About the Customer:
State Auto is a super-regional property and casualty insurance holding company headquartered in Columbus, Ohio that distributes personal and small commercial coverages in 33 states through approximately 3,400 independent agents. Their employees spend an exorbitant amount of time running complex actuarial analysis and reports. State Auto was looking for scalable analytics solutions to help manage this workload. They also wanted to enable secure data solutions on the public cloud by phasing out the burden of high Netezza costs spread across multiple data silos.
Customer Challenge:
Kohl’s was having several technological issues related to scalability, maintenance, performance, data recovery, disaster recovery, increased operational overheads, and governance while running their data on legacy data warehouses.They were also facing performance issues along with the high cost of the current Teradata environment.
Partner Solution:
Datametica built a single data platform on GCP to optimize network efficiency and eliminate problematic data siloing.
We used Eagle, our automated assessment tool, to analyze the existing Netezza Data Warehouse and reveal the complexity, data flow and overall system design along with formulating robust migration strategies, at a granular level.
We then used Raven, our code (SQL /Script) conversion tool, to convert complex undocumented Netezza scripts and Informatica jobs to Google BigQuery.
Finally, we delivered Pelican – our automated data validation tool, during parallel runs. Datametica ultimately completed the migration from Netezza to GCP BigQuery and its validation process within four months.
When we were evaluating GCP implementation partners, Datametica came out as the front-runner by a margin. Datametica, with their automation tools and accelerators, combined with their mature reference architectures, demonstrated faster migration capabilities at a lower cost together with the continuous validation tools to assure reliable delivery and result. At each step, they showcased their expertise on GCP and guided us through the journey of migrating our analytics.
Kishore Ravilla
VP, Enterprise Architecture , Stata Auto Insurance
Impact & Result:
- Development time was reduced by 45%
- Maintenance costs were cut by 55%
- Query execution time by nearly 90%
- Improvement was visible in the team’s effectiveness with the introduction of AI and advanced analytics through GCP.
- High security was enabled using the GCP platform
- The Data Lake on GCP was now scalable and able to handle unpredicted demand patterns.
- Support for disaster recovery and provisions were made available
- Faster processing and resolved tasks around Analytics and AI capabilities.
- The performance gains from Google BigQuery and GCP allowed users to efficiently evaluate and process customer insurance policies, leading to additional opportunities for revenue and customer growth.
- GCP’s Cloud Dataproc provided auto scaling and managed Apache Spark jobs, while Google Cloud storage offered a unified back-end location for internal data.
GCP Products Used
BigQuery
Cloud IAM
Cloud Storage
Stack Driver
Data Proc
Dataflow
