Data cleaning and data cleansing are both related to correcting errors in data. Cleaning fixes errors, and cleansing finds the root cause of data quality issues and prevents them.
In the world of data, where we make decisions, find insights, and build strategies, data quality is king. But raw data often has problems. Typos, missing info, and duplicates are common. These errors, even if small, can cause big problems. Think of a sales report with inflated numbers because of duplicates, or a marketing campaign that fails because of old customer addresses.
Bad data can do more than just cause small problems. Wrong customer info can lead to embarrassing mistakes or lost sales. Incorrect inventory data can lead to running out of stock and unhappy customers. In industries with lots of rules, bad data can even mean breaking those rules and paying big fines. When we use data to make decisions, clean and accurate information is super important.
To fight these problems, we use two main processes: data cleaning and data cleansing. People often use these words interchangeably, but they are different ways to improve data quality.
Data cleaning fixes errors automatically, like typos or missing values. Data cleansing goes deeper, ensuring data is accurate and complete, often with manual checks. It’s like adding extra details to make the data even better. Together, they make sure your data is not just clean, but also reliable.
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What is Data Cleaning
Data cleaning is the process of automatically fixing invalid or missing data in a dataset using methods like data validation, transformation and enrichment. Fortunately, many of these basic data quality issues can be addressed through automated processes, making data cleaning a seamless part of your data pipeline.
As data flows through the pipeline on its way to the data lake, data cleaning steps can be strategically integrated. These automated transformations can tackle a range of common problems. Missing data, for instance, can be filled in using imputation techniques or flagged for further review. Values in inconsistent formats, such as dates or addresses, can be standardized to ensure uniformity. Even full duplicates, often the result of processing errors, can be efficiently identified and removed. By incorporating these automated checks and corrections, data engineers ensure that the data arriving in the data lake is already in a much cleaner and more reliable state, paving the way for smoother analysis and decision-making.
What is Data Cleansing
Data cleansing improves data quality by manually reviewing and correcting individual records and then investigating the root causes of errors.
It goes further than data cleaning. It’s about finding and fixing hidden problems in your data. This often involves more manual checks to make sure the information is correct and complete. But it also means finding out why those problems exist in the first place.
For example, if you keep seeing the same errors in customer addresses, data cleansing might involve looking at how your users are entering that information. Maybe there’s a problem with your website form, or maybe your employees need better training. By fixing the root cause of the problem, you can prevent those errors from happening again.
Think of it like this: data cleaning is like fixing typos, while data cleansing is like doing detective work to make sure the facts in your data are correct and preventing those mistakes from happening again.
For critical data assets like customer or product databases, data cleansing is the gold standard. It aims to create “golden records”—single, validated, and up-to-date sources of truth. This might involve cross-referencing information with external sources, such as validating addresses against postal databases or enriching customer profiles with demographic data. In some cases, specialized tools like geocoding software can derive missing details, such as ZIP codes, from existing address information. Additionally, cutting-edge techniques like generative AI models are emerging as powerful allies in data cleansing, able to detect patterns and intelligently fill in missing values. This multi-faceted approach ensures that your most valuable data assets are accurate, complete, and ready to drive informed decision-making.
Tools for Data Cleaning and Data Cleansing
Effective data cleaning often involves a combination of specialized tools and skills. Data quality and profiling platforms play a crucial role in identifying inconsistencies, errors, and missing values. These platforms provide insights into the overall health of your data, highlighting areas that require attention. Additionally, data engineers may need to leverage their expertise in data transformation and programming languages like Python to implement cleaning scripts within data pipelines. These scripts can automate tasks such as standardizing formats, removing duplicate data, and filling in missing values, ensuring a smoother flow of clean data into the data lake.
Data cleansing, with its broader scope, demands a more diverse toolkit. Address validation platforms help ensure the accuracy of customer addresses, while geocoding platforms can enrich data with geographic coordinates. Integrating with external systems via APIs allows for real-time validation and enrichment of data. Master Data Management (MDM) platforms serve as central repositories for golden records, ensuring consistency and reliability across the organization. Moreover, the power of machine learning, particularly generative AI models, can be harnessed to intelligently fill in gaps and derive insights from existing data. For example, these models can learn patterns in customer numbers or product codes, enabling them to correct errors or predict missing values. To fully leverage these advanced techniques, proficiency in Python and machine learning becomes essential.
