Understanding Data Scrubbing: 7 Comprehensive Aspects

on Data Aggregation, Data Driven Strategies, Data Integration • October 21st, 2021 • Write for Hevo

Data Scrubbing cannot be overlooked especially when managing Databases because keeping clean data with consistent and accurate input is essential to having and running a smooth business. Digital data input is subject to errors as this is done by humans and it would be difficult to avoid making mistakes such as misspellings, having redundant entries, incomplete or missing values, and inconsistencies, therefore, there is always the need for clean up.

Institutions like banks, insurance, retail, telecommunications, transportation are driven by heavy data and cannot survive without Data Scrubbing as they constantly need to weed out data flaws by systematically and continually examining generated data.

This article aims at introducing you to the concept of Data Scrubbing and explains why it is important to have your data cleaned.

Table of Contents

What is Data Scrubbing?

Data Scrubbing
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Data Scrubbing, also referred to as Data Cleansing, is the act of correcting your data in a Database that has errors, is incomplete, not properly formatted, or has duplicate entries to make it usable before exporting it to another system. Data Scrubbing is an integral part of Data Science as it would be difficult working with impure data because this will lead to many challenges. A Database Scrubbing Tool generally includes programs that will help in the amending of specific types of mistakes. Data Scrubbing is done using algorithms, rules, using look-up tables, and other methods.

Importance of Data Scrubbing

Data Scrubbing for Data Analysis
Accurate results of Analysis are obtained by Data Scrubbing
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Data Scrubbing is important as there are numerous benefits. For you, as a data professional, having bad quality data would hinder your output and ultimately make you come up with a flawed Analysis which in-turn would affect your client’s or employer’s power to make the right decisions about future occurrences. Listed below are some benefits of cleaning up data:

  • Having clean data, free from errors helps increase your efficiency and allows you to have an optimal analysis which will improve your decision-making process. 
  • Having incorrect data would mean not having an accurate outcome. Even though your algorithm may be wonderful, it will be processing a wrong Dataset thereby causing you to waste time, effort, and resources as you will be required to carry out the Analysis all over again.
  • With Data Scrubbing, you can monitor errors as you will be able to see where the errors are coming from making it easy to fix wrong or corrupt data.
  • Data Scrubbing removes errors like duplicates that are inevitable when multiple sources of data are brought together in a Dataset hence streamlining your data to match what is required for usage.
  • When you clean up data before trying to get more information from them, your final deductions will be near accurate because they will be fewer errors and this will lead to happy clients, colleagues, employees/employers, management, etc.
  • With your data scrubbed, It gives you the ability to map its different functions and get clear insights into what the data is intended to do. 

Top 5 Data Scrubbing Tools

Here’s the list of the top 5 Data Scrubbing tools out there. This comprehensive list will help you decide the perfect tool for you.

Data Scrubbing vs. Data Cleaning vs. Data Cleansing

Many times the question arises what is the difference between Data Scrubbing vs. Data Cleaning vs. Data Cleansing. These terms are used interchangeably when it comes to practically applying them in the data preparation process. 

Data Scrubbing is more related to the number of specialized processes involved in the data preparation such as merging, translating, decoding, and filtering data. Data Cleaning involves the process of cleaning the raw data that involves, filling NULL values, identifying outliers, etc. 

We can use Data Scrubbing, Data Cleaning, and Data Cleansing internally as refer to the same process of data preparation because they have the same end goal. 

Customer Data Quality Impacts Business Processes

The customer data quality is directly proportional to the impact on business decisions, and it ultimately touches every facet of your business.

For example, take the Sales data. The sales team-high relies on quality customer data to deliver the context of the conversations that they have with clients. The low-quality data makes the process cumbersome and harms their ability to speak directly to the customer and address their issue.

Similarly, the Marketing data helps manage the Marketing Campaigns and low-quality data makes it difficult for Marketers and companies to create personalized Campaigns and avoid injecting them into your messaging.

The low-quality data highly impacts the business operations and lowers the benefits. This unscrubbed customer data grows rapidly and takes up more storage than the cleaned data. It will increase the costs of storage and computation and also slows the process making it harder to search through the data. Businesses miss out on $9.7 million on average due to bad data.

