Effective project management in data science
Unlocking the Complexities of Managing Data Science Projects
In a world that is extensively digitised, data science has become a paramount tool in helping businesses maximise their performance, make strategic decisions, and outshine their competitors. The role of project management within this sphere is critical but often met with unique challenges. In this article, we explore an answer to a fundamental question — How does project management work in data science?
To comprehend the complexity of project management in data science, we need to understand that traditional management methodologies don’t often fit perfectly into the realm of data science. Data science projects usually entail a long exploration phase with numerous unknown factors, which is starkly different from traditional software development where deliverables and timelines can be predefined.
Traditional project management methodologies are often categorised into:
- Waterfall — often represented by a Gantt chart illustrating tasks and dependencies.
- Agile — tasks are divided into short development cycles called sprints.
- Kanban — visualizes work progression from left to right, representing stages like to do, in progress, and done.
- CRISP-DM — A data science-centric approach involving: business understanding, data understanding, data preparation, modelling, evaluation, and deployment.
The significant limitations of these traditional methods lie in their rigidity that doesn’t cater to the iterative and explorative nature of data science projects. Hence, the key to succeeding in project management within the data science sector is flexibility and adaptability.
Advice on applying project management to data science:
- Avoid setting a rigid project structure right from the start. Allow an initial week or so to explore and understand the context of the project better.
- Assess Business Needs. Understanding what is needed by the business is crucial. The requirements may vary — a predictive model, standalone analysis, a full scale website, and API, and so forth. Remember to leave room for adjustments, as requirements may change at any stage of the project.
Data science projects typically involve the following phases:
- Understanding the context.
- Understanding the available data.
- Building a prototype.
- Defining KPIs and requirements in collaboration with the stakeholder.
- Refining the model and integration with real-time data.
- Testing and Deployment.
- Project completion and maintenance planning.
The necessity for a flexible approach means regular meetings and open, detailed communication with all stakeholders is essential. You must ensure both the business and data scientists are updated, and all required data, access, and cooperation are provided.
In conclusion, though traditional project management approaches such as Agile or Waterfall are beneficial, they must be adapted to suit the demands of data science projects. A mix of exploratory, iterative, and empirical methods are vital to make project management effective in data science.
For more insights on project management in data science, visit FastDataScience. You will also find resources such as in-browser Gantt chart generator, a project kickoff checklist, a roadmap planner, and much more to assist your journey.
