Global 500 Fleet Enterprise Data Architecture

Organization

XTIVIA’s client is an international car rental and fleet-management service that serves millions of customers annually with 5,000,000 vehicles throughout the past five years in more than 15,900 global locations. To simplify the operational and financial risk of such an array of locations and vehicles, the company licenses its brand out to roughly 60 international franchises. Additionally, the client also serves “Insurance Replacements” — for insurance companies such as Allstate and State Farm insurance — to a wide range of corporate customers.

Challenge

As part of their Data Governance initiative, we discovered that the data is siloed and duplicated across different functions with custom attributes based on the application needs. There was no enterprise definition of a logical data model specifically for master data entities. There was no enterprise standard for modeling and naming entities and attributes. Integration between different applications would be difficult with the lack of an enterprise standard logical model. There was no common definition for entities and features, and enterprise standards on Data Security and Privacy were non-existent. For any given data element, there was no common understanding of its classification. As our client was embarking on many enterprise transformation projects and digitalization, an Enterprise Data Architecture and Enterprise Data Model was essential to facilitate easier data exchange between systems.

Solution

XTIVIA, with decades of experience in Enterprise Information Management, created Enterprise Data Architecture and provided advisory services to the client’s Enterprise Data Architecture office. We created an enterprise logical data model with other supplemental artifacts to improve communication between IT and business stakeholders and enable improved data stewardship. As part of this effort, XTIVIA engaged with the client to:

• Create Data Architecture Principles
• Reverse engineered data models from different applications and created baseline data models at the application level
• Create conceptual data models for other subject areas
• Develop Enterprise Logical Data Model for different subject areas taking the current and future needs into consideration
• Review the conceptual and logical models with IT and business stakeholders
• Create business definitions for all the entities and attributes with documented entity and attribute naming conventions
• Create a CRUD matrix of applications, related data assets, subject areas, and responsible data stewards
• Create an information catalog in Excel (to be input into a Data Governance tool) with

BUSINESS RESULT

XTIVIA’s customer realized many benefits as a result of this project, including:

• An Enterprise Data Model, which, among other things, improved communication between IT and business stakeholders
• Provided the basis for creating semantic models for different needs, and drove convention uniformity across applications
• The availability of a canonical model for enterprise data and application integration
• Made data available for input into a Metadata Management tool
• Clarity on the data lifecycle for different subject areas

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