Using Cloud Data Warehouse for Insurance company

The customer

An insurance company focused strategically on innovation, quality, customer closeness, excellency and efficiency by offering highly specialized products on the insurance chilean market. This company is recognized for being oriented towards people and Pymes with a diverse portfolio of life, automotive and general insurance products.

The business challenge

Having limitations on compute and storage for data processing on-premises limit the business capacity on insights generation and decision making. By using cloud technologies, our customer was able to use corporate data in a safe, reliable, efficient recovery, managed strategy, which in turn is vital for the consolidation of a data strategy in the company.

As a summary, customer drivers for the implementation of this project were:

  • ETL automation. Data extraction from transactional databases is a manual and time-consuming job, limited by the use of on-premises resources.
    Data warehousing. Reports and analysis were using OLTP databases and thus increasing business risks and user limitations. Creating a data warehouse analytical model (OLAP) enables compute capabilities and users to obtain online data from different perspectives.
  • Operational efficiency. Reduce the operation and IT support expenses, relying on manual administration processes, such as database backups, copies, restauration, storage management, etc.
  • Data strategy aligned. Support a data architecture for the business in cloud, using best practices and pattern for data ingestion, transformation and visualization within AWS Cloud.

The results

By using AWS Redshift the customer has increased the analytics capabilities of cloud data analytics architecture, providing next business benefits:

  • Synchronization from Oracle on-premises database to Redshift in real time, giving the opportunity to users to query business data online.
  • High reduction on query times and faster business insights and responses.
  • BI tools integration using standard SQL dialect.
  • Faster query and retrieval times for data processing and analysis.
  • Support for scalar functions and Python stored procedures for business users.
  • Efficient Redshift best practices by optimization on keys, compression and data split capabilities.