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Come and join Alcine and Shane as they visit with British Columbia educators John Harris and Denise Augustine. We begin in the realm of story as Denise describes being situated “between generations” in her renowned Coast Salish family of carvers, artists, and leaders and John shares his experiences of growing up on the land and watching his father negotiate treaties as the official liaison for their community. Drawing on her legacy as the Superintendent of Indigenous Education for British Columbia, Denise provides powerful historical context for the work of the Truth and Reconciliation Commission in Canada, which created space for residential school survivors to tell their stories and led to 94 distinct “calls to action” in 2015. She pulls this thread into the fabric of educational change, illuminating how BC is leading the way in reconciliation through a Tripartide Education Agreement and the more recent Declaration of the Right of Indigenous Peoples Act (DRIPA), which requires that school districts create Indigenous Education Councils that view First Nations as “governing bodies”, not just “special interest groups.” From this exploration of reconciliation in education, John takes us into his own family’s legacy of the “Sixties Scoop”, in which his father was taken from his grandparent’s home nearly a dozen times, all the way to his family’s recent visit to the Field Museum of Chicago, which holds over 4,000,000 cultural artifacts, many of which were purchased from Indigenous Nations in the Pacific Northwest. John describes the unsettling experience “as if someone went into your house and took everything.” They end their visit discussing the nuances of place-based versus land-based education and the ways that John has woven his upbringing and community cultural wealth into his pedagogy, which is depicted in the integrative case study which concludes Shane’s forthcoming book, Pedagogies of Voice: Street Data and the Path to Student Agency (Corwin, 2025). Speaking to student agency, John reminds us that “When we give youth opportunities to give back to their communities, they really shine.” Join us for this incredible and luminous conversation reinforcing relationality and reciprocity as core values from Indigenous knowledge systems that hold the potential to transform education everywhere.

For Further Learning:

Learn more about John and his family’s artwork and clothing line at www.aylelum.com Learn more about indigenous ways of knowing and being by reading

Indigenous Storywork: Educating the Heart, Mind, Body, and Spirit by Jo-Ann Archibald Land as teacher: understanding Indigenous land-based education - UNESCO Canadian Commission June 21, 2021

See land-based education in action by following Land-based Education K-12 Plains & Woodland Cree Tanya McCallum on Facebook Learn more about the work of the First Nations Education Steering Committee in British Columbia, Canada Read up on the The United Nations Declaration of the Rights of Indigenous People Act

Today I’m chatting with returning guest Tom Davenport, who is a Distinguished Professor at Babson College, a Visiting Professor at Oxford, a Research Fellow at MIT, and a Senior Advisor to Deloitte’s AI practice. He is also the author of three new books (!) on AI and in this episode, we’re discussing the role of product orientation in enterprise data science teams, the skills required, what he’s seeing in the wild in terms of teams adopting this approach, and the value it can create. Back in episode 26, Tom was a guest on my show and he gave the data science/analytics industry an approximate “2 out of 10” rating in terms of its ability to generate value with data. So, naturally, I asked him for an update on that rating, and he kindly obliged. How are you all doing? Listen in to find out!

Highlights / Skip to:

Tom provides an updated rating (between 1-10) as to how well he thinks data science and analytics teams are doing these days at creating economic value (00:44) Why Tom believes that “motivation is not enough for data science work” (03:06) Tom provides his definition of what data products are and some opinions on other industry definitions (04:22) How Tom views the rise of taking a product approach to data roles and why data products must be tied to value (07:55) Tom explains why he feels top down executive support is needed to drive a product orientation (11:51) Brian and Tom discuss how they feel companies should prioritize true data products versus more informal AI efforts (16:26) The trends Tom sees in the companies and teams that are implementing a data product orientation (19:18) Brian and Tom discuss the models they typically see for data teams and their key components (23:18) Tom explains the value and necessity of data product management (34:49) Tom describes his three new books (39:00)

Quotes from Today’s Episode “Data science in general, I think has been focused heavily on motivation to fit lines and curves to data points, and that particular motivation certainly isn’t enough in that even if you create a good model that fits the data, it doesn’t mean at all that is going to produce any economic value.” – Tom Davenport  (03:05)

“If data scientists don’t worry about deployment, then they’re not going to be in their jobs for terribly long because they’re not providing any value to their organizations.” – Tom Davenport (13:25)

“Product also means you got to market this thing if it’s going to be successful. You just can’t assume because it’s a brilliant algorithm with capturing a lot of area under the curve that it’s somehow going to be great for your company.” – Tom Davenport (19:04)

“[PM is] a hard thing, even for people in non-technical roles, because product management has always been a sort of ‘minister without portfolio’ sort of job, and you know, influence without formal authority, where you are responsible for a lot of things happening, but the people don’t report to you, generally.” – Tom Davenport (22:03)

“This collaboration between a human being making a decision and an AI system that might in some cases come up with a different decision but can’t explain itself, that’s a really tough thing to do [well].” – Tom Davenport (28:04)

“This idea that we’re going to use externally-sourced systems for ML is not likely to succeed in many cases because, you know, those vendors didn’t work closely with everybody in your organization” – Tom Davenport (30:21)

“I think it’s unlikely that [organizational gaps] are going to be successfully addressed by merging everybody together in one organization. I think that’s what product managers do is they try to address those gaps in the organization and develop a process that makes coordination at least possible, if not true, all the time.” – Tom Davenport (36:49)

Links Tom’s LinkedIn: https://www.linkedin.com/in/davenporttom/ Tom’s Twitter: https://twitter.com/tdav All-in On AI by Thomas Davenport & Nitin Mittal, 2023 Working With AI by Thomas Davenport & Stephen Miller, 2022 Advanced Introduction to AI in Healthcare by Thomas Davenport, John Glaser, & Elizabeth Gardner, 2022 Competing On Analytics by Thomas Davenport & Jeanne G. Harris, 2007