What is Data Scrubbing: Examples, Techniques, & Tools

Organizations are flooded with large volumes of data from various sources, such as financial records, survey reports, sales, marketing, and social media applications. When consolidated together, this data enables businesses to uncover hidden patterns, identify trends, and make informed decisions to build scalable businesses.

However, as your organization's data grows, different issues, such as missing values, errors, or inconsistencies, may arise in your dataset. In such scenarios, you can implement data-scrubbing techniques to identify and eliminate inaccuracies in your data, thereby making it useful and reliable.

In this article, you will delve into the concept of data scrubbing, the various tools and techniques employed, and some popular examples.

What is Data Scrubbing?

Data scrubbing is the process of cleaning raw data to remove errors, missing or duplicate values, and redundancies from a dataset. Having a clean and accurate dataset empowers you to analyze your data efficiently and make data-driven decisions for your enterprise. It boosts the reliability and integrity of your data, thereby facilitating the operational efficiency of business processes.

Data scrubbing tools can automate these tasks, ensuring your data is consistently accurate, standardized, and high-quality.

Benefits of Data Scrubbing

Here are some of the key benefits of implementing a data scrubbing strategy to cleanse your data:

Ensure Data Consistency

Data consistency ensures your dataset is uniform and standardized across various databases and sources. You can utilize data-cleaning procedures to maintain data format, naming conventions, or other typographical errors.

Boost Decision-Making

With a clean and high-quality dataset, your organization can make informed decisions based on accurate data and gain a competitive edge in the dynamic market. Data scrubbing helps minimize misspellings, rectify syntax errors, remove blank cells, or fill up null values. This, in turn, lets you achieve an accurate and reliable dataset to perform seamless analytics and improve performance.

Streamline Data Integration

You can perform data scrubbing or cleaning during the data integration process as well. Data integration enables you to collect data from multiple sources, transform it, and finally load it into a centralized destination. In this transformation step, you can employ several data scrubbing techniques to clean your source data and make it compatible with the target system.

Cost-Effective 

While there might be an initial investment in data scrubbing tools and techniques, the long-term cost savings will help your enterprise use the money for other activities. Different data scrubbing techniques help you spot anomalies, eliminate redundant values, and remove duplicate data effortlessly.

Data Scrubbing Techniques

In this section, you will learn about some of the commonly used data-scrubbing techniques. Let's take a look at them briefly:

Data Standardization

Data standardization involves ensuring consistency in data formats across diverse sources. This might involve converting dates to a common format, using consistent units throughout the datasets, or applying a standard convention for data points.  

Data Enrichment

It is a critical technique that includes adding additional data points to existing records to enhance the value of datasets for analysis. This might include adding demographic information to customer records based on location details or enriching product information with customer reviews.

Error Correction

Error detection and rectification are important parts of the data-scrubbing process. This involves identifying and correcting errors within your data. You must always analyze your data for misspellings, extra spaces, incorrect values, or other outliers that fall outside the expected range. Data cleaning tools can help you to eliminate these errors and gain a high-quality dataset.

Data Masking

While scrubbing your sensitive data, data masking protects confidential information. To safeguard sensitive data, you can apply encryption techniques so that it is accessible only by your team as and when required.

Data Scrubbing Examples

Data-scrubbing helps address various concerns that you may encounter in your day-to-day business activities:

Standardizing Formats

This is a very common issue that may arise in your dataset, as it can lead to confusion during data analysis. For instance, suppose you gather data on customer purchases from multiple e-commerce platforms but the format of the date of purchase is different in each of them. Some might be in MM-DD-YYYY, YYY-DD-MM, or even DD/MM/YYYY format. In such a scenario, you can perform data scrubbing to standardize data to bring these dates into a single consistent format for easier analysis.

Handling Inconsistent or Null Values

Having a null or a missing value in your dataset makes it challenging to analyze the data. For example, you want to determine how many customers are eligible to buy a certain product based on their income slab. Depending on this parameter, they are placed in the applicable or non-applicable category.

Here, you may find some cells having null or an irrelevant value, which makes it difficult to calculate the total customers. You can easily leverage data scrubbing tools and techniques to fill a default or estimated value and continue your task without interruption.

Improving Data Quality Issues

Data quality issues can arise due to inconsistencies. Having data quality issues in your dataset decreases your operational efficiency and increases cost. Suppose you want to formulate a marketing strategy based on the customer data you receive from diverse sources. However, this data might have typos, extra spaces, missing information, or incorrect customer information.

