Data scrubbing is a crucial process that identifies and corrects inaccurate, incomplete, or irrelevant data. However, as high-quality data is relied upon to drive business decisions and scientific breakthroughs, ensuring data cleanliness can become an uphill task.
This is especially true for organizations venturing into data science without dedicated teams. To overcome the potentially overwhelming complexity of the task, and turn a cumbersome chore into an accessible process, it's become essential to leverage automated data scrubbing tools.
We'll take a look at the benefits of data scrubbing in this article, explore how automation plays a pivotal role in ensuring efficiency, and pinpoint the best practices for effective data scrubbing in 2023.
Data scrubbing, also known as data cleaning, is a vital process involving the detection and correction of inaccuracies, incompleteness, or irrelevance in a dataset. Its ultimate goal is to enhance data quality, ensure its reliability, and eliminate any errors or duplicates, thereby establishing a trustworthy foundation for your data science efforts.
Data scrubbing's importance is underlined in a myriad of applications:
Beyond these applications, the importance of consistently clean data manifests across several sectors:
The widespread application of data scrubbing reiterates its indispensability. Data scrubbing results in significant cost savings by reducing resources required for data storage or improving the quality of data-driven projects. Moreover, it enhances productivity by integrating into the data collection pipeline and flagging potential problems before they occur.
In short, data scrubbing isn't just one more step in the data management process—it's an essential part of maintaining and leveraging quality data.
Though often used interchangeably, the terms "data scrubbing," "data cleaning," and "data cleansing" do have subtle differences depending on the context and specific data processing goals.
They all share the objective of enhancing data quality and utility through the identification and correction of errors and inconsistencies, but their scope varies:
Before diving into best practices for data scrubbing, it's essential to address an important question: should you use a data scrubbing tool or attempt to handle the task manually?
Considering the potential for human error in large datasets, particularly as individuals grow weary from repetitive tasks, the answer leans heavily toward automation. Manual data scrubbing is not only laborious and time-consuming but also risky, seeing as it can lead to inconsistencies. For any sizable dataset (and most companies produce large amounts of data in this digital age), manual scrubbing can also be costly, requiring domain experts.
Automated data cleaning, on the other hand, offers a practical solution to these problems. An automated tool doesn't fatigue, allowing for consistency, and can be scaled according to your needs with just a few commands (say, launching more AWS instances).
Other benefits of employing automations for data scrubbing include:
Data scrubbing tools can also automate several tasks involved in the data cleaning process, including:
Akkio is a leading light in the realm of data scrubbing tools and a comprehensive predictive AI platform. Akkio empowers users to build robust Machine Learning (ML) models for extensive data analysis and facilitates seamless and automated data preparation, including data scrubbing.
One of Akkio's key strengths lies in its broad integrations. The platform can scrub data from various sources and easily merge disparate datasets. This capability is essential in today's data-rich landscape, where businesses often juggle information from numerous sources. Akkio's integrations streamline the amalgamating of this data, ensuring a unified and reliable dataset for your analysis needs.
But the pièce de résistance of Akkio's features is undoubtedly Chat Data Prep. This feature takes the complexity out of data transformation, offering a user-friendly interface that allows you to converse with your data as you would with a team member. Whether you need to combine columns, summarize records, translate languages, convert formats, or perform complex calculations, the Chat Data Prep feature facilitates all these operations with impressive ease.
By using natural language, you can instruct Akkio to carry out data transformations, thus eliminating the need for complex coding or technical skills. Your data transformation instructions can be as simple as "Combine these two columns," or "Summarize these records."
Data scrubbing is an integral part of maintaining data quality, and effective data scrubbing hinges on adopting best practices. Here are some key strategies to guide your data scrubbing efforts:
Every data scrubbing initiative should start with a clear definition of "clean" or "quality" data for your organization or project. This might entail specific standards for accuracy, completeness, consistency, and formatting. It's crucial to align these criteria with your organization's use cases and error tolerance.
For instance, while healthcare requires meticulous perfection, sales might allow a considerable noise margin. Clear goals and criteria allow you to steer your data scrubbing toward meaningful output that precisely meets your needs.
Data scrubbing shouldn't be an ad-hoc task, but a systematic and consistent process. This means devising standard procedures for detecting and rectifying errors, utilizing automated tools to expedite data cleaning, and defining roles and responsibilities for your team.
Data stewardship is central to efficient data cleaning, integrating data validation right from the data collection phase. ML models, particularly anomaly detection models, can scan incoming data for potential errors, triggering an expert's review.
Your data scrubbing approach should concentrate on errors with the most significant potential impact on your data analysis or decision-making. This means focusing on errors that are most common or severe, or those that influence crucial variables or relationships in your data.
As you make alterations to your data, documenting those changes and validating their correctness is vital. You may way to implement a change log, test the data post-cleaning, or compare the scrubbed data against external sources or benchmarks to ensure accuracy.
Maintaining data quality isn't a one-off task, but a continual process, and you’ll need to regularly monitor data quality to establish routines for constant data maintenance and updates. This could involve frequent data audits, implementing automated checks or alerts for possible errors, or setting up a system for users to report data quality issues.
The dynamics of your business might lead to changes in your data quality needs and priorities. Hence, regularly reviewing and updating your data quality metrics is essential. This ensures that your focus remains on the most important aspects of data quality and allows you to effectively measure progress toward your goals.
Ensuring the reliability and accuracy of your data is paramount, and to reach this goal, you'll need to implement best practices for data scrubbing. Adopting these practices allows businesses to transform raw, messy data into a polished, valuable asset.
However, the process of data scrubbing can be time-consuming and resource-intensive. That's why we highly recommend utilizing a dedicated data cleaning tool like Akkio. By leveraging Akkio's cutting-edge capabilities, you can automate the laborious task of data scrubbing, saving valuable time and resources, and enhancing the accuracy and consistency of your data.
Akkio's seamless integration with multiple data sources, intuitive Chat Data Prep feature, and capability to merge and transform datasets make it a comprehensive solution for all your data cleaning needs. With clean, trustworthy data at your fingertips, your business can make more informed, data-backed decisions that drive success.
So, why wait? Try Akkio for yourself today, discover the power of clean data, and unlock a new horizon of business intelligence.