Provide consistent unified access to data across different systems by using the flexible and semantically precise structure of the knowledge graph model
Implement a Connected Inventory of enterprise data assets, based on a knowledge graph, to get business insights about the current status and trends, risk and opportunities, based on a holistic interrelated view of all enterprise assets.
Quick and easy discovery in clinical trials, medical coding of patients’ records, advanced drug safety analytics, knowledge graph powered drug discovery, regulatory intelligence and many more
Make better sense of enterprise data and assets for competitive investment market intelligence, efficient connected inventory management, enhanced regulatory compliance and more
Connect and model industry systems and processes for deeper data-driven insights in:
Improve engagement, discoverability and personalized recommendations for Financial and Business Media, Market Intelligence and Investment Information Agencies, Science, Technology and Medicine Publishers, etc.
The main purpose of databases is to store related data, allowing users to easily process and analyze it to derive valuable insights from it.
Depending on the task at hand, there are different data management technologies. They answer the growing need of organizations wanting to better maintain, secure, manage, share and process their data. But often the question is – how to choose the best solution?
Here is a quick comparison between some of the major players in the game: Relational databases, Property graphs and RDF databases. For a in depth comparison, check our RDF vs Property Graphs Comparison presentation.