Data Strategy and Infonomics with Doug Laney – Episode 52

Data Leadership Lessons
Data Leadership Lessons
Data Strategy and Infonomics with Doug Laney - Episode 52
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This week on Data Leadership Lessons, we welcome Doug Laney, the author of the best-selling book, “Infonomics: How to Monetize, Manage, and Measure Information for Competitive Advantage.” This is a fascinating episode for executives and data nerds alike! Enjoy!

Watch this episode on YouTube: https://youtu.be/km8E5DCBexI

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About Doug Laney:

Douglas Laney is the data & analytics strategy innovation fellow with the consultancy, West Monroe. Formerly he was a vice president and distinguished analyst with Gartner’s Chief Data Officer (CDO) research and advisory practice. He is an accomplished practitioner and recognized authority on data and analytics strategy, and is a three-time recipient of Gartner’s annual Thought Leadership Award, and is regularly considered one of the top global influencers these topics. Mr. Laney specializes in and assists organizations with data monetization and valuation, open and syndicated data, data governance, and big-data based innovation. In 2001 he coined the “3Vs” of volume, velocity and variety, now commonly used in defining Big Data.

Nearly two decades ago, Mr. Laney originated the field of Infonomics, developing methods to quantify information’s economic value and apply asset management practices to information assets. He authored the book “Infonomics: Monetizing, Managing and Measuring Information as a Competitive Advantage,” and lectures at leading business schools on the topic. In addition to his dozens of Gartner research publications and blogs, Mr. Laney is a contributing author with Forbes and Information Management Magazine, and has been published in the Wall Street Journal and the Financial Times. He also edited and co-authored Forbes’ e-book on Big Data.

Prior to rejoining Gartner, Mr. Laney helped form and lead the Deloitte Analytics Institute, a global initiative to grow Deloitte’s multi-billion-dollar analytics business through marketing, social media, thought leadership, internal education, sales support, and recruiting. Today, Mr. Laney is also a visiting professor at the University of Illinois Gies School of Business and the Carnegie Mellon Heinz College of Business where he teaches graduate-level courses on analytics and infonomics, also available via Coursera.

Doug Laney at West Monroe – https://www.westmonroe.com/our-team/doug-laney

Infonomics Book – https://www.amazon.com/Infonomics-Monetize-Information-Competitive-Advantage/dp/1138090387

Episode Transcript

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anthony_algmin: Shoot, thirty five, forty five minutes. Um, yeah, I think you do everything.

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anthony_algmin: and uh,

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anthony_algmin: we will just take it from there. All right. Any other questions before we

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anthony_algmin: kicked us off A real.

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anthony_algmin: All right.

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anthony_algmin: here we go.

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anthony_algmin: Welcome to the data leadership lesson. Let’s try that again.

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anthony_algmin: Welcome to the Data Leadership Lessons podcast. I’m your host. Anthony. ▁j,

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anthony_algmin: Algmman Da is everywhere in our businesses and it takes leadership to make

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anthony_algmin: the most of it. We bring you the people, stories and lessons to help you

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anthony_algmin: become a data leader. Our show is produced by Algwman Business Media, where

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anthony_algmin: we make having your own video podcast as easy as joining a video call and

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anthony_algmin: sending an email at Algond Business Media. The stage is yours to day on data

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anthony_algmin: leadership lessons, welcome, Dug, lady, Doug is a well known thought leader

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anthony_algmin: on data and analytic strategy. He is the originator of the field of

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anthony_algmin: infomics, and is the author of the best selling book Infoms, How to

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anthony_algmin: monetize, manage and measure information for competive competitive

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anthony_algmin: advantage. Doug is the data and analytic strategy innovation fellow with the

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anthony_algmin: consultancy West Monroe, Doug. welcome to the show. It’s been a long time

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anthony_algmin: Com in

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doug_laney: I, Anthony, great to be with you.

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anthony_algmin: so like we do with all of our first time guesss. Would you please take a

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anthony_algmin: moment to tell the audience a bit more about your career and and how it led

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anthony_algmin: to the creation of infomics and everything else that you’re do these days?