The Difference Between Data Cleaning and Data Cleansing
Data cleaning is the process of fixing basic errors and inconsistencies in your data. It’s like tidying up a messy room, ensuring everything is in its proper place and free of obvious flaws. This might involve removing duplicate records, standardizing formats, and filling in missing values. Data cleaning is often automated, occurring as data flows through pipelines and into data lakes. It’s a crucial first step in ensuring your data is usable, but it doesn’t necessarily guarantee its accuracy or completeness.
Data cleansing, on the other hand, is a more comprehensive and investigative process. It goes beyond fixing basic errors to validate and enrich your data, ensuring it’s not only clean but also accurate and trustworthy. This might involve cross-referencing with external data sources, deriving missing information, and ensuring consistency with business rules. Data cleansing often requires manual intervention and collaboration with domain experts. It’s a deeper dive into your data, aiming to create a single source of truth that can be relied upon for decision-making and analysis.
Both processes are compared on the following infographic.
Best Practices in Automation
Both data cleaning and data cleansing can be labor-intensive processes, especially when dealing with large datasets. Data cleansing, in particular, often requires meticulous attention to individual records, making manual cleansing a time-consuming endeavor. The sheer volume of data can quickly overwhelm even the most dedicated team of specialists. Moreover, relying solely on manual intervention can lead to delays in identifying and rectifying issues, potentially impacting downstream processes and decision-making.
To address these challenges, automation becomes essential. By automating data cleaning and cleansing steps, organizations can ensure that data is fixed as soon as inconsistencies are detected, regardless of the scale of the dataset. This not only saves time and resources but also ensures that data quality is consistently maintained. A crucial aspect of automation is data quality monitoring. This involves implementing a platform that continuously evaluates datasets, running data quality checks, and flagging anomalies. When issues arise, the platform can automatically create tickets, assigning them to data stewards who can then investigate and determine the appropriate course of action. In many cases, this may involve collaborating with data engineers to modify data pipelines or working with business application owners to implement better validation checks at the point of data entry. This proactive approach ensures that data quality remains a top priority, minimizing the risk of errors and maximizing the value derived from data assets.
Data quality best practices - a step-by-step guide to improve data quality
- Learn the best practices in starting and scaling data quality
- Learn how to find and manage data quality issues
What is the DQOps Data Quality Operations Center
DQOps is a data quality platform designed to monitor data and assess the data quality trust score with data quality KPIs. DQOps provides extensive support for configuring data quality checks, applying configuration by data quality policies, detecting anomalies, and managing the data quality incident workflow. DQOps is designed in a unique way, providing client interfaces for automation within data pipelines, and a user interface for data stewards who are not familiar with coding.
The extensive API provided by DQOps allows full automation of all aspects of the platform, ranging from data discovery, data profiling, data quality testing, data observability, data quality incident management, and data quality reporting using 50+ data quality dashboards.
You can set up DQOps locally or in your on-premises environment to learn how DQOps can monitor data sources and ensure data quality within a data platform. Follow the DQOps documentation, go through the DQOps getting started guide to learn how to set up DQOps locally, and try it.
You may also be interested in our free eBook, “A step-by-step guide to improve data quality.” The eBook documents our proven process for managing data quality issues and ensuring a high level of data quality over time.
FAQ
Find answers to common questions about data cleaning and data cleansing below.
What is the Difference Between Data Enrichment vs Data Cleansing?
Data enrichment is actually one of the methods we use in data cleansing. Think of data cleansing as a toolbox filled with different ways to improve your data. Enrichment is one of those tools.
Data cleansing is all about finding and fixing incorrect or missing information. Enrichment helps us do that by adding information from other sources.
For example, imagine you have a list of potential customers with only their email addresses. With data enrichment, you can use those emails to find more information about those people, like their names, job titles, companies, and even where they’re located. This extra information can help you better understand your potential customers and make better decisions about how to reach them.
What are the Challenges of Data Cleaning?
Data cleaning definitely has its challenges! Here are a few of the big ones:
Huge amounts of data: Cleaning a small dataset is pretty easy. But when you have massive amounts of data, things get tricky. It can be hard to even identify all the errors, let alone fix them.
