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...
Really interesting insights Sergio Luis Conte Thank you for sharing them. I agree when you say that everything around data rests on other disciplines rather than on project management, but I also think that being data so decisive in projects, and more to be in the future, project managers are already shifting towards an understanding of how data behaves and affects their projects. It is as if until now, data-related issues were just another input coming from specific departments but now, project managers are trying to understand and influence those inputs since they affect their projects. What do you think?
Thanks for sharing, very insightful... Saving Changes...
Jim FuhringPMP | CSM | ITIL | RUP | Technology and IT Senior Leader| The Trade DeskOxnard, Ca, United States
Data being in inconsistent formats; incomplete data sets; opinion challenging the data...for example, lack of agreement on lessons learned. Saving Changes...
Jessie WhitlockProject Manager| Oregon Community Credit UnionEugene, Or, United States
I had not thought about a solution oriented version. Having agile, kanban, and predictive separated out and the data aligned with each category supports accurate data. Saving Changes...
Jessie WhitlockProject Manager| Oregon Community Credit UnionEugene, Or, United States
Jan 16, 2024 4:17 AM
Replying to Claudia Alcelay
...
Really interesting insights Sergio Luis Conte Thank you for sharing them. I agree when you say that everything around data rests on other disciplines rather than on project management, but I also think that being data so decisive in projects, and more to be in the future, project managers are already shifting towards an understanding of how data behaves and affects their projects. It is as if until now, data-related issues were just another input coming from specific departments but now, project managers are trying to understand and influence those inputs since they affect their projects. What do you think?
While I agree that it is up to data scientists to manage AI, they don’t know the ends and outs of the work of project management. I think of it as data scientists organizing the data to be most useful in the models. But the project manager must work in a close partnership to provide the data. It would have to be a team effort to be truely successful. Interested in others thoughts. Saving Changes...
Jessie WhitlockProject Manager| Oregon Community Credit UnionEugene, Or, United States
Just like with any data in any project, keep copies. Data changes or additions should go thru a robust change management process where experts in security etc are all at the table to discuss the change before it occurs. And back up the previous version before the change. Saving Changes...
Accurate real-time data is essential for effective decision-making in organizations. It enables leaders to respond quickly to market changes and operational challenges. By providing insights into performance, real-time data enhances operational efficiency and productivity. Understanding customer preferences through accurate data leads to improved experiences and loyalty.
Effective data governance ensures data integrity and builds trust among stakeholders. It also helps organizations comply with regulations, reducing legal risks. Accurate data aligns with strategic goals, allowing for better measurement of progress. Informed resource allocation prioritizes investments in high-potential areas. This efficiency leads to cost reductions, positively impacting the bottom line. Overall, prioritizing accurate data is critical for sustainable growth and competitive advantage. Saving Changes...
Anonymous
It's not unexpected, but data quality and completeness is typically the pitfall I encounter in my work.
We frequently have to make use of data from multiple systems, databases, sources, etc. This ends up making the team's resources take the time to refine the data before we can begin to use it for any actual analysis or problem solving.
In my previous project, the objective was to reduce obsolescence for database technologies across the globe aka many regions. The timeframe for communication with immense amount of data took two weeks cycle for response and plan of action. However the data kept growing as many assets were not listed in a particular region as API failed to retrieve information at the right time. Hence I took that region as a subset project with dedicated resources and fast tracked to bring the region at par with other regions. It was immensely challenging but at the end it was rewarding in terms of achievement and stakeholders appreciation.
I have also encountered unexpected challenges while using data in some projects.
One challenge was related to the data availability, and it affected also the accuracy of the performance analyze provided to customer.
Collaborating with the teams, who were responsible for data availability, we provided improvement plans, looking to mitigate this risk, by creating additional scripts to monitor data availability and correcting in time the performance analyzes delivered to customer. Saving Changes...
Yodit ZebeneSenior Program Coordinator| Office of Physician Scientist Development, Duke University School of MedicineCary, NC, United States
We often have encountered challenges with data quality, such as incomplete data that was not properly entered from the source, duplicated data, inconsistent data. Some of these issues can't be fixed because they originate from the source, which we have no control over. Another major challenge is dealing with regulatory requirements around identified data which complicates things when trying to integrate different datasets. When these issues arise, we focus on improving what we can, like cleaning the data or working with data owners to address the gaps. For the parts we can't control, we find ways to work around the limitations or look for alternative data sources. These challenges require a combination of technical problem solving and strategic compromises, especially when working with AI, where the quality of the data is key. Saving Changes...
Consultant| Canarys Automation LtdBangalore, Karnataka, India
In one of my past projects involving a data migration from a Relational DB (MySQL) to JCR, I encountered several unexpected data anomalies. These issues, once analyzed, revealed potential negative impacts on business operations. It was crucial to take immediate steps to address these anomalies, ensuring the data remained accurate and business processes were unaffected.
In my experience, I've found that prioritizing the business impact of data is key to navigating such challenges. Any time I work with data, I ensure the business implications are fully understood before taking corrective action. By keeping the business at the core of our decision-making, it becomes easier to identify, address, and mitigate the risks posed by poor data quality. Saving Changes...
PMI Team Member
Community Champion
