Cross-Industry Standard Process for Data Mining (CRISP-DM)- A Guide

EM Admin
Apr 25, 2025
0 Comments

Do you know what data-driven intelligence is? It’s data mining, which is based on deriving insights from intricate datasets to form new strategies or make decisions. Certainly, it’s beyond throwing data into algorithms for developing artificial intelligence. A disciplined approach helps transform raw data into structured models based on strategies. This is what Cross-Industry Standard Process for Data Mining (CRISP-DM) defines.

This strategic approach was evolved in the late 1990s. An association of SPSS, Daimler-Benz, and NCR developed it, which later became a revolutionary method for mining data or insights. A KDnuggets survey found a stunning fact, revealing that CRISP-DM is still the foremost framework for 43% of data scientists to work (source).

Let’s shed some more light on this approach.

What is CRISP-DM?

As it is already disclosed that CRISP-DM is short for Cross-Industry Standard Process for Data Mining. This is actually a wisely designed process of six stages, which makes a data mining project more meaningful, systematic, presumable, and successful. Let’s explain all phases below with some real-life examples.

Phases Involved in CRISP Data Mining and Analysis

The standard process of data mining involves six stages.

Phase 1: Business Understanding

The project begins with deciding the objective of why this process is to be carried out. Here, the concern is to understand the root of the problem before finding its solution. It’s crucial.

For example, a multinational company is facing a high attrition rate. So, the problem is here, attrition. Therefore, the goal must be to find the main cause of why employees are resigning. In essence, the objective must be the following for the process:

Discover objectives clearly
Analyse the situation.
Drawing the mining goals
Forming a strategy to mine data

With a foggy picture of goals in mind, the data mining & analysis won’t deliver meaningful insights. The finding will lead to unrealistic solutions.

Phase 2. Data Understanding

Recognising the goal shows the next step, which requires supporting data to collect and study. To carry out the data-gathering process, this strategy often proves effective:

Collect initial data resonating with the objective
Process the data to transform its structure.
Explore and verify data quality if it is comprehensive & relevant.
Identify outliers, anomalies, or missing values.

The soul of the process is to recognise patterns, correlate them, and find trends by thoroughly observing the data. Hypotheses can help in evolving this stage initially.

3. Data Preparation

At this stage, you need to focus on inconsistencies, typos, outliers, or anything that makes the data ready-to-use for modelling. The preparation goes through these steps:

Filter data resonating with your objective.
Run the data cleaning process.
Evolve necessary variables for comprehensive insights
Restructure datasets to prepare for modelling.

A viable model is prepared provided the records used in the process are hygienic, properly structured, and relevant. For real, accuracy is a must to create reliable models. All these steps are time-consuming, which requires data scientists to invest nearly 60-70% of their time on this phase and cleaning data

Phase 4. Modelling

Now comes the modelling of data, which can be done leveraging different methods, such as classification, clustering, regression, etc. The flow of the steps will be like this:

Select an ideal modelling technique (e.g., classification, clustering, regression)
Build relevant models
Test and tune parameters

So, this phase is dedicated to finding not just any, but an ideal model that can address the predefined problem. It can be simplified by using data mining tools like decision trees, neural networks, or clustering algorithms.

Phase 5. Evaluation

The result is a testing time, where the model is evaluated to be useful as expected. An insightful model genuinely addresses the problem without any latent errors or flaws. That is why evaluation is necessary. These are the steps involved in measuring the quality of the model:

Align and run the performance of the driven model against business objectives
Audit if it exactly meets the expectation, or seems overfit or underfit.
On the basis of the evaluation, finalise which model can be ideally deployed.

Phase 6. Deployment

The evolved algorithm or model must be deployed flawlessly into the real business environment once tested. Here is how data scientists practise it:

Integrate & run the model in operational systems.
Track its performance results.
Guide users on how to streamline it.
Review and update the model over time.

However, the deployment process can be intricate as well as simple. So, you cannot expect the best results unless it is properly deployed and tested.

Why CRISP-DM Still Matters Today?

Though new methods like agile analytics and modern data science workflows have evolved, the concept of CRISP-DM is still alive and relevant because of these reasons:

It’s industry-agnostic: It is utterly useful for banking, retail, healthcare, and manufacturing or any industry.
It’s flexible: The comprehensive design of this process helps in adapting to sudden changes or findings.
It’s easy to understand: It allows businesses, stakeholders, and technical teams to sit around the table and collaborate for its success.
It balances technical rigour with business focus: Data mining serves a major purpose, but not a small goal.

Real-world example:

A banking company is leveraging the CRISP-DM method to discover credit card fraud, eliminating prospective scams in a fraction of the time. All thanks to preparing data, models, and their successful deployment via this data mining process structure.

Challenges with CRISP-DM

Despite being extremely powerful, CRISP-DM has some limitations.

No native support for Big Data: This process does not work on big data because it evolved way before the big data explosion.
Vague on technology choices: It requires a technically trained team to harness data mining tools, which is a major drawback.
Lacks specific emphasis on data privacy: As it involves a tonne of data that may be sensitive, the whole process must comply with GDPR, HIPAA, or other regulatory frameworks.

Conclusion

In essence, it won’t be incorrect to say that almost every business needs data for scalability and success. The CRISP-DM framework is so trusted, flexible, and systematic that filtering insights is no big deal. Though it’s not for big data mining, businesses and diverse industries still rely on it for making crucial decisions.

Data

Data Mining