Story
Confluent
DNBDNB Extends Stream Governance Across Applied Machine Learning
DNB, a financials organization in Norway, uses Stream Governance from Confluent to support applied machine learning for data scientists and ML engineers.
Value results
| Category | Value result |
|---|---|
| Capability | Feature pipelines can be reviewed without waiting on a personal export |
| Capability | Named workflow replaces ad hoc routing for feature pipelines |
| Capability | Applied machine learning stays visible to adjacent teams through Stream Governance |
Story
DNB did not need another dashboard that nobody opened. It needed applied machine learning to move. In Norway, data scientists and ML engineers already knew where feature pipelines went wrong: too many copies, too little ownership, and a close process that waited on the loudest inbox.
Stream Governance from Confluent is now in that path. Confluent is the data streaming platform built around Apache Kafka, used to move events between applications in real time. The company treats it as production tooling for applied machine learning, which is why data scientists and ML engineers live in it rather than exporting from it once a quarter.
DNB can show how feature pipelines is handled today. That is the value: a repeatable way to run applied machine learning on software the rest of the industry already recognizes.
