From Data to Insights: A Roadmap for Project Managers
Bibliographic record
Abstract
Data in project environments presents challenges and opportunities that project managers should exploit to the benefit of the project. Firstly, the sheer volume and variety of data generated throughout the project life cycle can overwhelm traditional management approaches, leading to issues of data governance, quality and integration. Furthermore, the dynamic nature of projects often leads to fragmented data silos, hindering collaboration and decision-making. This then raises the question of how project managers can use data to make better project-related decisions. Two focus groups consisting of industry experts were conducted, one in South Africa and the other in Canada. The industry experts all had extensive experience in managing digital transformation projects. These types of projects are notorious for the abundance of data generated. Various challenges were raised, such as the skills of the project manager and team, the quality of the data, the life cycle of the data and the technology associated with the data, for instance data lakes. Among the skills that the project team require are data and process mining as well as visualisation of the data to gain insights. Overcoming these challenges requires a holistic strategy that encompasses technological, organisational and cultural dimensions. A roadmap, based on the socio-technical system, was developed to assist project managers in gaining insights from project-generated data. The roadmap incorporates aspects such as infrastructure, people skills and the necessary processes. The roadmap can be used by project managers to engage with project-related data in a meaningful way, allowing them to gain insights from the data and make better decisions to the benefit of all stakeholders. Researchers can use this roadmap to engage in-depth with the theory and provide specific guidelines for the various components of the roadmap.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.065 | 0.055 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.044 | 0.078 |
| Open science | 0.008 | 0.036 |
| Research integrity | 0.014 | 0.024 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".