What are the best data versioning practices for process automation?

1 Why data versioning matters

Data versioning is essential for process automation as it ensures the consistency and integrity of data across different stages and environments of the automation pipeline. It also allows you to document the history and provenance of your data, and trace back the source and impact of any changes or errors. Furthermore, data versioning facilitates collaboration between team members and stakeholders by enabling data sharing, synchronization, and review. Finally, it supports experimentation and innovation by allowing you to create, compare, and revert to different versions of your data.

2 How to version your data

To version your data effectively, you need to adhere to some basic principles and best practices. This includes defining clear and meaningful naming conventions and versioning schemes for files and folders, and using them consistently throughout the automation process. Additionally, a data version control system (DVCS) should be employed to store, track, and manage data versions, and be integrated with a code version control system (CVCS) such as Git or SVN. Data should also be organized in a logical and modular way, with metadata and annotations used to describe the content, context, and changes of your data versions. To reduce manual or ad hoc interventions, it is beneficial to automate the data versioning process as much as possible with scripts, hooks, or workflows.

4 How to get started

To get started with data versioning for process automation, you need to assess your data situation and identify your data sources, formats, sizes, and dependencies. Then, choose a tool that suits your use case, goals, and resources, and learn how to use it. Next, set up the environment and workflow, integrating it with code versioning and automation systems. Finally, test and monitor the process and make adjustments as needed.

5 How to improve

To improve your data versioning practices for process automation, you should review and update your data versioning policies and standards regularly, incorporating feedback from stakeholders and addressing any issues or challenges. Additionally, you can explore new features and functionalities, learning from best practices and case studies of other users and organizations. Furthermore, it is important to evaluate and measure the impact of your data versioning process, reporting on your achievements and lessons learned.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?