Set a Data Quality Target and make sure things actually get filled out! 🎯
Prerequisites ✅
If you've not done any of these steps, complete them first then come back here.
- Use the .PBIT (Power BI template) we've made for you to create your Projects Default Data Quality Report with our template reports
- Set up a Power BI online workspace
- Publish your report
- Set up scheduled refresh (optional)
So, if you've ticked those things off... well done!
You've got a new report, full of data, and you're probably wondering "where do I begin?". Good news; we're gonna walk you through each page.
Warning: Only data created using the out-of-the-box Project's "Default Data Quality" template will be shown in this report. To get data from the rest of your edison365 software, please check out our API documentation and the API portal to understand how to connect to and surface your custom data.
Tip: Need a recap on navigating the Power BI online user interface? Use Your Report
Overview 👀
Note:
If you want to change the Data Quality Target you will need to do so in Power BI desktop. This parameter is first entered when creating the report, but can be changed at any time by loading the report > Transform Data > Parameters (left bar) > Data Quality Target > edit the decimal value. In this example the target is set at 0.75 = 75%.
Summary
The Summary page is a great place to start. See your targets visualized and easily differentiate which projects are falling behind on their data quality.
Tip: Use the Program and Portfolio filters to drill down on specific types of projects without having to create a new report.
Distribution
See how all your projects measure up to your target. Spot which Projects are falling behind and specifically which parts of your projects need updating. In the example below I've selected the four projects that fall below the target line. The corresponding data is pulled up in the table.
Detail
Dig into the nitty-gritty details. Use the checkboxes on the left to restrict the data filter to specific parts of your projects. Seeing exactly which details are missing makes follow up tasks, poor compliance, and potential for process enhancements super clear.
Use the filters at the top (Stage, Project, Program, and Portfolio) to narrow down your focus even further on a particular type of data. Isolating and analyzing the specific information related to a single type, enables you to gain deeper insights. By selecting different types from the filter options, you can follow where the data takes you. This targeted approach enhances your ability to monitor progress, identify trends, and take corrective actions as needed.
Leaderboard
Make it competitive! See who needs to step it up with their details. The leaderboard offers a convenient list of individuals to follow up with, making it easier to track progress and encourage improvements. The target line gives you an easy visualization of the rat race.
Gap Analysis
This beefed up scatter plot provides a visual representation of the amount of missing data across your projects. So what are we looking at here?
Each circle on the plot corresponds to a section of your project data. It's position on the Y-axis shows you how much potential data there is in that part of a project. The size of the circle tells you how much data is missing (or, potential data - actual data). So, at a glance, you can see which part of your projects require the most input, and how well people are complying with that.
The larger the circle, the more data is missing, making it easy to pinpoint which parts of your project suffer from poor data quality, unearthing insights into reasons for poor compliance like training, configuration, and culture.
This gives you actionable data to quickly identify where the most significant gaps in data exist, allowing for targeted efforts to improve data completeness and accuracy.
Note: Check out how the Data Quality Percentage (top left) changes as different projects are selected.