About Us

The PM Data Repository is actually the product of a postdoctoral research project. Our idea was to use artificial intelligence to improve project management. We consider that the possibilities are very promising in this sense, and we understand that we are still taking the first steps on a long road that has enormous potential to change (for the better) the way we manage projects. It doesn’t matter if it’s using a traditional, agile, or hybrid approach.

As domain experts in project management, we know that there are many possible ways to apply machine learning throughout the lifecycle of a project. Having experience in the subject also helps a lot in feature selection and engineering, since almost all project management macro processes can (and need to) be optimized. In fact, we believe that the next big evolution in management will come from this data perspective. Not that AI will replace the manager, but it will definitely augment his/her management capacity.

It was like everything was perfectly in place: a good idea, a good mentor, Jupyter notebooks, and Google Colab to help. However, every time we developed and discussed a new algorithm, we ran into the same inexorable gap: the lack of data! There are some examples of databases on Kaggle and other sites, but nothing that represented exactly what we needed to develop a model that would actually be useful for project management.

Suddenly, a new idea came along: why don't we develop a database ourselves that could be useful for our research and also for all those who want to work with artificial intelligence in project management? Such a simple suggestion and so bold at the same time.

That's how we started to design this database that we affectionately call PM Data Rep. The idea is to popularize the base with as many projects as possible and make it public as it reaches a certain number of registered projects. We are fully aware that this is not a trivial target since many organizations are not mature enough to keep their own lessons learned in relation to the projects undertaken. And even if they do, they may not be interested in sharing their data for fear that it could somehow be misused.

It is for this reason that all data collected will NOT be associated with the organization of those willing to enter data from their projects. What we are really interested in is the quantity and quality of the data itself.

Speaking about that, we know that for AI projects, normally, the amount of data is super relevant. Actually, the quality too! However, we chose not to do any data cleaning or wrangling, since we understand that the researcher who is interested in the database will know how to do it so that it best meets his/her research interests. What we are going to make available is the base itself, in a .CSV format, to facilitate the use of libraries such as Pandas, Numpy, etc.

Anyway, that was the rationale behind the PM Data Rep. We hope that you will buy into this abstraction with us and help us turn this enormous latent potential of applying AI in project management into reality.

Live long and Prosper to Project Managers and the use of PM Data Rep!



André B. Barcaui is a Post-Doctor in Business Administration from FEA/USP, Doctor in Business Administration from UNR, master’s in systems management from UFF-RJ, with a degree in Information Technology and Psychology, with training in cognitive-behavioral therapy. He was project office manager at Hewlett-Packard Consulting, responsible for the Latin American region, and program and services manager at IBM. He is a founding member of the PMI Chapter Rio and is now a member of its Advisory Board. He is certified PMP, PMI-ACP and DASSM by the Project Management Institute, KMP by Kanban University, Master Coach by the Behavioral Coaching Institute (BCI), CSP-SM, PSO I by Scrum.Org, CP3P by APMG, SAFe Agilist, ICP-ACC by ICAGile, Data Science Project Lead (DS-TLF) and Certified Artificial Intelligence & Machine Learning PM (CPMAI).



André Soares Monat is an engineer from the Technological Institute of Aeronautics -ITA, holds a master’s degree in systems and computing Engineering from the Federal University of Rio de Janeiro /COPPE and a PhD in Systems and Computing Engineering - from the University of East Anglia, United Kingdom. He did his postdoctoral work at Bergische Universitat Wuppertal, Germany. He was regional secretary for Rio de Janeiro and Espírito Santo of the Brazilian Computing Society (SBC). He is a full professor at UERJ, where he joined in 1994. He coordinated the implementation of the Data Processing Technology course at UNIFESO in Teresópolis, RJ. He was professor and coordinator of the master's program in Computational Modeling at UERJ in Nova Friburgo. His main areas of expertise are Artificial Intelligence, multimedia design and information design.


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