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Enable easy discovery and reuse of existing Data & AI assets, provide access to usage context to build trust, track (re)use and incremental value.
Watch the video
How to bring added value to existing assets?
What level of trust do the users have in current Data & AI assets?
What are the long-term impacts and benefits of Data & AI?
How frequent is a data asset reused in projects?
What standards are put in place to ensure quality Data & AI reuse?
Maximize value generated
from Data & AI assets
Shorter time-to-market and
fewer risks of error for new projects
Foster Data & AI culture and data literacy to ease communication
Having access to existing assets, models, API, best practices, templates and more, Data & AI users can choose suitable initiatives to support use case executions regularly
The easiness to discover and browse Data & AI products avoids the organization from duplicated efforts.
Assist users to assess data products and educate them to reuse Data & AI products by providing usage guides, documentation and best practices.
Onboard new users and continuously support existing ones to learn how to use Data & AI initiatives to encourage the use and reuse of them.
Thanks to collaboration across functions and teams, submission and validation workflows become smoother and more comprehensive.
Govern assets by gathering feedbacks, enable peer review and emphasizing alignment in order to improve existing assets quality.
One of the common challenges to organizations is lacking metrics to track Data & AI usage frequency or if a product is ever used.
By using centralized usage metrics and context, the organization can drive the Data & AI portfolio, roadmap as well as monitor the use & reuse of products to better manage the knowledge they own.
