Content Styling and Centralization


LyftData Science and Machine Learning 

6 months 

Leading Mobility-as-a-Service platform looking to consolidate, clarify, and centralize content for its Data Science and Machine Learning businesses onto a single-service website.  

Steyer engaged client stakeholders across business groups to assess content needs and develop a clear content model that would accommodate a large, diverse, and dynamic content set. Success hinged on a strong ownership model of the content, style, and organization to foster long-term quality and maintenance. 

To achieve these goals, we: 

  • Explored content organization along different lines, including business teams, functional use cases, and tools to find the right model 
  • Selected a model driven by use cases due to the large use overlap between specializations, helping users, authors, and owners maintain current content and make new contributions via a single source 
  • Oriented content workflows to the new model via strict classification and appropriate assignment of ownership across teams 
  • Provided teams with authoring guidance and prompts, both separately and with formatted templates that integrate with the new website 
  • Developed publication guides, including directions on preparation and markup using Markdown, for multiple publication methods to GitHub 
  • Coached contributors in using the above model to both edit and refresh existing content while creating new content  
  • Provided original content as needed  
  • Contributed to developing the on-going maintenance strategy of the website