data cleansing

Automated data cleansing solutions is ideal for SME businesses that are constantly under pressure to improve their data management processes and reduce costs. For many SMEs, maintaining their data quality remains a significant struggle. They are unable to keep their CRM data clean, accurate, and reliable; and they still use Excel to compare data manually and perform basic functions to clean customer data. Some businesses outsource the business to consultants or agencies who charge exorbitant amounts of money, yet efforts are disjointed & the data is nowhere near ideal levels of accuracy.

This is where automated data cleansing solutions can be beneficial.

Let’s dig in.


Automated data cleansing uses software or tools to identify and correct or remove errors, inconsistencies, and inaccuracies in a company’s data. It involves automating the data cleaning process to save time and reduce the risk of human error.

Small and medium-sized enterprises (SMEs) need automated data cleansing because they often have limited resources, making it difficult to manually manage large amounts of data. A defining quality of an automated data cleansing solution is its code-free or zero-code approach that allows even business users to clean data as required. Some tools also allow automated data cleaning scheduling, so you don’t have to worry about missing a data cleaning date.

Data errors can lead to incorrect insights and decisions, resulting in lost revenue, wasted resources, and damaged customer relationships.

For example, suppose an SME business sells products online and collects customer data. If the data contains errors such as misspelled names, incorrect addresses, or inaccurate email addresses, it can result in failed deliveries, undeliverable emails, and unhappy customers.

According to a report by Experian, data quality issues cost US businesses an average of $15 million per year. Additionally, a survey conducted by IBM found that poor data quality costs the US economy more than $3.1 trillion per year.

Automated data cleansing can help SMEs:

✅save time and money by reducing the need for manual data cleaning

✅ensure that their data is accurate, consistent, and up-to-date.

✅make better business decisions by providing accurate and reliable data insights.

Some examples of tools SMEs can use for automated data cleansing include WinPure, Talend, and OpenRefine. These tools can help SMEs identify and correct errors in their data, remove duplicates, and ensure data consistency.


Small-to-medium businesses don’t have the manpower or budget to hire an experienced data professional to treat data. They either have to hire freelancers, use third-party resources OR have a business user (such as a marketing executive) manually clean and treat data using Excel. While this may work for small datasets, it becomes a bottleneck when a large dataset is required for an annual report or a forecast.

This is where UI-based software such as WinPure, can help to automate cleaning in a straightforward way. There is no need for programming experience! Business owners will not need to hire specialists or data scientists to run these processes.

Here’s a step-by-step guide on how you can use WinPure to clean data:

✅Import your data:
 The first step is to import the data you want to clean into WinPure. You can do this by clicking on the “Import Data” button and selecting your data file. WinPure supports a wide range of file formats, including CSV, Excel, and Access.

✅Select your data cleaning options: WinPure offers a range of data cleaning options, including deduplication, standardization, parsing, and case conversion. You can meet your data cleaning objectives by simply clicking on relevant options.

✅Set up your data cleaning rules: Want custom rules? Such as changing all LTD to Ltd? You can use Word Manager to create custom cleaning rules.

✅Clean multiple files simultaneously: No more cleaning one file at a time. Simply import your files and clean them all in rapid succession!

✅Create your own matrix: By ticking the appropriate boxes, you can create your own cleaning operation that can be run in one process using the “Run Clean” button  You can even save the matrix design, which can be subsequently applied to other datasets.

✅Review the statistics of your data: The statistics window is used to check the quality of your data. It will present you with a complete set of statistics which you can use to help clean and correct your data, and to prepare it better for data matching.

✅Match your data to remove duplicates: Even after cleaning and standardizing your data, you will still be left with duplicate data. WinPure allows the user to match data to remove duplicates using a combination of fuzzy, numeric, and exact algorithms.

✅Verify data: Once you’ve cleaned the data and removed duplicates, you can start with verifying address data, ensuring you’ve got accurate data to work with. WinPure offers international address verification, a feature none other competitors offer within an affordable price range.

In summary, WinPure’s data cleaning capabilities provide a user-friendly, automated approach to data cleaning. By following these steps, you can easily import, clean, and export your data, saving time and improving the accuracy and quality of your data.


Data cleaning is an essential process for businesses looking to extract insights and make informed decisions based on their data. However, there are several risks and limitations associated with using data-cleaning tools as highlighted above. Most of the tools available in the industry are designed for tech users and not for business users, which further aggravates risk factors.

WinPure was developed as a data cleaning solution that even business users can use. Here’s how the tool performs against potential limitations and risks:

Risk/Limitation WinPure Solution
Limited Knowledge WinPure allows the user to constantly add in new information, thus overcoming limited knowledge.
Unstructured Data WinPure is designed to work with semi-structured data, which is the most common type of data used in business contexts. While it cannot work with unstructured data such as images or audio files, this is a limitation of most data cleaning tools.
New Errors WinPure allows the user full control over the cleaning process, so there is no risk of new errors being introduced by the tool.
User-Friendly Interface WinPure has a user-friendly interface that is designed to be easy to use for non-technical business users. The tool includes built-in templates and wizards to guide users through the cleaning process, as well as drag-and-drop interfaces that make it easy to select and modify data fields.
Limited Support WinPure has a team of customer support professionals who are available to guide users on getting the best from the solution. The support team is available via phone, email, and chat, and can help users with a range of issues, from technical problems to questions about how to use the tool effectively.

Overall, WinPure offers a range of solutions that address many key risks and limitations associated with data cleaning tools. The tool is designed to be easy to use, flexible, and customizable, allowing users to clean their data efficiently and accurately. Additionally, the support team is available to help users with any questions or issues they may encounter while using the tool.


Cleaning CRM data has always been a critical challenge. When Adventure Marketing approached WinPure, they needed a quick, efficient solution that could clean and standardize CRM data, remove duplicates, and create a reliable source of truth for the team.

WinPure’s user-friendly interface helped the agency reduce 5+ working hours per week while improving efficiency and output.

Review case study here.  

Written by Farah Kim

Farah Kim is a human-centric product marketer and specializes in simplifying complex information into actionable insights for the WinPure audience. She holds a BS degree in Computer Science, followed by two post-grad degrees specializing in Linguistics and Media Communications. She works with the WinPure team to create awareness on a no-code solution for solving complex tasks like data matching, entity resolution and Master Data Management.

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