Achmea uses Elastic Observability to analyze its technical infrastructure and proactively resolve issues for 12,000,000 customers and 16,000 employees.
As Achmea invests in innovative cloud and digital solutions, Elastic Observability is used to maintain optimal performance and help support ROI from these tools.
Achmea uses Elastic machine learning capabilities to detect anomalies in system messages from applications, including data processing on ingest, built-in algorithms, and Data Visualizer for faster anomaly detection and to identify data fields that support machine learning. Using Elastic machine learning as an early warning system reduces Achmea’s MTTR.
One of the largest suppliers of financial services in the Netherlands, Achmea, founded in 1811, started as an insurance business to protect farmers from mishaps such as flooding and crop failures. Today, the company offers a wide range of services, including car, structure, and health insurance. Achmea has 16,000 employees worldwide and serves around 12,000,000 customers.
Much of Achmea’s growth in recent years has come from acquisitions, and the company now has nine Dutch and five international brands. The expansion has resulted in a highly complex IT landscape with multiple business systems that generate massive volumes of data stored in several locations.
Smooth integration between these systems is crucial for day-to-day business operations and to best serve customers. For instance, when a customer logs in to their account, the customer relationship management (CRM) and policy systems must communicate well with each other. If customers can’t access their information, or if the system is slow, unhappy customers and lost business can result.
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.
We recommended Elastic to Achmea because it provides intuitive data visualization with Kibana and navigation including purpose-built interfaces that let users interact with data in a flexible way. The tools also allow Achmea to filter logs for a certain application on a specific day. It all means better observability and ultimately better system performance for employees and customers.
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.
Now, if an interface or integration creates an issue, the team can quickly investigate, analyze, and monitor performance. We can count the volume and type of messages that flow around and resolve bottlenecks where they impact performance.
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.