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...
Vanessa ThomasCybersecurity Project Manager, Data Quality, Change Management| AssystOdenton, Md, United States
Jan 13, 2024 3:47 AM
Replying to Nikita Jha
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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.
Data fields must be able to coexist. Field names should be consistent and translate the same information. The slight difference in in naming conventions, can and will prolong the task. Saving Changes...
Pankaj .Consultant| Nippon Koei Company LimitedMabalacat City, PAMPANGA, Philippines
Handling large amounts of data is always a challenge. In one of my projects, my team and I dealt with a large amount of data with many discrepancies, resulting in the wrong output. To overcome this problem, we divided the data into smaller parts and started looking for the root cause one by one. Although tough, all the hard work paid off in the end, and we delivered the final model on time. Saving Changes...
The unexpected data challenges in projects can depend on the data sources, some quantity of the data might not always mean that it is correct, there should be a validation algorithm/ and we always backcheck data to confirm the results are in the range expected. Reliability of the sources and information also important. Saving Changes...
Anonymous
An interesting challenge is having project team members with the ability to opt out of data collection associated with their name. Makes for incomplete and inadequate project data. Saving Changes...
The main pitfalls that we can encounter when working with large amounts of data are inaccuracies, incoherency, and duplicated or outdated data. It's appropriate to use Data Quality Management Software to identify and correct these issues, and also monitor and synchronize data across the company, keeping database quality perfect.
Quality data without a doubt help solid foundation. It shows the difference between reaching the people and reaching the right people. Saving Changes...
Yu FujitaProject Manager| FUJITSU MISSION CRITICAL SYSTEMS LIMITEDKoto-Ku, Tokyo, Japan
In the field of project management, regardless of whether you use AI or not, you may encounter unexpected challenges based on data.
For example, in program development, the bug removal rate is far from normal in the quality measurement of the application.
In quality evaluation, if there is a situation where AI can be utilized, I feel that analysis such as why the bug removal rate is far from normal can be analyzed more efficiently. Saving Changes...
In our company, we regularly conduct the audits (continuous monitor), execute quality improvement (removing duplicates, correcting errors) and data augmentation (create new data from existing data) tasks in the AI projects.
One of my client has outsourced these tasks to a third party vendor, who owns compliance, privacy, and security of the client's users/customers data (risk transfer). Saving Changes...
It is important to understand the attributes of the data you are using and where the weaknesses may be. e.g.; Some data sources may not have all the attributes you want or may be incorrectly labelled. So verification must be done and decisions made regarding use of the data. I don’t want any AI hallucinations. I get enough hallucinating from bosses and customers. LOL.
Whenever I find a problem with data, I (or ask someone) look in more detail to try and determine whether it is one minor issue or a more pervasive problem. If the errors have already crept into the reports issued, it is critical to let stakeholders know and explain how and if possible when it will be remedied. Once the resolution has been determined, more communication is required to let stakeholders know what is going to happen and how it affects them.
Saving Changes...
V Srinivasa Rao APractice Manager| Itelligence India Software Solutions Pvt LtdIndia, India
One of the key issues while training legacy project data is confidentiality and security . When we use public GPT models , like chatGPT and others , its important that we keep the data free from client names , project names and specific information that would cause data breach relevant to the Organization. Saving Changes...
Project/Program/Portfolio Management could be related to all kind of areas/organ izations, here is a list of common data challenges and their solutions:
Data Quality Issues
1. Missing/inconsistent data: Implement data validation, use imputation methods, document quality issues 2. Outliers: Use robust statistical methods, investigate unusual values before removing 3. Inconsistent formats: Create standardized preprocessing pipelines, enforce data entry standards
Integration Problems
1. Incompatible systems: Build data transformation layers, use ETL tools 2. Schema changes: Design flexible schemas, maintain version control 3. Disparate sources: Create unified data models, implement strong metadata management
1. Automated testing: Data validation checks, pipeline monitoring 2. Version control: Track schema changes, code versions 3. Documentation: Clear data dictionaries, process documentation 4. Monitoring: Alert systems for data quality, pipeline health Saving Changes...
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