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...
It depends by the situation. I usually search in the scientific literature if something similar exist and then I tried to break down the problem to understand the causes. Or I tried to increment the number of data, or I analyze if the data are collected in the correct way. 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.
Thanks for your contribution to the group. Saving Changes...
Well, it was related to quality of data that lead to a very strange situation.
In an ASPAC country we had the output of addresses completely different from expectations
The Data Governance person had mis interpreted the instructions.
By contacting the various stakeholders in the project we found that in that specific ASPAC country the marketing function was responsible for the data, hence the odd output.
During a project daily stand-up we found the path to correcting this. Saving Changes...
I would promptly engage the project team to assess the nature and impact of the challenge. Establishing open communication channels ensures that team members feel empowered to share insights and potential solutions.
Next, I would prioritize a thorough analysis of the data issue, collaborating with relevant stakeholders, data scientists, and IT professionals. This involves identifying root causes, assessing the scope of impact, and evaluating alternative approaches. Simultaneously, I would work to manage stakeholder expectations by providing transparent updates on the situation and potential timelines for resolution.
Adaptability is crucial, and I would encourage the team to be flexible in adjusting project plans and timelines to accommodate the unforeseen challenges. Leveraging contingency plans, seeking external expertise if necessary, and implementing agile methodologies can facilitate a more responsive and effective resolution. Ultimately, maintaining a solution-oriented mindset and fostering a collaborative environment are essential for successfully navigating unexpected data challenges.
Mustafa addressed this issues really well.
Working with data everyday, it is important to ask the data analysts or business intelligence where and how they acquired the data. Ask them if they understand the data based on workflows and operational definitions in different operation areas. Data Quality often becomes compromised when the data pulled from different resources are inadequate when the agent did not understand the workflows, leading to pull the wrong data. Alot of old databases do not have the operational definition for each field, causing poor data quality..This is very important when working with new data analysis or business intelligence. Saving Changes...
Nirav PatelManagement| TeamuprightDahisar, Mumbai, India
In one of the project, we used company website as additional data source using RAG method. Company website had multiple response for the same questions due to old web pages. This resulted in an inaccurate response as sometime model used old webpages for responses. We need to create an index of pages to be considered for training and pages that should be ignored. Also we had to ask respective website team member to update the content an maintain the consistency across pages. Saving Changes...
Data quality and quantity are essential for successful AI projects. I've faced several challenges and employed strategies to overcome them:
1. Data Inconsistencies: I tackled missing values and duplicates by implementing automated data cleaning protocols and manual reviews.
2. Bias in Data: To combat biased data, I sourced diverse datasets and applied bias detection and correction techniques.
3. Data Privacy and Security: I enforced strict data protection measures, including encryption and access controls, and conducted regular compliance audits.
4. Data Integration: I used data integration tools to standardize and consolidate data from multiple sources.
5. Scalability Issues: I addressed scalability challenges by utilizing cloud-based solutions and distributed computing frameworks.
6. Real-Time Data Processing: I established data pipelines with appropriate technologies for projects requiring real-time processing. Saving Changes...
Engaging with team members and stakeholders for their input and leveraging past project insights can also help in making informed decisions and overcoming data-related challenges effectively.
Saving Changes...
Yasin Ali ShahPMP®, PMI-RMP® Certified Project Manager| SEPCO Electric Power Construction CorporationRas al khair, Eastern, Saudi Arabia
To navigate unexpected data challenges, I first analyze the issue to identify the root cause, whether it's data quality, integration, or compatibility. I then collaborate with relevant stakeholders to adjust the data collection or processing approach. Additionally, I implement corrective actions such as data cleaning or using alternative data sources to ensure project continuity. Flexibility and clear communication with the team are key to resolving issues quickly. Saving Changes...
PMI Team Member
