Catalog and Cocktails – Fashion Week… but for data

With the hype of graph databases and knowledge graphs, a common (mis)practice is to quickly migrate your existing siloed data into a graph database. But be careful! You may just be bringing the complexity of your silos into the graph.

Join Tim Gasper, Juan Sequeda and guest Jans Aasman from Franz Inc, the makers of AllegroGraph, for a conversation on why your graph-based machine learning and 360 projects should start with data modeling.

This episode features
– Data modeling approaches you should consider
– Tips to avoid data modeling pitfalls
– If you could be a top model for any product/brand, what would it be and why?

Key takeaways
– It’s “terrible” to start creating an ontology without knowing the application
– Intelligent people make the schemas… this is not easy
– Modeling is human problem solving!

Listen to the Podcast or Read the Transcript.

Here is the broadcast on YouTube: