Lowering risk with better anomaly detection
Elastic's schema on write, as opposed to Splunk's schema on read, allows analysts to find errors more quickly, boosting analyst morale and productivity.
Reducing licensing costs while increasing insights
Switching from Splunk to Elastic immediately cut annual license costs by $550,000, and freed up more data sources to be observed.
Elastic's support helped meet expedited launch goals
The Elastic hands-on support team provided a level of service beyond that of Splunk, ultimately allowing the bank to easily meet their aggressive production rollout timeline.
The Australia-based financial services organization is a unit of a global banking powerhouse in sustainability-oriented banking and a leader in financing for the food and agriculture sector.
Why Elastic instead of Splunk?
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.
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