The data science lifecycle follows CRISP-DM, the standard process for data science projects. myBytes works in its six phases and walks through an example of what happens in each phase.

CRISP-DM stands for Cross-Industry Standard Process for Data Mining. The process model breaks a data science project down into six phases: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation and Deployment. Each phase has a guiding question, defined tasks and a deliverable that the next phase needs. Since the 2000s it has been regarded as the de facto standard for data mining and data science projects.

CRISP-DM was developed from 1996 by Daimler-Benz, NCR and the tool vendor ISL, which later became part of SPSS, with the insurer OHRA as the fourth partner; the CRISP-DM 1.0 guide was published in 2000. The model is deliberately industry-neutral and tool-independent. That is exactly why it fits projects nobody had in mind in 1996: a returns model in fashion retail, a failure forecast in manufacturing and a turnover model in logistics all pass through the same six phases.

The newer process models build on CRISP-DM rather than replacing it. ASUM-DM from IBM adds operations and project management, TDSP from Microsoft adds roles, tasks and a project structure but has not been maintained since 2023, and DASC-PM from Nordakademie extends it with data provisioning and scientific grounding. Anyone who knows one of these models will recognise CRISP-DM in it.

CRISP-DM itself does not define roles; in practice the phases still fall to recognisable owners. Phases 1 and 2 need the business side: the business question, the success criteria and the knowledge of which data exist in-house and what a field in the merchandise system actually means come from the people who work with the data. Phases 3 and 6 are data engineering: pipelines, versioning, operations. Phases 4 and 5 are the work of the data scientists. No phase belongs to one role alone, and a project that staffs only the middle phases has neither a robust question nor an operation.

CRISP-DM is iterative, not linear. The arrows also run backwards. If the data assessment in phase 2 shows that the business goal from phase 1 cannot be answered with the available data, the project goes back to phase 1. If a model misses the success criteria in evaluation, it goes back to modelling or to data preparation. The outer loop closes after deployment: new data from operations raise new business questions.

A mid-sized fashion company with 1,800 articles and 18 markets sells jeans online. The return rate for jeans is 38 percent, and 34 percent of those returns are down to "does not fit".

Phase 1 · Business Understanding

The business question is not "we want AI" but: What share of fit-related returns can be avoided if the customer is recommended the right size at checkout? The success criterion is the return rate per category, measured before and after the launch. Phase 1 also sets the limit: new customers without a purchase history can initially only be served with category averages by such a model.

Phase 2 · Data Acquisition & Exploration

The data are in-house: purchase history, return reasons, the ordered and the kept size per customer and cut, plus the size charts per article. Phase 2 checks whether the size information in the returns data is complete. If it is missing, there is no model, only a statistic.

Phase 3 · Data Processing & Engineering

From the raw data, each customer gets a profile of kept and returned sizes per cut, and each article a calibrated size run. The model calculation assumes that a correct size recommendation would have been enough for 68 percent of fit-related returns. Whether that assumption holds is decided here, on the quality of the size fields.

Phase 4 · Exploratory Analysis & Modeling

The model is collaborative filtering with body-shape matching: customers with similar purchase and return behaviour are recommended the size that similar customers kept for this cut. The benchmark is the size chart without a recommendation, as a simple reference model before any more elaborate approach. If the model does not beat that reference, the effort is not worth it.

Phase 5 · Evaluation & Validation

In the model calculation the model picks the right size in 82 percent of cases. Fit-related returns fall by 18 percent, and the jeans return rate falls from 38 to 31 percent. A published vendor case reaches up to 20 percent fewer total returns; the model calculation deliberately stays below that. The evaluation also checks whether the effect appears in all categories or only for jeans.

Phase 6 · Deployment & Monitoring

The model runs as a widget on the product page: "Based on your previous purchases we recommend size 31/32." A second output goes to the product team: articles whose size chart deviates systematically. Monitoring keeps watching the return rate per category. The model calculation puts 61,824 avoided returns at 26 euros each, which is 1.61 million euros a year.

The next step in the same series shows the second iteration of the loop: a text analysis of 210,000 free-text comments breaks "does not fit" down into five causes and, together with the size recommendation, lowers the overall return rate of the model calculation across all categories and 18 markets from 28 to around 23 percent.