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...
Md Sheikh FaridCountry Head and Project Manager, Bangladesh| Bista Solutions IncDhaka, Bangladesh
Data quality, quantity, security and privacy is very important and the best way is to prioritize them in any type of situation of any particular project.Also adaptibility is very important for adjusting in project plan with timeline. Saving Changes...
Luis Fernando MirandaInternational Project Manager| Coca-Cola EuroPacific PartnersMadrid, Madrid, Spain
There are several data challenges possible when applying AI to Project Management. Some of them can be pretty obvious, such a differences in formats within historical data. Other obstacles could be more difficult to identify and resolve. One example of a use case: we try to evaluate and extract knowledge from the data by always using the same criteria or machine learning mechanisms while the criteria used to generate the data was different or changed over time. This would create noise in our AI model and in the outputs we get from AI. Saving Changes...
It's recommended to scrutinize the data before incorporating it into the model. However, when encountering unexpected data that could influence the outcome, it's prudent to refine your query to identify which data should be considered for a specific task. Additionally, categorizing your data and filtering out irrelevant information will ensure the model delivers the highest quality output.
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1 reply by Mevwerosuoghene Egbegbadia
Jul 23, 2024 4:25 PM
Mevwerosuoghene Egbegbadia
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This makes sense and applicable.
Saving Changes...
Janet HallSr. IS Project Manager| OhioHealth CorporationGalena, Oh, United States
We are not currently using GenAI for project management. Saving Changes...
Challenges often arise due to data biases, gaps, or inaccuracies, which can skew AI outcomes and lead to ineffective or unethical results. To address these issues, conducting thorough data audits to identify and mitigate biases and source additional data to fill gaps and enhance diversity is essential. Collaboration with data scientists to refine data collection and processing methods is critical. Implementing rigorous validation and testing processes helps the AI models perform as intended across diverse scenarios. When faced with data-related challenges, adopting a proactive and iterative approach to data management and model training, coupled with a solid ethical framework, enables effective resolution and ensures the responsible use of AI in projects. Saving Changes...
George FreemanThought Leader | Author | Architect| Florida, United States
Claudia,
One can have excellent quality, quantity, and diversity characteristics in their knowledge base and still objectively fall short of meeting stated project goals. When this occurs, the veracity of these characteristics and the analytic approach often get questioned, but the problem frequently lies in the “perspective view” housed in the data.
For instance, in an enterprise, analytics are often executed from the “back-office perspective,” as those objects (i.e., ERP data structures) are familiar and tangible to those consuming the data. However, the measures desired from the business, although not stated in the requirement, require a “commercial perspective” to be contextually relevant to future interrogations (i.e., questions asked of the data).
Although the business recognizes that analytics are compromised or questionable, the issue “under the hood” is not easily identified as the consumers consider the base data commercially relevant. Hence, the efforts to correct the problem only smooth the edges of specific “problem use cases.”
Bottom line: The “perspective” housed in the view of the data consumed by a system (e.g., generative AI) impacts interrogations. We should not assume that the interrogative system can make the transformational interpretations required; hence, the question of perspective should be dealt with upfront and be fully qualified.
I am starting a new project within my organization that involves create some kind of automation for the training process using the new techonology available. GenAI looks like good fit, but after seeing this part of the course I will guide my team more cautious about how we can develop the correct project implementation roadmap , considering all the data security that is required in the medical industry Saving Changes...
Mahalakshmi PavithraProject Manager| The Everly Putrajaya HotelCyberjaya, Selangor Darul Ehsan, Malaysia
Expect and Plan for the Unexpected: Develop contingency plans for potential data loss, corruption, or other complications. For instance, in our organization we consistently sharing backup data with relevant parties. Quarterly risk assessment meetings are conducted in order to proactively identify and address potential data challenges. Saving Changes...
I used ChatGPT to consolidate the collective wisdom from this board discussion into bullet points, added explanations where necessary, and provided a summary. The combined feedback is fascinating.
Feedback Bullets: Data Quality and Management: - Importance of data quality, quantity, security, and privacy in leveraging AI for projects. - Use of Data Quality Management Software to identify, correct issues, and synchronize data across the company. - The necessity for project managers to become increasingly data-savvy, emphasizing proactive data quality assurance and real-time monitoring.
Project Management Strategies: - Fast-tracking projects by allocating dedicated resources to handle growing data and API failures. - Adapting project plans and timelines to accommodate unexpected data challenges, emphasizing adaptability. - Engaging stakeholders and data experts early to understand and mitigate data challenges.
Collaboration and Communication: - Importance of cross-disciplinary collaboration, especially with data scientists and IT professionals, to ensure data integrity. - Developing communication skills to break silos and enhance collaboration across departments. - Continuous education and sharing of best practices within the community to improve data handling and project outcomes.
Technical Solutions and Approaches: - Leveraging AI and machine learning algorithms to predict and mitigate data-related issues before they impact project outcomes. - Conducting data audits to identify biases, inaccuracies, and fill data gaps. - Implementing rigorous validation and testing processes for AI models to ensure effectiveness across diverse scenarios.
Explanations: - The discussion emphasizes the critical role of data quality and comprehensive management in the success of AI-driven projects. Project managers are encouraged to adopt a proactive stance on data issues, leveraging technology and best practices to ensure data integrity. - Collaboration emerges as a key theme, with the need for project managers to work closely with data scientists, IT, and other stakeholders to navigate data challenges effectively. - The feedback highlights the importance of adaptability, suggesting that project teams should be prepared to adjust their approaches as new data challenges arise. This includes the use of advanced technologies and methodologies to anticipate and address potential issues.
Summary: The collective insights from the board discussion underline the complexity and importance of managing data challenges in AI-driven projects. The key takeaway is the need for a holistic approach that combines robust data management practices, proactive project management strategies, and strong collaboration across disciplines. By focusing on data quality, security, and privacy, and by fostering an adaptable and communicative project environment, project managers can navigate unexpected data challenges more effectively. Furthermore, the integration of advanced technologies and continuous learning are essential for enhancing decision-making processes and ensuring project success in the evolving landscape of AI and big data.
Saving Changes...
Victor FragaSenior IT Project Manager| Maples GroupWest Bay, Grand Cayman, Cayman Islands
Regardless of the use of AI as part of the project work and deliverables, data is always a key element even before making a decision to start one. I usually rely on data management specialists withing the organization for guidance on that as well as Information Security and Privacy groups to avoid pitfalls. It has been working fine this far, but again, not without its challenges. Saving Changes...
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