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...
When faced with data that appears to be not accurate a strike team will be asked to address why the data has changed. They will determine if there was a different data source or if there were requirement changes in the report and provide a report. If the changes are not agreed to then a complete review and consensus will determine how to proceed. Saving Changes...
Recenlty worked on a project where we got stuck because lacked enough quality data to help us build the system we needed: Facing an unexpected quality data shortage during this project's development delayed it's roll out , we identified key data owners and secured permissions to access their datasets. This collaborative effort significantly increased our data pool, enabling us to successfully develop and launch the project. Saving Changes...
To navigate unexpected data challenges in our projects, we employ the following strategies:
Root Cause Analysis: Quickly identify and analyze the source of the data issue.
Data Cleaning and Preprocessing: Implement robust data cleaning and preprocessing steps to handle inconsistencies.
Backup Plans: Maintain backup data sources and alternative datasets to ensure continuity.
Expert Consultation: Engage data experts and stakeholders to gain insights and solutions.
Agile Methodology: Use agile practices to adapt quickly to changes and iterate solutions.
Continuous Monitoring: Set up automated monitoring to detect and address issues early.
Documentation: Maintain thorough documentation to track challenges and solutions for future reference. Saving Changes...
In the domain of clinical trials, data quality is extremely important as research results inherently depend on collected data. Major issue in data collection is to identify anomalies by combining and evaluating data from specific collections. One way to ensure continuous quality check is to establish and execute data accuracy checks that will be automatically triggered when any related data item is added, updated or deleted. Saving Changes...
Inti SeguraSenior Project Manager| Selt employeeSanta Ana, San Jose, Costa Rica
Jan 16, 2024 4:17 AM
Replying to Claudia Alcelay
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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?
So, what is the data for? and how the data is coming today to the decision-makers. I'll say a better source is better data, nothing new, right but the source is still changing, so enterprise data awareness and alignment could support the way of handling data into information nowadays. Saving Changes...
Really interesting insights Sergio Luis Conte Thank you for sharing them
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1 reply by Sergio Luis Conte
Jul 18, 2024 5:14 AM
Sergio Luis Conte
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You are welcome Nisar
Saving Changes...
Sergio Luis ConteHelping to create solutions for everyone| Worldwide based OrganizationsBuenos Aires, Argentina
Jul 17, 2024 6:39 AM
Replying to Nisar Ahmed
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Really interesting insights Sergio Luis Conte Thank you for sharing them
You are welcome Nisar Saving Changes...
Amber McMillanFounder/CEO| The Feisty Project Manager, Inc.Victoria, British Columbia, Canada
Face the challenges head on. Seek subject matter expertise and be prepared to fail fast and learn. Saving Changes...
David NelmsProject Manager - MIS/Gaming Systems| NC Education LotteryAngier, Nc, United States
We have some continual testing projects that we had the vendor to anonymize production data so that we could have better load tests as well as better overall testing. We have not gotten to the data model with AI at this time. Saving Changes...
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