Ford Data Model of the Future
Situation
Managing information architecture for a website like Ford.com is a tall order. The data modeling for such a large platform, involves balancing performance, security, scalability, and adaptability. Ford seeks to provide a seamless experience for diverse audiences in the public car-buying market and existing Ford customers as well as internal and external users at the Ford Motor Company and its contractors.
Task
I led a small team of content architects and marketing tech workflow specialists to create the Ford Data Model of the Future with accompanying user workflows to manage data from a variety of sources and publish them to the web platform.
Key Requirements
Operate at Scale and Volume: Catering to a vast audience and serving an immense amount of content at scale requires robust data models that allow for efficient intake, organization, storage, and retrieval.
Handle Complex Integration: Data comes from a variety of sources such as vehicle specifications, user profiles, dealer inventory, service records, and more. Incorporating diverse data streams into a single cohesive model is complex, requiring understanding data relationships, controlling consistency, and avoiding redundancies.
Provide a Path for Graceful Schema Evolution: As Ford Motors evolves, so do its data requirements. Adding new vehicles and features or modifying existing ones necessitates adjusting the data model. Backward compatibility has to be ensured while accommodating schema changes.
Anticipate Complex Query Responses: Serve diverse users: comparison shoppers, dealer locators, financing calculators, and more. Data modeling should use polymorphic relationships to handle complex queries efficiently across different use cases.
Establish Content Governance: Define ownership of sources, create data standards, and ensure data quality.
Action
Create a Master Data Model
Define data relationships by grouping base-level content with packages, offers, and associated models while adhering to the Ford Motors brand standards and style guide.
Map incoming data to model from a wide variety of sources. E.g. dealer inventory, dealer services, marketing asset libraries, engineering data, legal disclosures, schema.org, etc.
Create Tooling and documentation for use.
Create workflows within a customized Atlassian Confluence platform for data entry workflows
Develop automated lookup and population of Schema.org content
Enable export to Adobe Experience Manager (AEM) Platform
Result
Boosted business outcomes by improving content consistency across all web experiences.
Increased efficiencies by reducing duplicate data entry across systems by a factor of four.
Reduced exposure to risk from possible errors and omissions regulated content.
Estimated net savings per quarter: mid-six figures.
