Is it just us, or are data products becoming all the rage? Is Google Trends a data product that could help us answer that question? What actually IS a data product? And does it even matter that we have a good definition? If any of these questions seem like they have cut and dried answers, then this episode may just convince you that you haven’t thought about them hard enough! After all, what is more on-brand for a group of analysts than being thrown a question that seems simple only to dig in to realize that it is more complicated than it appears at first blush? On this episode, Eric Sandosham returned as a guest inspired by a Medium post he wrote a while back so we could all dive into the topic and see what we could figure out!

Articles and Other Interesting Items Mentioned in the Show

• (Article) Reasoning models don’t always say what they think


• (Article) China Says No to AI Hype by Ignacio de Gregorio


• Snowflake Cortex AI


• (Podcast) The Third Party Show (Featuring Tim!)  from Josh Silverbauer


• (Data Viz) Based on a True True Story?


• (Film) Widow Clicquot


• (Article) Pivoting to Data Products by Eric Sandosham

Photo by eduard on Unsplash

Episode Transcript

0:00:05 .8 Announcer : Welcome to the Analytics Power Hour. Analytics topics covered conversationally and sometimes with explicit language.

0:00:16 .2 Tim Wilson: Hi, everyone. Welcome to the Analytics Power Hour. This is episode number 273, and I’m your host, Tim Wilson. You know, we occasionally get asked how we decide which hosts are going to be on any given episode, and our process for that has varied over the years. One of the considerations is ensuring that we’re keeping kind of a reasonably balanced rotation of different co-host combinations. There are various other considerations, more than you’d think. We’re not going to go into it. That sort of prevent us from having a strictly formulaic process. But for the rotation tracking, which co-hosts are on at any given episode, I built a chart that automatically updates, that visualizes the frequency of different combinations of co-hosts. It dynamically updates, but it’s all within this highly sophisticated platform called Google Sheets. It’s kind of a unique chart. It’s a derivation of what’s called an upset chart, which I’d never heard of until I was trying to figure out what visualization to use, but it turned out to be pretty useful. So does that mean that I built a data product?

0:01:22 .4 Tim Wilson: I mean, it’s just one chart, but maybe I did. Maybe I’ll know one way or the other by the end of this episode, because the topic of this show is just that, data products. I’m joined by a couple of co-hosts. Val Kroll, what’s your initial reaction? Can a single chart be a data product?

0:01:41 .2 Val Kroll: I cannot wait to get into this because old Val would have said, no, it’s just a chart. But I feel like we’re going to challenge that on this one. So maybe it is.

0:01:52 .9 Tim Wilson: Ooh, the perfect ambiguous, ambivalent answer. And we’re also joined by Moe Kiss from Canva. Moe, what say you? Can a single chart be a data product?

0:02:03 .5 Moe Kiss: Well, also, I’ve been having this exact discussion with my team over the last week. So I don’t think I have a strong opinion yet, but let’s see where I land.

0:02:13 .7 Tim Wilson: All right. Well, by the end of this show, I’m expecting us all to have a definitive answer one way or the other. Otherwise, we really whiffed on our guests. But I know we didn’t because we have had Eric Sandosham, founder and partner at Red & White Consulting Partners on the show before. Way back on episode number 254, he was on for a lively discussion about benchmarks. And it turns out that it was Moe, Val, and me as the co-host for that episode, too. In addition to his consulting work, Eric is on the adjunct faculty at Nanyang Technological University, Singapore Management University, and the Wealth Management Institute. We’re actually capturing him right mid two-day session with the Singapore Management University. So he has been talking a lot yesterday and he’ll be talking a lot tomorrow. He was previously the Customer Intelligence Practice Lead for North Asia for SAS. And before that, was the managing director, head of decision management at Asia Pacific Consumer Bank at Citibank Singapore. And he’s now well into year two of publishing a weekly article on Medium about data and analytics. I look forward to reading each installment when it lands in my inbox every Saturday.

0:03:27 .4 Tim Wilson: But one of those recent articles was about, shocker, data products. We’re excited to have him back on the show. So welcome, Eric.

0:03:35 .7 Eric Sandosham: Thank you. Thank you for having me back. Yeah, it was a real pleasure.

0:03:40 .4 Tim Wilson: We’ll also say that it is early morning for Eric as we’re recording this. So he said that was fine, but I’m wondering if that was… It sounded fine when we were scheduling it. And now if he’s second guessing that decision.

0:03:54 .7 Eric Sandosham: Yeah.

0:03:55 .9 Val Kroll: He’ll also be here for some hot takes.

0:03:56 .9 Tim Wilson: Yeah. So Eric, in that Medium post that I just referenced, you started off by trying to put together kind of a reasonably tight and clear definition of what a data product is. We already covered that maybe we’re not all that clear. So maybe we can start there. Like, where you landed, maybe what you found problematic in some of the other definitions that you found.

0:04:22 .0 Eric Sandosham: The article actually started in a casual conversation with a in-house data lake person. And even before, even during my time in Citi, and from an infrastructure perspective, they would say, how can we up our game? How can we provide more value, more utility? Can we create products? And I suppose it also fits into thinking about this space in IT and data warehouse. If you think about the role in Amazon, for example, in AWS, they call themselves engineers. So they are making stuff as opposed to my time when I grew up, IT was IT. We didn’t refer to them as engineers. And so nothing wrong with that. So it got me thinking, do you really have products if you’re part of the infrastructure or your role is sort of managing data and all of that? And even then, if you flip it to the front end, if you’re a data scientist, are you truly making products or doing analysis? I mean, if you’re building a predictive model, is that a product? And then, it sort of got me into a little bit of a rabbit hole. And I realized at the very core of it is this lack of clarity between what would seem as a product versus a feature, even in the physical world, actually.

0:05:47 .9 Eric Sandosham: Maybe less so in the physical world because you can touch and feel something. It’s a chair. But if I took the chair and I had to replace the legs, for example, and it’s not bespoke, right? And it’s sort of part of a modularized kind of chair. Would that be a product? So like a chair that could be swapped out and swapped in, would that be a product? I