Data science teams usually need to manage two different things: the analytical lifecycle—from understanding a problem through modeling and evaluation—and the day-to-day coordination of people, priorities, and feedback. CRISP-DM or Microsoft’s TDSP can structure the analytical work; Scrum, Kanban, stage gates, or a tailored hybrid can help coordinate it. The best fit depends on uncertainty, delivery requirements, stakeholder needs, and governance—not on a universal ranking of methodology labels.
What project management methodology means in data science
A project lifecycle describes the work and its relationships: what needs to happen, and how one activity informs another. A coordination method describes how a team selects, tracks, and reviews work. These are related but not interchangeable. IBM describes CRISP-DM as a methodology with typical project phases, tasks, and relationships among tasks; it does not prescribe every team practice needed to coordinate people or operate a delivered system.
This distinction answers a common question: “What is the data science equivalent of the SDLC?” There is no single equivalent that covers every project. A team can use a data-science lifecycle to make analytical work visible, then separately choose how to prioritize tasks, involve stakeholders, make decisions, and deliver results. [IBM’s CRISP-DM overview; Data Science PM’s 2025 evaluation]
How the main approaches differ
| Approach | What it contributes | Question to ask before using it |
|---|---|---|
| CRISP-DM | A process model for organizing typical data-mining phases, tasks, and relationships. | What coordination, stakeholder communication, deployment, monitoring, and governance practices must be added? |
| Scrum or other Agile practices | Iteration and prioritization; an IEEE study proposed combining Scrum with CRISP-DM for data science work. | Can the work be organized into useful increments while allowing for discovery and uncertain data preparation? |
| Kanban | A way to make and prioritize work visible in a CRISP-DM-based process. | Would continuous flow suit the team better than committing to fixed sprint cycles? The cited source names Kanban as an option but does not compare its outcomes with other approaches. |
| Microsoft TDSP | An iterative lifecycle, standardized project structure, and resources aimed at productionized predictive analytics and intelligent applications. | Is the project building a production data product, and which lifecycle steps are unnecessary for exploratory or ad hoc work? |
| Hybrid or tailored approach | Can combine data-science lifecycle phases, formal stage gates, and Agile iteration. | Which decisions require formal approval, and where should the team preserve experimentation and frequent feedback? |
These approaches are not mutually exclusive. Microsoft notes that exploratory or ad hoc projects may not need every TDSP step, and that teams may continue using CRISP-DM or a custom lifecycle. The available sources do not establish a controlled head-to-head ranking of all the options. [Microsoft TDSP lifecycle detail; IEEE’s 2020 Scrum study; Data Science PM’s evaluation]
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When to use CRISP-DM
Use CRISP-DM when the team needs a recognizable structure for analytical work and a way to discuss how tasks relate. It can help make discovery and modeling visible rather than treating a model as the entire project. It is best understood as a lifecycle foundation, not a complete operating handbook: the cited evaluation says CRISP-DM does not itself define team roles, and it cautions against excessive documentation.
Decide explicitly how the team will handle stakeholder updates, work prioritization, approval points, deployment, and monitoring. Set role ownership and documentation to match the project’s risk and actual need rather than assuming the lifecycle defines them. [IBM CRISP-DM overview; Data Science PM, 2025]
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When Agile coordination helps—and where it needs adaptation
Scrum or other Agile practices can help teams prioritize work and create regular opportunities for review. An IEEE 2020 conference paper studied an integration of Scrum with CRISP-DM and evaluated the proposed method through expert interviews in three organizations. That is evidence of a studied adaptation, not proof that Scrum fits every data science team.
Before committing to sprint-sized deliverables, consider whether a meaningful increment can be demonstrated and reviewed. Some tasks—such as data preparation or validation—may need sequencing and may not translate neatly into user-facing increments. The PMI/NASSCOM playbook discusses both horizontal slicing and iterative stakeholder management; teams can use small end-to-end slices where useful without pretending every task is independently shippable. [IEEE, “Applying Scrum in Data Science Projects”; PMI South Asia and NASSCOM playbook]
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Kanban is another coordination option named in the CRISP-DM evaluation. Consider it when making work visible and managing continuous flow is more useful than fixed sprint commitments. The available source does not establish that Kanban produces better outcomes than Scrum or another method. [Data Science PM, 2025]
When TDSP fits
Microsoft describes the Team Data Science Process (TDSP) as an iterative lifecycle for predictive analytics and intelligent applications. Its standardized project structure and supporting resources can be useful when a team is building productionized predictive work and needs a lifecycle oriented toward delivery.
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Do not treat TDSP as a mandatory checklist for every analysis. Microsoft says exploratory or ad hoc projects may not need every step and acknowledges that a working CRISP-DM or custom lifecycle can remain in use. Match the process to the project’s delivery needs rather than adding ceremony without a purpose. [Microsoft TDSP lifecycle detail]
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When a hybrid approach makes sense
A hybrid can preserve analytical iteration while adding formal checkpoints for decisions that carry governance, delivery, or stakeholder risk. In a PMI South Asia and NASSCOM study published in 2020, 76% of organizations in the study used a customized methodology combining the CRISP-DM lifecycle with waterfall-style stage gates and Agile iteration. This is a finding from that study, not a current global adoption rate.
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The playbook also reports a perceived gap in frameworks tailored to data science and AI work. As it attributes the observation to Sayani Nag, retail analytics head at one of India’s largest conglomerates: “There is a lack of a tailored framework for DS/AI projects. Most of these frameworks are related to software development.” The practical implication is to define which decisions truly need gates and which work benefits from short feedback loops, rather than forcing the entire project into one cadence. [PMI South Asia and NASSCOM, November 2020]
How to choose and tailor a method
- Agree on the problem and intended outcome. Define the decision, capability, or result the project is meant to support before choosing a workflow.
- Map the analytical lifecycle. Use CRISP-DM, TDSP, or a working custom lifecycle to make discovery, data work, modeling, evaluation, and delivery visible.
- Set stakeholder checkpoints. GitLab identifies requirements gathering, implementation planning, and presenting model results as useful points for stakeholder check-ins. Make owners and decisions for each checkpoint clear.
- Choose a coordination cadence. Use Scrum when planned iterations and review points fit the work; consider Kanban when continuous flow and visible work fit better. Keep room for investigation when data or results are uncertain.
- Identify the gates that matter. Add formal approval or governance gates where risk or organizational needs justify them; avoid turning every analytical task into a sequential sign-off.
- Plan validation, delivery, and monitoring. A project is not necessarily complete when analysis or a model is presented. Domino’s lifecycle includes validation, delivery, and monitoring; TDSP is oriented toward productionized predictive work.
- Scale roles and documentation to risk. Clarify ownership and record decisions proportionately. CRISP-DM does not define roles, and the cited evaluation cautions against documentation that becomes excessive.
[GitLab Data Science Project Development Approach; Domino Data Lab project-management guide; Microsoft TDSP lifecycle detail; Data Science PM, 2025]
A practical default
For a team without an established process, start with a data-science lifecycle the team understands, then add only the coordination practices the project needs. Make the problem, stakeholder checkpoints, evaluation, delivery, and monitoring explicit. Choose fixed iterations, continuous flow, or stage gates according to the work and its risks. Revisit the process when it obstructs useful discovery or leaves delivery responsibilities unclear.
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