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...
Yes, I’ve encountered data quality challenges in AI projects, particularly with ensuring diversity and comprehensiveness.
In one project, we found gaps in demographic representation, which could have skewed our model's predictions. To address this, we analyzed the data to pinpoint biases, expanded our data sources, and incorporated third-party datasets to fill in gaps. Through iterative testing, quality checks, and collaboration with experts, we improved data diversity and accuracy, ensuring our model produced more balanced, reliable results. This experience reinforced the importance of a flexible, data-centric approach to AI.
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Murali DamodaranSenior Project ManagementChicago,IL, United States
The main data issues we face are with Master Data, such as SKU details. SKU data often contains large amounts of inaccuracies, duplicated entries, obsolete data, and inconsistencies. It’s appropriate to use Data AI Software to identify and correct these issues, as well as to monitor and synchronize data across the company, ensuring the database remains high-quality and secure Saving Changes...
Once I had to classify a large amount of data.
The difficulty I had was that the description of the data was not good, which made it difficult to classify, which impacted the time it took to develop the task.
I believe that, to mitigate the above, the description process should have a standard format that helps its subsequent classification. Saving Changes...
Agreed that data quality and quantity are particularly important. Some challenges have been:
Incomplete Data: Sometimes, key pieces of data are missing. This can skew results or make analysis difficult. One way to handle this is by using data imputation techniques to estimate the missing values or by collecting additional data to fill the gaps
Data Inconsistencies: Inconsistent data formats or values can lead to errors in processing. Implementing strict data validation rules and using data cleaning tools can help in standardizing the data Saving Changes...
In previous projects, I have encountered challenges in maintaining data availability and consistency within systems operating 24/7. This necessitates meticulous planning of maintenance and update tasks to minimize service disruptions for clients and anticipate potential delays or issues during these activities Saving Changes...
Pham Thi Hai VanHead of Building Service Engineering| Inros-Lackner VietnamHanoi, Viet Nam
We use DVC (Data Version Control) to keep track of data versions and enable easy rollback when unexpected issues arise. This ensures that we can quickly return to a stable version if new data introduces inconsistencies. Saving Changes...
Most nearly all data sets have some quality issues. Challenges and pitfalls should not be unexpected; rather they should be sought out. Being able to identify data quality issues is an obvious first step to gaining and maintaining data quality, but sometimes the identification of data inconsistencies, variances, and errors is not so obvious, particularly with very large data sets. Data has to be explored, investigated, and compared in diverse ways, agnostic to the discoveries. If the goal is to continuously improve, then reaching perfection is not, nor need it be, attainable. Achieving 100% data quality should not be the goal. Achieving an aggregate trend in the direction of 100% data quality is the goal. PMs and their teams should "Always Be Checking the Data" to achieve this goal.
To overcome previous data challenges, I have always had to take a multifaceted approach. For instance, I had to learn from the system SME, question the users, exhibit the behaviors and explore the workarounds with the developers, implement data transformation and enhancements, and give the data analysts creative license to view the data in ways that may not immediately seem relevant. Data errors can cascade, so it's important to continuously re-render you and your teams understanding of the data landscape and lifecycle. Saving Changes...
Project Manager| AWR Development (BD) Ltd. Cox's Bazer , Bangladesh
Hi Claudia Alcelay,
i do navigate unexpected data challenges in your projects are following
1. Identify issues and engage stakeholders.
2. Conduct data audits for resources.
3. Develop contingency plans.
4. Leverage technology.
5. Foster a quality culture.
Data hygiene as an input into projects is important for my team because it affects the project's priority based on impact. In our case, we had to do a deep dive into our CRM to make sure data was being accurately categorized and not being falsely inflated or deflated. Saving Changes...
Anonymous
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.
Si si he encontrado dificultades, principalmente cuando he querido integrar el project profesional, con teams y con power bi. Saving Changes...
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