A Complete Guide to Data Cleansing

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Farah Kim • February,2023

Often find yourself spending hours trying to find scripts on StackOverflow to clean or process data? Whether you’re a data analyst, or an IT manager, you’re bound to spend a significant amount of your time “cleaning” data before using it for reporting, insights, or analytics.

You’re not alone.

In fact, according to multiple articles and reports, data analysts spend 80% of their time cleaning data, earning the not-so-endearing term of a “data janitor.” 

Don’t want to be a data janitor for the rest of your career?

This guide has everything you need to know about data cleansing approaches, solutions, and techniques you can use to get the job done faster and with accuracy. 

Let’s get started. 

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Understanding Dirty Data

Before we get into the method and approach for data cleansing, we’ll need to address the elephant dominating the room, i.e, dirty data.


Data is inherently dirty, caused by many factors that are often out of one’s control. It can occur for various reasons, including human error, changes in business processes, or system errors. Human error may occur when manual data entry is required, leading to inaccuracies and inconsistencies between records. Changes in business processes over time can also result in mismatched fields as newer data recording methods are adopted. System errors can occur when software glitches lead to unexpected outputs, such as incorrect calculations or duplicate entries.

Here is an example of data entry errors:

data matching table Image 3


Even if you can control human error, the collected raw data will always contain errors or duplication. For example, if you’re collecting customer data through a web form, there is a 98% chance for the same customer to have multiple emails or locations. Your data then holds duplicate information on the same customer, some that may be obsolete and irrelevant to your business requirements. You could easily spend weeks sorting this data out, which causes unnecessary friction between departments, affects productivity and kills efficiency.


The consequences of ignoring these data problems can be dangerous. Your organization could face legal suits, and security risks, be penalized for missing sanctions and GDPR compliance, lose money because of inaccurate insights or predictions, and many other problems. According to a survey by Price Waterhouse Coopers in 2001, 75% of 599 companies suffered losses due to dirty data. Although this is a 22-year-old study, the problem remains a challenge for organizations today.


Moreover, incorrect or missing information due to dirty data can lead to a variety of technical problems, such as difficulty in matching the duplicate records across multiple datasets, wrong selections when using filters and searches within a dataset, unidentified duplicated records, invalid relationships established between different types of objects, incorrect calculations due to errors in input parameters, and outdated information being used without warning.


All of this stresses the need for organizations to prioritize data cleansing. But the traditional way of handling dirty data is not adaptable to a large dataset. As organizations dabble in AI, ML, and Big Data, they need innovative ways to tackle dirty data

What is the Data Cleansing Process & Why Does it Matter?

Data cleansing is the process of transforming the quality of your data, making it reliable, accurate, and usable for business purpose.


Data cleansing is necessary because errors affect information quality – i.e, the insights you’ll get from the data would be flawed. Unfortunately, even with specific processes put into place for data entry and acquisition, error rates are still around 5% or greater. Legacy/existing data may require additional solutions beyond those used during initial acquisition to achieve satisfactory accuracy levels.


It’s important to understand though, that data cleansing is more than just updating a record. Serious data cleansing involves breaking down and reassembling the data.


In theory, data cleansing is similar to a water treatment process. Water must be cleansed of impurities before it can be directed to water systems for residential use. Without the cleaning process, you’d be unable to use water safely. Similarly, without data cleaning, your data would be unfit for insights, analytics, reports, campaigns, and any other data-driven business purpose.

Five Data Cleansing Steps

The data cleansing process can be categorized into five steps: 


Data analysis or profiling: The first step in data cleansing is analyzing the data to identify the errors and inconsistencies in the database. In other words, this phase is called data auditing where this phase will find all types of anomalies inside the database.


Create transformation workflow and mapping rules: define the detection and elimination of anomalies through a sequence of steps that involves correcting typos, fixing data fields, and other activities depending on the errors in the data.


Data match: identify duplicates or consolidate disparate sources: a critical step in removing redundancies and merging/purging data as needed.


Verification & validation: this phase may require human intervention where the results of duplicate records need to be manually assessed for its correctness before any further action is taken.


Create master records: Once the data is verified and validated, an updated master record can be accessed by users within the organization.


For any sizable data set, accomplishing these tasks manually is impossible and expensive. Yet, most organizations will still hire data analysts to use traditional methods to clean data, resulting in delayed outcomes and overwhelmed teams.


Organizations are crammed with a massive volume of information, which is being collected every day at an unprecedented scale. This situation decreases the value of collected data and indirectly affects data quality and analysis. This also implies traditional methods, such as coding and programming scripts to clean data are no longer efficient or effective enough.