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anthony_algmin: Um, and then we’ll We’ll certainly get into a whole bunch more about

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anthony_algmin: infomics and and all the details as we continue through the conversation.

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doug_laney: Yeah, I actually started my career at Arthur Anderson back in the day before. It

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doug_laney: was Uh, even before was Anderson consulting, and you know, of course before

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doug_laney: ecenture, and uh now I’m with West Monroe, which kind of feels like coming home

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doug_laney: because Uh, West Monroe is um formed by some former Arthur Anderson consultants.

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doug_laney: so Um, great form, very happy to be very happy to be here, Really innovative.

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doug_laney: And and that’s you know, one of the reasons I joined is Uh, Westmoro, wase a

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doug_laney: great great platform for doing some really innovative things around around data

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doug_laney: and analytics. Um. So I spent a you know number of years at At At Arthur

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doug_laney: Anderson, Uh, Anderson, consulting, and then got into the software industry, Uh,

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doug_laney: A a number of years and and some of the early Uh, pioneering expert systems,

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doug_laney: artificial intelligence, Uh type companies where I did some um, uh knowledge

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doug_laney: work there, and then Uh got pulled into data warehousing, which I thought was

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doug_laney: kind of boring, but uh it ended up finding it to be a pretty pretty exciting

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doug_laney: field, Uh, building, um large. Well, at the time large, we would call them large

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doug_laney: today, they

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anthony_algmin: Have, and

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doug_laney: warehouses for Uh, for for major companies, I got a chance to uh, to move to

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doug_laney: Australia with one of the companies I was with and and run the Asia Pacific

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doug_laney: business there. Um, while I was there, I started doing some more speaking and

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doug_laney: writing, and uh, at one of the events, Uh, I think at a Dc. I conference if you

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doug_laney: remember those. Uh, Somebody came up to me after I spoke and he said, Hey, did

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doug_laney: you come up with those slides yourself and I said Well, y. yeah. Why I was

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doug_laney: talking about datawehousing methodology. Um, and uh, he said, Do you wantnna

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doug_laney: join a Meta group, which was a spin off of Gardener at the time, which got

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doug_laney: reabsorbed by Gardener. Later on, I said Yeah, that sounds cool. So onm, moving

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doug_laney: back to the U. S. I ended up joining Uh, Meta group and I spent Uh, the better

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doug_laney: part of twelve years at Um, at Uh, Metta and and Gardnener as a research analyst

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doug_laney: and adviser. Uh, doing a lot of speaking and writing, And then the the second

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doug_laney: time I, I, I joined Gardener, I said Listen. I’ve been working on this idea

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doug_laney: about data as an actual corporate asset, Um. and they said Well, Well, what do

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doug_laney: you mean? I said Well, back at Metta Group, Um was during the Nine Eleven terror

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doug_laney: attacks and

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doug_laney: some clients, Um. I, in the twin towers, you know, lost their data. They lost

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doug_laney: their their, uh. uh. They didn’t know who their customers were anymore. You

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doug_laney: know, the data was all on site. Uh, you know, onram, and without offse backups.

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doug_laney: And so so these companies didn’t know who their customers were. They didn’t know

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doug_laney: who their employees were. They lost their contracts. They lost. It became a real

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doug_laney: existential uh event for them and not to be callous, but you know they. they

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doug_laney: told me they said. You know, you know, we. we. We definitely mourn. Obviously

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doug_laney: more in the the loss of of some of our employees, But um, you, we hire more

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doug_laney: employees. We just can’t for get our data back. It’s gone.

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anthony_algmin: Mhm.

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doug_laney: And so what um, they did was submit claims to their insurers for the value of

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doug_laney: the data they lost, and the insurers said You know. Sorry, we don’t consider

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doug_laney: data to be property. Therefore’, not going to cover it as part of your your your

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doug_laney: P and C, uh policies, So that really got my attention and I got to thinking

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doug_laney: about. Well, isn’t Da a property? Isn’t it an asset? Um? Why not? I help some of

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doug_laney: the businesses in the Twin Towers value their data so that they could kind of

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doug_laney: determine what their their loss was? Um. And they got to thinking more about.

