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Project Management

The role of automation in maintaining data integrity

By now, we’ve all heard about how important data is to organizational success–with McKinsey & Company finding that data-driven teams are over 23 times more likely to outperform competitors in the area of new business acquisition. But how do you ensure that the data your team relies on is accurate and reliable? That’s where data integrity comes in. 

What is data integrity?

Simply put, data integrity is understanding whether or not you have the highest-quality information about your business at any given time. In the project management world, this means data like project status, assignee, project details, etc. Data integrity can also apply to data from other sources, such as Salesforce, GitHub, PowerBI, and more. 

The impact of data loss: a real-world example

Let’s take a step back and consider a real example of how data integrity, or a lack of it, can impact a business and the bottom line. We recently worked with an organization of approximately 100 people internally divided into smaller teams. Because of data inaccuracies in their project management system, they became stuck and could not progress through project work. 

These inaccuracies stemmed from the team’s overreliance on manual updates–which only became less sustainable as the workload and project scope increased. To fix this, they needed to address a number of unclear dependencies that got lost in the mix of project work – critical information that affected numerous subtasks, creating a problematic pattern. 

Figuring out how to find the dependency was not only additional work for the team but also delayed current work in flight. It resulted in a significant loss in revenue and productivity, proving that even a small overlooked data error can wreak havoc in the larger business sense. 

When you work within a close team daily, it’s easier to communicate and work without losing important details. But, once you scale even a little bit, those granular details can easily be lost. That’s when automation becomes essential. Had the team properly automated workflows, they could have gained back valuable time in preventing and addressing any potential data errors faster, should they occur.

The role of automation in ensuring data integrity

Right now, a lack of data integrity is primarily caused by human error. In the project management sense, this might mean Issue descriptions that aren’t filled out, missing due dates, no estimates, and out-of-date statuses. 

Automation’s role in addressing this is reducing the number of human touches required when inputting data. For example, instead of worrying if Team A has alerted Team B about a project update, automation ensures that the update is automatically relayed without any additional thought or work required. 

Examples of how Zenhub is using automation for data integrity

Let’s look at how automation can improve data integrity in project management using Zenhub as an example.

Automatic data categorization

Issue and task categorization is one of the simplest–yet most frequently overlooked–project management data points your team relies on to keep work flowing smoothly. This is another great application of AI for maintaining data integrity, as it has the ability to suggest missing data points.

Example: Zenhub AI labels auto-generate label options for your team to choose from, ensuring that each issue is correctly labeled.

Automate data between platforms

Platform integrations are one of the most well-known forms of automation in project management, ensuring that data can flow from one platform to another with minimal human intervention. From a data integrity standpoint, this ensures that data from a primary source is just as up-to-date in other tools where it might be used. 

Example: Zenhub’s GitHub integration provides a seamless flow between what’s happening in GitHub and what appears in Zenhub. This means that the moment a dev changes status in GitHub, the rest of the team can instantly see that change in Zenhub. When a pull request is merged in Github, for example, the issue that it’s connected with automatically updates as well – keeping your team’s data cohesive from one end to another. 

Automate data between teams

Data is often most vulnerable when it’s in transition, such as when its making its way from one team to another. Automation features make it easy to outline custom “rules” that ensure that the right information gets to the right people.

Example: Using Zenhub’s workflow automation, you can automate the flow of Issues from one team’s board to another. 

Automatically update data when changes occur

Data changes can also occur whenever data is moved around within a single system, such as between pipelines (columns) on a Kanban board. Automation helps ensure that the right information is updated whenever a user makes these changes.

Examples: Zenhub Smart Pipelines allow teams to set up triggers within their boards, updating metadata (like Assignee, status, etc.) when Issues are moved from one pipeline to another. Similarly, in Zenhub’s automated sprint planning feature, when a sprint comes to an end, unfinished Issues are triggered to then move to the next sprint.

Automate data visualizations

When data is constantly changing, visual representations of that data sometimes remain stagnant until the visual model is manually updated. Automation, however, can make these updates for you, ensuring that visual data representations are always up-to-date. 

Examples: Zenhub roadmaps always reflect up-to-date information on project progress, updating when scope changes, and flagging when projects are at risk. Similarly, Zenhub reports always automatically update themselves according to the most current project data. 


Automation has emerged as a key player in ensuring data integrity within project management systems. By minimizing human error and streamlining data processes, automation not only enhances the accuracy and reliability of crucial information but also boosts overall efficiency and productivity. 

As shown in some of the Zenhub examples we provided, automation can facilitate seamless data transitions, real-time updates, and more accurate data categorization. To learn more and see how these automations work in action, book a demo of Zenhub.


What causes a lack of data integrity? 

As long as we rely on humans to maintain, enter, and verify information within data-housing solutions of any kind, the potential for inaccurate data and errors inherently exists. Yet, certain risk factors can make a team more likely to struggle with maintaining data integrity, such as:

  • Scaling too quickly: As teams scale, managing bigger volumes of data can lead to inaccuracies. 
  • Process failures: Migrating to a new project management/software tool too quickly with proper onboarding can lead to data being recovered and handled properly. 
  • Transfer losses: As data is migrated into a new platform, it can be lost in transfer if careful precautions aren’t put in place. 

Why is data integrity important?

Regardless of your team’s tech stack, the purpose of the tools remains the same: to act as a central source of truth for essential data. These granular datasets–everything from the plans teams have to individual attributes and task details–are tracked within a project management system. 

Each piece contributes iteratively to a larger goal—if any of those links are weak or inaccurate, it has implications for each project and the broader organization as a whole. Think about a string of dominos, with each data point representing a string in a larger chain of implications. 

Accurate data is the key to keeping larger business operations running smoothly. Collaboration across teams, for example, hinges on clarity from both sides on project progress, especially when dependent tasks and issues are concerned. 

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