Data Scrubbing: Definition, Purpose and Benefits

By Indeed Editorial Team

Published January 3, 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

A company's data can inform its biggest business decisions, like where to focus marketing efforts, how to reach more customers and what its current metrics are. Performing an intensive data cleanup can help ensure correctness and improve overall business performance. Learning the details about scrubbing data can help you identify if this task might help your organization. In this article, we define data scrubbing, discuss what it can help solve, explore its key benefits and share steps you can take to perform a data scrub on your own databases and systems.

What is data scrubbing?

Data scrubbing is the process of fixing data in a database. This involves reviewing the currently stored data, fixing any errors, removing unimportant data and adding information to ensure accuracy. Companies might scrub their data manually by reviewing its current records or using a software tool that can check for common issues.

What common errors can you fix with data scrubbing?

There are several common errors you might fix with data scrubbing, including:

  • Duplication: Databases may contain multiple records with the same information. Scrubbing tools may help identify duplicate records, though someone may need to identify which one to keep.

  • Inconsistencies: Data can have inconsistencies like formatting or text inconsistencies. For example, if records should have a date, a data scrub can help ensure they all have the same formatting.

  • Redundancy: Repeated data differs from duplicated data, as you might have similar information within one record or records that convey the same information in different ways. Scrubbing data can help you identify and keep only the most relevant information.

  • Mistyped data: Common with manual entry data, mistyped data can include typos or grammatical inconsistencies. Scrubbing can identify records with errors that you can fix.

  • Missing data: Records in a database might require several pieces of information or metadata. Scrubbing tools can help you identify if you're missing entire records or accompanying information.

These errors might happen for several reasons. For example, systems with many manual data entry fields might cause more typos or inconsistencies without specific guidelines. Organizations might also merge systems together, which can cause duplication or inconsistencies depending on how users handled input in each.

Related: Data Governance: Definition, Benefits and Best Practices

Benefits of data scrubbing

There are several benefits you can get from scrubbing your data:

  • Improving experience: Creating accurate and consistent records for all of your products, employee information and other data requirements can improve how employees and customers interact with systems and information. This can build trust with external individuals and improve employee mood and performance, as this could eliminate common frustrations experienced with incorrect or missing data.

  • Improving decisions: Accurate data can help businesses make better business decisions. For example, accurate sales data with complete metadata can help an organization understand where it's most successful and where it might improve.

  • Increasing revenue: As scrubbing can help reduce costs from additional production, rework or customer frustration, it can also help increase revenue. For example, accurate product data on a company's website can guide customers to purchase their products and improve their shopping experience.

  • Improving productivity: With a clean database, employees can find records easier, understand data trends and focus on their responsibilities rather than manually cleaning data. This can improve productivity in the workplace, which can improve output.

Related: 5 Stages of Data Life Cycle Management (Plus Benefits)

How to scrub data

Here are some steps you can take to scrub data:

1. Audit your records

Before correcting data, you might perform an audit on your database. This can help you identify common issues and define the scope of your project. You might use a scrubbing tool or perform this audit manually. During this audit, you may identify the different systems or databases where you store data, who inputs and maintains it and what an ideal database looks like.

Related: Data Analysis: Purpose and Techniques

2. Create rules

Once you understand your common issues and where you store data, creating strict rules about data input and management can keep your records consistent and correct. You might define items like:

  • Formatting guidelines

  • Grammar rules

  • Input roles

  • Access roles

  • Required fields

  • Metadata or tagging requirements

Your list of rules can guide you when correcting your data and help you maintain it after the scrub. Consider business goals when creating this list. For example, if you hope to fix pricing issues, you might include specific dollar formatting and ensure each product has a price to reduce missing data. You might meet with several teams to determine what data would be most useful to include.

Related: What Is Data Profiling? Definition and Types

3. Correct the data

With your rules, you can manually fix your data or explore automated tools that can fix them. This can include inputting missing information, fixing typos, adding metadata or eliminating duplicate records. You might dedicate a separate team to do this, different from those who normally input data, to change any records objectively using the defined rules. If you're merging systems or decommissioning one, consider only updating the data in one system or after the merge to ensure you only correct the errors once, in the correct location.

4. Validate the data

Once you correct the data, you can perform validation to ensure everything is correct. This is especially important if you used software to scrub your data, as it might only follow strict rules and might not identify every issue, like correctness. For example, you might have identified and input product data for every product you sell, but you might need experts to verify that specifications or metadata are correct in each.

5. Create reports

Many databases or systems allow you to create reports with your data. Software tools that perform scrubs might also have reports that specify the issues identified and progress for correcting them. This can help if you hope to perform a data scrub periodically, as you can learn how much time it might take and what it might cost. Reports can also show trends, like where there are more common issues with specific data fields.

6. Communicate requirements

Once you complete your scrub, you can consider how to communicate your findings and adjust any processes or documentation to help avoid some of the common issues. You might meet with leadership to see if you can assign specific roles for inputting data, identify any technical support resources you might need and create documentation for data requirements. These can all help improve the quality of data in the future and can save you time and money during another data scrub.

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