AI and ML Analytics abstract representation.
Solutions / Use Cases

AI / ML Analytics

Accelerate your advanced analytics

The use of knowledge graphs and ontologies is fast becoming a best practice for the future of data management and advanced analytics.

Traditional data management techniques lack the capability to abstract business logic and context to facilitate data analytics. Most analytics are dependent on the underlying schema of the data warehouse or data lake. Kobai's Knowledge Graph platform provides a codeless and self-service interface to create the business context and logic, so data scientists can accelerate their analytic insights. 

Reduce data wrangling time

By reducing the time to create ad-hoc queries to minutes and combining different types of data from multiple sources, Kobai significantly reduces the wrangling time of data scientists by 70%. This time savings provides more time to focus on the development of the actual analytics.

Close the analytic loop

The output of a predictive or  forecasting analytic is a very important business insight that requires actions to be taken or decisions to be made. However, this output usually ends up in a database table or a chart on a dashboard. The missing link is a means to easily leverage the results for subsequent business decisions. The Kobai platform provides a mechanism to feed the analytic outputs and findings back into the knowledge graph's ontology model in order to build new business queries. This allows you to immediately incorporate new learnings and create, potentially previously hidden, net new insights, thus closing the AI/ML loop and making analytical outputs more actionable.

Quickly socialize and share learnings

Each query created on the Kobai Platform can be visualized as a card or published as an API endpoint. This allows for easy democratization of analytic insights as they can be socialized and shared across different functions, enabling teams to easily collaborate and identify the actions needed to be taken.

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