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Why Is CRISP-DM Gaining Grounds

Why Is CRISP-DM Gaining Grounds

CRISP-DM is a popular methodology that follows a standard, end-to-end structured approach to solving a problem that requires data science. More precisely, CRISP-DM or CRoss-Industry Standard Process for Data Mining focuses on the data mining part of the operation.

Industries and organisations have been undergoing machine learning-driven approaches for a few years now. However, this report from last year suggests that 85% of AI projects won’t deliver for their sponsors due to reasons like low quality, lack of development process, less functional in real-world applications, among others. 

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Some of its popular instances are after spending 62 Million, IBM Watson AI Health was cancelled in 2019 due to wrong recommendations on cancer treatments, in 2018 Uber’s self-driving car killed a woman in Arizona, and more.

Due to these issues, organisations have started using alternative methodologies in their machine learning applications. This is where CRISP-DM comes into play. The utilisation of this methodology has been witnessing exponential growth for a few years now. 

How It Works

CRISP-DM defines a framework for denoting data mining projects and sets out activities to be performed to complete a product or service. The activities consist of six phases, which are Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation and Deployment. 

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The successful completion of a phase initiates the execution of the subsequent activity. Also, the methodology includes iterations of revisiting previous steps until success or completion criteria are met. 


Data science demands a top-down, solution-oriented approach to solve problems. According to the latest Data Science recruitment survey,  the open jobs figure for Data Science and Analytics reached a maximum in February-March 2020, reaching a high of approximately 113,000 in the first week of March and rising steadily from a figure of 97,000 last year.  

In this field, data plays a very important role and processes like data mining help in generating actionable insights, extract patterns and identify relationships from large datasets. CRISP-DM is designed to be domain-agnostic and has been widely used by industry and research communities.

The distinctive characteristics have made CRISP-DM to be considered as ‘de-facto’ standard of data mining methodology and as a reference framework to which other methodologies are benchmarked. One of the important factors of using this method in Data Science is that it is a cross-industry standard for which it can be implemented in any Data Science project regardless of its domains.

This methodology remains a dependable method to develop data science solutions for enterprise problems. Also, the flexible and iterative approach of the method makes it a future-proof alternative for anyone looking to solve data science problems. 

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Benefits of Using CRISP-DM

  • This method is cost-effective as it includes a number of processes to take out simple data mining tasks.
  • CRISP-DM encourages best practices and allows projects to replicate.
  • This methodology provides a uniform framework for planning and managing a project.
  • Being cross-industry standard, CRISP-DM can be implemented in any Data Science project irrespective of its domain.

Wrapping Up

CRISP-DM is becoming the de-facto industry standard process model for data mining, with an expanding number of applications, such as in quality diagnostics, warranty, and others. 

However, recently, a team of AI researchers from Max Planck Institute for Information and others claimed that they identified two shortcomings of CRISP-DM. First, CRISP-DM does not cover the application scenario where an ML model is maintained as an application. Second, CRISP-DM lacks guidance on quality assurance methodology. 

To mitigate such issues, researchers further proposed CRISP-ML(Q) or CRoss Industry Standard Process for the development of Machine Learning applications with Quality assurance methodology. 

CRISP-ML(Q) is a process model for machine learning applications with a quality assurance methodology, that helps organisations to increase efficiency and success rate in their machine learning projects. The CRISP-ML(Q) methodology is also organised in six phases and expands CRISP-DM with an additional maintenance phase. 

It guides machine learning practitioners through the entire machine learning development life-cycle, providing quality-oriented methods for every phase and task in the iterative process including maintenance and monitoring.  

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