Governance with Dataiku

A Central Control Tower

Dataiku Govern is a single place where data and analytics leaders and project managers track the progress of multiple data initiatives and ensure the right workflows and processes are in place to deliver Responsible AI. 

As your company scales its AI footprint, centralized program oversight is crucial for maintaining visibility and reducing risk.  

Standardized Governance Plans and Workflows

Dataiku Govern is a single place where data and analytics leaders and project managers track the progress of multiple data projects. With Dataiku, you leverage standardized project and workflow templates with clear steps and gates to explore, build, test, deploy, and maintain AI projects. Assign stakeholders, capture notes, and attach relevant documentation to each stage of a workflow to ensure the process is documented and tracked, from design to delivery. 

Structured Sign-off and Approvals

In governed workflows, project owners request and collect sign-offs on models or project bundles prior to promoting them to production, in order to ensure audit-readiness on deployment decisions. Without appropriate reviews and sign-off, a deployment will be blocked until proper approval is obtained. 

Getting stakeholder approval for data projects can be challenging to manage and track, but is necessary to ensure both projects and models align with business needs, are auditable, and follow responsible AI best practices. 

Model and Bundle Registries

Dataiku’s model registry provides a centralized way to see all models (whether developed in Dataiku or externally) in one place, versioned, and with performance metrics and project summaries for leaders and project managers. 

The bundle registry delivers the same benefits for project bundles, so versions of analytics pipelines and project artifacts can be governed and managed according to a defined workflow. 

Project Value & Risk Qualification

In Dataiku, stakeholders assess project value and risk using a standardized qualification framework. With limited resources to execute a growing number of AI project requests, a single value-risk matrix helps leaders compare initiatives, determine oversight requirements, and determine which projects should be prioritized for investment.