5 Best Data Scrubbing Tools

In this section, you will read about the best Data Scrubbing Tools that you can use to clean data. The top 5 Data Scrubbing tools are listed below:

1) Hevo Data

Data Scrubbing - Hevo Data Logo
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Hevo Data, a No-code Data Pipeline helps to integrate data from 100+ sources to a data warehouse/destination of your choice to visualize it in your desired BI tool. Hevo is fully-managed and completely automates the process of not only loading data from your desired source but also Scrubbing the data and transforming it into an analysis-ready form without having to write a single line of code.

Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss. It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. It allows you to focus on key business needs and perform insightful analysis using a BI tool of your choice.

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Check out what makes Hevo amazing:

  • Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
  • Schema Management: Hevo takes away the tedious task of schema management & automatically detects schema of incoming data and maps it to the destination schema.
  • Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
  • Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
  • Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
  • Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
  • Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
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2) Winpure

Data Scrubbing - Winpure logo
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Winpure is a popular Data Scrubbing tool that helps companies eliminate duplicate data, clean large datasets, and seamlessly correct and standardize the information. It can easily integrate with Access, Dbase, and SQL Server, spreadsheets, CRMs, and more.

3) Cloudingo

Data Scrubbing - Cloudingo Logo
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Cloudingo is the best Data Scrubbing tool if your company you Salesforce. It can perform Data Migration, delete duplicates, etc. Cloudingo can handle businesses of all sizes and eliminates all human errors. There’s even additional support available for application programming interfaces (API) with REST and SOAP frameworks.

4) Trifacta Wrangler

Data Scrubbing - Trifacta Wrangler Logo
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Trifacta Wrangler is a Data Scrubbing tool that focuses on less formating time and analyzing data. It helps Data Analysts clean data quickly with accuracy so that they can analyze and generate insights from it. Trifacta Wrangler uses Machine Learning algorithms for Data Scrubbing by suggesting common transformations and aggregations.

5) Data Ladder

Data Scrubbing - Data Ladder logo
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Data Ladder is a Data Scrubbing tool that is known for its fast speed and accuracy. It features an easy-to-use interface that gives users the power to seamlessly clean, match and deduplicate data. It also taps into an impressive collection of algorithms to identify fuzzy, phonetic, and abbreviated data issues.

Who Should Employ Data Scrubbing, and Why?

Data Scrubbing is an essential part of managing data in a well-mannered format. Different industries and sectors require data to be clean to run their daily activities efficiently. But some sectors such as Banking, Finance, Retail, and telecommunication are data-intensive industries that make Data Scrubbing a high-priority stage.

Let’s go through some of the common sources of Database errors listed below:

  • Human errors during manual data entry.
  • A lack of company-specific or industry data standards.
  • Older Systems with obsolete data.
  • Merging Databases.

Some facts about data quality are listed below:

  • Due to bad quality data Ingestion, businesses lose up to 20% of their revenue.
  •  Managing data quality is a time-consuming process, and employees waste about half of their working time handling bad quality data.
  • In an hour, almost 5 dozen of companies change addresses, names and 50 new businesses open, which creates data inconsistency.

Data Scrubbing for Effective Data Management Processes

Data Scrubbing plays a vital role in Data Management Processes. Some of the effective processes are listed below:

Data Integration

Data Integration is the process of combining data from multiple data sources into a unified single platform that can store huge volumes of data. The raw data from the data sources is low-quality data that needs to be structured and transformed to a common format. Data Scrubbing cleans the raw data and transforms it into a standard format so that it can be integrated with other data.

Data Migration

Data Migration is the process of transferring data from one system to another. It is essential to maintain Data Integrity and consistency while migrating data from one system to another. It ensures that correct data with the right format and no duplication is replicated to the target system. Data scrubbing tools help clean your data efficiently, ensuring better data quality throughout the enterprise.

Data Transformation 

Data Transformation is the process of transforming data to a standard or common format before loading it into a target system or Data Warehouse. The format is chosen based on the business requirements. These Data Transformation processes involve filtering of data, cleaning, and preparation so that it can be further used for Data Analysis. The Data Scrubbing tools available in the market help this data to clean using built-in transformations.

Data Scrubbing in ETL Processes

Data Scrubbing plays an important in Data Analytics and decision making. While the ETL (Extract Load Transform) process takes place Data Scrubbing to ensure that only high-quality data passes through and loads into Data Warehouse. High-quality data can be seamlessly used by BI tools, Data Analysts, and Data Scientists for making smarter and better data-driven decisions. Data Scrubbing tools detect anomalies and inconsistencies in data and rectify them automatically. Now cleaned data can be loaded into Data Warehouse or other destinations using an ETL process.

