CRISP-DM vs Microsoft TDSP - Data Science Project Methodologies Compared
Comparing CRISP-DM and Microsoft Team Data Science Process (TDSP) for structuring data science projects, covering phases, team roles, and practical guidance.
Choosing a methodology for data science projects matters more than most teams realize. Without structure, data science work drifts into exploratory dead ends. CRISP-DM and Microsoft TDSP are the two most widely adopted frameworks. They share DNA but differ in important ways.
Overview
| Aspect | CRISP-DM | Microsoft TDSP |
|---|---|---|
| Origin | Industry consortium (Daimler-Benz, ISL/SPSS, NCR, OHRA), conceived 1996, version 1.0 published 1999/2000 | Microsoft, 2016 |
| Focus | Vendor-neutral data mining process | End-to-end data science lifecycle |
| Phases | 6 phases | 5 lifecycle stages |
| Team Guidance | Minimal | Detailed role definitions |
| Tooling Opinions | None | Azure-oriented but adaptable |
| Documentation Templates | None | Extensive templates provided |
Phase Comparison
CRISP-DM defines six phases: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The process is explicitly iterative - arrows connect every phase to every other phase, acknowledging that data science work rarely proceeds linearly. The published standard has never received an official revision (there is no CRISP-DM 2.0), so its longevity comes from being a stable, vendor-neutral reference rather than an actively maintained product. The original founders were Daimler-Benz, Integral Solutions Ltd (ISL, later acquired by SPSS and then by IBM), NCR, and the Dutch insurer OHRA.
TDSP defines five stages: Business Understanding, Data Acquisition and Understanding, Modeling, Deployment, and Customer Acceptance. TDSP merges CRISP-DM’s Data Understanding and Data Preparation into a single stage and adds an explicit Customer Acceptance stage that CRISP-DM lacks.
| CRISP-DM Phase | TDSP Equivalent | Key Difference |
|---|---|---|
| Business Understanding | Business Understanding | TDSP adds structured templates for defining success metrics |
| Data Understanding | Data Acquisition and Understanding | TDSP combines exploration and preparation |
| Data Preparation | Data Acquisition and Understanding | TDSP includes data pipeline guidance |
| Modeling | Modeling | TDSP adds feature engineering as explicit step |
| Evaluation | Customer Acceptance | TDSP frames evaluation from the customer perspective |
| Deployment | Deployment | TDSP includes CI/CD and MLOps guidance |
Team Structure and Roles
This is where the frameworks diverge most significantly. CRISP-DM says almost nothing about team structure. It assumes someone will do the work but does not specify who or how teams should be organized.
TDSP defines four explicit roles: Group Manager, Team Lead, Project Lead, and Individual Contributor. Each role has defined responsibilities, and TDSP provides onboarding documentation for each. For organizations building their first data science team, this guidance is valuable. For mature teams with established roles, it can feel prescriptive.
Tooling and Templates
CRISP-DM is deliberately tool-agnostic. This is both its greatest strength and its greatest weakness. Teams can apply CRISP-DM with any technology stack, but they get no starter templates, no repository structures, and no automation guidance.
TDSP ships with a standardized project structure for Git repositories, document templates for each phase, and utilities for data exploration. While originally designed around Azure Machine Learning, the templates work with any platform. The TDSP project template on GitHub includes directories for code, data, docs, and sample reports.
A practical caveat as of 2026: TDSP is stable but no longer actively developed. The original Azure/Microsoft-TDSP GitHub repository was archived (made read-only) in October 2023, and the canonical guidance now lives in the Microsoft Learn Azure Architecture Center under the data science process section. The lifecycle, role definitions, and templates remain available, but do not expect new features or updates to track Microsoft’s latest tooling (for example, the shift toward Azure Machine Learning v2, Microsoft Fabric, and generative AI workflows). Treat TDSP as a settled process reference rather than a living framework.
When to Choose CRISP-DM
CRISP-DM works best when your team already has established processes and needs a lightweight conceptual framework rather than prescriptive guidance. It is also the better choice when you need a vendor-neutral methodology that does not imply any particular cloud platform. Academic research projects, cross-platform consulting engagements, and experienced data science teams benefit from CRISP-DM’s flexibility.
When to Choose TDSP
TDSP is the stronger choice for teams that need structure. If you are building a data science practice from scratch, TDSP’s role definitions, templates, and repository structures save months of process design. Teams already invested in the Azure ecosystem get native integration. Organizations that need to standardize across multiple data science teams also benefit from TDSP’s opinionated structure.
Practical Recommendation
Many teams use both. CRISP-DM provides the mental model for how data science projects flow. TDSP provides the operational scaffolding for how to run them. Start with TDSP’s templates and repository structure, but use CRISP-DM’s iterative phase model to set expectations with stakeholders that data science work does not proceed in a straight line from requirements to deployment.
The biggest risk with either methodology is treating it as waterfall. Both frameworks emphasize iteration, but project managers accustomed to linear delivery often flatten the phases into sequential gates. Resist this. The value of both frameworks is in acknowledging that you will revisit earlier phases as you learn from data. For more on that tension, see /comparisons/agile-vs-waterfall-ai-projects/.
One gap worth naming: both CRISP-DM and TDSP predate the modern MLOps era, and neither was designed for continuous training, model monitoring, or drift detection on deployed models. CRISP-ML(Q), the Cross-Industry Standard Process for the development of Machine Learning applications with Quality assurance, is a more recent adaptation that keeps the familiar phase structure but adds an explicit Monitoring and Maintenance phase and a quality-assurance step at every stage. If your concern is the operational life of a model after deployment, pair either methodology here with MLOps practices (see the /guides/mlops-getting-started/ guide) rather than expecting CRISP-DM or TDSP to cover that ground on their own.
Sources
- What is the Team Data Science Process?, Microsoft Learn - the current canonical TDSP documentation, including the five lifecycle stages.
- Team Data Science Process roles and tasks, Microsoft Learn - the four personnel roles: Group Manager, Team Lead, Project Lead, and Individual Contributor.
- Azure/Microsoft-TDSP, GitHub - the original TDSP repository, archived and read-only since October 2023, with a deprecation notice pointing to the Microsoft Learn documentation.
- Cross-industry standard process for data mining, Wikipedia - the six phases and the founding consortium (Daimler-Benz, ISL/SPSS, NCR, OHRA).
- CRISP-ML(Q): The ML Lifecycle Process, ml-ops.org - the quality-assured adaptation of the CRISP-DM phase model for machine learning, including the added Monitoring and Maintenance phase.