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...
Data quality and quantity is very important for accurate results when using GenAI. However, for unexpected changes observed in data, the following steps can be taken;
1. Assess the impact on the project's scope, timeline, and resources.
2. Open communication with team members
3. Prioritize tasks
4. Adjust Plans
5. Solve Creatively
6. Monitor Progress Saving Changes...
Cynthia Kaye BanishCynthia Banish| Healthcare Program and Project ManagementPlano, Tx, United States
Good day. Most of my projects require validation of the data by SMEs and Business owners, so one of my constant challenges is to make sure the data is within a format where it can easily be determined whether it meets the criteria we need. Clarity as to which fields names are involved has saved time and money continually over the years. Simple but important. Saving Changes...
Eddretta DorseySr. System Engineer| AT&TAtlanta, Ga, United States
as currently stated, I am not using AI at this time in my current role however, I do use other data related forecasting tools such as Power BI.. I have experience challenges with data, especially when the data was provided to me. The biggest challenge is making assumptions about the data. Understanding data feels and how it is intended to be used as critical. Saving Changes...
Anonymous
I navigate by getting risk management plan and alternative workarounds in track. 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pitfalls that we can encounter when working with large amounts of data are inaccuracies, incoherency, and duplicated or outdated data.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.
Most large organizations have a strong data governance standard in place. This has to be kept upto date as we encounter new scenarios and threats. Ensuring that all the risks are captured and channeled to the right stakeholders responsible in keeping the standards updated. A robust training program is a must Saving Changes...
We have strict measures, policies and procedures in place to prevent data leaks or data breach in our company. Employees are trained on new policies and procedures regularly. Therefore I have not yet encountered unexpected challenges or pitfalls while using data in our projects. Saving Changes...
It is important to understan what kind of data we are talking about, it is discrete data, continuous data or qualitative data in order to define the level of quality required and what quantity is enough to have an accurate analysis. Saving Changes...
Diana GarciaSenior Analyst and Developer| Deacero S.A.P.I.Monterrey, Nuevo León, Mexico
Yes, there is data that has restricted access even within the organization and to which only certain people or specific areas of the company have access. But this data is relevant for some analysis that is to be carried out as part of improvement projects. In these cases, I have had to look for the "owners" of the data and together with those involved in the project, justify why access to the data is important. Then, we have agreed on the means and mechanisms through which the data will be shared for the purposes of the project, without compromising the confidentiality of the data. Saving Changes...
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