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...
Ming Kam NgChief Advisory Officer| EcoMetrics ConsultingN.T., Hong Kong
Challenge to identify the right outcome across multiple scenarios, so I would try to use multiple diversified questions to validate the outcomes over comparison their nuances. Saving Changes...
Elaine ThanNoneFoster City, California, United States
Before inputting the data, I once noticed an inconsistency between the new data and the previously inputted data. Upon further investigation, I discovered that the reason was the different contexts from which the data were pulled. I then rectified the discrepancy by placing the new data under a different set of rules. Data quality in utilizing AI is very important. I think it is an ongoing process of vetting and monitoring, starting from evaluating the source and context of the data to then identifying and rectifying any inconsistencies that may arise. Saving Changes...
Anonymous
I validate data or compare data with others to confirm what exactly I am looking at, if I am doing it correctly, if I am using the correct data and sometimes this leads to finding issues with other's data.
I always takes notes explaining my data too, so if there are any questions on how I got my numbers I can recall how I got the data. Saving Changes...
Managing unanticipated data difficulties in projects necessitates a combination of adaptability, problem-solving abilities, and good communication. When faced with unexpected data issues, it is critical to first determine the nature and scope of the problem. This entails working with the project team, stakeholders, and data experts to obtain a thorough grasp of the difficulties at hand. Once the difficulties are identified, a methodical strategy is required.
I agree Biwi, its essential to have a very good communication between the teams' members and the stakeholders, by leading the project manager, several times the stakeholders (managers, chiefs, suppliers, analyst, etc.), provide poor or incorrect data, in this case is responsibility of PM to implement a set of procedure to correct and validate the data, along the whole project. Saving Changes...
Candice ShubbieConsultant| PROJECT40 ConsultingOntario, Ca, United States
Feb 27, 2024 12:47 AM
Replying to Gaurav Dhooper
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Data quality, quantity, security and privacy have always been important and the best way is to prioritize them in any type of situation, project. They serve as the backbone of any organization's reputation and business.
Gaurav, you make a great point that prioritization is key. Data informs decision-making and the quantity of data allows companies to make informed decisions, however, poor quality data can lead to flawed analysis and bad conclusions. Clear priorities help make collecting, aggregating, and effectively using data mor efficient. Data security is also extremely important as it ensures that sensitive information is protected from nefarious use and is compliant with any necessary regulatory body. While AI may bring about new challenges, the data challenge will remain the same. Saving Changes...
Daniel HilburnChief Technology Officer| Research Innovation UnlimitedMesa, Az, United States
As a seasoned PMO, Program, and Project Manager who consults for Fortune 100 companies and advises the C-suite, I have encountered my fair share of unexpected data challenges in AI projects. Here's how I navigate these challenges:
Proactive Risk Management: I incorporate robust risk management practices into my project planning, identifying potential data-related risks early on. This includes assessing data quality, availability, security, and compliance issues. By anticipating challenges, I can develop contingency plans and allocate resources accordingly.
Collaborative Problem-Solving: When faced with unexpected data challenges, I bring together a cross-functional team of experts, including data scientists, engineers, and domain specialists. By leveraging collective knowledge and fostering open communication, we can brainstorm innovative solutions and adapt our approach as needed.
Agile Methodologies: Implementing agile project management methodologies allows me to respond swiftly to data challenges. By breaking down the project into smaller, manageable iterations, I can quickly identify and address data issues, ensuring that the project stays on track and aligned with the organization's goals.
Continuous Monitoring and Evaluation: I establish a comprehensive monitoring and evaluation framework to track data quality, integrity, and performance throughout the project lifecycle. This enables me to detect anomalies, inconsistencies, or deviations early on and take corrective actions promptly.
Stakeholder Engagement and Communication: I maintain transparent and regular communication with stakeholders, including the C-suite, to keep them informed about data challenges and their potential impact on the project. By engaging stakeholders in the problem-solving process, I can ensure their buy-in and support in navigating complex data issues.
Leveraging Industry Best Practices: I stay abreast of industry best practices and standards in data management, security, and governance. By aligning my projects with these practices, I can minimize the risk of unexpected data challenges and ensure compliance with relevant regulations and guidelines.
Continuous Learning and Improvement: I treat each data challenge as an opportunity to learn and improve my processes. By conducting post-project reviews and capturing lessons learned, I can refine my approach and build a repository of knowledge to better anticipate and mitigate data challenges in future projects.
By proactively addressing data challenges, fostering collaboration, and maintaining a flexible and adaptive approach, I can successfully navigate unexpected data hurdles and deliver AI projects that drive value for Fortune 100 companies and meet the strategic objectives set by the C-suite.
Saving Changes...
Abiola AKINLADEConstruction Project Manager| DCSA PROJECT MANAGEMENT CCJohannesburg, South Africa
This being an introduction to the concept of AI to me, I am yet to encounter a practical situation as described. From a theoretical perspective however, establishing a culture of respecting data - capture, generation, collation, use, storage and disposal is non-negotiable if AI is to be introduced on a project or in an organisation. Thinking aloud, the use of AI requires a sea-change in how a project organisation treats data. Saving Changes...
By Continuously monitor model performance in production.
Set up alerts for unexpected behavior.
Implement feedback loops to retrain models as new data arrives. Saving Changes...
Shea KileyEducator| Massachusetts Department of CorrectionMassachusetts, United States
A great many data pitfalls. I tend to try and find a good start point after implementing change. I use that start point for clean data reflective of the reporting need, and archive or make legacy data of the rest of the database. Saving Changes...
When faced with unexpected data challenges, for example missing data in a project, it is critical to identify what caused the issue and to be agile in working around this challenge, applying key assumptions to keep the project on track.
I would also research the subject of my missing data for my project to obtain as much data as possible. This also involves engaging key stakeholders and the project team.
I would also apply data security and control systems, to avoid a re-occurrence of the challenge and to safeguard existing data.
It would also be important to capture and share lessons learnt from this challenge. Saving Changes...
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