Data Innovation @ AXA Germany: Journey Towards a Data-Driven Insurer
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Situation faced: AXA is transforming towards a data-driven insurance company to fully unlock the potential of its data. However, the transformation faces several challenges: Firstly, implementing a living data-driven decision-making system, demands a cultural change in the business lines. Secondly, the heterogeneous infrastructure complicates the deployment of advanced technologies. Data harmonization techniques are outdated, causing low computing performance, and high maintenance costs. Thirdly, insurers have to fulfill new legal regulations ensuring strict data protection. Since the functional roles to address the aforementioned challenges are not clearly assigned, a new organizational entity within AXA Germany was required.
Action taken: The Data Innovation Lab was founded to focus on these tasks. It is a cooperation of the units Data Analytics, Data Management Office, and Data Engineering under one transversal roof. Data Analytics drives innovative data analytic projects and designs new solutions for complex business challenges. The Data Management Office is concerned with process efficiency, compliance, stability, and evolution. This includes tasks such as initiating activities for data quality improvements, providing data architecture and prioritizing data protection. Data Engineering builds the technical infrastructure, accelerates the evolution of the IT landscape and implements a data lake.
Results achieved: A target operating model shows how AXA Germany operates the tranformation towards a more digital, data-driven, and customer-centric organization. The target operating model (TOM) of Data Analytics states the tasks, role definitions and a cooperation model of how to operate innovative data analytic projects. The TOM of Data Management Office states the tasks, role definitions and disciplines of how to provide an efficient and compliant data organization. The TOM of Data Engineering states the tasks, role definitions, and a cooperation model of how to develop and operate the data lake.
Lessons learned: It is essential to build up an interdisciplinary work environment as Data Analytics and the Data Management Office operate at different speeds. A bottom-up transformation, which actively involves every member of the enterprise, is required to establish a cross-sectional data culture. The funding is allocated depending on several factors: Data initiatives have financial incentives but also an experimental orientation. Most of the data management activities are necessary due to future regulatory requirements. Furthermore, a major success factor on the data-driven journey is the support and the commitment of the top-management.
KeywordsData Management Office Target Operating Model (TOM) Lake Data Business Lines Data Architecture
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