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.
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.
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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.
Why Use CRISP-DM
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.
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.
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.
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.