Best Practices for Data Scrubbing in 2023

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.

What is data scrubbing?

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.

Applications of data scrubbing

Data scrubbing's importance is underlined in a myriad of applications:

• Data analysis: The accuracy of data analysis and insights hinges on the data's quality. Data scrubbing removes any inaccuracies or incomplete data, thereby reducing misleading results.
• Data migration: In migration projects, data scrubbing ensures data's cleanliness before it's transferred to a new system or platform. This prevents issues such as data loss, corruption, or conflicts during migration.
• Data warehousing: Data scrubbing acts as good housekeeping for large centralized repositories used for analysis and reporting. Eliminating even 1% of low-quality or mislabeled data can lead to significant savings by discarding or storing it on a different medium.
• Data integration: Integrating data scrubbing into the data collection pipeline (by flagging erroneous data for manual review) aids in identifying and rectifying errors such as inconsistent data formats, missing data, or duplicates before integration into a new system.

Industry-wide impact

Beyond these applications, the importance of consistently clean data manifests across several sectors:

• Marketing: Data scrubbing helps clean up customer databases, improving the accuracy of segmentation and targeting, leading to successful marketing campaigns and higher ROI.
• Sales: By ensuring accurate and current customer information, data scrubbing reduces the risk of failed communication attempts and lost sales opportunities.
• Finance: Given the critical need for data accuracy in the finance industry, data scrubbing helps financial institutions clean up databases by identifying and correcting errors in customer data, transaction data, and other financial data.
• Healthcare: Patient data accuracy is paramount for quality care. Data scrubbing, with the help of anomaly detection tools, flags potential errors in medical records.
• Government: Data scrubbing helps government organizations maintain accurate and reliable data for decision-making and compliance. It assists in cleaning up databases by identifying and correcting errors in citizen information, tax records, and other government data.
• E-commerce: Data scrubbing helps e-commerce businesses clean their product databases by removing outdated or duplicate product listings, correcting errors in product information, and standardizing data formats.

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.

Differences between data scrubbing, cleaning, and cleansing

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:

• Data scrubbing: This comprehensive procedure goes beyond identifying and rectifying errors and inconsistencies. Data scrubbing also involves standardizing and transforming data to improve its usability. Think of it as deep cleaning your home, where every nook and corner is scrutinized, and necessary adjustments are made for better living conditions.
• Data cleaning: This term is considered a subset of data scrubbing, primarily focusing on eradicating errors and inconsistencies. Using our home cleaning analogy, this would be akin to basic daily chores like vacuuming or doing the dishes. It maintains the order but does not necessarily involve deeper transformations or standardizations.
• Data cleansing: While often used interchangeably with data cleaning, it can also denote a broader process encompassing data scrubbing, enrichment, and validation. In this case, data cleansing would be akin to a full-home maintenance service, including deep cleaning, repairs, and even home improvements.

Should you use a data scrubbing tool?

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:

• Efficiency: Data scrubbing tools automate many aspects of data cleaning, enabling quick and efficient cleaning of large datasets. Automating this process eliminates human errors arising from fatigue or boredom.
• Consistency: By employing a data scrubbing tool, you ensure the cleaning process is consistent across your data, preventing inconsistencies or errors arising from different interpretations during manual cleaning.
• Scalability: Manual data scrubbing becomes increasingly challenging as datasets grow. A data scrubbing tool facilitates scalability in data cleaning, allowing you to manage larger datasets quickly and accurately.
• Accuracy: Data scrubbing tools detect errors and inconsistencies that might be challenging to identify manually. A tool could identify data patterns suggestive of errors, outliers, or even inconsistent data values. With the rise of generative AI, tools can detect content created by bots such as ChatGPT or StableDiffusion, offering an added layer of data scrutiny.
• Repeatability: Using a data scrubbing tool ensures the cleaning process is repeatable and auditable, which is especially crucial for regulatory or compliance purposes.

Data scrubbing tools can also automate several tasks involved in the data cleaning process, including:

• Removing duplicates: Many tools have algorithms to identify and eliminate duplicate records in a dataset.
• Correcting spelling and grammar errors: Some tools can automatically correct spelling and grammar errors in text-based data.
• Standardizing data formats: Tools can standardize formats for data such as dates, phone numbers, and addresses, facilitating easier comparison and analysis.
• Identifying outliers and errors: Data scrubbing tools can identify values outside expected ranges or inconsistent with data patterns, flagging them for review or correction.
• Filling in missing data: Some tools can infer missing data values based on data patterns or using external data sources—a process called imputation.
• Merging datasets: Tools can merge multiple datasets, resolving conflicts and duplicates to create a unified dataset. They can also detect when datasets are too different for merging.
• Validating data: Tools can validate data against specific criteria or quality standards, flagging data that falls below the threshold for review or correction.
• Reporting: Tools can generate reports summarizing the cleaning process, including the number and types of errors identified and corrected, the percentage of data cleaned, and the time it took to complete the process.

Introducing Akkio: A leading data scrubbing tool

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."

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Akkio in action

1. Head to the Akkio homepage to get started. You'll need an account, so decide whether a free or paid plan suits your needs, and run through the registration process.
2. Once you've logged in, select 'Create New Flow'.

3. Akkio gives users options for connecting their datasets – you can upload them directly or use a data collection tool like Salesforce or Snowflake.

4. Next, you'll need to clean and prepare your data, and Akkio has all the tools you need.
5. Akkio's Data Cleaning tool provides users with several pre-made options that can be used to clean data. You can take your pick of configurations, preview the adjustments to your table, and ensure that any duplicate or erroneous entries are quickly resolved. The Data Cleaning feature is a quick and easy way to combat human data entry errors that can result in empty cells, repetition, and coding mistakes.

6. The Chat Data Prep tool allows you even more granular control over your dataset—and you won’t need any coding experience. Chat Data Prep allows users to input directions directly to transform data tables. You can easily reformat data, combine and merge columns, summarize records, and, of course, clean data by creating filters.

Best practices for data scrubbing

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:

Define clear goals and criteria for data quality

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.

Practice data stewardship

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.

Prioritize high-impact errors

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.

Validate and document changes

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.

Monitor data quality over time

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.

Regularly review and update data quality metrics

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.

Get started with Akkio and benefit from automated data scrubbing

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.

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