Boeing OSHA Dashboard

Boeing’s production line was busier than ever in 2013, and with union issues and the pressure to match delivery timelines, workplace injuries were on the rise. As a result, a lot of injured workers were off the lines and it took time to get new workers on-boarded. They needed a deep analysis of why these injuries were happening and how they could be resolved.

ClientBoeingServicesEnvironment Health and SafetyYear2013


  • Injury data existed in Excel spreadsheets and couldn’t be read easily
  • Solutions were reactive rather than preventive
  • The real reason for recurring injuries was difficult to learn
  • Excel was difficult to visualize data easily. We needed a better tool to visualize over 80 years of data related to industrial accidents


  • We started off by creating an Ishikawa diagram showing the root cause analysis of accidents
  • By leveraging Tableau’s leading analytics platform, it was easier for us to show production line supervisors injury metrics linked to various scenarios
  • A visual dashboard allowed us to design data according to a narrative that could communicate the information better

Data Designer

I was the data designer for Boeing EHS and employed the following methods:

  • Worked across five engineering teams to understand the complexity of the production line, and established faster analytical reporting while cultivating a healthier and more productive partnership between engineering and data design.
  • Led all levels of data design review with engineering and Boeing EHS leadership.
  • Designed and executed all user research activities including interviews, validation testing, longitudinal contextual walkthroughs.

Tableau, Microsoft Excel, Omnigraffle


We need to go through the incident reports and visualize as to why the injuries were recurring even after fixes were being made according to OSHA regulations. We decided to use Ishikawa diagrams to assimilate the findings of our walkthroughs and interviews. They are causal diagrams  that show the causes of a specific event.

Privacy Preference Center