A data project is not only about technologies or algorithms. Its success relies above all on clear organization, well-defined objectives, and effective coordination between business and technical stakeholders. With a structured methodology in place, teams benefit from a framework that guides every step, facilitates collaboration, and ensures alignment with the company’s strategy.

Two methodological frameworks prove particularly effective and complementary. CRISP-DM provides the reference structure for running a data project end-to-end, from defining business needs to deployment. SCRUM, as an agile framework, introduces an iterative way of working that allows teams to deliver tangible results quickly and adapt to changes. Used together, they create a robust and pragmatic approach that maximizes the chances of success.

CRISP-DM: a reference methodology for structuring data projects

Read the complete guide : https://docs.eaqbe.com/project_management/crisp_dm

Business understanding

The first phase of CRISP-DM is about clarifying the organization’s objectives and needs. It goes beyond stating a general goal and requires defining precise evaluation criteria for the project. These criteria specify how success will be measured: numerical indicators, timelines, and scope.

A key component is setting up a steering committee. This committee brings together key decision-makers, relevant operational managers, and an internal project leader. Its mission is to ensure cross-functional communication and quickly remove obstacles that may hinder progress.

In parallel, semi-structured qualitative interviews are conducted with different departments involved. These interviews help uncover needs beyond those explicitly stated, deepen understanding of business processes, and identify implicit expectations. Such discussions often reveal additional opportunities or critical constraints to consider.

Data understanding

Once objectives are framed, the next phase focuses on exploring the available data. This involves read-only access to relevant sources and performing univariate descriptive statistical analyses. These analyses provide a clear picture of each variable: distributions, outliers, completeness rates.

At this stage, a data quality report is produced. It highlights strengths and weaknesses in the data, provides short-term recommendations to improve quality, and suggests medium- to long-term improvement paths. This report serves as a valuable resource not only for the current project but also for the company’s future data governance.

It is important to note that phases 1 and 2 often feed into each other. Discovering limitations in the data may lead to reframing certain objectives or adjusting the initial scope. Likewise, clarifying new business needs may prompt the exploration of additional data sources.

Subsequent phases

The data preparation phase represents the largest share of technical work, covering cleaning, transforming, and creating suitable variables. Modeling involves testing multiple approaches and configurations to select the one that best meets defined objectives. Evaluation compares results against business criteria established in the first phase, ensuring technical work remains aligned with strategy. Finally, deployment ensures production implementation, user training, and documentation, with the goal of making the model operational and sustainable.

CRISP-DM therefore provides a global, complete, and flexible framework, but remains general in its execution. This is where SCRUM brings complementary value.

Combining CRISP-DM and SCRUM

Read the complete guide : https://docs.eaqbe.com/project_management/scrum

SCRUM is an agile framework for managing projects iteratively and collaboratively. Its principle is to break work into short cycles called sprints, usually lasting two to four weeks. At the end of each sprint, the team must deliver an increment: a concrete, usable, or demonstrable outcome that moves the project closer to its final objective.

At the heart of SCRUM is the backlog, which is simply a prioritized list of requirements and features to be developed. The backlog is dynamic: it evolves throughout the project based on user feedback and learnings. Its items, called user stories, describe simply what needs to be done. At each sprint, part of this backlog is selected, developed, and validated.

Team organization is structured but flexible. The Product Owner represents the business vision and prioritizes backlog items. The development team carries out the tasks needed to turn requirements into tangible increments. The Scrum Master ensures adherence to the framework and helps remove obstacles.

The process is paced by rituals that maintain coordination. Sprint planning defines the objectives of the sprint and distributes tasks. The daily scrum is a short meeting each day to synchronize the team. The sprint review presents results to stakeholders and allows priorities to be adjusted. Finally, the retrospective helps the team improve its practices.

This framework offers full transparency on project progress, ensures strong user involvement, and enables quick adaptation to unforeseen events. In data projects, it prevents the tunnel effect and guarantees that each iteration delivers measurable value.

The two approaches naturally complement each other. CRISP-DM defines structure and overall phases, while SCRUM organizes the team’s day-to-day work. Each sprint can correspond to a sub-step of a CRISP-DM phase ; for example, one sprint may deliver a report of business interviews, another a descriptive statistical analysis, and another a model prototype.

This combination ensures both methodological consistency and operational agility. Objectives remain clear and aligned with the company’s strategy thanks to CRISP-DM, while SCRUM ensures the team progresses in short, measurable steps.

By combining CRISP-DM and SCRUM, organizations equip themselves to turn data projects into true performance drivers. They benefit from a recognized framework that secures the process and ensures alignment with business needs, while also leveraging an agile mode of working that delivers results quickly, keeps users engaged, and adapts to unforeseen challenges.

This integrated approach gives data projects a double strength: the rigor of a structured methodology and the flexibility of an agile framework. For businesses, it is a way to fully harness data and make it a sustainable engine for decision-making and competitiveness.