Fast, high-value business insights with cloud data warehouse modernization solutions.
Company
BCI Insurance
Industry
Financial Services
Location
Santiago, Chile
Use Case
Data Analytics
Web site
https://www.bciseguros.cl/
Author
Jaime Florez
jflorez@arkho.io
Company
BCI Insurance
Industry
Financial Services
Location
Santiago, Chile
Use Case
Data Analytics
Web site
https://www.bciseguros.cl/Author
Jaime Florez
jflorez@arkho.io
BCI Seguros is the leading general insurance company in Chile and the second largest in non-life insurance. The Spanish group Mutua has just acquired a majority stake and the brand is composed of BCI Seguros, ZenitSeguros and Auxilia. The company has more than 1,000 employees and branches throughout Chile.
Business challenges
BCI Seguros faced challenges common to modern organizations, including difficulty in synchronizing multiple data sources, unmet critical information analysis needs, and barriers to data warehousing in local IT infrastructure. Due to limitations in computation, scarce storage and data processing capacity, business analytics were severely restricted in generating knowledge, making decisions and, therefore, generating competitive advantages in the market.
Additionally, the generation of business insights required a fast, agile and flexible process with support for normal information modifications and with response times according to the dynamism of the insurance industry. The business information coming from different sources with large volumes of data needed to be analyzed in a limited period, the scaling and availability needs were critical to enable the expert analysis of the information and thus adjust the product offering according to relevant market variables.
The following business challenges summarize the previous state:
- Slow business analytics. The extraction and processing of data from transactional databases was a manual and slow job, limited by the use of local resources and high investment of man-hours.
- Difficulties in storing and processing data at scale. Reporting and analysis used OLTP databases and therefore increased business risks and created user limitations. The creation of an analytical data warehousing model (OLAP) enabled IT capabilities and users to obtain online data from different perspectives.
- Low operational efficiency. The need to reduce operating and IT support expenses, incurred in manual administration processes, such as database backups, copies, restoration, storage management, etc., generated high costs for the company.
- Data strategy not aligned with business needs. Old data architecture is needed to support business needs and facilitate industry disruption.
Results
By forming an integrated work team, BCI Seguros and Arkhotech, as strategic allies in achieving business objectives, integrated a multidisciplinary team for business disruption through data.
Arkho used a cloud-based ingest, processing, and consumption strategy with multiple backbones that will enable the generation of business-critical insights for years to come:
- Synchronization of business data to the cloud, in a process with low impact on production processes and low response latency.
- Creation of enterprise data lake structures that will allow future incorporation of new analytical needs by reusing the same information.
- Distributed processing for optimal information model generation and business data enrichment.
- Data consumption in BI tools already used by the business.
Benefits
As a result of the project, the business was able to capture the needs for generating business insights more agilely, using large volumes of information and enriching the data in automatic transformations. Response times for business inquiries have been optimized by 95% compared to previous times. In addition, the analytics structure created makes it possible for the business to continue innovating in predictive and disruptive use cases in the market.
On the other hand, the solution generated different features in the optimization of data processing:
- Online data replication in AWS S3, allows the creation of a data lake for the company, in addition, the use of the parquet format improves efficiency by 60% and reduces storage space requirements.
- Online data replication in AWS S3, in turn, has allowed the data source to be reused for the creation of temporary test databases, data backup, and creation of more complex queries for business intelligence. Going forward, this structure allows for maximum reuse and more complex analytics use cases.
- Near real-time synchronization of the local Oracle database with Redshift, allowing users to perform online queries.
- Having a subset of RAW data in Redshift has allowed users to decouple from OLTP data sources and stop competing for resources on transactional bases, obtaining results in less time even with aggregated information.
- Metrics for data replication are now a common language for IT and business operations teams.
- Alarm creation and online monitoring have enabled users to shift from reactive to proactive operations models.
- Athena allows you to query and search for results quickly, with virtually no infrastructure.
Ana Isabel Prieto
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