Learn why updating your data dictionary is an essential artifact in every sprint of a CRISP-DM and Scrum hybrid project to maintain accurate AI development.
Question
Table of Contents
In a CRISP-DM + Scrum hybrid, which artefact should be captured each sprint?
A. Data dictionary update
B. Project charter
C. Vendor-contract renewal
D. Employee-satisfaction survey
Answer
A. Data dictionary update
Explanation
When combining the CRISP-DM methodology with the Scrum framework for an artificial intelligence project, updating the data dictionary stands out as a critical artifact to capture during every single sprint.
CRISP-DM provides the structured phases required for data science—such as data understanding, data preparation, and modeling. Scrum provides the agile, iterative delivery engine. In this hybrid environment, data is never static. Throughout a two-week sprint, data scientists and engineers constantly manipulate information. They engineer new features, drop irrelevant columns, impute missing values, and merge entirely new datasets to improve the machine learning model’s predictive accuracy.
Because the underlying data structure evolves so rapidly, the data dictionary must evolve right alongside it. A data dictionary acts as the central source of truth for the project. It defines exactly what each variable means, how it is formatted, and where it originated. If the team alters the data during a sprint but fails to update the dictionary, a dangerous disconnect forms. Developers might misinterpret a metric, or compliance officers might lose track of how sensitive customer information flows through the algorithm. By mandating a data dictionary update as a sprint artifact, project managers enforce strict data governance, prevent technical debt, and ensure that everyone—from the data engineers to the business stakeholders—speaks the exact same language.
The alternative options do not fit the chronological workflow of an agile sprint cycle. A project charter serves as the foundational document that officially authorizes the initiative. Leadership drafts and signs this document once at the very beginning of the project; it is not something a team rewrites every two weeks.
Similarly, vendor-contract renewals operate on completely different timelines. Procurement departments and legal teams handle these agreements on an annual or multi-year basis, operating far outside the technical sprint cadence of a development team. Finally, an employee-satisfaction survey belongs strictly to human resources. While measuring team health holds value, it is an administrative metric rather than a tangible technical artifact produced by data science work.
In a fast-paced AI environment, clear documentation keeps the project alive. Updating the data dictionary at the close of each sprint ensures the models remain transparent, reproducible, and ready for deployment.