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doug_laney: You know if data is an asset, then you know Why isn’t on on the balance sheet?

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doug_laney: And uh. And and what does that mean to organizations? because they don’t measure

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doug_laney: data as an actual asset? Does that? A? Is that is that really why so many

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doug_laney: companies don’t really manage their data with the same kind of discipline as the

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doug_laney: way they manage their other assets? Um. And then I said, Well, why do you manage

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doug_laney: any asset? You manage any assets so that you can generate Um value from it? You

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doug_laney: know the old adages you can’t manage what you don’t measure, Um, and that you

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doug_laney: can’t Uh. I think you know it follows that you can’t M. You can’t monetize what

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doug_laney: you’re not managing Well, And that’s really the core of of Infoms is about

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doug_laney: getting companies to measure their data so that they can get the support um

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doug_laney: budgets resources that they need to manage it like an actual asset. Uh, and then

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doug_laney: put them in a better position to generate Uh, optimal economic value from it. So

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doug_laney: that’s the Uh. The the crux of the book. Uh, Gardner supported me in writing the

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doug_laney: book and and publishing it and it was. It’s been uh, very well received. And

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doug_laney: then I got to know the folks at Westmanroe and they said, Listen, we, we want to

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doug_laney: build a practice based on you know these concepts and I said Well, I guess I’ve

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doug_laney: had enough just talking about at Gartner. Um, it’s enough to actually put it

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doug_laney: into practice And so uh, I joined Uh, Westmanerro exactly a year ago today.

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anthony_algmin: Oh, congratulations, and in full disclosure I did work for Westmann Roe for

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anthony_algmin: a period of time in the early Twent tens. And so I know that organization

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anthony_algmin: well, still have a lot of friends over there. Um. really excited to see what

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anthony_algmin: what

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doug_laney: Yeah,

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anthony_algmin: you all are doing. And and and how it’s Um. how that’s

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anthony_algmin: progressing. I remember a year ago when I had heard that Uh, Westman Road

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doug_laney: yeah, yeah, its great people, great culture.

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anthony_algmin: brought you on it. It was.

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doug_laney: Yeah,

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anthony_algmin: it was great.

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doug_laney: a great culture, great people. it’s super inclusive, Um. collaborative

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doug_laney: supportive, even egalitarian like everybody’s voice matters at

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anthony_algmin: Yeah,

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doug_laney: Westmanro it’s not. It’s not like some of the other. I won’t name names, a

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doug_laney: consulting firm that I, I’ve been with

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anthony_algmin: yeah. well, and that the energy I think is is is really um, impressive and

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anthony_algmin: and that

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anthony_algmin: fits your, um. your style very well. I, I know. so I want to talk a little

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doug_laney: Yeahks,

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anthony_algmin: bit about this. Uh. The. The. The concept fundamentally of of data is an

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anthony_algmin: asset Because

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doug_laney: Mhm,

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anthony_algmin: I think that clearly it resonates around it has you know. data clearly has

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anthony_algmin: real value. You’re You’re preaching to the choir in this audience of you

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anthony_algmin: know

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doug_laney: right,

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anthony_algmin: data as an asset. Um, you know having real value, but there’s obviously a

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doug_laney: Mhm, you must not be an accountant, Thencause, the accountants don’t agree,

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anthony_algmin: very big difference.

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anthony_algmin: Well, well, I mean, e. yeah, we. I don’t know. we have some accountants of

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anthony_algmin: the audience. I’m sure, um, uh of the show, but definitely um. have. uh. You

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anthony_algmin: know a lot of folks who who build their careers working with data, and I

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anthony_algmin: think that’s become even more prevalent. Uh, these days where most

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anthony_algmin: businesses have a substantial investment in what they’re doing with data,

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anthony_algmin: Um, because they see the value in it. And and I think that, Um, you know

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anthony_algmin: that that’s something that we can. We can treat as a as a

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doug_laney: Mhm,

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anthony_algmin: as a assumption at this point, but I think what what I’m curious about. And

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anthony_algmin: and you know I know the book you know talks about this. Some is is assets

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anthony_algmin: typically

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anthony_algmin: are something you consume and then they go away. We is with data in in many

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anthony_algmin: ways. If you use it

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anthony_algmin: and do things with it, you actually create additional value and additional

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anthony_algmin: data and it almost works backwards to the consumption of regular assets.

