046 - How Steelcase’s Data Science, UX, & Product Teams Are Helping Customers Design Safer Office Workplaces Informed by Covid-19 Recommendations w/ J...

When you think of Steelcase, their office furniture probably comes to mind. However, Steelcase is much more than just a manufacturer of office equipment. They enable their customers (workplace/workspace designers) to help those designers’ clients create useful, effective, workplaces and offices that are also safe and compliant. Jorge Lozano is a data science manager at Steelcase and recently participated as a practitioner and guest on an IIA webinar I gave about product design and management being the missing links in many data science and analytics initiatives. I was curious to dig deeper with Jorge about how Steelcase is enabling its customers to adjust workspaces to account for public health guidelines around COVID-19 and employees returning to their physical offices. The data science team was trying to make it easy for its design customers to understand health guidelines around seat density, employee proximity and other relevant metrics so that any workspace designs  could be “checked” against public health guidelines. Figuring out the what, when, and how to present these health guidelines in a digital experience was a journey that Jorge was willing to share. We covered: Why the company was struggling to understand how their [office] products came together, and how the data science group tried to help answer this. The digital experience Steelcase is working on to re-shape offices for safe post-pandemic use. How Steelcase is evaluating whether their health and safety recommendations were in fact safe, and making a difference. How Jorge’s team transitioned from delivering “static data science” outputs into providing an enabling capability to the business. What Steelcase did to help dealer designers when engaging with customers, in order to help them explain the health risks associated with their current office layouts and plans. What it was like for Jorge’s team to work with a product manager and UX designer, and how it improved the process  of making the workspace health guidelines useful.   Resources and Links: Steelcase: https://www.steelcase.com/ LinkedIn: https://www.linkedin.com/in/jorge-lozano-flores/   Quotes from Today’s Episode “We really pride ourselves in research-based design” - Jorge “This [source data from design software] really enabled us to make very specific metrics to understand the current state of the North American office.” - Jorge “Using the data that we collected, we came up with samples of workstations that are representative of what our customers are more likely to have. We retrofitted them, and then we put the retrofitted desk in the lab that basically simulates the sneeze of a person, or somebody coughing, or somebody kind of spitting a little bit while they're talking, and all of that. And we're collecting some really amazing insights that can quantify the extent to which certain retrofits work in disease transmission.” - Jorge “I think one of the challenges is that, especially when you're dealing with a software design solution that involves probabilities, someone has to be the line-drawer.” - Brian “The challenge right now is how to set up a system where we can swarm at things faster, where we're more efficient at understanding the needs and [are able to get] it in the hands of the right people to make those important decisions fast? It's all pointing towards data science as an enabling capability. It's a team sport.” - Jorge  

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Are you an enterprise data or product leader seeking to increase the user adoption and business value of your ML/AI and analytical data products? While it is easier than ever to create ML and analytics from a technology perspective, do you find that getting users to use, buyers to buy, and stakeholders to make informed decisions with data remains challenging? If you lead an enterprise data team, have you heard that a ”data product” approach can help—but you’re not sure what that means, or whether software product management and UX design principles can really change consumption of ML and analytics? My name is Brian T. O’Neill, and on Experiencing Data—one of the top 2% of podcasts in the world—I offer you a consulting product designer’s perspective on why simply creating ML models and analytics dashboards aren’t sufficient to routinely produce outcomes for your users, customers, and stakeholders. My goal is to help you design more useful, usable, and delightful data products by better understanding your users, customers, and business sponsor’s needs. After all, you can’t produce business value with data if the humans in the loop can’t or won’t use your solutions. Every 2 weeks, I release solo episodes and interviews with chief data officers, data product management leaders, and top UX design and research professionals working at the intersection of ML/AI, analytics, design and product—and now, I’m inviting you to join the #ExperiencingData listenership. Transcripts, 1-page summaries and quotes available at: https://designingforanalytics.com/ed ABOUT THE HOST Brian T. O’Neill is the Founder and Principal of Designing for Analytics, an independent consultancy helping technology leaders turn their data into valuable data products. He is also the founder of The Data Product Leadership Community. For over 25 years, he has worked with companies including DellEMC, Tripadvisor, Fidelity, NetApp, Roche, Abbvie, and several SAAS startups. He has spoken internationally, giving talks at O’Reilly Strata, Enterprise Data World, the International Institute for Analytics Symposium, Predictive Analytics World, and Boston College. Brian also hosts the highly-rated podcast Experiencing Data, advises students in MIT’s Sandbox Innovation Fund and has been published by O’Reilly Media. He is also a professional percussionist who has backed up artists like The Who and Donna Summer, and he’s graced the stages of Carnegie Hall and The Kennedy Center. Subscribe to Brian’s Insights mailing list at https://designingforanalytics.com/list.