Our Insurance Data Warehouse is designed to evolve alongside your business. As your data needs grow, our flexible architecture adapts seamlessly to accommodate new data sources.
We provide comprehensive Data Warehouse (DWH) implementation and ongoing maintenance, ensuring you stay ahead of emerging trends and can effectively manage increasing volumes of data.
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Real‑time and batch data ingestion
Easily gather and process data from various sources, whether in real‑time or at scheduled intervals. It means you'll always have up‑to‑date and reliable analytics at your fingertips.
Custom KPI dashboards
Track the insurance metrics that matter most to you with personalized dashboards. These dashboards are tailored to align with your business goals and workflows, making it easier to spot trends and insights.
Role-based access and audit trails
Your data security and compliance are top priorities. With user‑specific access controls, you can ensure that only the right people see the right information, and you'll have clear visibility into all system activity.
Prebuilt compliance reporting modules
Save time and hassle by quickly generating regulatory reports with ready‑to‑use templates that meet industry standards.
Scalable cloud‑native or hybrid architecture
As your needs grow, so can your infrastructure. Our flexible system can adapt to changing data volumes, technology stack, and security needs.
Complete operational control
A web app gives teams full control and real‑time visibility into the data warehouse environment. At the same time, a bot ensures instant monitoring and automated issue response through Jira integration.
Our data warehouse enables the insurance company to leverage data‑driven insights, optimize operations, and enhance customer experiences, ultimately driving strategic growth and a competitive advantage in a rapidly evolving market.
Our DWH solution consolidates data from various systems — claims, underwriting, customer service, and finance — into a single, reliable source of truth. This unified view enables business leaders to analyze trends, monitor key performance indicators (KPIs), and make data‑driven decisions quickly. With accurate and up‑to‑date insights, insurers can better allocate resources and respond to market changes proactively.
By integrating and automating data workflows, a custom data warehouse (DWH) reduces reliance on manual processes and eliminates the need for fragmented spreadsheets. Teams can access consistent, real‑time data without waiting on IT or data analysts. It leads to faster cycle times, fewer errors, and better collaboration across departments.
With centralized and enriched data, insurance companies gain a holistic view of customer behavior, preferences, and risk profiles. This enables more precise segmentation, targeted marketing, and personalized policy offerings. As a result, customer satisfaction increases, and retention improves.
A custom data warehouse (DWH) is built to align with the insurer's specific data landscape and business objectives. It can evolve alongside new data sources, technologies, and analytics needs, offering long‑term flexibility. This scalability ensures that the data infrastructure can support strategic initiatives such as digital transformation and AI adoption.
Data warehousing in insurance consolidates disparate data sources to facilitate easy access, analysis, and usage. It enhances insurers' decision‑making, fraud detection, compliance with regulations, and customer understanding, resulting in more effective operations and personalized customer services.
Utilize high‑quality data across all insurance reporting, risk analysis, planning, and business decision‑making processes.
Employ standardized calculations for premiums, risk scoring, loss ratios, and other critical metrics.
Keep track of changes over time and allow for the recovery of past data to support analysis and compliance.
Generate complex reports quickly to facilitate efficient decision‑making for management and stakeholders.
Leverage clear business intelligence (BI) dashboards that provide actionable insights for effective management oversight.
Supply clean, standardized, and structured data to other systems and applications that rely on consistent and accurate information.
Our Insurance Data Warehouse (DWH) is developed as a coherent engineering platform with aligned tools, structure, and operations. Below are the key characteristics of DWH architecture, engineering, process, and architectural maturity.
The Insurance DWH is implemented as a single, monolithic system, where all major layers (STAGE, ODS, CORE, MIS, ADM) reside within a single Oracle database.
Temporary data area
Ingestion andOperational Data Store
Structured operational view of recent data for reporting and accessIntegrated historical data
Transformation, calculations, and historicizationManagement Information System
Data marts for analytics, BI, and downstream systemsAdvanced Data Mining
In Data Flow
Out Data Flow
Optional Data Flow
Note: In our implementation, a monolithic architecture means that all layers reside within a single Oracle instance without being spread across separate platforms, external storage, or data lakes.