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anthony_algmin: Could you talk about that

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doug_laney: Mhm,

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anthony_algmin: dynamic and how that’s so unique and and interesting with the Datpase

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doug_laney: Sure you know, it’s hard. It’s hard to go a day or a week or a month without

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doug_laney: hearing somebody talk about data as being the.

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doug_laney: the, The, The new oil right, and um,

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doug_laney: uh. well, That certainly reflects an understanding appreciation that data is a

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doug_laney: driver of the economy at a macro level. The way oil perhaps was that the you

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doug_laney: know, um early in the you know or later in the Industrial revolution, but um.

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doug_laney: it. it misses the point, like you say. that data has these unique economic

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doug_laney: characteristics. That data that that oil doesn’t have you know. when you consume

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doug_laney: a drop of oil, you can only consume it one way at a time. and when you consume a

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doug_laney: drop of oil, it turns into heat energy. you know, and and pollutants. Um. and

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doug_laney: that when you consume a drop of oil, it, like you say, it doesn’t create more

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doug_laney: oil doesn’t create more you know of Um of some kind of asset. And so, Uh, data

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doug_laney: iss very unique. That way, it’s what economists would call a non rival risk. You

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doug_laney: mean you can use it multiple ways simultaneously? Non depleting doesn’t go

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anthony_algmin: Mhm?

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doug_laney: away when you use it, and A A the progenitive asset, meaning it creates more of

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doug_laney: some M. more of itself when you use it, And uh, the companies that are really

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doug_laney: thriving in the digital economy, Date economy, whatever you want to call it

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doug_laney: today, are the ones who are taking advantage of that fact. Jeff Bezos has a a

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doug_laney: Basos, has a um, a w. A term for that, he calls it, you know, a fly wheel

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doug_laney: effect,

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doug_laney: but

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doug_laney: so yeah,

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anthony_algmin: So so as

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anthony_algmin: people understand this kind of reinforcing behavior that that

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anthony_algmin: data has

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doug_laney: Mhm,

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anthony_algmin: is that relevant to the work we do with data? Or doesn it even matter Is is

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anthony_algmin: data work to quickly influenced by this economic function? And should

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doug_laney: mhmm.

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anthony_algmin: we be paying more attention to that than maybe our data administrators and

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anthony_algmin: and data analysts typically do

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doug_laney: Yeah, I think that’s where the measurement part come comes in. Um. according to

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doug_laney: your current accounting standards, Um. you can no longer capitalize or or

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doug_laney: recognize the value of your company’s data on your balance sheet. Uh, so that

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doug_laney: puts a lot of companies into a bit of a quandary, Okay, we’ve got this this

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doug_laney: thing recalling an asset, but it, according to accounts, it’s not really an

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doug_laney: asset and therefore you know H. How do we get the budgets and resources that and

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doug_laney: support that we need to actually treat it like an asset, and interestingly after

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doug_laney: nine eleven,

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doug_laney: Um, the Uh insurance industry realized it was a bit exposed, and uh. it updated

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doug_laney: the commercial general liability policy template used by all insurers for their

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doug_laney: their Uh, the Uh. A and they, so they updated the template to explicitly exclude

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doug_laney: Um data from P and C. policies. They did that a month after nine eleven,

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doug_laney: and then not to be outdone. the accountants said, Oh, if uh, the insurance

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doug_laney: industry isn’t going to recognize it as an asset and the courts are confused on

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doug_laney: the matter. I’ll talk about that in a moment. then, um. we can’t recognize it as

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doug_laney: A as an asset, So they updated a key Fin, financial standard, Um, f, a s, or I,

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doug_laney: a s. thirty eight, which deals with how to recognize intangibles, Um to to

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doug_laney: expressly say that, Um, your data can’t be capitalized. The courts, like I said,