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Traditional vs Modern Data Cleaning Methods

Traditional data cleansing methods are not suitable to handle a massive amount of data. These methods have resource constraints, scalability issues, and manual labor dependencies. Manual labor is often necessary, especially when assessing data match results, which can be time-consuming and prone to human errors.


In the traditional methods, a user would have first to audit the data to detect
discrepancies using an auditing tool like Unitech Systems’ ACR/Data or Evoke Software’s Migration Architect. Then they would either write a custom script or use an ETL (Extraction/Transformation/Loading) tool like Data Junction or Ascential Software’s DataStage to transform the data, fixing errors and converting it to the format needed for analysis.


The data often has many hard-to-find special cases, so this process of auditing and transformation must be repeated until the “data quality” is good enough.


These methods rely on multiple tools to do the job in isolation. There is no connected, interactive interface and no way for the user to view the state of their data.


Suffice to say, traditional methods are not cutting it for modern data types.

Modern Data Cleaning Tools Compared with Traditional Methods

While there has been a plethora of data cleaning tools, most can only handle a limited amount of data at a time with limited functionality. You would have to use a combination of tools, methods, and scripts to clean, match, deduplicate, and consolidate data. 


Modern data matching and cleaning solutions, however, are significantly more advanced as they incorporate machine learning algorithms allowing them to automate numerous tasks that were once done manually, such as sorting, filtering, standardizing, and removing duplicates from data sets. This enables greater accuracy and faster turnaround times than traditional methods. In addition, modern solutions are often cloud-based and easily scalable, so they can handle larger datasets with less cost and without investing in expensive IT infrastructure or personnel.


To summarize, here’s a table highlighting the difference: 

Traditional Data Cleansing Methods Modern Data Matching and Cleaning Tools
Time-consuming and labor-intensive Automated and efficient
Less accurate results due to manual errors Greater accuracy due to machine learning algorithms
Scalability is an issue due to high investments in IT infrastructure and personnel Scalability is not an issue, as these solutions are easily scalable for handling large datasets
High operational costs Low operational costs with savings over time through labor cost reduction

Some of the key benefits of using modern data cleaning solutions as compared to traditional methods include: 


Automation: Data matching and cleaning solutions today are automated, compared to traditional data cleansing methods which are largely manual and time-consuming.


Accuracy: Today, data matching and cleaning solutions can be more accurate than traditional data cleaning methods as they can utilize machine learning algorithms to detect patterns that may not have been visible to a human eye.


Scalability: Data matching and cleaning solutions today can handle large datasets much better than traditional data cleansing methods, which tend to be limited in terms of scalability due to resource constraints or manual labor requirements.


Cost efficiency: Investing in a data matching and cleaning solution is often cost-effective because it provides greater accuracy, automation, and scalability than traditional methods over more extended periods.


Flexibility: Data matching and cleaning solutions are also more flexible when it comes to working with different types of datasets, while traditional methodologies may require additional resources or modifications if used on different types of data sets


Tools for data matching and cleaning have advanced significantly over the past few decades. It’s time for businesses to empower their teams with the right automation tools to get more done at a fraction of the cost. 

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A Cost-Benefit Analysis of Using Modern Data Cleaning Tools Over Traditional Methods

Traditional methods have lengthy implementation times, which can add up to additional costs in terms of labor and resources. This can lead to delays in project completion, resulting in decreased customer satisfaction and reduced ROI.


On the contrary, modern data cleaning tools offer a significant reduction in implementation times, reducing it by up to 50%. This leads to faster project completion and improved customer satisfaction rates. Additionally, organizations can benefit from increased productivity due to the streamlined nature of no-code solutions.


A simple cost-benefit analysis shows: 

Traditional Methods Modern Data Cleaning Tools
Development Costs High Low (up to 50% savings)
Labor Costs High Low (35-45% savings)
Implementation Times Long Short (25-50% reduction)
Return on Investment Variable 20-30% ROI


How to Choose the Right Data Cleaning Tool

No-code data cleaning tools come in many different forms, each offering its unique set of features. When comparing different solutions, it is essential to consider factors such as: 


Cost Evaluation – Evaluate the costs associated with each option and determine which offers the most cost-effective solution for your needs.


Data Quality – Ensure that the technology’s data profiling, standardization, and matching capabilities can handle any quality issues in the data sets.


Security – Make sure that the solution has strong security protocols in place to protect sensitive or confidential data.


Usability – Choose a solution that is easy to use and understand by all users.


Integration – Ensure that the solution can easily integrate with existing systems and tools, making the data-cleaning process seamless.


