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...
Dependency on established tools hurst the most when data security is breached. A cyber-attack at our company resulted in unexpected data challenges that caused the company to shut down for months and also not being able to retrieve all affected data. I cannot underestimate the importance of data security. GenAI puts a whole new layer of responsibilities on data security. Saving Changes...
An essential step is to quickly analyze the issue and communicate it to the teams and stakeholders. Ensure that the right approach to identify and resolve the issue gets established and mitigate impacts as early as possible.
Establish best practices for data governance, modeling, architecture, quality and Security as part of the project plan. Saving Changes...
Anonymous
At the end the rules about cybersecurity assets applies and fits with GEN AI projects. Saving Changes...
Navigating unexpected data challenges in projects requires a proactive and adaptive approach. Saving Changes...
Anonymous
Analyzing with the project team the data challenges to manage & control them. It's important a good communication with all the partys involved and move forward to find the proper solution. Saving Changes...
Periklis KydonakisTechnology and Transformation Manager| Veraltis Asset ManagementAthens, I, Greece
Our industry relies heavily on data to monitor performance and formulate strategic direction, so its protection lies at the heart of our culture. However, unexpected challenges have emerged that were solved through collaborative communication with all stakeholders involved. As we are heavily regulated, our policies and procedures cover data governance concerns through protocols that should be followed. Long story short, policies and procedures augmented by collaborative communication navigated our efforts to overcome and resolve the issues. Saving Changes...
Tim ArmstrongTechnical Engineering Manager | | Principal Consultant| Toray CMA | | Eos Astraeus LLC |Steilacoom, WA, United States
This is a large question that covers a lot of ground. Data challenges can come in the form of bias, inaccuracy, or lack of precision. You must first ensure proper sampling & collection is unbiased and also the measurement system is actually measuring correctly. Take into consideration the risk of going forward inaccurate and low precision. A lot of times the data is accurate and precise but still risky because of a few data points. Those times require an agile mindset where you iterate to lower the risk while attaining value as soon as possible. You must as a project manager accurately represent the data risk to stakeholders appropriately. Saving Changes...
In my current project we are implementing GDPR for an European bank. Major challenges faced during data collection and its consistency due to the variety of data scattered across both in mainframe and distributed system. And, this data has to fed into our models / algo that can obfuscate the data (anonymize + synonymize) to make them GDPR compliance.
To make the data consistency across platforms, we have implemented an automation process to check data consistency before feeding into our build models. Saving Changes...
Norman MoserSenior Manager Digital Platform| BB Hotels Germany GmbHGermany
In my experience, data challenges often arise from unanticipated sources. For example, on one project we found systematic labeling errors in a subset of the training data provided by a third-party vendor. This required significant effort to identify the problematic data and re-label it correctly.
The key is to thoroughly audit and understand your data sources upfront, implement robust data governance practices, and have processes to continuously monitor data quality over time. Saving Changes...
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