Community Champion
Guessing is not a strategy: How to build decision velocity with AI and real-time data
June 10, 2026 | Live Webinar
You probably work in an organisation that has a lot of data. We have customer info, staff info, prospects and leads, marketing data, utilisation data - you name it, there’s probably a view in your data analytics tool for it.
But the data is only useful if it’s truly believable. And that’s where data integrity comes in.
What is data integrity?
Data integrity refers to the accuracy, consistency, and reliability of data throughout its lifecycle, from initial collection to final archiving or deletion.
For projects, that means making sure that project-related data (e.g., budgets, timelines, tasks, resources, and deliverables) is reliable and uncorrupted at every stage of the project.
Actually, that’s the easy part. It’s the data yours project uses and creates that is trickier. Migrating data from one system to another? We had a whole workstream dedicated to data clean up on one of my projects. Capturing new data as a result of this project? Where does it go? How does it get incorporated into existing reports or new dashboards?
And then there’s the disposal. When you decommission a product or software tool, we have to make sure data is removed, archived, made searchable or deleted according to the prevailing restrictions on data storage.
Why data integrity matters
Data awareness should be part of the fabric of your project. Ask yourself where it is coming from and where it is going to. What’s the lifecycle of a piece of data – can you map it?
On a project, we use data to assess progress, allocate resources, and make adjustments, so we need it to be reliable because data errors can lead to poor decisions. That’s the same in other areas of the business too. The data inputs and outputs of our project need to work effectively so that decision makers get what they need.
Data integrity means we can hold people accountable. Whether it’s tracking benefits, performance, deadlines… knowing that you can trust the baseline is important.
When you’ve got confidence in the data, it builds trust with stakeholders and internal or external clients, assuring them that the project is on track and meeting objectives.
What I’ve noticed is that it’s pretty easy for data to not be accurate. Test data slips in and needs to be deleted. A report has a field missing and suddenly your formula doesn’t count anyone in the north – small things like that make big differences.
What to do now
It’s one thing to agree that data integrity matters, but that’s just lip service unless the team comes together and takes it seriously. Small changes help create an integrity mindset:
- Agree naming conventions
- Use version control
- Set clear ownership for who is responsible for each dataset.
Create a data workstream on every project, and include relevant milestones, such as checkpoints for data validation during testing and user acceptance phases.
Think about how you’ll monitor ongoing data quality too, so this can be included in the ops handover at the end. Maybe the BAU team want automated checks, exception reporting, or something else. Talk to them about how they will use the data going forward and build that into your schedule.
During the project, a monthly review of key project data elements and fields can highlight issues early – for example, we do a scan through of risks to see when they were last updated, and overdue milestones flag themselves automatically, which is very handy! What can you do to build data integrity throughout your project and ensure it sticks once the project is closed?
Posted on: November 08, 2025 12:00 AM |
Permalink