Automated Data Cleansing for SME Businesses

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Farah Kim • February,2023

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. For example, many SMEs are unable to keep their CRM data clean, accurate, and reliable. 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.

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What is Automated Data Cleansing and Why Do SMEs Need It?

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.

The Benefits of Automating Data Cleanses

Let’s consider some further benefits to automating data cleanup.

Boost productivity and efficiency

Automating data cleaning means there is less risk of data being irrelevant or duplicated. This means agents can avoid contacting the same customers multiple times!

It also means that agents can find information easier. Of course, this means that they can effectively complete more projects in a given timeframe.

Free up critical time

Less time spent manually filling and cleaning data is always a boon. By automating the process, you free up availability for other tasks and processes.

The less time companies spend on menial tasks, the better. Data cleaning, while crucial, is repetitive. Repetitive manual work can lead to human error. It is simply not worth the risk!

Increases confidence

Automating data cleans automatically provides business owners with the tools they need to operate confidently. By delegating this work to a program, they can rest easy without the fear of human error.

This, in time, will likely result in more confident business decisions. As automated cleansing can scale up and down, it can also change and evolve with you.

Many business owners have more confidence in machines than people! For data-intensive tasks such as these, relying on programs is likely the better option.

Save money

As we know, dirty data is expensive. Therefore, any cleaning solution is likely to save money. However, automation can take these savings one step further.

Automated data cleansing removes the need for specialist data scientists. Because of this, there is no need for additional expert hires. There is also no need to spend excessive money training staff. Automation requires little guidance!

It’s always updating

It’s not easy to know precisely when data is starting to ‘decay.’ While talented data scientists will likely know the signs, everyday operators might not.

Smart programs and UI automation can learn how to spot dirty data. They can continue to update and learn in the background.

Therefore, automated cleaning programs will know what to look for and how to remedy problems. There is no need for excessive training or education.

Automated cleaning is simple to use

Providing one uses a UI-based system, autonomous data cleaning is effortless. It is easy enough to set tasks running in the background to ensure all records stay up to date.

How to Clean Data Autonomously – and Efficiently

Many business owners find that running scripts can be pretty complex. Besides, it is challenging to scale up automated cleaning through scripts alone.


Script building requires expert knowledge. This isn’t always something that a business operator will necessarily have. Scripts are also relatively inefficient. There is always going to be a requirement for someone to activate them!


Therefore, true automation should remove this need. Script automation can also confuse matters. It is not always seamless to see who has ownership of tasks this way!


Through these problems, cleaner, more user-friendly UI automation was born. UI automation effectively takes away the need to go deep into the code. This means everyone can start cleaning data!


By building automated cleaning into a simple UI, there is less risk of confusion. You’re also likely to avoid human errors making matters worse.


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: 

    1. 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.
    2. 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. 
    3. 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. 
    4. Clean multiple files simultaneously: No more cleaning one file at a time. Simply import your files and clean them all in rapid succession! 
    5. 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.
    6. 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.
    7. 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. 
    8. 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.

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Potential Limitations, Risks and Solution

While automated data cleaning solutions can solve a lot of our data cleaning problems, here are some risks, and limitations to look out for. Potential solutions are also provided against risks. 


Potential Risks Potential Solutions
Loss or corruption of critical data if there is a software error or crash Use data backups to regularly save copies of your original data. Always save the data after you’ve performed a step. 
Incorrect corrections made to data especially if rules are not defined If you don’t want the software to automatically change standards, you must use definitive rules that can be saved for all your data set later. 
Introducing bias into the data cleaning process Audit the data cleaning process to ensure it is free from bias, especially when it comes to non-English names of entities. 
Over-reliance on automation for data cleaning Automated tools are not meant to replace human monitoring. You must always review the outcome of a cleaning process before saving it as a final record.
Incomplete cleaning of the data Use multiple data cleaning tools and perform manual checks to ensure completeness


Some limitations with most data cleaning tools that you also have to look out for: 

    1. Limited to known issues: Because these solutions have pre-built templates, it is quite possible they may not be able to detect a new kind of data error. For example, an automated data cleaning tool may detect and correct misspelled names in a dataset, but it may not be able to detect a new type of change (such as Google renamed as Alphabet) which means you will have to manually add these rules. 
    2. Works only on semi-structured data:  Automated data cleaning tools cannot be used on unstructured data. So you will have to transform unstructured data (such as product text entries like: free-form text from sources such as emails, social media posts, customer feedback, and survey responses) before running it through an automated solution. 
    3. May introduce new errors: Automated data cleaning tools can sometimes introduce new errors into the data if they are not carefully validated. For example, an automated tool may identify a particular field in a dataset as being invalid and remove it, but in doing so, it may inadvertently remove a field that is valid and should be included in the cleaned data.
    4. Cannot replace human judgment: While data cleaning tools can automate many aspects of the cleaning process, they cannot replace the judgment and expertise of human data professionals. Human judgment is still needed to interpret the cleaning process results, validate the cleaned data, and make decisions about handling complex data issues.
    5. Poor user interface: Some data cleaning tools may have complex navigation or user interfaces that are difficult for non-technical users to understand or use effectively. For example, a tool may require users to write complex scripts or code to define the cleaning process, which can be daunting for non-technical users who are not familiar with programming languages.


To mitigate these limitations of automated data cleaning, data professionals should carefully evaluate the data cleaning tools they plan to use, invest time and effort into preparing the data for cleaning, carefully validate the cleaned data, and audit the data cleaning process to ensure that it is free from bias and accurately represents the original data.

How Does WinPure Perform Against These Limitations & Risks?

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 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.

Case Study - Automated Data Cleansing for a Marketing Agency

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 3+ working hours per week while improving efficiency and output. 


According to the Operations Manager,

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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, data deduplication, and MDM.

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