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Strise with Marit Rødevand

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Priyanka Vergadia hops back into the host seat this week, joining Mark Mirchandani to talk to Marit Rødevand of Strise. Strise is an AI-driven enterprise company using knowledge graphs to gather and analyze massive amounts of information, depositing it into a web-based interface to help large clients such as banks solve data-driven problems. Strise’s web-based data platform is customizable, flexible, and capable of keeping up with the fast-paced world of technology so enterprise companies aren’t constantly putting time and resources into reworking old or building new software. To do this, Strise uses knowledge graphs rather than typical databases to create what Marit calls a future-proof data model.

Marit explains knowledge graphs in detail, emphasizing that they can reduce training of machine models, allow new data to be input easily, and make analyzing unstructured data much easier. Knowledge graphs take data that would normally only make sense to humans and in effect translate it for computers. Using banking as an example, she details how information about customers can be collected and analyzed thoroughly to help the bank come to conclusions about credit-worthiness or possible criminal activity.

On Strise’s platform, Marit tells us that the information is now available to the end user who provides feedback to the system, marking things as relevant or irrelevant, rather than leaving those decisions to a data scientist outside of the client’s field. This means that massive amounts of information could be stored in the knowledge graph, across many industries, and each user only gets the data he or she needs.

Google Cloud tools such as Kubernetes Engine, Dataproc, and Pub/Sub have played an integral roll in the creation of the Strise data pipeline. Marit explains how Strise gets their data, how it’s input into the knowledge graph, and how these Google tools help to keep Strise running.

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