020 - How Human-Centered Design Increases Engagement with Data Science Initiatives

Ahmer Inam considers himself an evangelist of data science who’s been “doing data science since before it was called data science. With more than 20 years of leadership experience in the data science and analytics field at companies including Nike and Cambia health, Ahmer knows a thing or two about what makes data science projects succeed—and what makes them fail.

In today’s episode, Ahmer and I discuss his experiences using design thinking and his “human-centered AI” process to ensure that internal analytics and data science initiatives actually produce usable, useful outputs that turn into business value. Much of this was formed while Ahmer was a Senior Director and Head of Advanced Analytics at Nike, a company that is known as a design-mature organization. We covered:

• The role of empathy in data science teams and how it helps data people connect with non-technical users who may not welcome “yet another IT tool”
• Ahmer’s thoughts on Lean Coaching, Scrum Teams, and getting outside help to accelerate the design and creation of your first data products and predictive models
• The role of change management in the process of moving data products into production
• Ahmer’s two-week process to kick-start data product initiatives used at Nike
• How model accuracy isn’t as important early on as other success metrics when prototyping solutions with customers

Resources and Links

How Analytics Are Informing Change At Nike

LinkedIn

Quotes from Today’s Episode

“Build data products with the people, for the people…and bring a sense of vulnerability to the table.” — Ahmer

“What I have seen is that a lot of times we can build models, we can bring the best of the technologies on optimal technology it’s in the platforms, but in the end, if the business process and the people are not ready to take it and use it, that’s where it fails.” — Ahmer

“If we don’t understand people in the process, essentially, the adoption is not going to work. In the end, when it comes to a lot of these data science exercises or projects or development of data products, we have to really think about it as a change management exercise and nothing short of that.” — Ahmer

“Putting humans at the center of these initiatives drives better value and it actually makes sure that these tools and data products that we’re making actually get used, which is what ultimately is going to determine whether or not there’s any business value—because the data itself doesn’t have any value until it’s acted upon.” — Brian

“One of these that’s been stuck in my ear like an earworm is that a lot of the models fail to get to production still. And so this is the ongoing theme of basically large analytics projects, whether you call it big data analytics or AI, it’s the same thing. We’re throwing a lot of money at these problems, and we’re still creating poor solutions that end up not doing anything.” — Brian

“I think the really important point here is that early on with these initiatives, it’s important to figure out, What is going to stop this person from potentially engaging with my service?” — Brian