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doug_laney: are confused on the matter. Some courts have fallen on the side of Uh. you know,

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doug_laney: W, where there have been cases related to data being misused or damaged or

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doug_laney: stolen, or Um. the courts sometimes have have issued rulings along the lines of

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doug_laney: data constituting property. Um, because it can be represented by bubbles on an

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doug_laney: optical disk or it can be printed. Other courts have said well, since electrons

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doug_laney: have negligible mass,

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doug_laney: Um, Uh, data shouldn’t be considered property. So that’s the kind of crazy

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doug_laney: confusing world we live in. And you know, if you look F to the government for

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doug_laney: some help, there really hasn’t been a lot of help other than in privacy

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doug_laney: regulations. In fact, there was a s. U. S. I wrote about this in the book The A

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doug_laney: U S. Senate Sub Committee hearing on how to evolve a nineteen thirty style

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doug_laney: accounting system to the twenty four centuries. Why? Why nineteen thiries? Well,

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doug_laney: that’s when the asset classes were defined by the S. E C coming out of the Great

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doug_laney: depression. Um to formalize you financial reporting a bit more. And at that time

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doug_laney: it was before the information age, so there was no really real reason to think

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doug_laney: about data as a separate kind of asset. It was all in books and magazines and

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doug_laney: ledgers, and so forth. So Um, you know, here we are ninety years later in the

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doug_laney: accounting profession, Um, the courts, insurance companies. The government

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doug_laney: haven’t come around to fully acknowledging data as property, or as uh, or as uh,

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doug_laney: an asset.

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anthony_algmin: well. And and what’s interesting is I think about the data management hats

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anthony_algmin: that we often

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doug_laney: Mhm,

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anthony_algmin: wear, and with data governance is that a lot of those activities tend to be

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anthony_algmin: Um.

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anthony_algmin: driven by data as a liability as much as data as an asset. Because of

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doug_laney: yeah,

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anthony_algmin: the the governor, the the G, d, P, r and C, Cp. And

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doug_laney: Mhm,

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anthony_algmin: like the privacy concerns and all that people see, Oh

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doug_laney: right,

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anthony_algmin: da is a big risk. But then it’s also an asset. Is this is this unique to

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anthony_algmin: today

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doug_laney: yeah, y,

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anthony_algmin: to two or there other assets. That that parallel that? as both, I, I think

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anthony_algmin: they, the energy, um,

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doug_laney: mhm.

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anthony_algmin: analogy can apply where you know you have a lot of

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doug_laney: Mhm,

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anthony_algmin: energy. Are you creating a battery or youre creating a bomb? you know, and

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doug_laney: Yeah, here, here’s we get into a littleyt tactic, um, I, uh, conundrum, which is

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anthony_algmin: so

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doug_laney: W. When people say liability, they often mean risk, but in an accounting sense a

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doug_laney: liability is something that you owe someone else,

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anthony_algmin: very true. Very true.

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doug_laney: so uh, okay, so if we’re mixing and matching the termal liability and asset, we

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doug_laney: should be talking about liability and En counting terminology, which is

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doug_laney: something you owe someone else. Data very rarely, if ever, something that you

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doug_laney: owe someone else, Um, But in in in Com, you know

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anthony_algmin: Yeah,

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doug_laney: liability is a risk, so Yes, data can pose a a risk, Um. Just having it poses a

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doug_laney: risk. you know, using it improperly or handling it improperly poses a risk, but

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doug_laney: not a not a balance sheet liability.

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anthony_algmin: right. right. well, I appreciate that clarificationcause.

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doug_laney: Yeah,

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anthony_algmin: I do think, especially those of us who are not as deep in accounting will

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anthony_algmin: often make that kind of linguistic slip and

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doug_laney: right,

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anthony_algmin: confuse the accountants where they think very literally, And this is. This

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anthony_algmin: is a

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anthony_algmin: consulting thing where you have words that you may use in a more colloquial

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doug_laney: y,

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anthony_algmin: way

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doug_laney: mhm.