Missing information with no way to fill it: Sometimes you have missing values that you just can’t fill. Imagine you need a customer’s phone number, but it was never collected. If you don’t have a reliable source to find that information (like a third-party data provider), you might be stuck.
Data coming from different places: Many companies collect data from lots of different sources. This can create a real mess because each source might have its own set of errors and inconsistencies. For example, your accounting system might have perfect information about customer invoices, but no data about what those customers do on your website. Or your website might track users by their login names, which your accounting system doesn’t know anything about. This can lead to duplicate records and make it hard to get a single, accurate view of your customers.
How to Perform Product Data Cleaning?
Product data cleaning is all about making the information about your products accurate and consistent. Here’s how to do it:
- Find the problems: Look for things like missing images, inconsistent descriptions, or duplicate entries.
- Set standards: Decide on rules for your data, like how long descriptions should be or what size images should be.
- Use the right tools: Spreadsheets, scripting languages, and specialized data quality tools can help.
- Clean the data: This might involve standardizing formats, removing duplicates, correcting errors, and filling in missing information.
- Keep it clean: Regularly check your data and use validation rules to prevent new errors.
How to Perform Database Cleaning?
Database cleaning is like taking out the trash and tidying up your database. It’s about removing things you don’t need anymore and making sure everything is organized. Here’s how it usually works:
Remove unused tables: Over time, databases can accumulate tables that no one uses anymore. These leftover tables can clutter things up and make it harder to find what you need. Removing them simplifies the database and makes it easier for everyone to use.
Archive or delete old data: Many databases store information that’s no longer actively used. This old data takes up space and can slow things down. Archiving or deleting it frees up valuable disk space and can significantly improve the performance of your database. This can make your dashboards, reports, and business applications run much faster!
To find unused tables and outdated data, you can use tools like:
Data catalogs: These tools help you understand what data you have and how it’s being used. They can often identify tables that haven’t been touched in a while.
Data observability tools: These tools track things like how fresh your data is. If a table hasn’t received new data in a long time, it might be a candidate for archiving or deletion.
What are the Data Cleansing Steps?
Data cleansing involves a bit of detective work to make sure your data is accurate and reliable. Here are the main steps involved:
- Inspection: Start by looking closely at your data. What are the problems? Are there missing values, inconsistencies, or errors?
- Prepare a fix: Once you know the issues, figure out how to fix them. This might involve setting rules for your data or finding ways to fill in missing information.
- Fix the data: This is where you actually make the changes to your data. You might correct errors, standardize formats, or remove duplicates.
- Find the root cause: Data cleansing isn’t just about fixing errors; it’s about preventing them. Try to understand why the errors happened in the first place. Was it a problem with how the data was collected? A bug in your software?
- Prevent future errors: Once you know the root cause, take steps to fix it. This might involve changing your data collection process, training your employees, or updating your software.
Why is Data Cleaning Important?
Data cleaning is super important for a couple of big reasons:
It helps your business run smoothly. Think of it this way: bad data leads to bad decisions. If your data is full of errors, you might misunderstand your customers, misjudge your sales, or even make costly mistakes with your inventory. Clean data helps you understand what’s really going on so you can make the right choices.
It keeps you on the right side of the law. In some industries, like healthcare or finance, there are strict rules about how data needs to be handled. Clean data helps you meet those rules and avoid big fines or legal trouble.
What are the Data Cleaning Techniques?
Data cleaning involves a whole bunch of techniques to whip your data into shape. Here are a few of the most common ones:
Data conversion: This is about making sure your data is in the right format. For example, you might need to convert dates from European format (DD/MM/YYYY) to American format (MM/DD/YYYY), or change text to numbers.
Case unification: Making sure text is consistent in terms of upper and lower case. For example, changing “john smith” to “John Smith” or “JOHN SMITH” to “John Smith”.
Data enrichment: Adding missing information to your data by using other sources. For example, if you have a list of customer names but no addresses, you might use a third-party data provider to fill in the missing addresses.
Value transformation: This involves changing the values in your data to make them more consistent or useful. For example, you might replace all instances of “New York City” with “NYC” or convert all temperatures from Celsius to Fahrenheit.
