Next DPLC Live Webinar & Discussion @ 1pm ET on Feb 27, 2024
The Data Product Leadership Community is excited to host Karen Meppen of Hakkoda and DPLC founding member to discuss Immature Data, and Immature Clients: Are Data Products the Right Approach? We'll hear from Karen and then participate in an open dialog. Like all DPLC live sessions, it's fully recorded and transcribed too, and you can keep the chat going in our 24-7 Slack. For members only.
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What can McDonald’s teach you about prototyping?

As both a musician, and a product designer, I loved a scene in the recent movie, The Founder.  This film discusses the rise of McDonalds restaurants, and how the restaurant focused on its design and operations to enable speedy service to customers.

In this scene, the restauranteurs chalked (designed) a speed-optimized layout for a kitchen on a tennis court, with real employees literally walking through order/cook/delivery scenarios to help inform the ideal locations for food stations, fryers, refrigerators, cash registers, sinks, etc. It's design prototyping in action!  They interacted (actively) with real kitchen workers, and they measured the design against success criteria (that being primarily speed in their case). The owners didn't waste money and time building a new kitchen day 1, only to learn the sink or fryer was in the wrong place.

You can design and prototype like McDonalds did with your data or analytics product too. Or, on just a single feature.

Before you invest tons of money into engineering, data science, architecture, data collectors, and all the other plumbing, get your design in front of some people, test it with some pass/fail criteria, and reduce the risk of launching a poor first iteration. The MVP mindset doesn't mean you have to, or should, take a WAG (wild-ass guess) and pray for results.  That's just a waste of money and time. While I value the "just ship" mentality and listening for customer feedback after launch, you have to remember that not all of your customers are going to give you useful feedback, or feedback at all. It usually requires interpretation. When they do give you input, they're likely to share symptoms with you that do not directly identify the real problem you may have. They might say, "it's crowded by the fryer." You typically have to actively observe users to get to the real problems that need design changes ("the refrigerator location and door swing is what is causing personnel backups near the fryer.") Customers aren't going to typically offer the latter, nor are they trained to do so.

Watch the tennis court scene here:

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