The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model that serves as the base for a data science process. It has six sequential phases:
- Business understanding – What does the business need?
- Data understanding – What data do we have / need? Is it clean?
- Data preparation – How do we organize the data for modeling?
- Modeling – What modeling techniques should we apply?
- Evaluation – Which model best meets the business objectives?
- Deployment – How do stakeholders access the results?
Published in 1999 to standardize data mining processes across industries, it has since become the most common methodology for data mining, analytics, and data science projects.
Data science teams that combine a loose implementation of CRISP-DM with overarching team-based agile project management approaches will likely see the best results.
I. Business Understanding
Any good project starts with a deep understanding of the customer’s needs. Data mining projects are no exception and CRISP-DM recognizes this.
The Business Understanding phase focuses on understanding the objectives and requirements of the project. Aside from the third task, the three other tasks in this phase are foundational project management activities that are universal to most projects:
- Determine business objectives: You should first “thoroughly understand, from a business perspective, what the customer really wants to accomplish.” (CRISP-DM Guide) and then define business success criteria.
- Assess situation: Determine resources availability, project requirements, assess risks and contingencies, and conduct a cost-benefit analysis.
- Determine data mining goals: In addition to defining the business objectives, you should also define what success looks like from a technical data mining perspective.
- Produce project plan: Select technologies and tools and define detailed plans for each project phase.
II. Data Understanding
Next is the Data Understanding phase. Adding to the foundation of Business Understanding, it drives the focus to identify, collect, and analyze the data sets that can help you accomplish the project goals. This phase also has four tasks:
- Collect initial data: Acquire the necessary data and (if necessary) load it into your analysis tool.
- Describe data: Examine the data and document its surface properties like data format, number of records, or field identities.
- Explore data: Dig deeper into the data. Query it, visualize it, and identify relationships among the data.
- Verify data quality: How clean/dirty is the data? Document any quality issues.
III. Data Preparation
A common rule of thumb is that 80% of the project is data preparation.
This phase, which is often referred to as “data munging”, prepares the final data set(s) for modeling. It has five tasks:
- Select data: Determine which data sets will be used and document reasons for inclusion/exclusion.
- Clean data: Often this is the lengthiest task. Without it, you’ll likely fall victim to garbage-in, garbage-out. A common practice during this task is to correct, impute, or remove erroneous values.
- Construct data: Derive new attributes that will be helpful. For example, derive someone’s body mass index from height and weight fields.
- Integrate data: Create new data sets by combining data from multiple sources.
- Format data: Re-format data as necessary. For example, you might convert string values that store numbers to numeric values so that you can perform mathematical operations.
What is widely regarded as data science’s most exciting work is also often the shortest phase of the project.
Here you’ll likely build and assess various models based on several different modeling techniques. This phase has four tasks:
- Select modeling techniques: Determine which algorithms to try (e.g. regression, neural net).
- Generate test design: Pending your modeling approach, you might need to split the data into training, test, and validation sets.
- Build model: As glamorous as this might sound, this might just be executing a few lines of code like “reg = LinearRegression().fit(X, y)”.
- Assess model: Generally, multiple models are competing against each other, and the data scientist needs to interpret the model results based on domain knowledge, the pre-defined success criteria, and the test design.
Although the CRISP-DM Guide suggests to “iterate model building and assessment until you strongly believe that you have found the best model(s)”, in practice teams should continue iterating until they find a “good enough” model, proceed through the CRISP-DM lifecycle, then further improve the model in future iterations.
Whereas the Assess Model task of the Modeling phase focuses on technical model assessment, the Evaluation phase looks more broadly at which model best meets the business and what to do next. This phase has three tasks:
- Evaluate results: Do the models meet the business success criteria? Which one(s) should we approve for the business?
- Review process: Review the work accomplished. Was anything overlooked? Were all steps properly executed? Summarize findings and correct anything if needed.
- Determine next steps: Based on the previous three tasks, determine whether to proceed to deployment, iterate further, or initiate new projects.
A model is not particularly useful unless the customer can access its results. The complexity of this phase varies widely. This final phase has four tasks:
- Plan deployment: Develop and document a plan for deploying the model.
- Plan monitoring and maintenance: Develop a thorough monitoring and maintenance plan to avoid issues during the operational phase (or post-project phase) of a model.
- Produce final report: The project team documents a summary of the project which might include a final presentation of data mining results.
- Review project: Conduct a project retrospective about what went well, what could have been better, and how to improve in the future.
Some argue that it is flexible and agile and while others see CRISP-DM as rigid. What really matters is how you implement it.
Waterfall: On one hand, many view CRISP-DM as a rigid waterfall process – in part because of its reporting requirements that are excessive for most projects. Moreover, the guide states in the business understanding phase that “the project plan contains detailed plans for each phase” – a hallmark aspect of traditional waterfall approaches that require detailed, upfront planning.
