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Data Cleaning – A Comprehensive Guide for 2024 [Free PDF Inside]

data cleaning ensures quality of your datasets
Data cleaning has grown into a complex activity as data sources and types keep multiplying. With dirty data muddying data reliability, B2B data aggregators today are adopting new database cleaning strategies and automation to protect the quality, credibility, and saleability of their datasets.

Cleaning dirty data is a critical responsibility of B2B data aggregators, and its results directly affect their market credibility. Customers come to buy clean, standardized, organized and comprehensive datasets. And any presence of corrupt data makes those datasets unusable. Lack of diligence in data cleaning can make a data seller lose both reputation and customers.

The job is not easy. Data volumes are growing exponentially and forecasted to reach 572 Zettabytes by 2030, which is around 10 times more than the volumes today. To handle this tsunami of data, B2B data aggregators need to use advanced data cleansing solutions backed with tools, technology and automation. But to use modern tools effectively, they also need to have a holistic understanding of dirty data and data cleaning operations.

We have created this data cleaning guide to walk you through the fundamentals of data cleansing, explain why it’s needed, demonstrate the benefits and challenges and provide examples and a primer on how to clean data.

Table of Contents

What is data cleansing?

Raw data from disparate sources comes in with missed values, format inconsistencies, duplicates, typos, and erroneous entries. Data decay is another major issue affecting datasets. And data cleaning must be done thoroughly before the datasets reach the hands of companies who’ll use the data for analysis and business decisions.

Data cleansing is the process used to identify and rectify all corrupt, inaccurate, irrelevant, incomplete, duplicate, and inconsistent data. It is a process where the database is made accurate and is also enriched with additional information. The process helps in data maintenance, improving data quality, and keeping data clean, accurate, up-to-date, and sellable. The job is best done by data specialists who are well-versed in different data cleansing techniques.

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Data cleaning vs. data cleansing vs. data scrubbing

Data cleansing, data cleaning, and data scrubbing are mostly used interchangeably and often understood as the same thing. But going deeper into the terms, data scrubbing has a slightly different meaning. Data cleaning or data cleansing is the process where data experts monitor the database, identify problems, figure out solutions and implement data hygiene best practices.

Data scrubbing is a subset of data cleansing that uses tools and techniques for much deeper cleaning. It refers to a specific part of the data cleaning process, such as removing duplicates or corrupt data from the datasets.

Why is data cleaning important?

Clean data is the key to effective marketing campaigns. Any inaccuracy in B2B data can lead to inaccurate customer metrics, misleading customer records, and incorrect market segmentation. These lead to missed opportunities, damaged relations, and finally revenue loss. Therefore, data cleaning is not a onetime process, but run regularly to check errors and data decay.

Data cleaning is extremely important for several reasons:

  • Managing data decay: Business contacts and company data change at an incredible speed, leading to data decay and stale data. So, keeping the data updated, validated, and refreshed continuously is very important.
  • Keeping the data enriched: Data sources and data fields keep multiplying at a high speed. It is a difficult job to continuously keep adding new data without set processes.
  • Data authenticity for SMEs: The authentic data of public companies is easier to find, as they have to make their audited information public. However, in privately held companies, and especially those of smaller and medium size, it is very difficult to find authentic data.
  • Data comprehensiveness: Every day new start-ups are launched but not reported adequately. Mining the data and capturing information about these companies is a challenge.

Different data handling activities that require data cleaning

  • Data migration: Data migration is the process of moving data or files from one system to another. Being a high-risk activity, data cleansing forms an integral part of data migration. Data is validated and verified at the pre-migration and post-migration stages for quality, consistence, and usability.

  • Data integration: Combining data from diverse sources into a single view is called data integration. Data cleansing is crucial in this process as chances of errors like duplicates and incomplete or incorrectly formatted data can easily creep into the system. Data cleaning techniques ensure the data is free of errors and standardized before moving to the destination site.

  • Data transformation: Data transformation happens when data is converted to match the target or destination database. It is the process of converting data from one format or structure into another format or structure. Data extracted from diverse sources can have errors, meaningless data, or contradictory data and new errors may creep in during format conversion. Data cleaning helps remove these errors before and after structuring the data.

  • Data debugging in ETL processes: Data cleansing is extremely important while preparing data for the (ETL) ‘Extract, Transform, and Load’ process. This involves extracting data from multiple sources, then transforming and loading it in the target data warehouse. Data cleaning ensures the availability of only high-quality data for any business decision or analytics.

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What constitutes accurate, comprehensive, and up to date B2B data?