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anthony_algmin: that people have very loaded meanings To. I once said, In an energy Me, I

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anthony_algmin: was working with an energy company and I actually made a point around how

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anthony_algmin: this thing that we wanted to do was a was a critical decision to make, and

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anthony_algmin: as soon as I said critical, their eyes lit up and they’re like. What do you

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anthony_algmin: mean critical? and I’m like, Oh,

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doug_laney: Um, yeah, yeah, yeah,

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anthony_algmin: what have I stumbled into? And it actually connoted a a critical

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anthony_algmin: infrastructure, which is a

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doug_laney: mhm,

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anthony_algmin: specialized term in the

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doug_laney: mhm.

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anthony_algmin: energy space,

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doug_laney: yep.

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anthony_algmin: And I will never forget that as long as I live, because I, It was just be

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doug_laney: right.

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anthony_algmin: me you know, doing that. And and I mentioned critical, and the entire tone

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anthony_algmin: of the room changed and I, I learned my lesson at it.

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doug_laney: yeah, so language is important and um, you know that kind of gets us to the

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doug_laney: topic of dayta literacy thing being critical as well. Um, getting people not

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doug_laney: only don’t understand you know that business terminology, but uh, the the

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doug_laney: glossery and and concepts around around data. We’ve been doing a lot of work

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doug_laney: lately, Um. setting up data literacy programs for organizations and it’s not

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doug_laney: just a matter of generic training them on. you know, B. I terminology and and

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doug_laney: stuff, What is a

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anthony_algmin: Mhm.

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doug_laney: Pichart versus a barchar versus the lineart? You know, Um, but Um, really

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doug_laney: embedding it into their organization And but it all starts with language,

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doug_laney: ensuring that that that there’s a common set of vernacular in the way that we

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doug_laney: talk about data within an organization.

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anthony_algmin: Yeah, I, it gets me thinking is as we get older and our colleagues tend to

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anthony_algmin: get younger. We, um, we know that these. Uh, the the younger generation has

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anthony_algmin: grown up with a deeper technology sophistication as consumers. Uh, versus

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anthony_algmin: older generations, there is just a natural comfort there. But what I found

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anthony_algmin: and I’m curious what your perspective is is that Just because we are

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anthony_algmin: sophisticated consumers on our personal lives or in our even our work, it

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anthony_algmin: doesn’t nessarily mean we have all of the tools necessary to be data

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anthony_algmin: literate and to be effective data creators or data stewards, as we

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anthony_algmin: have to take on these responsibilities for these data assets. Do you think

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anthony_algmin: that the way we deliver data literacy today has changed as a result of that?

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anthony_algmin: And and would you even at first agree with the um. hypotheses that I that

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anthony_algmin: I’m thrown out there,

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doug_laney: Yeah, yeah, the those first hypothesis was that you know the the digital

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doug_laney: natives, right, Um, are much more comfortable with the concept of handling and

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doug_laney: using data, and and privacy and all that. I would say. Yeah, pro prob. probably

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doug_laney: so. Um.

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doug_laney: but they’re also tend to be more bi, more casual with it. As long as you’re not

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doug_laney: doing anything bad, right, Um, then it doesn’t really matter which what you post

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doug_laney: or how you handle your personal data too much, Um.

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doug_laney: And and most digital natives have found a way to generate benefits from sharing

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doug_laney: and exposing their data right, And that could translate into the business world.

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doug_laney: I. I would. I would. I would suppose, um, Co, individuals who are comfortable

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doug_laney: with sharing their browsing habits or whatever in order to get better offers.

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doug_laney: You know that that certainly can translate into the into the business world. I

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doug_laney: mean, we even see when you go into the grocery store, you scan your your loyalty

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doug_laney: card and you get a discount for you know, on your on your groceries, right, but

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doug_laney: you know we know it’s really happening and probably I would imagine digital

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doug_laney: natives. Also, you don know. no, no, what’s what’s happening? Which is, it’s

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doug_laney: really a barter transaction you’re exchanging information of about you and your

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doug_laney: purchase for for free food for some free food? Um, we don’t call it a barter

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doug_laney: transaction ’cause that doesn’t feel good to us. Right discount feels much much

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doug_laney: better.

