Achmea accelerates their digital transformation with Elastic Observability

To identify and resolve these types of issues, Achmea previously deployed a series of custom-developed observability systems that aggregated and inspected telemetry data. But over time, these tools struggled to keep up with the ever increasing volume of data, which today generates 3,000 systems messages per second and two terabytes of storage per day. The observability challenge increased with deployments of additional technologies as well as the increased workload caused by the shift to remote working during the COVID-19 pandemic. The growth of multiple customer-facing channels such as web, mobile, and chat added even more to the complexity.

Marc Rekers, Project Coordinator Integration IIB and API Gateway at Achmea, sums up the situation, “Our previous observability solutions were under-equipped to manage our growing and changing environment. We wanted something more streamlined that could also scale to meet message and data volumes.”

Rekers called on Achmea’s long-term IT service provider Atos to collaborate on a solution. Following an analysis of Achmea’s requirements, Atos recommended the Elastic Cloud Enterprise SaaS implementation. This includes the Elastic Search Platform and a Logstash data events pipeline. Achmea also uses Kibana for data visualization, including Lens, dashboards and the Discover application for fast, detailed data insights.

With Elastic, Achmea can quickly and accurately search entire databases for a specific piece of information, such as a customer relationship number or policy number. It has also added several metadata fields to these types of system messages. This includes the integration, server, and platforms associated with the message as well as performance times for fine-tuned observability. Every new service and integration that the team develops will be tracked using Elastic.

For example, if a customer isn’t able to create an insurance proposal on the website and contacts the call center, the call center creates an incident report to investigate the front- and back-end systems. At the integration layer, Achmea uses Elastic to examine systems in detail and find a solution that can be passed back to the call center and the customer.

In the future Achmea anticipates making use of Elastic machine learning capabilities. The Elastic Stack processes data upon ingest, ensuring that Achmea has the metadata it needs to most quickly identify root causes or add context to any event. Elastic also includes algorithms that work at scale and built-in tools like Data Visualizer that help find fields in data that pair well with machine learning.

Above all, Elastic supports Achmea’s business success objectives as it navigates ongoing technical growth and evolution while delivering innovative products and customer service via the latest platforms.