Real-time access to insurance claims data insights case study

This leading medical insurance company is a reputable organisation with a long-standing history of providing comprehensive medical insurance to clients all over Australia. The company has a team of highly experienced professionals and maintains local offices throughout the country.

In 2020, the company engaged Altis Consulting to provide a detailed architecture plan for an implementation of a new cloud-based data and analytics platform, with the aim of supporting the company’s national mission to derive greater business value from its data with increased agility.

Given Altis Consulting’s extensive expertise in designing and implementing data platforms, the medical insurance company enlisted Altis’ services to establish a modern Azure-based data platform and to migrate the identified subject areas to the new platform.

The opportunity

The medical insurance company was facing challenges in analysing its core business datasets due to limitations of its aging on-premises data analytics platform, which lacked a scalable and efficient process for ingesting data from multiple sources. Additionally, limited self-service reporting capabilities hindered the ability to make informed, data-driven decisions. To further compound the issue, there were unclear definitions and inconsistent understanding of the key performance indicators being reported. Challenges are summarised below:

• Lack of flexibility in analysing core business data sets (e.g. Claims, Policy)
• Aging on-prem data analytics platform lacking a scalable and streamlined ingestion process from multiple sources
• Limited self-service reporting capability, which didn’t allow decision makers to explore data to make smart and data-driven decisions
• Unclear definitions and inconsistent understanding on the reported KPIs

The solution

The solution introduced for the medical insurance company includes the following components:

1. Altida Data Load Accelerator (DLA)
   A metadata-driven framework that automates and simplifies the ELT process using pre-built logic and data setups. It is integrated with popular Data Warehouse tools such as Snowflake, SQL Server, Synapse and Redshift, as well as ETL tools like Azure Data Factory and Matillion.
2. Azure Data Lake
   A complete copy of data from all sources is stored in this layer, consisting of untransformed copies of source system data and standardised/consolidated copies of data.
3. Azure Data Factory
   It is utilised in the ETL process to move and transform data through the Data Platform.
4. Serverless Azure SQL DB
   Used for storing persisted data that has been cleaned, integrated, and transformed, offering a consolidated view of source data at an atomic level.
5. Power BI
   It provides self-service analytics through interactive business intelligence dashboards and has the ability to integrate with the data cloud platform.

The outcome

With the integration of various systems, a daily refresh pipeline has been established to provide real-time access to Claims data insights for business users. This streamlines the process, eliminating the need for manual requests and providing a more efficient approach to data analysis.

• Self-service reporting: Enabling informed, data-driven decision making through the availability of data insights for decision-makers.
• Azure-based data platform: Facilitating efficient data development processes through the scalable data platform.
• Integration of Power BI with modernised data platform: Enhancing decision maker support through this integration.
• Integration of data catalogue, platforms and governance: Achieving operational efficiency through this integration to create a harmonious system.

Benefits

The project delivered a range of benefits to the organisation by enabling analytics, data integration and self-service capabilities.

• Firstly, the organization now can streamline the data ingestion process, using the Altis Data Load Accelerator and its metadata driven approach to acquire data into the platform, embedding industry best practices, enabling scalability, and reducing the cost of maintenance.
• Moreover, the project enabled self-service capabilities for Claims data that decreased the resource and technical bottleneck of acquiring and reporting on data, enabling the organization to self-analyse and interrogate Claims data.
• The end result was an organisation that was better equipped to make informed, data-driven decisions, leading to increased productivity and business success. The outcome of the project also supports continuous improvement by providing ready access to consistent and high-quality data, thereby removing the burden of data collection and distribution from departments, and allowing them to focus on their core business activities.

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