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...
Suey Yee LimSenior Project Manager| Australia and New Zealand College of AnaesthetistsMelbourne, Victoria, Australia
Data might be incomplete, inconsistent or subject to data privacy considerations. The ability to continuously track and monitor such risks, ensuring mitigating strategies are in place is crucial to minimizing these risks. Saving Changes...
Anonymous
Great Options Saving Changes...
Thomas HopwoodProject Manager, level 2| ConectaSan Mateo, Costa Rica, Costa Rica
Thanks to all for your input. I appreciate the comments as this is a new frontier for my company. I hope to be the trailblazer in implementing AI tools for our data collection implimentations. Saving Changes...
Yasin Ali ShahPMP®, PMI-RMP® Certified Project Manager| SEPCO Electric Power Construction CorporationRas al khair, Eastern, Saudi Arabia
In a project, I deal with unexpected issues in the data by reasoning out what happened, investigating the source, and inquiring of the stakeholders. By using various tools, techniques, and knowledge, I construct implementable solutions while focusing on the objective of the project. Adaptability and clarity in communications assure minimum disruptions and effective decision-making. Saving Changes...
First we validated the data. Next we looked at the data and our opttions for next steps based on the data including keeping stakeholder in the loop. We also exained what data was collected and if there might be better data /indicator available to reflect status or other desired information. Saving Changes...
Taslim KhanProject Manager| Saudi Water AuthorityRiyadh, Saudi Arabia
I my opinion, a good data is really important when using AI in projects. It's about having a mix of different and complete data. When facing unexpected data problems, it's key to identify the issue quickly, working within our team to solve it, and making sure to test and adapting the solutions as needed. Saving Changes...
I assessed the projects data and artifacts that were generated by the PMs that worked for the 2 previous Program Managers along with the data points shared in the weekly status reports and reporting format. With the PMs and Leads input and following best practices, we defined the repository for projects data, templates to use and standardized the status reports cadence, dates, etc. included a redesign of the project page with links to relevant information. Also nudging the BAS and development teams to fill in specific project related information in Jira (Epics, stories, etc.) to be able to gather Jira analytics, build queries, reports and dashboards that feed into Smartsheets and Roadmunk, etc. Saving Changes...
Anecdotally, I observed on other projects that the quality of data which drew from less than reputable sources was unreliable. Saving Changes...
Mikaella DarumOperations Effectiveness Manager and Change Manager| RELX Reed Elsevier (Philippines)Quezon City, Philippines
I have not used big datasets for GenAI (as I mostly use qualitative ones for insights generation for short-term reports) but a common pitfall we encounter are inconsistencies in data, especially historical data where there is a change in unit of measure. What we usually do is to assess the effort of data alignment. If it is truly not possible, we advise to simply go with the most recent data format that has been agreed on by the business moving forward.
Another challenge is data not being available, in which case we'd recommend to conduct a time and motion study to establish a baseline. Saving Changes...
Siva GDirector, EBS Supply Chain Manufactirng| ORACLE INDIA PRIVATE LIMITEDIndia
One challenge is to safeguard sensitive information like PII. When the data collected has PII that is not necessary for a particular project, it would be better not to consume that information at all. This would eliminate the risk of handling such sensitive information (that is not at all relevant for that project). Saving Changes...
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