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anthony_algmin: right.

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doug_laney: I, and it glosses over what’s actually happening with our data. But that kind of

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doug_laney: um. that kind of scenarios happening much more in the B to B world, and the kind

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doug_laney: of thing that we at West Moro are are helping our clients with, which is to

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doug_laney: monetize their date in A in a broader um, broadn amount of ways, And that does

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doug_laney: take a degree of literacy, helping

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anthony_algmin: Mhm,

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doug_laney: them understand. What are the different patterns of data monenitization? What

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doug_laney: are the different ways that you can generate economic value from data and even

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doug_laney: you mentioned, G, d p r. Right. so g, d p R makes A and the California consumer,

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doug_laney: uh, pro, uh, uh, protection act. Um.

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doug_laney: It

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doug_laney: limits to some degree what you can do with your your customer data, but it

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doug_laney: doesn’t mean that you can’t monettize it anymore. So, when clients have come to

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doug_laney: us and said right, we can’t monetize our customer data because of these

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doug_laney: regulations, I said Well, you haven’t really thought about it creatively enough

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doug_laney: right. let’s

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anthony_algmin: Mhm,

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doug_laney: flip that model on its head. You know you can’t sell your data to others, but

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doug_laney: you can sell other stuff to your customers without ever exposing who those

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doug_laney: customers are. So I refer to that as a inverted data monitization model. So like

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doug_laney: we’re working with a hospital who knows who its diabetes patients are right.

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doug_laney: They can’t sell that data or even share that data with anyone, but it can share

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doug_laney: others, Uh, offerings and products and services like healthy meal plans or gym

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doug_laney: memberships or at home glucose monitoring test kits. They can share that with

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doug_laney: their own customers, their own new members, and and patients, Um, and take a cut

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doug_laney: of that action right without ever exposing who those people are. So,

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anthony_algmin: Yeah, Well, definitely is something that we’ve talked about on the show

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anthony_algmin: before around. There’s different layers of

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anthony_algmin: data, graularity or aggregation that start to trigger different levels of

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doug_laney: Mhm, Mhmm,

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anthony_algmin: privacy or scrutiny, or regularulator, or what have you and? and to your

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anthony_algmin: earlier point, like some of those systems are a little bit behind, Uh, to be

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anthony_algmin: generous on on where they are compared to the complexity of data, And

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anthony_algmin: that’ll lead me to another question in a moment. Uh, but it it makes me

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anthony_algmin: recall, I had a. I had another pair of headphones, Uh, that I had at that

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anthony_algmin: broke. Uh, the other day one of the earpieces just stopped working and I’m

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anthony_algmin: like Okay. Well, let me go and look where I bought that from and I went to

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anthony_algmin: the internet site that they had had, and I’m like Okay. Let me see if

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anthony_algmin: there’s a warranty on these things Because I think I got themem about a year

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anthony_algmin: ago and Itve been. They’ve been great, but this thing is just weird. They

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anthony_algmin: just stopped working, so I went and looked at the the webs. I found the

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anthony_algmin: order, found the. Um, uh, the headphones and I hadn’t been to that website

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anthony_algmin: in a long time, and I noticed after I had ended to their credit, they gave

361
00:20:43,449 –> 00:20:46,889
anthony_algmin: me a a warranty replacement. I’ve got to go ship and back and all that, but

362
00:20:46,500 –> 00:20:47,500
doug_laney: Mhm, Mhm.

363
00:20:47,289 –> 00:20:52,409
anthony_algmin: I noticed that from literally that afternoon, and for the next several days

364
00:20:52,809 –> 00:20:58,409
anthony_algmin: I have been seeing advertisements for those headphones that I went to are

365
00:20:58,489 –> 00:21:02,009
anthony_algmin: that I already owned that I’ve that are just popping up all over the

366
00:21:02,009 –> 00:21:04,169
anthony_algmin: internet for me. Now it’s like you really should look at these

367
00:21:04,249 –> 00:21:08,089
anthony_algmin: headphones and I’m like, wait a second. And then if I said that the cart

