In relational databases, data is stored in various tables connected through joins and keys.
In traversal graph databases, nodes are linked via explicit relations called edges. For example, the nodes Jane and Pizza can be joined by logical relations such as “likes”, “eats” or “is allergic to”.
In both cases, someone must define the relations and prioritize them based on expected queries.
Having to many relations, the databases will lose speed, and the data model will be harder to manage.

When Capish was founded, we wanted to create software for the curious.  Designed for investigating cause and effect within data with many parameters. 

No existing database could provide us with instant answers to questions no one expected to be asked. Hence, we had to develop a unique type of graph database. This approach also required new methods for modelling and indexing data, enabling unprecedented possibilities for exploring information quickly and freely.

This session will cover:
• How ancient Greek philosophers helped us with the data modelling
• The pros and cons of the Capish graph database
• From a data manager’s perspective: lessons learned in data integration
• The technology applied, live demonstration of the data exploration software Capish Reflect

Speaker:
Eva Kelty is the CEO of Capish, holds a Bachelor of Science in Statistics, and boasts an extensive
career in Life Sciences. With expertise spanning biostatistics, clinical study design, and management
roles at CROs, she recognizes the pivotal role of rapid access to democratized data in driving project
success. View profile on LinkedIn.