Learning & Innovation Research Manager| Project Management Institute (PMI)Spain
Data quality and quantity is particularly important as we think about leveraging AI on projects. Considerations include the diversity and comprehensiveness of the data that is available to us.
Have you ever encountered unexpected challenges or pitfalls while using data in your projects? How did you navigate the situation and find a resolution? Saving Changes...
I work on BPMS implementation projects. The BPM continuous improvement cycle and the use of agile strategies in project implementation helps mitigate the challenges associated with massive data use. Saving Changes...
Anonymous
I verify the data and solicit the expertise of the compliance members. Saving Changes...
Data quality and quantity can make or mar any project. This is because it is simply a case of garbage in, garbage out. If the data is too little and not robust enough, it could introduce bias. On the flipside, if it is too large, it may include errors and inconsistencies/outliers, and the output will be incorrect.
Human error can be a huge deal when collecting data especially with lack of adequate training on the specific collection methods. Automation processes can be employed where necessary especially for repetitive tasks leaving humans to do the critical analytical work.
Proper communication/collaboration among team members as well as all project stakeholders is also critical to ensure adequate data is collected. The “man in the middle” has a crucial role to play to ensure data accuracy which in turn ensures accurate output.
It is also important incorporate feedback loops to ensure continuous improvement, periodically reviewing the data quality, refining the collection methods if necessary. Saving Changes...
Navigating unexpected data challenges in projects requires a combination of *proactive planning, adaptability, and a clear problem-solving approach. Over the years, I’ve developed a few strategies that help mitigate the risks associated with unexpected data issues and ensure that projects stay on track, even when faced with data-related challenges. Saving Changes...
Anonymous
Yes, we have projects to improve our data quality and also to align data meaning, in few words, many projects referenced with different names depending of the department. i.e. Sales calls a project PV_San Thomas, Operations calls it 123_23_PV_Clean, etc. Saving Changes...
The most important thing is to ensure that the data is treated appropriately with the security levels that imply maintaining the confidentiality of the cases. Saving Changes...
PMI Team Member
