Govern all your products and use cases end-to-end

Define and monitor value, risks and costs

Deploy a data catalog, define policies and processes, setup the governance roles and responsibilities. All organizations today need an appropriate governance workflow.

Governance is a transversal act of enforcing policies. YOOI supports multiple workflows per concept to enable governance on the full lifecycle of Data & AI investments across all stakeholders.

On top of intrinsic risks carried by data itself, additional risks in ethical, environmental, security aspects induced by the use of data need to be monitored closely as well. (project governance, model governance, analytics governance, risk governance, etc.)

Define and follow comprehensive requirements and deliverables checklists

Consolidate risk guidelines & evolutions for meeting cybersecurity, privacy and alignment needs

Connect assets with business use cases to identify value contribution

Prioritize efforts or decommission items base on combined value and risks

Ensure validation and checkpoints are enforced and tracked

Cannot track asset usage in the value chain and risk to lose assets

Outdated data & analytics initiatives may be used and sabotage the business

Unclear costs, value, risks of Data & Analytics

Establish a value-driven governance approach

Consolidate risk guidelines & evolutions

With more and more data protection and cybersecurity guideline being implemented, organizations need to translate policies into practices and procedures.

Effective end-to-end governance allows all actors in the value chain to use Data & AI in ethical and responsible way.

The alignment with current and anticipated use of Data & AI initiatives ensures organizations’ continuous compliance with the regulations and requests.

Orchestrate workflows
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Comply with various policies by using workflows to define requirements and deliverables checklist and ensure stakeholders validations and checkpoints are enforced along the way.

This enables efficient approval at every critical step along with tracking the deployment and results.

Prioritize efforts by its value and risks

Using combined value and risks, managers and data teams can notice which data sets matter most to the organization. Then, allocate additional resources and efforts in leveraging the data asset to its full potential.

On contrary, end-to-end governance also allow them to spot Data & AI initiatives that are no longer a positive assets and remove them promptly.