Here, you can leverage data scrubbing tools to identify and filter your dataset for invalid or incorrect entries. This will provide you with an error-free and accurate dataset, thereby facilitating the creation of personalized campaigns.

3 Top Data Scrubbing Tools

Till now, you have explored the advantages of data scrubbing, its techniques, and a few examples. In this section, let's take a look at some of the popular data-scrubbing tools that you can leverage to maintain the quality and accuracy of your organization's data.

OpenRefine

OpenRefine is a free and open-source data cleaning platform. It is a Java-based tool that allows you to handle messy and large data with ease. You can transfer your dataset to its platform and perform data cleaning according to your business requirements.

It helps you achieve a clean and accurate dataset by removing inconsistencies using its data-cleaning feature. You can employ this feature to merge duplicate values, fix misspellings, remove redundant entries, or rectify errors in a dataset.

Astera Centerprise

Astera is a no-code data integration platform that lets you consolidate data from various sources and load them into a destination system. You can leverage its 50+ library of connectors to automate your data pipeline creation within minutes.

It offers multiple data-cleaning features to provide accurate, consistent, and high-quality datasets. You can effortlessly remove duplicate values, inconsistent data types, whitespaces, incorrect numbers or words, and formatting issues in your dataset.

Data Ladder

Data Ladder is a robust platform that offers solutions for data profiling, cleansing, matching, and deduplication. Its intuitive and user-friendly interface enables you to seamlessly clean, merge, delete, edit, or update values in your dataset.

You can employ its data cleansing solution to detect anomalies such as null values, blank spaces, or punctuation errors and rectify them. In addition to cleaning, you can also leverage its data profiling solution to understand the structure, quality, and content of your dataset. 

Transform You Journey from Messy Datasets to Accurate Insights with Airbyte

Airbyte is a cloud-based data integration platform that uses a modern ELT approach to move data. You can seamlessly extract data from multiple sources, such as flat files or databases, and migrate it to a destination like a data lake or warehouse.

To facilitate this process, it provides a rich catalog of 350+ pre-built connectors that you can use to build data pipelines without writing a single line of code. If you can’t find the connector of your choice, you can create custom ones using CDK within a few minutes. You can also request a new connector by reaching out to its sales team.

As an open-source platform, Airbyte has a large community of 15000+ data practitioners and engineers. These members are responsible for developing and maintaining the platform to facilitate advanced data integration practices. You can collaborate with others to resolve queries arising during data ingestion and provide articles or resources for knowledge sharing.

Some of the key features of Airbyte are:

• Data Replication Capabilities: You can employ the CDC functionality to identify and capture changes from the source data and replicate them in the target system. This ensures that your data is updated and consistent across various platforms, thereby maintaining data integrity.‍
• Handle Different Data Types: Airbyte supports data sources that manage semi-structured, unstructured, and structured data to adjust to modern integration workflows.‍
• Support for Data Transformation: You can integrate with dbt to execute your transformation. It enables you to manage your data transformation workflows flexibly and according to your specific needs.‍
• Manage Data Pipelines: You can build and manage your data pipelines in four ways using Airbyte. If you have programming knowledge, you can employ API, Terraform Provider, or PyAirbyte, whereas for quick and easy workflows you can leverage the user-friendly interface.

Final Words

Data scrubbing is a long yet important process to keep your datasets integrated and consistent. You can leverage various scrubbing tools to add, delete, edit, or update values in your dataset, thereby making it consistent and analytics-ready.

It is an indispensable process that your organization must incorporate to ensure data accuracy and integrity. With a clean dataset at your disposal, you can effortlessly draw actionable insights, formulate robust strategies, and unlock the full potential of your business data.

Frequently Asked Questions

What is the data scrubbing method?

The process of removing inaccurate, redundant, incomplete, or otherwise inaccurate data from a data set is known as data scrubbing. It involves discovering data mistakes and fixing them by adding, eliminating, or altering the data.

Is data scrubbing beneficial?

Data scrubbing, when performed routinely, maintains data accuracy and integrity for a long period. If you are dealing with large data volumes consolidated from multiple sources, you must perform scrubbing as frequently as possible.

What role does the data cleansing procedure play in ETL?

Data scrubbing is performed in the transformative step of the ETL (extraction, transformation, and loading) process. Here, the unprocessed data is cleansed to make it compatible with the target systems or downstream applications.

What is a data scrubbing tool?

Data scrubbing tools assist you in eliminating errors by employing automated procedures that carefully examine the data and use various algorithms to fix issues. This, in turn, allows your enterprise to improve its decision-making skills and streamline workflows.