Indeed, if you follow CRISP-DM precisely (defining detailed plans for each phase at the project start and include every report) and choose not to iterate frequently, then you’re operating more of a waterfall process.
Agile: On the other hand, CRISP-DM indirectly advocates agile principles and practices by stating: “The sequence of the phases is not rigid. Moving back and forth between different phases is always required. The outcome of each phase determines which phase, or particular task of a phase, has to be performed next.”
Thus if you follow CRISP-DM in a more flexible way, iterate quickly, and layer in other agile processes, you’ll wind up with an agile approach.
Example: To illustrate how CRISP-DM could be implemented in either an Agile or waterfall manner, imagine a churn project with three deliverables: a voluntary churn model, a non-pay disconnect churn model, and a propensity to accept a retention-focused offer.
CRISP-DM Waterfall: Horizontal Slicing
In a waterfall-style implementation, the team’s work would comprehensively and horizontally span across each deliverable as shown below. The team might infrequently loop back to a lower horizontal layer only if critically needed. One “big bang” deliverable is delivered at the end of the project.
CRISP-DM Agile: Vertical Slicing
Alternatively, in an agile implementation of CRISP-DM, the team would narrowly focus on quickly delivering one vertical slice up the value chain at a time as shown below. They would deliver multiple smaller vertical releases and frequently solicit feedback along the way.
Which is better?
When possible, take an agile approach and slice vertically so that:
- Stakeholders get value sooner
- Stakeholders can provide meaningful feedback
- The data scientists can assess model performance earlier
- The project team can adjust the plan based on stakeholder feedback
Definitive research does not exist on how frequently data science teams use different management approaches. So to get an idea on approach popularity, we investigated KDnuggets polls, conducted our own poll, and researched Google search volumes. Each of these views suggests that CRISP-DM is the most commonly used approach for data science projects.
Bear in mind that the website caters toward data mining, and the data science field has changed a lot since 2014.
KDnuggets is a common source for data mining methodology usage. Each of the polls in 2002, 2004, 2007 posed the question: “What main methodology are you using for data mining?”, and the 2014 poll expanded the question to include “…for analytics, data mining, or data science projects.” 150-200 respondents answered each poll.
CRISP-DM was the popular methodology in each poll spanning the 12 years.
Our 2020 Poll
For a more current look into the popularity of various approaches, we conducted our own poll on this site in August and September 2020.
Note the response options for our poll were different from the KDnuggets polls and our site attracts a different audience.
CRISP-DM was the clear winner, garnering nearly half of the 109 votes.
Given the ambiguity of a searcher’s intent, some searches like “my own” could not be analyzed and others like “tdsp” and “semma” could be misleading.
For yet third view into CRISP-DM, we turned to Google Keyword Planner tool which provided the average monthly search volumes in the USA for select key search terms and related terms (e.g. “crispdm” or “crisp dm data science”). Clearly irrelevant searches like “tdsp electrical charges” or “semma both aagatha” were then removed.
CRISP-DM yet again reigned as king, and this time with a much broader margin.
So CRISP is popular. But should you use it?
Like most answers in data science, it’s kind of complicated. But here’s a quick overview.
From today’s data science perspective this seems like common sense. This is exactly the point. The common process is so logical that it has become embedded into all our education, training, and practice.
-William Vorheis, one of CRISP-DM’s authors (from Data Science Central)
- Generalize-able: Although designed for data mining, William Vorhies, one of the creators of CRISP-DM, argues that because all data science projects start with business understanding, have data that must be gathered and cleaned, and apply data science algorithms, “CRISP-DM provides strong guidance for even the most advanced of today’s data science activities” (Vorhies, 2016).
- Common Sense: When students were asked to do a data science project without project management direction, they “tended toward a CRISP-like methodology and identified the phases and did several iterations.” Moreover, teams which were trained and explicitly told to implement CRISP-DM performed better than teams using other approaches (Saltz, Shamshurin, & Crowston, 2017).
- Adopt-able: Like Kanban, CRISP-DM can be implemented without much training, organizational role changes, or controversy.
- Right Start: The initial focus on Business Understanding is helpful to align technical work with business needs and to steer data scientists away from jumping into a problem without properly understanding business objectives.
- Strong Finish: Its final step Deployment likewise addresses important considerations to close out the project and transition to maintenance and operations.
- Flexible: A loose CRISP-DM implementation can be flexible to provide many of the benefits of agile principles and practices. By accepting that a project starts with significant unknowns, the user can cycle through steps, each time gaining a deeper understanding of the data and the problem. The empirical knowledge learned from previous cycles can then feed into the following cycles.
