Data cleansing is a tough job! Ask any data analyst and they will tell you they have to spend 80% of their time “cleaning data,” instead of preparing the data for business use.

In this guide, we want to help you see how you can clean data without wasting 80% of your time!

Here goes.

P.S Watch how to use WinPure to clean and transform 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 but not limited to:

❌ human error: when data is entered manually leading to duplicates, typos, and poorly structured data.

❌business processes: when new systems, software, or tools are introduced and data is merged or exported without a data quality strategy in place.

❌poor data collection: when data collection is not governed or monitored, such as a web forms where there are no data controls.

In a spreadsheet or database, dirty data would look like this 👇

data matching table Image 3

In this table, you can see the data fields have incomplete information, names with multiple spellings, duplicate entries, and invalid postal codes. You could spend weeks sorting this data, leading to delayed outcomes and poor organizational productivity.

Additionally, 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, and lose money because of inaccurate insights or predictions, among many other problems.

All of this stresses the need for organizations to prioritize data cleansing. But the traditional way of handling dirty data through manual scripting, using complex regular expressions, or Excel is not scalable and cannot be applied to large datasets that are needed for AI, ML, or big data projects.


Data cleansing, therefore, is the process of treating raw, dirty data and transforming it into records that are reliable, accurate, and fit for business.

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.

If you value information quality, you need to prioritize data cleansing as a strategic activity consisting of several steps. Contrary to most practices, data cleansing must not be treated as a simple “error-fixing” activity. You must understand the context of your data before you can treat or clean it. Once you know the context and end goal, you can follow a step-by-step process to clean and make the data fit for purpose.


The data cleansing process has five key steps:

data cleaning

✅ Data analysis or profiling: The first step is analyzing the data to identify the errors and inconsistencies in the database. For example, records with text in numbers (or vis-a-vis), with a mix of upper and lower case, incomplete fields, etc are detected using data profiling functions.

✅ Create transformation workflow and mapping rules: define the detection and elimination of anomalies through a sequence of steps that involves correcting typos, removing odd characters, and defining mapping rules such as combining First Name and Last Name into a Full Name field for marketing purposes.

✅ Data match: use fuzzy, exact, and numeric matching algorithms to identify duplicates in different data sets. Data match can also be used to consolidate disparate sources: a critical step in removing redundancies and merging/purging data as needed.

✅ Verification & validation: using verification modules on phone, address, or email data to ensure its validity.

✅ 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 expensive & time-consuming, especially when organizations are crammed with a massive volume of information, collected at an unprecedented scale. This causes a catch-22 situation where data is needed, but it’s not fit-for-purpose until it is cleansed, but because data analysts don’t have the time, they end up doing batch fixes or cleaning the data only when there is a business request. The data is treated in isolation, devoid of context and business understanding.

In an era where digital transformation is happening at breakneck speed, companies need to adopt faster methods to clean, prepare, and process data.

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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