No centralized definition for key metrics
The Semantic Model is critical to the success of any self service initiative. Because context is critical to accuracy and accuracy is critical to self-service trust.
Finding data is difficult for business users
The Semantic Model is critical to the success of any self service initiative. Because context is critical to accuracy and accuracy is critical to self-service trust.
Data verification/knowing it is a trusted source
The Semantic Model is critical to the success of any self service initiative. Because context is critical to accuracy and accuracy is critical to self-service trust.
Matching data to a business user’s preferred way of interaction
The Semantic Model is critical to the success of any self service initiative. Because context is critical to accuracy and accuracy is critical to self-service trust.
No way to provide guardrails or control responses of AI on data
The Semantic Model is critical to the success of any AI initiative. Because context is critical to accuracy and accuracy is critical to self-service AI trust.
Developers are spending time on lower level problems, e.g. rewriting the same queries
Data models speed up technical users, due to reusability & extensibility. Tortoise & the hare.
Systems to provide context to AI are isolated to those AI platforms and not reusable
The Semantic Model is critical to the success of any AI initiative. Because context is critical to accuracy and accuracy is critical to self-service AI trust.
It takes time to build models to answer new questions from business users
Data models speed up technical users, due to reusability & extensibility. Tortoise & the hare.
Data team is bottlenecked by having to answer tickets, do data engineering work at warehouse layer, or other technical types of work
Shorten the gap between the technical teams & business teams - business team creates metrics via context that can fundamentally change the view of the business.
Engineering teams cannot keep up with ongoing maintenance / sync between systems.
Interoperability across the integrated modern data stack.
Broken dashboards as the result of changes in the DW lead to engineering emergencies.
Interoperability across the integrated modern data stack.
Change Management
Integrated SDLC workflow and managing content at scale
Lack a way to robustly deploy updates
Integrated SDLC workflow and managing content at scale
Things are breaking and we lack visibility to know what is broken or what will break with upcoming changes
Integrated SDLC workflow and managing content at scale
Uptime
Integrated SDLC workflow and managing content at scale
Firefighting / constant scrambling to fix broken content
Integrated SDLC workflow and managing content at scale
Lack of ability to revert mistakes and know what happened (version control)
Integrated SDLC workflow and managing content at scale