  • Validity – The data should not be outdated
  • Accuracy – Data should be close to the true value and up to date
  • Completeness – Data should be complete with no missing information
  • Consistency – Data should have a uniform format
  • Uniqueness – Data should be free of any duplicate entries

Top benefits of data cleaning

Data cleansing helps B2B data aggregators. These are some top benefits:

  • Brand credibility from a high-performing decay-resistant database: By providing accurate and validated data to B2B companies, you build credibility in the market. You gain the confidence of your clients, thus building long-term associations.
  • Higher customer retention: Providing clean data helps companies communicate better with their customers and increase conversions. This leads to improved customer satisfaction, driving higher retention.
  • Readily usable data with proper standardization and segmentation: Having a system that keeps validating the data based on set rules and algorithms ensures the accuracy of B2B data. With all processes in place right from sourcing to standardizing, segmenting, and validating, the data is always ready for sale.
  • Optimized ongoing data maintenance cost: Ongoing B2B data validation keeps the remediation cost in check, resulting in a higher return on investment in data management.
  • Enhanced revenue, profitability, and ROI: Having a structured B2B data validation plan saves time and effort and proves cost-effective. Your data stays sellable all the time, driving enhanced revenues and profit.
  • Structure validation helps in easy integration with client’s databases: Structure validation of data collected from multiple sources ensures uniform format for easy integration with the client database.

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Types of dirty B2B data and case-specific examples of cleaning

1. Duplicate data

Duplicate data is any record that repeats the data in another record in the database. There could be an exact carbon copy of records, or it could be partial duplicates. Duplicate data makes your data dirty and can harm your business in multiple ways. The lack of a single customer view can lead to confusion among customers and missed sales opportunities.

duplicate data example

Example: There could be two records of the same person wherein one record has an email while the other a phone number. There could be a case where the customer has already subscribed to your newsletter and while transferring data from CRM, a duplicate record is created. Sometimes, when the same customer’s name is written in different ways, you create multiple records of the same person.

Solution: Arbitrarily removing duplicate records can lead to loss of data. The data has to be matched using advanced matching techniques. Invest in an automation platform that detects duplicates and cleans up data. Use a merge/purge process where records are merged, eliminating duplicates, and retaining all valuable information from all records.

2. Invalid or outdated data

Data decays at a quick pace. Old data that is no longer relevant handicaps decisions. People change jobs, companies merge, promotions happen, and emails and phone numbers change. All these, if not corrected, make your database outdated.

invalid outdated data example

Example: When a customer moves to a different location or organization and the email address changes, or the person changes his or her mobile number. So, the records in your database become invalid.

Solution: Purge and cleanse data before migrating it or integrating into new systems. Use automated tools to keep updating old records. Constant real-time database updates are essential to keep the database current and address data decay issues. Scheduled macros and bots can constantly gather and change data. Automated scheduled crawlers can trigger alerts for any change in data wherein automatically old data is replaced with new and relevant data.

3. Incomplete data

Information gaps where critical data or records are missing result in incomplete data in databases. Records with missing fields like phone number, email, industry name, job title, etc. impact the customer connection rates. Companies also look for additional data on customer preferences, demographics, attitudes, etc. for accurate and personalized targeting. Without a complete customer overview, a database is of limited utility to companies.

incomplete data example

Example: When you have the customer’s phone number, but his email address is missing. This can create a problem when you need to send any campaigns through email. Somebody filling out a survey form can miss a few questions leading to incomplete data. To send personalized mails to the client, companies often look for information on client sentiment data, and if that is missing, the data may not work.

Solution: Use technology to scan your CRM for missing data. Automatically capture valuable missing information to create a better pipeline. Use data enrichment techniques to capture additional information like demographic, firmographics, chronographic, technographic, behavioral, social media, intent, market intelligence, etc. for accurate targeting. Data harvesting using scrapers and crawlers works effectively to capture additional data. Multi-sourced data acquisition helps keep data comprehensive and all-inclusive.

4. Inaccurate data

Inaccurate data is created when, despite capturing data correctly, you end up with fake or wrong data. It is the biggest challenge for B2B data aggregators, as often data collected even from most authentic sources can be incorrect and need validation. Data authentication of private and small companies is more challenging, as they are often not listed.

Inaccurate data example

Example: When a prospect enters a fake or wrong mobile number. When a company has changed its location, or a contact person has changed organization.

Solution: Constant data verification and validation is a key step to weed out inaccuracies from the database. Multi-layered verification processes and technology-enabled checks help in maintaining data accuracy. Rule-based macros and bots make the validation process effective. It is also important to check data at the entry point itself. Automated real-time data capture platforms provide a comprehensive solution for this problem.

