Companies have widely built data lakes – which often lack a semantic “understanding” of data points and their interconnections. Analytics and reporting require manual model definitions by data scientists – largely limiting capabilities and speed of analytics.
Establish a semantic core graph and scale it up with ontologies, coveringbusiness structures and models as well as external graphs.
(e.g., lines of business, products, customers)
Apply real-time graph and event analyticsto derive actionable insights and business process support.
(e.g., emerging risks, trigger events, sentiment)
Automate processes based on robotic process automation (RPA) and higher-value analytics.
Apply Artificial Intelligence to data lakes and create a truly comprehensive, agile Knowledge Base ready to power pro-active analytics, business applications, and processes with reliable but dynamic models and up-to-date information.