Whether you're a data analyst, data scientist, data engineer, or entrepreneur, it's important to know just how essential quality data is. High-quality data helps organizations better understand their customers, markets, and operations, enabling them to respond to changes in real time.
However, maintaining good data quality can be a challenge.
In this article, we’ll explore data scrubbing, a key technique for improving data quality within an organization.
What Is Data Scrubbing?
Data scrubbing is the process of transforming raw, “dirty” data into a clean, usable state. Dirty data can include duplicate entries, inconsistent formatting, missing values, or inaccurate information. By addressing these issues, data scrubbing ensures the dataset is reliable and ready for further analysis or application.
Importance of Data Scrubbing
Clean data is the foundation of any meaningful data analysis. Without scrubbing, errors and inconsistencies in raw data can compromise the validity of insights, which de facto leads to misleading conclusions. Additionally, dirty data can disrupt the performance of predictive models and analytical processes, making them ineffective or entirely unusable.
Common Challenges for Data Quality and Data Scrubbing
Duplicate data is a frequent problem that skews analysis. These errors are often human mistakes, such as entering the same information multiple times or mishandling files during data transfers. Machines, too, can generate duplicates due to system malfunctions or design flaws. Regardless of the source, duplicates inflate datasets unnecessarily and distort results.
Another pervasive issue is inconsistent formatting. Take location data as an example: "NYC," "New York," and "New York City" may all refer to the same place but will be treated as separate entities unless standardized. Similarly, datasets that mix currency units, such as US dollars, euros, and pounds, create roadblocks for straightforward analysis. Without consistent formatting, analytics tools struggle to deliver accurate results, misinterpreting variations that should represent the same value.
Missing data is another challenge, arising from incomplete surveys, technical glitches, or poor data collection design. Gaps in datasets disrupt analysis, making it harder to uncover patterns or draw reliable conclusions. While some missing values might seem minor, like a blank field in a survey, they can cumulatively lead to biased or incomplete insights.
Lastly, there can be incorrect values, which are frequently the result of misinterpretation or non-standard data entry practices. These values deviate significantly from the norm and can sometimes indicate errors. For example, a customer database might show an age of minus 30, an impossible value requiring correction. Such issues make datasets unusable for meaningful analysis until corrected.
Businesses can make sure their data is correct, consistent, and ready for in-depth analysis by dealing with these issues. The first step toward solving these is figuring out what caused them, whether it's human error, machine malfunction, or just poor collection methods.
Data Scrubbing Process
The process typically involves four main tasks:
Removing Duplicates
The first step in the data scrubbing process is removing duplicates. Duplicates are problematic because they distort data analysis and can lead to incorrect insights. Removing duplicates will make sure that each record is unique, preventing repeated data from skewing analysis or model outcomes.
Before removing duplicates, check that they truly represent errors. Sometimes, repeated information may not be a duplicate but rather a result of missing data. Always verify whether the repeated entries are genuine duplicates before deleting them.
Once duplicate records are removed, you can proceed to the next task of the data scrubbing process.
Formatting Records
After removing duplicates, the next task is to ensure your data is formatted consistently. Proper formatting is essential because inconsistencies in your data can lead to errors during analysis. If the data is not uniformly formatted, it becomes difficult to accurately summarize or analyze the information. Data analytics tools rely on consistent formatting to interpret and process data correctly. Without it, you risk your outcomes being all over the place.
Consistency isn’t the only consideration. Data must also align with the correct data types, such as text, numbers, or dates. Each type requires specific formatting rules to be properly interpreted by software and databases.
By standardizing formats and verifying data types, you eliminate ambiguities and allow your tools to interpret and process the information accurately. Before you proceed to exploring or analyzing your data, always check for formatting consistency and adherence to the correct data types.
Solving for Missing Values
Next, you need to check for missing values. This is a critical part of cleaning your data.
Missing values are common and can result from a lack of information or machine errors. Regardless of the cause, it’s important to identify missing values and address them to prevent disruptions in your analysis.
By addressing these gaps, you know that your data is as complete and accurate as possible, which lays the foundation for reliable analysis.
Checking for Obviously Incorrect Values
As the last step in scrubbing your data, you should identify and address values that are clearly incorrect. To determine if a data point is invalid, you must consider the context in which the data was collected and the expected range of values. For instance:
- Unrealistic Values: A person's age exceeding 150 is clearly invalid.
- Illogical Values: Negative values appearing where they don’t make sense should also be flagged as incorrect.
However, understanding the context of your dataset is equally important to avoid flagging valid data as incorrect.
Data Scrubbing Techniques
The data scrubbing process relies on specific techniques to clean, format, and refine data that will ensure accuracy and reliability.
