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...
Roberto PuglieseDeputy General Coordinator, Director of ICT| Elettra Sincrotrone Trieste S.C.p.A.Duino-Aurisina, Trieste, Italy
The most important thing here is to take seriously and not for granted these aspects. Saving Changes...
Anonymous
Part of the methods I have used in controlling and navigating data challenges on my projects by first ensuring that project team are aware of our organization data privacy policy, security and the governance structure and any specific project data requirements by the customer. As a default, our employees typically undergo a company-wide yearly online training on data privacy, ethics and compliance to ensure everyone understands the expectations. We use encryption methods to exercise controls and prevent unnecessary access and data breaches, amongst other means. Saving Changes...
Maria Thompson-SaebSenior Manager Governance, Risk, and Compliance| Illumio, Inc.Laguna Niguel, Ca, United States
One of the biggest challenges for us is not having enough data to use with GENAI. Saving Changes...
Jose DiazDirector of Information Technology| The Rockefeller FoundationBrooklyn, Ny, United States
Every project I've led or work on, data / information management are included as key deliverables and success factors. I embrace a continuous improvement mindset and recognize that data/information requirement will evolve as project work continues. Ensuring key questions are raised throughout and ensure delivery of agreed outcomes, which often includes data/information components are key to all this. Saving Changes...
Karen BradleyProject Manager, Sr.| AnthemVirginia Beach, Va, United States
As a project manager, maintaining data security is a critical responsibility. Ensuring that all project data is stored, handled, and shared securely protects sensitive information and ensures compliance with relevant data protection regulations. This includes implementing appropriate security measures, regularly reviewing data access permissions, and promoting a culture of awareness regarding data security among team members. It's essential to stay informed about potential threats and enforce rigorous security protocols to safeguard project data throughout its lifecycle. Saving Changes...
Rajesh JainSr Business Process Consultant| SAPBangalore, Karnataka, India
Data security and Privacy are very important focus area for PMs and PMs need to ensure that proper controls are in place to ensure this. Saving Changes...
Missing data, incomplete datasets, and issues with data diversity are a few areas we've encountered.
When identifying missing or incomplete data, we engage with stakeholders to fill gaps where possible.
Use automated scripts/tools to clean the data, ensuring accuracy and consistency before input into models.
Build continuous feedback loops in to assess data quality throughout the project lifecycle.
Being flexible and adaptable in our approach. Saving Changes...
Rohit KatkeProject Manager| Dinero Software Pvt LtdMumbai, India
Navigating unexpected data challenges in projects often comes down to a few key factors: open communication, flexibility, and a willingness to adapt quickly. When unexpected issues arise, it's important to first assess the impact and work closely with stakeholders and data experts to understand the root causes. Implementing a contingency plan, especially for data validation and integrity checks, can help mitigate risks. Additionally, maintaining transparency with stakeholders about challenges and potential timelines is crucial for managing expectations and finding a resolution that keeps the project on track. Saving Changes...
Anonymous
We place a high priority on bias and ethics evaluations to guarantee equitable results, data privacy and compliance audits, and quality control pilot testing to determine efficacy when integrating Gen AI. We track performance using customizable tools like scalability metrics and prompt engineering. Finally, in order to upskill teams in the responsible administration of Gen AI operations, training programs are crucial. Saving Changes...
Amanda JohnsonMBA, MSM, PMP, PSM I, LSSYB| NoneMontevideo, Departemento de Monevideo, Uruguay
I'm not really in a position to answer this question, since I haven't worked anywhere that was even beginning to implement the use of GenAI in any capacity, let alone for Project Managment assistance. Saving Changes...
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