Benefits of Data Scrubbing Tools

Data Scrubbing manually is a tedious and time-consuming process as it involves manual chacking of data entries row by row, which makes it very time-consuming, and there are high chances of human errors. 

Data Scrubbing tools makes all the process hassle-free as it automate all the Data Scrubbing or data cleaning process by systematically inspecting data based on different rules and algorithms. It makes the data cleaned and ready for analysis. 

Many Data Scrubbing tools are available in the market but choosing a good one that suits the company’s requirements is still a confusing part. Enterprises use Data Scrubbing tools to automate their data cleansing process and save time.

5 Key Components of Quality Data

Quality Data
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The components of quality data are measured by certain characteristics. Clean data has the following characteristics:

1. Validity 

This is explained as the degree to which your data agrees with defined business rules, constraints or requirements. For example, having a defined format for phone numbers and only data that conforms to this would be viewed as valid.

2. Accuracy 

Even though data may be valid based on it fulfilling the requirements, it may not be accurate. Data is said to be accurate when it is close to the true value. Going with the example of phone numbers, you may input the correct format for phone numbers which makes it valid, but have a wrong phone number for a particular client which means the data is not accurate (true).

3. Completeness

This is the degree to which you know all the required data or values. That is every field has a value inputted in it. Completeness at times may be nearly impossible as you may not have some information about a particular entry.

4. Consistency

To ensure quality data, make sure you have consistent data within the same Dataset and/or across multiple Datasets. You can measure consistency by comparing two similar data systems, check data values within the same Dataset, or through relational means.

5. Uniformity

This is the degree to which a Database follows a specific unit of measurement. This is to ensure that all values entered in your Dataset are in the same units. For example, if you are using the SI units for measurements, the Imperial System cannot be used throughout the Dataset.

Steps to Perform Data Scrubbing

You can perform Data Scrubbing, by observing the following:

1. Removal of Duplicate or Irrelevant Values 

Removing unwanted entries like duplicates and irrelevant data to a given Dataset for further review is a form of Data Scrubbing. Duplicate data easily occurs when you have a collection of Datasets from various sources which will increase the volume of your load while irrelevant data can be described as data that does not fit a specific solution, therefore, it is not needed for that Analysis.

2. Avoid Structural Errors

Structural errors include typos, wrong naming conventions, incorrect capitalization, string size, etc. It is good to fix these errors as they can cause categories and classes to be mislabeled.

3. Convert Data Types 

Another way of Scrubbing Data is to ensure that all data types are uniform across the Dataset. Where a String is applicable, only String values should be inputted as a String cannot be Numeric, neither can a Numeric value be a Boolean and vice versa. In situations where you cannot convert a specific data value, ‘Not Available (NA) value’ should be used.

4. Handle Missing Values

A lot of algorithms do not accept missing data and there will be missing data in a Dataset and this has to be handled before Analysis can be carried out. Ignoring missing values would be a grave mistake as they can contaminate your data. You can deal with missing value by doing the following: 

  • Dropping fields that have missing values especially when it is enormous. Doing this might mean losing information so you have to think through and be careful before embarking on this.
  • You can input the missing values based on observations from the other values like using an average or a range. Having said that, inputting missing values may slightly alter the integrity of the data because you may be operating based on assumptions and not facts.
  • Thirdly, you can use null values where there are missing values. For example, in cases where Numeric values are needed, 0 can be used to fill up those missing values but you should make sure to ignore these values during Statistical Analysis.

5. Inform Your Team and Co-Workers

After Scrubbing your data, it is important to inform your fellow team, co-workers, etc. of changes done to the data as this would help promote adoption of the new protocol and create a culture of having quality data within the organization to avoid making similar errors as in the past.


Since most of the work now revolves around data, it is ultimately important more than ever that Databases are as close to perfection as possible. Wrong Data Analysis and submissions can adversely affect a lot in society and this may arise as a result of faulty data.

In this blog post, you learned about Data Scrubbing and how vital it is to always make sure your Database is devoid of mistakes that can greatly affect insights and truncate your efforts and productivity. Procedures on how to clean your data were also touched upon.

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Clean data will require protocols and algorithms which will require long lines of codes and having the right sets of skills. These however can be done easily without having to write any code through Hevo Data, a No-Code Data Pipeline. Hevo Data does all the work of cleaning up your data and guarantees that your data will be clean, consistent, and ready for Analysis. !

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