Weaknesses & Challenges
In a controlled experiment, students who used CRISP-DM “were the last to start coding” and “did not fully understand the coding challenges they were going to face”
- Rigid: On the other hand, some argue that CRISP-DM suffers from the same weaknesses of Waterfall and encumbers rapid iteration.
- Documentation Heavy: Nearly every task has a documentation step. While documenting one’s work is key in a mature process, CRISP-DM’s documentation requirements might unnecessarily slow the team from actually delivering increments.
- Not Modern: Counter to Vorheis’ argument for the sustaining relevance of CRISP-DM, others argue that CRISP-DM, as a process that pre-dates big data, “might not be suitable for Big Data projects due its four V’s” (Saltz & Shamshurin, 2016).
- Not a Project Management Approach: Perhaps most significantly, CRISP-DM is not a true project management methodology because it implicitly assumes that its user is a single person or small, tight-knit team and ignores the teamwork coordination necessary for larger projects (Saltz, Shamshurin, & Connors, 2017).
CRISP-DM is a great starting point for those who are looking to understand the general data science process. Five tips to overcome these weaknesses are:
- Iterate quickly: Don’t fall into a waterfall trap by working thoroughly across layers of the project. Rather, think vertically and deliver thin vertical slices of end-to-end value. Your first deliverable might not be too useful. That’s okay. Iterate.
- Document enough…but not too much: If you follow CRISP-DM precisely, you might spend more time documenting than doing anything else. Do what’s reasonable and appropriate but don’t go overboard.
- Don’t forgot modern technology: Add steps to leverage cloud architectures and modern software practices like git version control and CI/CD pipelines to your project plan when appropriate.
- Set expectations: CRISP-DM lacks communication strategies with stakeholders. So be sure to set expectations and communicate with them frequently.
- Combine with a project management approach: As a more generalized statement from the previous bullet, CRISP-DM is not truly a project management approach. Thus combine it with a data science coordination framework. Popular agile approaches include:
Dive Deeper: Explore key actions to consider
for Data Science projects using CRISP-DM
A few years prior to the publication of CRISP-DM, SAS independently developed Sample, Explore, Modify, Model, and Assess (SEMMA). Although designed to help guide users through tools in SAS Enterprise Miner for data mining problems, SEMMA is often considered to be a general data mining methodology (Tiwari & Dixit, 2017). SEMMA (8.5%) was the third most popular methodology per the 2014 KDnuggets poll, but its use is down from 13% in 2007.
Compared to CRISP-DM, SEMMA is even more narrowly focused on the technical steps of data mining. It skips over the initial Business Understanding phase from CRISP-DM and instead starts with data sampling processes. SEMMA likewise does not cover the final Deployment aspects. Otherwise, its phases somewhat mirror the middle four phases of CRISP-DM. Although potentially useful as a process to follow data mining steps, SEMMA should not be viewed as a comprehensive project management approach.
KDD and KDDS
Knowledge Discovery in Database (KDD) is the general process of discovering knowledge in data through data mining, or the extraction of patterns and information from large datasets using machine learning, statistics, and database systems.
In 2016, Nancy Grady of SAIC, expanded upon CRISP-DM to publish the Knowledge Discovery in Data Science (KDDS). “As an end-to-end process model from mission needs planning to the delivery of value”, KDDS specifically expands upon CRISP-DM to address big data problems. It also provides some additional integration with management processes. KDDS defines four distinct phases: assess, architect, build, and improve and five process stages: plan, collect, curate, analyze, and act (Grady, 2016).
KDDS can be a useful expansion of CRISP-DM for big data teams. However, KDDS only addresses some of the shortcomings of CRISP-DM. For example, it is not clear how a team should iterate when using KDDS. In addition, its combination of phases and processes is less straight-forward. Adoption of KDDS outside of SAIC is not known.
- Blog Post: What is a Data Science Life Cycle?
- Blog Post: What is a Data Science Workflow?
- Blog Post: What is the Data Science Process?
- Blog Post: Steps to Define an Effective Data Science Process
- Blog Post: CRISP-DM for Data Science – 5 Actions to Consider
- Blog Post: 10 Ways to Manage a Data Science Project (Part I – Traditional)
- Blog Post: CRISP-DM is still the most Popular Framework
- Blog Post: Data Science vs Software Engineering
- Explore the Data Science Team Lead course and consulting services to learn CRISP and other processes
- (external): Official CRISP-DM Guide
Data Science Team Lead Certification
There’s more than just CRISP-DM
CRISP-DM is a great starting point to understanding data science projects. But there’s a lot more.
To learn these frameworks, how to apply them, and how to deliver data science outcomes, enroll in the Data Science Team Lead course.