4 Considerations to Maintain Clean Data Across Multiple Systems

When developing a database for your organization, it's crucial to maintain clean data across multiple systems to ensure readability and understanding for everyone involved in the project. However, "clean data" can mean different things to different people. How can you ensure everyone is on the same page?

There are a few key considerations that you should implement to maintain clean data across systems. These small changes make a huge difference.

4 Considerations to Maintain Clean Data Across Multiple Systems

1. Define Standards

First, establish clear standards for data presentation. This includes everything from date formats to naming conventions. For example, dates should consistently be formatted as MM-DD-YYYY or DD-MM-YYYY. When you decide on a specific naming convention, you should document this in a shared file that the whole team has access to. This shared doc can serve as a reminder for team members as they add new data.

Assigning specific users or teams to maintain data integrity can help enforce these standards. If possible, reduce the number of employees entering data - you may be able to reduce this number by having these team members provide data to designated team members for system uploads. This will also help reduce inconsistencies in data.

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2. Standardize Backend Code

The backend code should also adhere to defined standards. Consistent code formatting is essential for readability and debugging. For instance, writing all functions in capital letters, using brackets around table names, and indenting code appropriately can help maintain clean data.

Like with your general data standardizations, we strongly recommend having a shared file that documents these coding conventions. Having a shared file is a fantastic way to keep all team members on the same page and elminate some of the human error of needing to rely on memory.

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3. Implement Data Integration Tools

Using data integration tools like ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) pipelines can streamline data formatting before it is loaded into the database. These tools ensure that data is consistently formatted across tables, facilitating easier joins and reducing discrepancies.

There are a number of tools available, but for Power BI users or Fabric Users, we strongly recommend Azure's Data Factory or Microsoft SQL Integration Services (SSIS). Both of these ETL tools integrate with other Microsoft tools, making it even easier to migrate data from one platform into a Power BI environment. 

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4. Train Users and Enforce Data Entry Standards

Training users on proper data entry practices and implementing validation checks in the user interface can prevent bad data from entering the database. For example, ensuring that fields are correctly filled out and validating data types can reduce errors. 

Going back to our first recommendation: if you assign specific users or teams to maintain data integrity, these teams can also help enforce data entry standards. With training it's important to remember it's not a one-and-done scenario. Holding regular sessions to reinforce data entry standards keeps them top of mind and helps ensure they remain consistent. Many of our clients have great success with quarterly group working sessions.

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Conclusion

There are several ways to keep your data clean across multiple systems. Implementing these practices can help maintain data integrity and readability. Superior Consulting, with over 27 years of experience, can provide best practices and insights to ensure your data remains clean. Contact us for a no-obligation meeting to discuss your specific needs and challenges.  We also offer introductory courses which include modules on entering and maintaining clean data. 

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