Reporting – Check if the solution provides comprehensive reports on all activities, allowing users to monitor their progress.


Flexibility – Pick a solution that is flexible enough to accommodate any changes in your data processing requirements as needed.


Scalability – Consider a solution that can scale up or down as required for future demands.


Support Services – Check if they offer ongoing support services in case of any difficulties or technical issues.

Selecting the right data-cleaning solution is essential for businesses to ensure that their data sets are accurate, secure, and up-to-date. Careful evaluation of the above factors can help you identify the most suitable option for your needs.

Effective & Efficient Data Cleaning Techniques Using WinPure

WinPure is an all-in-one, no-code data match and cleaning solution that can clean, match, standardize, verify, and validate data within a single, user-intuitive interface. With WinPure, analysts can quickly find, identify, and correct any errors without the need to code scripts to identify errors. 


The software checks millions of records every minute while still maintaining a high degree of accuracy and speed. It uses fuzzy, numeric, exact logic algorithms with proprietary algorithms to spot patterns in your dataset so you can quickly fix any inconsistencies or typos you might have missed when editing your files manually. 


Some of the key features of WinPure include: 


No-code: Match complex data, define custom rules, and perform extensive data standardization and cleansing tasks without the need to code a single line! It’s so easy that even your business users could merge, purge, and clean their departmental data. 


Multiple views: Gone are the days of flipping between tools, systems, or sheets to assess your data. With WinPure’s single-view interface, you can profile, clean, and transform data. 


Advanced profiling and statistics: Get clarity on the percent of fields affected by data quality issues. Track how many fields are affected by odd characters, how many have numbers or alphabets mixed up, how many have incomplete information, and so on. Access advanced statistics you’d never have with a traditional method.


Advanced cleaning & customization: The cleaning matrix has sections that let you simultaneously perform cleaning operations on multiple columns. You can also use the Word Manager to create your custom dictionaries and spelling checkers, use any language you wish, and then save the settings for other projects within the WinPure system.


Data match using match definitions: Match names and addresses using fuzzy matching, zip codes and phone numbers using exact or numeric matching. You can match between tables or within tables as required. All you need to do is select the columns and Run Match.


International address verification:  The Verify (Address Verification) module will allow you to check the validity and deliverability of every physical address on your mailing list. It will automatically correct and add all missing address elements, comparing it to the latest country data, adding ZIP+4 info, Latitude/Longitude,  Carrier Route info, LOT Codes, County Names and Codes, Congressional Districts, and much more.


WinPure’s no-code, intuitive interface makes it easy for non-technical users to take full advantage of its features without extensive training or coding knowledge. The platform contains a library of ready-made rules which can be applied to any dataset to reduce errors and inconsistencies; this helps improve data quality within minutes instead of days or weeks. 

Data Cleansing Best Practices

Despite the best tools and technologies, data cleansing is mostly a process performed by specialists. If you’re new to data cleaning and are attempting to clean data using a software like WinPure, here are a few best practices to remember. 


✅Always save a backup of the data before beginning any cleaning process. That way, if something goes wrong, it can be reversed in order to resume working from where you left off without having to start from scratch. Better yet, always use sample data before treating your actual data. 


✅ Use data validation techniques such as range checks and format checks to ensure that only valid values are included in the dataset. This will help to identify errors more quickly and it also prevents inconsistencies which can cause issues further down the line.


✅Use fuzzy matching algorithms when dealing with text-based data containing spelling mistakes or typos. Fuzzy matching algorithms can help reduce noise and improve accuracy by recognizing variations on similar words in meaning and context.


✅ When dealing with missing values, always opt for the most reasonable approach depending on the context of the dataset and how it is being used (e.g., imputation vs deletion). Making an informed decision in this regard can ensure that results aren’t skewed due to incorrect assumptions made while handling missing values. 


✅ Take into account outliers when performing exploratory analysis or predictive modeling tasks by either discarding them (if deemed appropriate) or applying data transformations such as logarithmic scaling, min-max scaling, etc., so as to minimize their influence on the model’s performance metrics like accuracy, recall, etc.


Always review the quality of match results. You will need to manually review any false negatives or positives before making final changes to your master record. 


In Conclusion 


Data cleansing is necessary if you want reliable and accurate data to work with. However, the traditional methods of data cleaning, where only a team of specialists can come up with scripts and codes to clean data, is no longer effective, efficient, or even scalable. You need modern data cleaning solutions like WinPure that allow you to match, clean, and consolidate data without having to waste time. 

Remember, clean data improves outcomes. Dirty and unreliable data causes chaos. 

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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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