The bank, whose parent is among the world's 30 largest financial institutions, moved to a new digital banking platform to remain competitive and needed a world-class observability solution to keep it running. After an expensive and slow-paced year using Splunk, they moved to Elastic and haven't looked back.
- Choosing Elastic to remain competitive with Australia's trusted local banks. This banking subsidiary's combined analytics platform uses Elastic for machine learning, alerting, and security.
- Elastic provides lightning-fast issue discovery and mitigation. Elasticsearch's schema-on-write system creates indexes for search and discovers problems upon ingest, while Splunk utilizes a schema-on-read approach—leading to significant delays for issue discovery, diagnosis, and mitigation.
- Elastic's flexible, open source platform made onboarding and development easy. Splunk's closed source proprietary solution is limited in terms of development and integration. The switch to Elastic paved the way for immediate integration with other applications, as well as allowed the bank to begin data visualizations with custom dashboards.