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If you’re a leader tasked with generating business and org. value through ML/AI and analytics, you’ve probably struggled with low user adoption. Making the tech gets easier, but getting users to use, and buyers to buy, remains difficult—but you’ve heard a ”data product” approach can help. Can it? 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 designer’s perspective on why creating ML and analytics outputs isn’t enough to create business and UX outcomes. How can UX design and product management help you create innovative ML/AI and analytical data products? What exactly are data products—and how can data product management help you increase user adoption of ML/analytics—so that stakeholders can finally see the business value of your data? Every 2 weeks, I answer these questions via solo episodes and interviews with innovative chief data officers, data product management leaders, and top UX professionals. Hashtag: #ExperiencingData. PODCAST HOMEPAGE: Get 1-page summaries, text transcripts, and join my Insights mailing list: https://designingforanalytics.com/ed ABOUT THE HOST, BRIAN T. O’NEILL: https://designingforanalytics.com/bio/
Episodes
Tuesday Aug 25, 2020
Tuesday Aug 25, 2020
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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