Validation
Validation makes sure that data conforms to expected formats and values. For example, you need to check for missing data and ensure that any required fields, such as dates, product IDs, or location names, are properly filled. This can include manually inspecting records for discrepancies or using software tools to flag errors. In the context of scrubbing, validation is the first step that shows if your data is formatted correctly and ready for further cleaning.
Transformation
Transformation helps so that your data follows a consistent structure. This might involve reformatting dates into a standard format or ensuring numeric values align with appropriate units. Transformation helps eliminate inconsistencies in the dataset and allows for accurate analysis across the entire dataset.
Enrichment
Enrichment is the process of adding additional information to incomplete or missing data points. This technique ensures that data is as comprehensive and accurate as possible. For example, if certain fields in a customer database are missing, enrichment may involve sourcing external data to fill in those gaps. This can also involve adding context to the data to enhance its value. The goal of enrichment is to create a richer dataset that provides deeper insights when analyzed.
De-duplication
De-duplication is one of the most important techniques in data scrubbing, as duplicate records can distort analysis and lead to inaccurate conclusions. The process involves identifying duplicate records, manually or automatically, and removing them. This helps preserve the integrity of your dataset, making sure that each data point is unique and accurate.
Best Practices for Effective Data Scrubbing
When performing data scrubbing, adopting a set of best practices can help speed up the process, improve accuracy, and ensure that your data remains reliable for analysis.
Understand Your Data
Knowing the source, structure, and intended use of your data will guide you in making informed decisions during the scrubbing process. This understanding helps you determine what data points are essential and how they should be treated for consistency and accuracy.
Backup Your Data
Always make a copy of your raw data before starting any scrubbing process. Having a backup allows you to revert to the original dataset if needed and prevents the permanent loss of valuable information.
Ensure Consistency
Consistency is key when scrubbing data. Date formats should be uniform, naming conventions should be consistent, and numeric values should follow the same unit of measurement.
Automate Repetitive Tasks
Automation can help to simplify processes such as removing duplicates and filling in missing values, making your data scrubbing more efficient and reliable. Automating these steps reduces the need for manual intervention and the possibility of human error, and it improves consistency across large data sets.
Iterate the Scrubbing Process
Data scrubbing is an iterative process that should be revisited multiple times as new data is added or as your analysis progresses. This ongoing refinement helps your data stays accurate and up to date.
Start Early in Your Data Pipeline
Data scrubbing should not be left until the end of the data pipeline. Addressing data quality concerns early allows you to build a solid foundation for your analysis, minimizing the chances of major corrections later in the process.
Cross-Validate Regularly
Cross-validation allows you to confirm that the scrubbing process hasn’t altered or removed important information. It helps identify discrepancies between the cleaned and raw datasets, making sure that the final dataset is accurate and complete.
Collaborate Across Teams
If you're working within a team, collaboration is essential. Communicate the changes you make so everyone is on the same page. Shared documentation and clear communication help maintain consistency across the scrubbing process, especially when multiple team members are involved. This way, everyone’s actions align with the overall data cleaning objectives and the final dataset is of high quality.
Benefits of Data Scrubbing
By investing time in data scrubbing, organizations can:
- Improve decision-making: Rely on accurate insights.
- Prevent costly mistakes: Avoid misinformed actions based on flawed data.
- Optimize models and tools: Ensure smooth operation of advanced analytical techniques.
Data Scrubbing Tools and Software
Let's look at a few data cleaning tools that can help you clean and organize your data by removing unwanted or incorrect entries.
OpenRefine: OpenRefine is a highly popular open-source data cleaning tool that helps organizations convert data between different formats while preserving its structure. It enables easy transformation, exploration, and matching of large datasets. OpenRefine also allows you to extract data from the web and work with it directly on your machine.
IBM InfoSphere QualityStage: IBM InfoSphere QualityStage is a data quality tool designed to cleanse and manage databases, helping build consistent views of key business entities like customers, vendors, and products. It's particularly useful for big data, business intelligence, and data warehousing.
Cloudingo: Cloudingo is a Salesforce data cleaning tool that automates the process of keeping Salesforce data clean. It can delete outdated entries, automate cleaning on a schedule, and update records in bulk, making it suitable for companies of all sizes.
Data Scrubbing With Acceldata
Clean data is the basis of any successful project and scrubbing your data will make sure it's correct, reliable, and free of mistakes. Organizations can make smarter choices, avoid mistakes that cost a lot of money, and essentially get the most out of their data-driven projects by giving this process the most attention.
Acceldata’s data observability platform makes data scrubbing easier with features like real-time quality insights, automated error detection, and data lineage analysis. It integrates smoothly with existing systems, helping you maintain reliable and accurate data effortlessly.
Ready to improve your data quality? Request a demo today!
This post was written by Alex Doukas. Alex’s main area of expertise is web development and everything that comes along with it. He also has extensive knowledge of topics such as UX design, big data, social media marketing, and SEO techniques.
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