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Accessibility Analytics – Being Data Driven With VPATs

Date & Time
Thursday, March 16, 2023 - 9:20 AM PST  
Location
Platinum 7-8  
Description
Historically, VPATs or (Accessibility Conformance Reports) have been the way in which to measure and document compliance with a number of standards including (WCAG, US 508, and EN 301 549). Companies which produce many products often produce many VPATs to help meet the needs of customers and internal stakeholders. Within VPATs, there is a wealth of data generated that, if fully leveraged, can provide insights into a company‚Äôs accessibility estate. Data can be derived from VPATs such as the distribution of checkpoints that are fully, partially, and not supported. This data can be mined, aggregated, and sliced in ways to help provide larger insights. Elsevier has a large number of products, and we used this data analytics approach to compare and rank using data from dozens of accessibility evaluations. We will share the story of why this became necessary for us, and the eventual decisions that led to our current iteration. We will also go over the benefits of ranking products against each other and how it breeds competitiveness, especially in an organization that takes accessibility seriously. We will also share the success stories of how this comparison has driven accessibility remediation efforts across numerous products. We will also go over the limitations of these analytics and any current issues they still have.  
Audience
  • Higher Education
  • Information & Communications Technology
  • Media & Publishing
Audience Level
Intermediate  
Session Summary (Abstract)
Elsevier will share our approach to managing VPAT data and analytics across dozens of products. We will share our ranking and graphing capabilities, which can help drive accessibility remediation efforts.  
Primary Topic
Digital Accessibility  
Secondary Topics
  • Law, Compliance, and Policy
  • Leadership & Strategy
  • Web
 
Session Type
General Track  

Presenters

  • Jay Nemchik
    Elsevier
  • Ted Gies
    Elsevier

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