Note: The STAGE and ODS layers operate at the same logical level, and both contain raw data. Their difference lies in how data is loaded:
Among key architectural approaches to developing a DWH, we chose the most common ones — Kimball and Inmon because a hybrid architecture combines the strengths of both.
The hybrid approach to building an insurance data warehouse enables us to do the following:
To ensure efficient support and complete control over the Insurance Data Warehouse, we built DWH SUPPORT — a custom web app based on the Oracle APEX platform. It improves day‑to‑day operations, making it easier for teams to monitor, diagnose, and manage complex data flowing across the DWH environment.
With real‑time visibility into ETL processes, interactive dashboards for load status, and built‑in error diagnostics, DWH SUPPORT helps quickly identify and solve issues. It also enables the secure performance of operational tasks directly through the interface — decreasing downtime, enhancing data reliability, and improving operational efficiency.
To enable rapid incident response and real‑time platform monitoring, a specialized bot — DWH ALERTS — was built and integrated with Jira. It provides instant tech alerts, automatically creates support tickets, and gives real‑time updates on data load statuses. So issues are detected and resolved more quickly.
The DWH platform uses Oracle BI as its core business intelligence solution. Our team not only delivers the required data infrastructure — such as data marts, aggregations, and metrics — but also has a dedicated BI team that is responsible for the following tasks.
Involved in the entire development lifecycle, our BI engineers ensure the accuracy and reliability of analytical solutions by leveraging strong domain knowledge and technical data warehouse (DWH) expertise.
We build enterprise‑grade data warehouses, analytics platforms, and data processing systems of any complexity. From classic on‑premise to hybrid and cloud‑native architectures — we deliver end‑to‑end solutions tailored to your business.
An enterprise data warehouse for insurance enhances data governance, accelerates reporting, and facilitates business intelligence initiatives. It enables trend analysis, risk assessment, and personalized service delivery. With DICEUS, insurers benefit from a scalable architecture, CI/CD workflows, and real-time data integration via tools such as Kafka.
DICEUS employs a hybrid architecture that combines Inmon's and Kimball's methodologies, providing both stability and flexibility. Features like a unified Oracle-based architecture, CI/CD automation, ETL process monitoring, and the custom-built DWH_SUPPORT app ensure system reliability and operational efficiency.
The DICEUS Insurance Data Warehouse employs a multi-layered, monolithic architecture (STAGE, ODS, CORE, MIS, ADM), all of which reside within a single Oracle instance. This design enables faster ETL processing, simplified monitoring, and improved performance through the full utilization of Oracle's features.
Business intelligence is powered through Oracle BI EE (OBIEE). The dedicated BI team at DICEUS manages repository layers, develops key performance indicators (KPIs) and dashboards, and ensures that all analytical outputs meet business needs. The solution is fully integrated into the development lifecycle for consistent quality and accuracy.
DICEUS offers a coherent engineering platform aligned with modern development standards, including CI/CD, Git integration, automated testing, and architectural clarity. The solution is built to scale with your business, handle complex data environments, and support seamless integrations with downstream systems.
Yes! DICEUS offers personalized demos to showcase how our insurance DWH solution works. You'll see real-time data workflows, reporting capabilities, and BI dashboards, as well as how the platform supports your specific business needs.
A modern DWH tech stack includes multiple core components to handle data from ingestion to analysis. For data ingestion, tools like Fivetran, Airbyte, Apache Kafka, and AWS Kinesis collect batch and streaming data. Data transformation is handled using dbt, Apache Spark, Databricks, AWS Glue, or Azure Data Factory, often coordinated by orchestration tools like Airflow or Prefect. Processed data is stored in cloud-based warehouses such as Snowflake, Google BigQuery, Amazon Redshift, or Azure Synapse. For business access and insights, the stack includes a semantic layer with dbt Semantic Layer or LookML, and BI tools like Power BI, Tableau, and Looker. Governance is managed with platforms like Collibra, Alation, or Apache Atlas, while Monte Carlo, Great Expectations, and DataDog ensure data quality and monitoring. Security is enforced through IAM, encryption, and data masking, and for enterprise data management, solutions like Informatica MDM or Reltio are used. This modular and scalable setup is suitable for the insurance industry and adaptable across other sectors.
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