Story
Qlik
OlamOlam Uses Talend for Feature Pipelines
Olam, a consumer staples organization in Singapore, uses Talend from Qlik to support applied machine learning for data scientists and ML engineers.
Value results
| Category | Value result |
|---|---|
| Productivity | Handoffs in applied machine learning sit in a shared queue instead of a mailbox trail |
| Risk and compliance | Talend is the governed place data scientists and ML engineers use for applied machine learning |
| Capability | New joiners can see how applied machine learning actually runs |
Story
Inside Olam, applied machine learning used to depend on whoever still had the latest file. That pattern is common in consumer staples groups working out of Singapore. Data scientists and ML engineers needed a system that would still make sense after the original project team moved on.
Olam uses Talend from Qlik as the working layer for feature pipelines. Qlik provides analytics and data integration software, including Qlik Sense and Talend, for interactive analysis and pipelines. The practical change is simple: applied machine learning has a home, and reviews happen there instead of in a forwarded thread.
Nothing in this writeup invents a savings number. What Olam gets from Qlik is a durable place to run applied machine learning and a way for data scientists and ML engineers to see the same feature pipelines at the same time.
