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...
Katty BibicProject Manager| TeocoHuntersville, Nc, United States
Indeed, I have encountered such scenarios in a couple of projects I worked on. Having unreliable data delays projects and increases efforts as well. I think the use of AI is going to help enormously overcoming or reducing the impact on such scenarios. If the data validity can be validated in advance via AI, this will reduce the response time to the provider of the source who needs to fix the data, as well as to the team who is going to work with the data to develop the final solution intended for the customer. Saving Changes...
DIVINE KAYIIProject Manager ( Principal Project Engineer)| Shell Development CompanyPort Harcourt, Ri, Nigeria
We have been quite careful to manage available data from the field and the the lessons learn data base. i would encouraged our team of expert to conduct a thorough assessment of the data to identify any inconsistencies, errors, or missing values if any observe and unusual pattern that suggest error.
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1 reply by Bingye Yu
Jun 04, 2024 7:34 PM
Bingye Yu
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Inconsistent is really a thing, especially multiple people touch the same area with different preference methods. Cost longer time to maintain in long run.
Handling unexpected data challenges in AI projects requires a strategy that emphasizes adaptability, effective data management, and proactive problem-solving. Addressing data biases is crucial, necessitating the pursuit of diverse data sources and regular bias assessments to ensure AI models are balanced. Additionally, the evolving nature of data demands a solid data governance framework for timely updates, validation, and maintenance, ensuring data remains relevant for AI applications. Data silos present another hurdle, often impeding data accessibility and analysis. Overcoming this involves fostering cross-departmental collaboration and implementing interoperable data platforms that facilitate comprehensive data integration. A proactive approach, anticipating potential data issues and incorporating flexible contingency plans, is vital. This may include allocating resources for data cleansing, or establishing external partnerships for data augmentation, to fill in any gaps swiftly.
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Emmanuel UdoOther| Inflatus Consultants and Partners Port Harcourt, Nigeria
Jan 13, 2024 7:19 AM
Replying to Sergio Luis Conte
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Key to understand is what you stated: "Data quality and quantity is particularly important as we think about leveraging AI on projects". This is key in AI from long time ago, from 1970s at least. But all related to data rest on other discipline that today is called Big Data to put it under and umbrella. And it is independent of you use AI or not. So, let me say, nothing new below the sun. Just to understand that data has to be converted into information. Again, nothing new. It was analized by Claude Shannon in the 1940s.
AI integration is still new to us. Saving Changes...
Basil MelibariPMO Director | SAL (Saudi Logistics Services Company)Jeddah, Makkah, Saudi Arabia
Certainly, I've personally encountered challenges with data quality and quantity in various projects across different industries. For instance, we faced issues with incomplete datasets and discrepancies between sources, impacting the reliability of our AI-driven analyses. To overcome this, we implemented rigorous data validation processes, leveraging statistical methods and domain knowledge to identify and rectify discrepancies, in addition to some random manual checks of the data. These efforts stressed the critical role of accurate data management in maximizing the effectiveness of AI applications in projects to get the maximum outcomes.
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Farhad AbdollahyanManaging Director| Cyrus Associados Apoio em ProjetosSao Paulo, Sp, Brazil
Navigating unexpected data challenges amid the VUCA (Volatility, Uncertainty, Complexity, and Ambiguity) and permacrisis context requires a strategic and adaptive approach. Here are some tips to help us address such challenges effectively:
1. **Flexible Data Strategy:**
- Develop a data strategy that is agile and adaptable to changing circumstances.
- Emphasize data collection, processing, and analysis flexibility to accommodate unforeseen variations or disruptions.
- Consider implementing real-time data monitoring and adjustment mechanisms to respond to dynamic situations.
2. **Robust Data Governance:**
- Strengthen data governance practices to ensure integrity, security, and compliance under uncertain conditions.
- Establish explicit data quality control, verification, and validation protocols to mitigate risks associated with inaccurate or incomplete data.
- Implement backup and recovery strategies to safeguard critical data assets in volatile environments.
3. **Collaborative Approach:**
- Foster collaboration across multidisciplinary teams, including data scientists, subject matter experts, and decision-makers, to address complex data challenges collectively.
- Encourage open communication and knowledge sharing to leverage diverse perspectives and insights in problem-solving.
- Prioritize teamwork and coordination to navigate uncertainties and address data issues collaboratively.
4. **Adaptive Analytics Techniques:**
- Utilize advanced analytics and machine learning algorithms to extract actionable insights from unconventional or sparse data sources.
- Explore adaptive modeling approaches that can adjust to real-time data patterns and trends.
- Leverage predictive analytics to anticipate potential data challenges and proactively address them before they escalate.
5. **Risk Management and Contingency Planning:**
- Conduct thorough risk assessments to identify vulnerabilities and potential disruptions in data processes.
- Develop contingency plans and alternative strategies to mitigate the impact of data challenges on project outcomes.
- Implement robust monitoring and early warning systems to detect anomalies and deviations in data streams.
6. **Continuous Learning and Improvement:**
- Foster a culture of continuous learning and experimentation to enhance the project team's data literacy and problem-solving capabilities.
- Encourage feedback loops and post-mortem analyses to reflect on past data challenges and identify opportunities for improvement.
- Prioritize adaptability and resilience in responding to unforeseen data issues to drive ongoing project success.
By embracing adaptability, collaboration, innovation, and risk management strategies, one can effectively navigate unexpected data challenges in the current VUCA and permacrisis context, ensuring the resilience and success of projects despite the uncertainties and complexities encountered. Saving Changes...
Anonymous
It was repeated several times throughout this course Garbage in, garbage out. That truth has remained constant for the many years I have been working as a PM in IT. Data cleansing through knowing your source. Data edit and reasonability. The best way to navigate unexpected data challenges in your project is to develop a methodology for keeping bad data out. There is no cut and dry answer. Keeping data out can lead to unintended biases and a gap in model learning and understanding. As we learned in the course low-quality data will lead to low-quality results. Always has, always will, time has proven the best course of action is to try and mitigate the low-quality upfront, it is the least expensive and least time consuming. Saving Changes...
David BrezlerOwner| Brezler, LLCWhite Plains, Ny, United States
Absolutely have run into challenges. The best example of this was the fact that I was an early adopter of data visualization on the PMO where I was working. Since I had responsibility for not only operating programmatic tasks, but also the data reporting and monitoring of the data sets, I had to have an implicit understanding of what was going on in multiple portions of the program simultaneously. I developed a dashboard for one director level office after the responsible officer had been looking at spreadsheets for several months, and avoiding a decision on a key operational item. Once they saw the visualized data, it changed their perception, and they made the decision I had been recommending. Never underestimate the power of a good visualizaiton! Saving Changes...
Allison ParkDelivery Manager| MicrosoftFalls Church, Va, United States
I am an IT consulting portfolio manager. Alot of our projects will include data from our customers. If we are using customer data for any reason such as testing/modeling we engage the privacy team at our company to provide guidance on appropriate storage and access. Saving Changes...
Jeffrey RossCyber Software Engineering, Senior Advisor| PeratonAlide, Va, United States
Data privacy is one ot the first considerations we have to have on our projects. Saving Changes...
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