Web research and other data sources are used to identify irrelevant, invalid, or obsolete data. Manual, as well as rule-based validation, is used for real-time data validation. And constant monitoring mechanisms are a must keep the data accurate all the time.

5. Inconsistent data

Data captured from multiple sources and formats needs to be standardized for easy integration with the target database. Inconsistent data doesn’t adhere to predefined rules and can be non-standardized. If, after all the drills of data collection, verification, validation, and enrichment, the data is not made consistent, it will not be of much use.

Inconsistent data example

Example: For instance, the CMO, Chief of Marketing, and Chief Marketing Officer are all versions of the same data field, but entered in different formats.

Solution: Create a standard file-naming convention. Standardize and normalize format for data consistency. Have a well-planned data integration system where data from different sources are standardized, providing the user with a unified view of the data.

Beginning of a data cleaning project

Managing dirty data is a complex process. It requires constant monitoring, identification of problems, and finding appropriate solutions. You need to have a well-planned system in place for database cleaning. Data can be cleaned using applications like Excel, Google sheets, MySQL or PostgreSQL, MongoDB, any data server, platform, or software like Tableau. Though the rules may vary according to application, the core targets and techniques of data cleaning remain the same.

  • Develop a data audit plan: Develop a data assessment plan and create key performance indicators (KPIs) to track data health for quick identification of problem areas.
  • Focus on data input to minimize the collection of bad data: Keep track of all data entry points and develop systems and techniques to prevent the entry of dirty data into your database.
  • Use a good mix of data sources: Collect data from multiple sources to help keep the data relevant and accurate.
  • Constantly monitor and clean your data: Maintain a consistent data monitoring schedule and regularly for errors. Immediately fix any found errors to maintain a clean database.
  • Deploy real-time data cleansing tools: Take a look at the tools we’ve identified in this article and find the best one for your business needs.
  • Look for automated solutions available with experts: Use data hygiene experts to maintain the quality of your B2B database.

Standard data cleaning project steps

  • Document your workflow: A documented workflow makes it simpler for your team members to understand and follow the set procedures.
  • Validate the accuracy of your data: Integrate rule-based systems to identify incorrect data and rectify using data validation and verification techniques.
  • Remove irrelevant data: Identify only the data needed for your analysis and remove any unnecessary data fields. Any irrelevant data can give you incorrect results and slow down the process. For example, if you are analyzing the location of your customers, you would not require data on their age. Remove any unnecessary data before you start your analysis.
  • Devise a strategy to remove duplicates:Use merge and purge techniques to remove data duplications. CAUTION: Removing duplicate records arbitrarily may lead to record loss so be careful. The definition of a duplicate will vary according to context and database, and any removal needs to be done very carefully.
  • Handle missing data: Run your data through a program to identify missing data such as blank spaces, missing answers in survey forms, missing emails, or phone numbers, etc. Understand the need for missing fields and add the information.
  • Translate data to one language: Natural Language Processing (NLP) is used to interpret human language. The tools used by B2B companies like chatbots, spam filters, social media monitoring tools, etc. require machines to understand language. But they cannot process multiple languages. Therefore, translate all data into one language for easy processing.
  • Fix structural errors: Make sure that your data collected in different formats is standardized to a common format and is consistent with your client’s needs. Check for things like spelling mistakes, different naming formats, etc. as these can make a big impact.
  • Enrich records with the most reliable source possible: Understand the needs of your customer and append your database with important data points such as revenue, company profile, social media behavior, preferences, intent, etc. Here’s a great example of how Hitech BPO implemented data hygiene best practices for a large US-based data aggregator which resulted in a 100% accurate and updated 50-million-record database for a US-based data aggregator.

3 quick tips for cleansing data

  • Standardize upper case, lower case: Inconsistent data can create ambiguities and often irrelevant categories are created. Set rules regarding capitalization so that your data stays consistent across the database. For instance, you can set a rule that all initials and customer names should be capitalized. Suppose, you have a customer with the name Bill and if it is not capitalized, there could be confusion between the name ‘Bill’ and the invoice bill.
  • Convert data types to avoid misinterpretation: Convert all your numbers from text form to numerical form. This problem mostly comes with dates, money amount, etc. Analysis algorithms can’t work on texts, they require numerals.
  • Clear formatting: B2B data collected from diverse sources comes in different formats. Processing this type of data is not possible for machine learning models. The best option is to clear all formats and start with a completely clean format.

