Establishment of AI/ML Resource Governance for Organizational accountability and risks mitigation

Democratization of AI/ML-enabled AI citizens to fully utilize the AI/ML resources. With this, there is a need for a streamlined approval process in order to access the resources which are time-consuming.

This case study explains how D Cube Analytics resolved various pain points around Resource Provisioning and Governance.

categories created by well-curated request & approval workflows to provision resources

waiting time on Enhanced visibility on resource provisioning

“With the support for seamless collaboration and bundled approval workflows enabling transparent and frictionless governance, we were able to reduce the waiting time and improve productivity.”


A leading pharma company has democratized the AI/ML within the organization as there is an increase in the need for a variety of data science capabilities across the organization.

Industry tool capabilities did not provide the required visibility and governance across the various data science resources. This resulted in the need for a streamlined approval process with reduced process friction to bring more productivity.


D Cube Analytics surveyed the needs of several departments having 600+ Data Scientists across departments and performed market research and conducted workshop sessions involving relevant stakeholders, domain experts and analysts to brainstorm multiple use cases to capture & identify functional requirements and customizations.


  • D Cube Analytics has created a Unified Data Science platform that enabled the governance across all the various Data Science Resources resulting in frictionless provisioning of the data and other assets
  • This has provided the leadership and information governors more visibility on the usage and cost of the data science resources across the enterprise
  • The Data Science platform allows departmental heads to make different kinds of assets available for its users
  • The platform leverages a project-based structure which uses well-curated request and approval workflows to gain accessibility to various Data Science resources like project access, dataset access, vendor data access and custom compute provisioning
  • The Data Science platform also lets the owners of the resources restrict the accessibility at project level/department Level or Enterprise Level


D Cube Analytics delivered a high impact by creating the Unified Data Science platform on the cloud which offered a very little to no process friction enabling better visibility on the Data Science resources across Enterprise-wide making the Democratization even more productive.

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