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...
Swati SharmaSanta Clara, California, United States
While working on a project for performance testing for multiple projects, I would get into issues like metrics not defined properly, teams lacking communication, creating a baseline for each project, not using effective tools for proper data management and processing.
I started to notice this trend and it was directly impacting my work, so I set up meetings with relative stakeholders to bring consistency in the process and leveraged tools that would help bringing more automation into the process. Saving Changes...
Pallab MajiSenior Engineer| Carrier CorporationBengaluru, KA, India
1. Identify the Root Cause
🔍 Assess Data Quality – Check for missing, inconsistent, or inaccurate data.
🔄 Trace Data Sources – Identify if the issue originates from collection, processing, or integration.
⚠️ Review Dependencies – Determine if external systems or APIs are causing disruptions.
2. Immediate Mitigation Strategies
🛠️ Quick Fixes – Implement temporary workarounds (e.g., manual corrections, alternate data sources).
📊 Data Cleansing – Use automated tools to standardise and validate data.
🔔 Alert Stakeholders – Communicate issues proactively to manage expectations.
3. Long-Term Resolution
💡 Enhance Data Governance – Implement clear policies for data validation and ownership.
🤖 Leverage AI & Automation – Use AI-driven tools to detect and correct anomalies.
🔄 Improve Data Pipelines – Optimize data collection, storage, and transformation processes.
4. Lessons Learned & Future Prevention
📚 Document Issues & Fixes – Maintain a knowledge base to prevent recurrence.
📈 Set Up Monitoring & Alerts – Automate anomaly detection for early warnings.
👥 Conduct Training – Educate teams on best practices for handling data challenges. Saving Changes...
We have several challenges in preparing good quality data for modeling, We have partially automated several steps in the laboratory process however not 100%. When there is a problem occurred, our goal was to narrow down the problem with the available metrics. But because of the non-availability of metrics in each sub steps, we have hard time in preparing the data. It is beyond the scope to work on every single step in the process to capture all the quality metrics. We are still exploring the best solution. Saving Changes...
Anonymous
I don't have such kind of experience. I understood the importance of data protection. Saving Changes...
Hugh WiegelAgile Leader and Release Train Engineer| Florida Power & LightBaltimore, Md, United States
Jan 13, 2024 3:47 AM
Replying to Nikita Jha
...
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.
Have you ever encountered unexpected challenges or pitfalls while using data in your projects? How did you navigate the situation and find a resolution?
Have developed many solutions for regulated industries whereby our offshore teams couldn't see the data without masking, scrambling or at all. Had to be quite creative in the use of different environments, demonstrations and development techniques. Saving Changes...
We collect financial data for business modeling for healthcare clients. We typically find challenges in data based on the consumers assumption and interpretation of their data. After we analyze the data, we have to adjust our predictions based on more accurate data. It's typically in the form of net % collection of revenue - what was billed vs. what was collected from payors. In almost all case, what was contractually agreed upon does not always show up in the financial data, so it's imperative this data is reviewed. Saving Changes...
We collect financial data for business modeling for healthcare clients. We typically find challenges in data based on the consumers assumption and interpretation of their data. After we analyze the data, we have to adjust our predictions based on more accurate data. It's typically in the form of net % collection of revenue - what was billed vs. what was collected from payors. In almost all case, what was contractually agreed upon does not always show up in the financial data, so it's imperative this data is reviewed. Saving Changes...
We collect financial data for business modeling for healthcare clients. We typically find challenges in data based on the consumers assumption and interpretation of their data. After we analyze the data, we have to adjust our predictions based on more accurate data. It's typically in the form of net % collection of revenue - what was billed vs. what was collected from payors. In almost all case, what was contractually agreed upon does not always show up in the financial data, so it's imperative this data is reviewed. Saving Changes...
We collect financial data for business modeling for healthcare clients. We typically find challenges in data based on the consumers assumption and interpretation of their data. After we analyze the data, we have to adjust our predictions based on more accurate data. It's typically in the form of net % collection of revenue - what was billed vs. what was collected from payors. In almost all case, what was contractually agreed upon does not always show up in the financial data, so it's imperative this data is reviewed. Saving Changes...
We collect financial data for business modeling for healthcare clients. We typically find challenges in data based on the consumers assumption and interpretation of their data. After we analyze the data, we have to adjust our predictions based on more accurate data. It's typically in the form of net % collection of revenue - what was billed vs. what was collected from payors. In almost all case, what was contractually agreed upon does not always show up in the financial data, so it's imperative this data is reviewed. Saving Changes...
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