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...
Norman JenningsSenior Project Manager| Catalent Pharma SolutionsLawrence, Ks, United States
This seems to underscore the value of working in an organization with dedicated data governance and IT security teams. Saving Changes...
Richard KaluleSenior Officer Planning, Monitoring and Evaluation| Public Procurement and Disposal of Public Assets AuthorityKampala, 102, Uganda
Often times, we are faced with inadequacies in the data where the need to collect data on a given variable arises midway during the project Saving Changes...
We are following below steps.
Run data security report at every iteration of deployment.
Prepare tool which verify data consistency and security after specific interval of time. Saving Changes...
Data surprises happen! Identify the issue, assess its impact, and explore solutions like cleaning, integrating, or creating synthetic data. Collaborate with the team, adapt if needed, and learn for next time. Saving Changes...
Compliance within the existing framework of the GDPR makes for interesting conversations among the many groups considering the Global Outreach of some of the projects that we are considering, I look forward to continuing the discussion once the C-Suite gives us the go-ahead. Saving Changes...
MAHESH LIMAYESelf employed| Self employedLynn, Ma, United States
In one of my previous projects, the process data was sensitive and had to be given restricted access. Data architect was engaged to ensure the deployment of data architecture to have the process data was available for analytics to the process engineers but not to the other staakeholders. Saving Changes...
Eduardo Romero ParraMBA / Digital Solutions Consultant| IndependentBogotá, Dc, Colombia
This fenomeno is likely to happen in current projects whre companies has relyed on legacy systems or need to involve external sources, specialty when they are not been intensly used before. What we can do is conduct a previous check based on templates and quality criterias in order to have a preliminary "state of the art" report. This will not ensure that everything will go without surprices, but will point to reduce dramatically the probability of happening or at least the impact that can be omn our project. Yet is important to have an accountable of realice and oversee this process permanently, moreover in projects with distributed fronts of works. Saving Changes...
Mohamed El-ZanatyQA/QC Manager| Kharafi National SAEAlexandria, Egypt
Navigating unexpected data challenges in projects, especially when leveraging AI, is a common aspect of project management. Here's how I typically approach such situations:
1. Identify the Challenge: The first step is to clearly identify the unexpected data challenge. This could include issues such as missing data, data inconsistency, data bias, or inadequate data quantity or quality.
2. Assess Impact: Understand the impact of the data challenge on the project's objectives, timeline, and deliverables. Assess whether the challenge requires immediate attention or can be addressed within the project's existing framework.
3. Root Cause Analysis: Conduct a root cause analysis to understand why the data challenge occurred. Was it due to data collection methods, data processing errors, system limitations, or external factors? Understanding the root cause helps in developing effective solutions.
4. Engage Stakeholders: Communicate the data challenge and its potential implications to relevant stakeholders, including project team members, data scientists, data engineers, and business stakeholders. Collaborate with them to brainstorm potential solutions and gather insights.
5. Explore Alternatives: Explore alternative data sources or data collection methods that could help mitigate the impact of the data challenge. This could involve supplementing existing data with external sources, improving data collection processes, or leveraging data augmentation techniques.
6. Data Preprocessing and Cleaning: Implement data preprocessing and cleaning techniques to address data quality issues such as missing values, outliers, or inconsistencies. This may involve data imputation, outlier detection, data normalization, or deduplication, depending on the specific challenge.
7. Iterative Approach: Adopt an iterative approach to data analysis and model development, allowing for continuous refinement and improvement based on feedback and insights gained from addressing data challenges. This helps in adapting to evolving data dynamics and requirements.
8. Monitor and Evaluate: Continuously monitor the performance of AI models and data-driven solutions to assess their effectiveness in addressing the data challenge. Evaluate key metrics and performance indicators to determine whether the solution meets the project's objectives and requirements.
9. Document Lessons Learned: Document lessons learned from navigating unexpected data challenges, including the strategies employed, the outcomes achieved, and the insights gained. This helps in building institutional knowledge and improving future project planning and execution.
By proactively addressing unexpected data challenges and employing effective problem-solving strategies, project managers can mitigate risks, enhance project outcomes, and ensure the successful implementation of AI-driven projects. Saving Changes...
Timothy ThurstonSenior Product Engineering Program Manager| Hewlett Packard Enterprise Missouri City, Tx, United States
Data quality is extremely important so there is a rigorous process to ensure the data used is appropriate for the application. When the data quantity is low we are cautious about its impact on the project's outcome. Saving Changes...
In my previous project, the objective was to reduce obsolescence for database technologies across the globe aka many regions. The timeframe for communication with immense amount of data took two weeks cycle for response and plan of action. However the data kept growing as many assets were not listed in a particular region as API failed to retrieve information at the right time. Hence I took that region as a subset project with dedicated resources and fast tracked to bring the region at par with other regions. It was immensely challenging but at the end it was rewarding in terms of achievement and stakeholders appreciation.
As an old adage goes ‘garbage in garbage out’, so what you put in determines what comes out’. I did encounter few incidences where Geotech reports were not spot on. One instance the depth of roots was quite deep and extensive. And still in another instance, the first 300m of an access road on a landfill site was as per Geotech the rest we discovered an average of 900mm layer plastics. In both instances time and money were lost on the project in redesign, and a substantial amount of additional earthworks resulted. It was suspected, the existing data was used, or even extrapolated without actual site investigation. Many things could have happened in between when data was first recorded to a time later when the data was used. Indeed, recent updated quality and quantity data can leverage AL usage also on construction projects, but there will always be aspects that requires physical human intervention to verify or observe salient features. Saving Changes...
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