can be simple or complex.

Data Quality Checks in ETL Tools

Although data transformation and data cleansing are two separate terms, many ETL tools offer advanced data profiling and cleansing capabilities along with data transformation functionality to cater to complex data management scenarios, such as data migration and master data management.

Astera Centerprise is an enterprise-grade data management solution that enables users to evaluate the integrity of critical business data with its flexible data quality and validation features, which enhance the data processing and cleaning during the ETL process and provide accurate data for business intelligence.

Features to Consider

Many industries, including banking, insurance, retail, and telecommunications, generate heaps of data sets every day and need accurate insights for strategic decision-making. Hence, data scrubbing or cleansing is a crucial step.

However, going through millions of records manually can be a daunting task. Therefore businesses require an intelligent tool for data cleansing in a data warehouse that can identify any discrepancies based on custom rules.

Free data cleansing tools may be the right choice for basic errors and simple sorting of data. However, enterprises that need to check data on a granular level according to specific business-defined rules would require robust data cleansing tools.

The key to selecting the right data cleaning tool is research. Browsing through review websites like Capterra, G2 Crowd, etc., will give you a fair idea of what data cleaning methods are available in the industry.

Here are the essential features that a successful data cleansing strategy or tool should possess:

  • Extensive Data Profiling and Cleansing Capabilities

An end-to-end data cleansing solution should include data profiling and cleansing features, which can automate metadata identification and provide clear visibility into the source data to pinpoint any discrepancies.

  • Advanced-Data Quality Checks

Data quality checks are objects or rules that you can integrate into the information flow for monitoring and reporting any errors that may occur while processing data. During the data cleaning process, robust data validation rules are applied to ensure data integrity.

  • Easy Data Mapping

To ensure that your data is being cleansed with accuracy, it is essential to correctly map or match data from source(s) to transformation(s) and then to the destination(s). Tools featuring a code-free, drag-and-drop, graphical user interface can support such functionality. Easy data mapping also enhances the usability of a data scrubbing tool.

  • Enhanced Connectivity

A data cleansing tool should provide support for the commonly-used source data formats and destination data structures, including XML, JSON, EDI, etc. Connectivity to popular destination formats allows you to export the cleansed data to versatile destinations, such as SQL Server, Oracle, PostgreSQL, and BI tools, like Tableau and PowerBI. This enables your business to access high-quality information faster for timely decision-making.

  • Workflow Automation

This will help automate the entire data cleansing project starting right from the profiling of incoming data to its conversion, validation, and loading to the preferred destination.

Comparison of Tools

When searching for the best data cleansing software for your organization make sure to check its features, pricing, and maintenance cost.

A few aspects to look for in a data cleansing software are – or the questions that you should ask the data cleansing tool vendor include:

  • What features does the data cleansing software offer?
  • Does it have API connectors to directly get data from systems?
  • Is it a visual data cleansing platform? Will the user need to learn how to code?
  • Does it offer integration capability?
  • What will be the yearly charges of data cleansing software?

It is even better if you start by listing down your requirements and then search for data scrubbing software that meets those requirements.

In most cases, you would need data integration software with data cleansing & transformation functionality. Because if your data is available from multiple sources, you will need to map it to the target destination after cleansing. A data integration software with a staging area can easily fulfill this requirement.

Astera Centerprise – The Smarter Way to Cleanse Data

Astera Centerprise, one of the top data cleaning tools, is a complete data integration solution that offers data cleansing and transformation features in a unified platform, ensuring data reliability and accuracy.

The advanced data profiling, cleansing rules, and quality capabilities allow users to ensure the integrity of critical business data, speeding up the data scrubbing process in an agile, code-free environment.

With the right data cleansing strategy, Astera Centerprise can help businesses cleanse data in multiple ways. The following steps can also be used as a data cleaning plan template:

  • Identification of errors

The first step of every data cleansing process is data profiling i.e. to identify data inconsistencies. The Data Profile transformation allows the user to examine source data and get detailed statistics about the content, structure, quality, and integrity of data.


Figure 1: Data Profiling 

The screenshot below shows the data profiling results of sample customer data. Users can study the source data and determine the error count, blank count, data type, duplicate count, etc. This information cleansing is important for advanced data analysis.


Figure 2: Data Profiling Results

  • Correcting Duplicates in Data

Data duplication is one of the major causes of poor-quality data and is often caused due to manual data entry or data consolidation from multiple sources.

To get a unified view of data, you must merge the complementing datasets and remove the duplicates. For deduplication, you can use the Distinct transformation, which is designed to identify and remove duplicates from data.


Figure 3: Distinct Transformation 

  • Correcting Incorrect Information

Businesses receive data from multiple sources that might contain incorrect information, thus affecting data accuracy. Moreover, using this data for reporting and analytics can lead to misleading results.

Using the wide selection of advanced transformations available in Astera Centerprise, users can tackle any data cleansing scenario.


Figure 4: Data Cleansing Rules being applied in Astera Centerprise

For example, in this scenario, the source data contains incorrect email addresses. Now, the company wants to ensure that all email addresses contain ‘@.’

The Expression transformation can help fix these errors. With the help of ‘ReplaceAll’ expression, ‘@’ can be added to the records as shown in the screenshot below.


Figure 5: Expression Builder

Want to know more about how Astera Centerprise can help improve the quality of your enterprise data?

Get in touch with our solution architects to work out a plan to scrub, clean, and validate data using advanced automation techniques and ensure your business users get accurate analytics using an efficient data cleansing strategy. Test drive our data preparation tool for free.

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