Recorded Webinar: Building a Semantic Layer for Your Enterprise Data Estate
The democratisation of data has encouraged engineers to think about how to make their data estates more accessible and useable for non-technical business end-users. Translating intention into data action requires careful configuration that enables consumers to mine insight, analytics and value without having to use precise technical terminology.
Data engineers achieve this through semantic layers, a set of digital instructions that act as an intermediary between consumers and the tech stacks of organisations.
Not only do they permit non-technical personnel to interact with data, converting it from its raw form to business-meaningful information, they also create a unified view of that data across an enterprise. This in turn optimises data quality, creates the fundamentals for self-service analytics and facilitates a host of powerful applications and use cases.
Building a semantic layer takes precision to ensure it aligns with the business requirements of the organisation. In this webinar, leading practitioners will look at how semantic layers can bring value to organisations and examine how they can most effectively be configured for an enterprise.
Among the topics discussed will be:
- Best practices for building a semantic layer
- How such structures can be monitored and validated
- The kinds of modelling tools used in their creation
- Construction challenges and how to overcome them
Speakers:
More about our speakers:
Jez Davies
Chief Information Architect
Northern Trust
Jez is driving Northern Trust to turn data into a competitive advantage through semantic models, observable pipelines, knowledge graphs, and AI-ready foundations. He leads a team building the semantic foundations that make enterprise AI actually work.
Matej Matoulek
Senior Product Manager
Ataccama
Matej Matoulek is a Senior Product Manager at Ataccama, driving product strategy to help organizations maximize the value of their data estates. Passionate about democratizing data, he focuses on scaling unified, AI-ready data management solutions that bridge technical complexity and build the self-service foundations that make enterprise AI actually work.
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