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...
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.
Of course, I find that despite basing the project’s design assumptions on trends, the reality of the project can often be affected by any changes in aspects that seemingly have no relation to the project. In those cases, the project planning is reconsidered, as long as it still aims for the same development objectives.
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Anonymous
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Danuta Kierek-JaszczukFounder and Principal Consultant| Retired from iSavva, Inc.Lemont, Il, United States
Hi Cludia,
Data quality and quantity are always important, whether we process the data on projects in a semi-automated way using standard data evaluation programs such as Excel, Statistica, Matlab, Monte Carlo e.g., or in a more automated way using AI-powered engines. You are right: with the latter, data challenges may occur unexpectedly more often so constant monitoring and safeguarding data integrity, from their input, through transformation and analysis to the final result, becomes a necessity. The published data sets that provide solved results for a variety of data transformations and evaluations can serve as analytical standards against which both, semi-automatic or AI-assisted data processing, can be verified. This may be a good way to find resolution to unexpected challenges with data quality. I would like to hear from the members of the Community more about the challenges they encounter with the quantity of data processed with the help of AI models and how they go about solving these challenges. Saving Changes...
Badri N SrinivasanAVP and Lead - Agile Center| Societe Generale Global Solutions CenterBangalore, Karnataka, India
Data quality and quantity are important when we think about leveraging AI on projects.
There have been unexpected challenges or pitfalls while using data in our projects. One of the steps is to have regular data backups to avoid data loss and to also roll back to the previous version set, when required. This helps to resolve some data issues. Other elaborate procedures may be used to manage data quality and quantity of data. Saving Changes...
Tareq ShelbayehProgram Manager| RTAAbudhabi, Az, United Arab Emirates
Hi , I would like to response in English and Arabic .
Yes, I've encountered challenges with data quality and diversity when leveraging AI in projects. In one case, the data was biased, leading to skewed AI predictions. To resolve this, we conducted a data audit, identified gaps, and sourced additional diverse data. We retrained the models iteratively, validated them rigorously, and established continuous monitoring. This approach ensured the AI's outputs were accurate and unbiased, ultimately enhancing the project's success.
Arabic version نسخة بالعربي
نعم، لقد واجهت تحديات تتعلق بجودة وتنوع البيانات عند استخدام الذكاء الاصطناعي في المشاريع. في إحدى الحالات، كانت البيانات متحيزة مما أدى إلى توقعات غير دقيقة من الذكاء الاصطناعي. لحل هذه المشكلة، أجرينا تدقيقًا للبيانات، وحددنا الفجوات، وحصلنا على بيانات إضافية متنوعة. أعدنا تدريب النماذج بشكل تدريجي، وقمنا بالتحقق منها بدقة، وأنشأنا نظام مراقبة مستمر. أدى هذا النهج إلى ضمان دقة مخرجات الذكاء الاصطناعي وعدم تحيزها، مما عزز نجاح المشروع.
A few years ago, in a gold mining company, the data from the exploration process was confidential and had to have restricted access. Therefore, the data architect was hired to ensure the implementation of the data architecture so that the process data was available for analysis by the project team. Saving Changes...
In my last Database assignment/project, effective data cleaning was done using Microsoft Excel, Power BI with Generative AI in order to boost the quality of the data used. As we all know, poor quality data will deliver a defective project; garbage in - garbage out. Saving Changes...
By continuously monitoring and evaluating data during and after the project Saving Changes...
Patricia WhiteEducator/Trainer| UMUCOrange Park, Fl, United States
Have you ever encountered unexpected challenges or pitfalls while using data in your projects? How did you navigate the situation and find a resolution?
I actually have encountered some pitfalls with data while working on a project; I was using big data, lots of it, structural data namely but it was from various spreadsheets, and I felt it was not consistent. I had to cipher through it, clean it, delete data, address null values, etc., in order to use it. I used BigQuery & SQL, & R to deal with the data to remove anomalies, etc. It was a task to say the least but eventually had some data that was useful. Saving Changes...
Data quality, quantity, security and it's privacy is always important in our projects. They serve as the backbone of any organization's reputation and business as well as the success of any project. Saving Changes...
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