Overview of Common Frameworks

To tackle complex data science projects efficiently, the industry relies on well-established frameworks. Among these, CRISP-DM (Cross-Industry Standard Process for Data Mining) is the most widely used. It outlines a step-by-step sequence that helps professionals move logically from understanding the business to deploying and monitoring models.

Several other frameworks also exist, such as OSEMN, SEMMA, and the Data Science Lifecycle from IBM, each offering a slightly different perspective.

CRISP-DM

This framework consists of six major phases:

1. Business Understanding

Clearly define the business problem, success criteria, constraints, and objectives. Without a solid understanding, data efforts may produce irrelevant results.

2. Data Understanding

Explore the available data, inspect its quality, identify gaps, detect anomalies, and form early hypotheses.

3. Data Preparation

Clean, transform, and engineer features. This is the most time-consuming stage because real-world data is messy and inconsistent.

4. Modeling

Select algorithms (regression, classification, clustering, etc.), train multiple models, and evaluate their performance.

5. Evaluation

Assess whether the model meets business goals, not just mathematical accuracy. This includes interpreting results and identifying risks.

6. Deployment

Integrate the solution into real workflows, dashboards, or applications, followed by monitoring and maintenance.

CRISP-DM is popular because it is flexible, industry-neutral, iterative, and easy to understand. It emphasizes the importance of continuously revisiting previous steps as new insights emerge.

Other Frameworks

1. OSEMN (Obtain, Scrub, Explore, Model, Interpret)

A practical, hands-on approach commonly used by analysts and data scientists in tech. It emphasizes data exploration and interpretation.

2. SEMMA (Sample, Explore, Modify, Model, Assess)

Originally developed by SAS, this methodology focuses heavily on statistical modeling and is widely used in enterprise analytics.

3. IBM Data Science Lifecycle

A modern approach that includes stages such as “Gather,” “Analyze,” “Visualize,” “Model,” and “Deploy.” It is optimized for cloud-based AI and big-data ecosystems.

Together, these frameworks provide data professionals with structured paths to navigate complex tasks. Organizations choose the one that aligns with their workflows, tools, and team culture.

Regardless of the specific framework, all emphasize understanding the problem, preparing data effectively, building models thoughtfully, and deploying solutions responsibly.