This state government client is a statutory body that provides risk management and insurance to government departments and authorities.
The client recently implemented a new core insurance system and reporting platform. However, the reporting platform could not fully utilise the data from the new system due to a mismatch of design, terminology, and processes inherited from the phased switch from legacy to the new core system.
Moreover, the business used different definitions of data entities and calculation metrics between areas of the business, which led to some confusion and uncertainty about the data, requiring extra effort to reconcile and validate.
Exposé was tasked with undertaking a thorough analysis of existing systems, as well as business and reporting requirements, to establish a conceptual data model for the business. We then developed an Enterprise Data Model (EDM) with clear and consistent data definitions, which would provide an ideal target for their future data analytics initiatives.
Analysis was done through the exploration of existing systems, their data and reports. We conducted a series of workshops with key business stakeholders and SMEs from multiple business domains.
Exposé recommended the use of SqlDBM as the modelling tool to design the EDM, which had appropriate integration with Snowflake, Alation and Azure DevOps (the tools used as part of the client’s data platform).
Additionally, exposé produced a gap analysis and recommendations exercise to provide a roadmap for the client to move towards a solution encompassing the EDM.
Improved decision-making: By having access to high-quality, consistent insurance and customer data, the client can make better decisions about their operations. This will lead to increased surpluses, reduced costs, and improved customer satisfaction.
Increased efficiency: The EDM helps to streamline the client’s operations by eliminating the need to manually re-process data for reporting and analytics, saving time and money, and will help to respond more quickly to changes in the market.
Reduced costs: By reducing data duplication and improving data accuracy, money is saved on data storage, data cleaning and extraction processes, and data analysis.
Improved customer service: By having a better understanding of their customers, their businesses can provide better service, leading to increased customer loyalty and repeat business.
Increased compliance: By having a well-defined data model, the client can more easily comply with data privacy regulations and continue to enhance their data governance and management practices.
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