CRISP-DM Explained: A Beginner’s Step-by-Step Guide
Jericho Katende
Data science is the process of identifying actionable insights in an organization’s data by combining specialist programming, sophisticated analytics, artificial intelligence (AI), machine learning, and math and statistics with subject matter expertise.
Even with its potential, a lot of data science initiatives fall short. According to a 2019 Gartner study, 85% of AI and data science projects fail because of inadequate data quality, ambiguous goals, and an unstructured methodology. According to a 2020 survey, 82% of data science teams do not adhere to any formal procedure, which leads to time wastage, priorities that are not aligned, and undesirable outcomes.
Trying to contribute to an open source project with no info or methodology
In order address these issues, a number of frameworks have been created to organize data science projects, enhance teamwork, and guarantee consistency. CRISP-DM, KDD, SEMMA, Scrum, Microsoft TDSP, and Kanban are a few examples.
The most popular of them is CRISP-DM (Cross-Industry Standard Process for Data Mining), which offers a clear road map for converting unprocessed data into useful business insights.
In fact, a KDnuggets survey found that 43% of data science practitioners still rely on CRISP-DM, making it the most widely used methodology for analytics and data mining projects.
In this guide, we’ll break down CRISP-DM into its six stages, explain key tasks in each phase, highlight strengths and weaknesses, and provide recommendations for successfully managing data science projects.
AI brings the energy — let’s go! 🔗 Source: GIPHY
What is CRISP-DM?
CRISP-DM (Cross-Industry Standard Process for Data Mining) is an industry-independent framework that simplifies and streamlines data science projects. First introduced in 1999, it has stood the test of time due to its simplicity, flexibility, and dual focus on business objectives and technical execution.
The framework outlines the key activities required to carry a data science project from start to finish, while allowing teams to pause, iterate, and resume work between phases without losing context or momentum. This structured yet adaptable approach ensures projects stay organized, repeatable, and aligned with both technical and business goals.
The Six Phases of CRISP-DM
A visualization of CRISP-DM
- Business Understanding
This phase’s primary focus is on understanding the project’s objectives and requirements. Like creating the foundation of a house, success depends on having a firm knowledge of business.
Key tasks:
Establish your company’s goals: Recognize the client’s goals and establish success criteria.
Evaluate the situation: Determine requirements, risks, and resources. Then, do a cost-benefit analysis.
Establish the project’s objectives: Convert business objectives into technical metrics for data analysis success.
Create a project plan: Choose technology and tools and establish phase-by-phase plans.
Gather preliminary information: Obtain the required data and, if required, import it into analytical tools.
2. Data Understanding
The goal of this phase is to find, gather, and examine project-related datasets.
Key tasks:
Describe the data: Analyze the format, number of records, and field identities of the data and documents.
Examine the data: Analyze, visualize, and identify relationships within the data.
Verify the quality of the data: Evaluate the completeness and cleanliness, and record any problems.
3. Data Preparation
Often referred to as data munging, this stage prepares the final dataset(s) for modeling. Typically, 50–80% of project effort is spent here.
Selecting Data: Identify and choose the relevant data sources.
Cleaning Data: Fix or eliminate mistakes to avoid garbage-in, garbage-out.
Feature Engineering: Create new, more informative variables from existing ones (e.g., calculating BMI from height and weight)..
Integrating Data: Combine data from different sources to create a unified view.
Formatting Data: Ensure all data is in the correct format for the modeling algorithm, e.g. through converting categorical text data into numerical values.
4. Modeling
This is often the most exciting phase, where teams apply and assess different modeling techniques.
Key tasks:
Choose Modeling Techniques: Determine and pick suitable algorithms (such as neural networks and regression) according to the problem and the properties of the data.
Design Testing Strategy: Create training, validation, and test sets to prepare the dataset and ensure accurate model performance evaluation.
Model Training and Iteration: Use the prepared data to train the chosen machine learning algorithms, adjusting their parameters to get the best predictive performance..
Assess model: Compare models using domain knowledge, success criteria, and test design.
5. Evaluation
Beyond technical performance, evaluation considers the models’ wider business relevance.
Key tasks:
Evaluate results: Determine if models meet business success criteria.
Review process: Ensure all steps were properly executed and document findings.
Determine next steps: Decide whether to deploy, iterate, or start a new project.
6. Deployment
Deployment ensures that the model delivers actionable results to stakeholders. This can range from sharing reports to implementing real-time predictive systems.
Key tasks:
Plan deployment: Develop and document deployment, monitoring, and maintenance plans.
Produce final report: Summarize project findings and insights.
Putting a model into use does not mean the end of the project. A full retrospective is needed to review the project, find out what worked and what didn’t, and write down what was learned so that the data science process can keep getting better.
It’s your turn now! Do you use CRISP-DM for your projects, or do you like a different method better? Please leave a comment below with your thoughts, and don’t forget to clap if you found this article helpful! 👍
References
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