Data cleaning tools and technologies

Data cleaning tools are used to correct data errors and improve the quality of data used in applications. These tools are used to organize, clean, structure, and enrich data. Here are the top 7 data cleaning tools:

  • OpenRefine: A powerful, open-source tool that provides an intuitive interface for cleaning, transforming, and integrating data from a variety of sources.
  • Trifacta: A cloud-based data cleaning tool that uses machine learning algorithms to automate enterprise-level data cleaning, making the process fast and efficient.
  • DataWrangler: A browser-based data cleaning tool that provides simple, powerful, and flexible data cleaning capabilities.
  • Talend: A comprehensive, open-source tool that offers a range of capabilities for data cleansing, normalization, standardization, and various transformation processes.
  • KNIME: An open-source data analysis platform that provides a suite of data cleaning tools to enable data scientists and data engineers to clean, transform, integrate, and prepare data.
  • Dataddo: A cloud-based data cleaning and integration tool that provides a simple and efficient way to clean and integrate data from a variety of sources.
  • RapidMiner: A data science platform that provides a suite of data cleaning tools to help data scientists and data engineers clean and prepare data for analysis. It also provides tools for text mining, data visualization, and machine learning algorithms.

Each of these tools has its own strengths and weaknesses and the choice of tool depends on the size and complexity of the data, the skills of the users, and the specific requirements of the data cleaning task.

Important data cleansing operations tools

  • Conversion tables: A table of equivalents for changing units of measure or weight into other units is perfect for converting and formatting data types. The conversion of data into uniform formats is important so that the data can be easily read and understood by any software.
  • Histograms: A graphing tool that helps illustrate data distribution. It is a graphical representation of data points organized into user-specified ranges. It condenses data series into an easily interpreted visual. A histogram is of great use when dealing with large data sets where it can detect any gaps in data or ambiguity. It can summarize discrete or continuous data measured on an interval scale.
  • Algorithms: Setting up a procedure or a set of rules for specific tasks helps in data cleansing. With algorithmic programming, one can write a set of rules that instructs the computer on how to perform each task. For instance, if you want all the dates in a numeric form, you can create a rule to convert every date entered in the date column into numeric form.

Future of data cleansing

The future of data cleaning will be characterized by the increasing use of artificial intelligence (AI) and machine learning (ML) technologies. These technologies will power automated and efficient bulk cleaning of data, handling data volumes much higher than what humans can manage.

Robotic Process Automation (RPA) and AI solutions based on ML will be leveraged to deliver clean, standardized, and accurate data output at high speed and scale. All repetitive and logic-based tasks, such as data acquisition, cleansing, validating, de-duplicating, integrating, and others will now be automated.

With the explosion of data from IoT devices and other sources, the need for scalable and efficient data cleaning solutions will become even more pressing, making it a key area of focus for businesses and organizations. As a result, data cleaning solutions that prioritize privacy, security, and accuracy will see increased demand.

Hitech BPO: The one stop solution for outsourced data cleaning

At Hitech BPO, our teams have decades of experience in providing reliable data cleaning services for global B2B data aggregators. We help clients drive a data culture based on quality by providing accurate and affordable solutions to combine and clean their data. We have dedicated data cleansing teams run by veteran data scientists, who can handle a wide range of B2B data challenges.

Our data professionals have expertise in B2B data quality, and the need to clean and manage it for you to make efficient and effective business decisions. With an excellent track record of over two decades in running data cleaning projects of different magnitudes, our data cleansing team can help you get your data updated and back on track quickly!


Investing in B2B data cleansing solutions is a must for every B2B business maintaining large data sets. Data cleansing is a complex process involving multiple tasks such as checking for accuracy, removing invalid and duplicate data, adding in missing values, enriching the data with important qualifiers, and finally standardizing the data to make it consistent.

Considering the huge volumes of data most businesses have to manage, traditional and manual processes are not feasible. Data aggregators should either invest in the right technology, infrastructure, automation, and AI or hire specialized data cleansing companies to do the job.

With data cleansing processes improving every day, keeping up with the ever changing technology can be a tough job for data aggregators. Remember this is a specialized field, and whenever possible it is best to use experts such as the team at Hitech BPO for your organizational data cleansing to save you time and money.

Author Snehal Joshi
About Author:

 spearheads the business process management vertical at Hitech BPO, an integrated data and digital solutions company. Over the last 20 years, he has successfully built and managed a diverse portfolio spanning more than 40 solutions across data processing management, research and analysis and image intelligence. Snehal drives innovation and digitalization across functions, empowering organizations to unlock and unleash the hidden potential of their data.

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