368
00:21:04,260 –> 00:21:05,260
doug_laney: Yeah, it’s

369
00:21:08,249 –> 00:21:10,729
anthony_algmin: reminders and things like I didn’t put it in the carpe, But they’re like

370
00:21:10,969 –> 00:21:13,369
anthony_algmin: you. You’re looking at these. You really should consider buying these. These

371
00:21:13,449 –> 00:21:18,329
anthony_algmin: are great and I’m like this is where you’re almost at this line of stepping

372
00:21:18,489 –> 00:21:20,729
anthony_algmin: over this creepy factor of like

373
00:21:20,340 –> 00:21:21,340
doug_laney: yeah,

374
00:21:21,209 –> 00:21:25,609
anthony_algmin: data, getting a little bit weird in how they’re they’re reacting to, Because

375
00:21:25,220 –> 00:21:26,220
doug_laney: Mhm, Mhm,

376
00:21:25,769 –> 00:21:29,369
anthony_algmin: I’m in a y, like my situation is a little bit outside of their norm.

377
00:21:28,240 –> 00:21:31,120
doug_laney: And they know you already bought these and they know that you already return

378
00:21:31,280 –> 00:21:34,160
doug_laney: them. Why would they be trying to sell you the same thing over and over again.

379
00:21:34,320 –> 00:21:35,760
doug_laney: That happens to me all the time and

380
00:21:35,389 –> 00:21:36,389
anthony_algmin: Yeah,

381
00:21:36,000 –> 00:21:39,360
doug_laney: I think it’s there’s some some bad data science going on there,

382
00:21:39,609 –> 00:21:42,169
anthony_algmin: there is well, and it just shows that we’re still a bit in the

383
00:21:41,860 –> 00:21:42,860
doug_laney: Mhm.

384
00:21:42,249 –> 00:21:44,409
anthony_algmin: infancy of the Um. the

385
00:21:44,020 –> 00:21:45,020
doug_laney: right,

386
00:21:44,569 –> 00:21:47,129
anthony_algmin: logic behind how they’re doing these things. They was like. Ooh,

387
00:21:46,900 –> 00:21:47,900
doug_laney: Yeah,

388
00:21:47,289 –> 00:21:49,929
anthony_algmin: we saw eyeballs on this thing. Let’s go marketed to them. It.

389
00:21:49,860 –> 00:21:50,860
doug_laney: right, right,

390
00:21:50,089 –> 00:21:53,689
anthony_algmin: It reminds me what I bought. I bought a uh humidifier. Just a room who

391
00:21:53,929 –> 00:21:58,409
anthony_algmin: humiifier from Amazon years ago and and Amazon’s largely considered as some

392
00:21:58,069 –> 00:21:59,069
anthony_algmin: of the

393
00:21:58,340 –> 00:21:59,340
doug_laney: Mhm, Mhm,

394
00:21:58,729 –> 00:22:01,689
anthony_algmin: best in the business as far as managing Medta and doing, you know, Acros

395
00:22:01,929 –> 00:22:05,049
anthony_algmin: promotals and all that stuff. But I will tell you, after I shopped for the

396
00:22:05,049 –> 00:22:06,169
anthony_algmin: hudfier, I am

397
00:22:05,780 –> 00:22:06,780
doug_laney: Mhm,

398
00:22:06,329 –> 00:22:08,009
anthony_algmin: as I bought the humidifier on

399
00:22:08,020 –> 00:22:09,020
doug_laney: Mhm, Mhm,

400
00:22:08,169 –> 00:22:13,849
anthony_algmin: Amazon. I then proceeded to get all of this marketing for humidifier filters

401
00:22:13,949 –> 00:22:14,949
anthony_algmin: for every

402
00:22:14,660 –> 00:22:15,660
doug_laney: right,

403
00:22:14,969 –> 00:22:19,049
anthony_algmin: humidifier ever made. They were like this guy is a huge humidifier fan, But

404
00:22:19,449 –> 00:22:20,889
anthony_algmin: they, they missed some key data

405
00:22:20,500 –> 00:22:21,500
doug_laney: No,

406
00:22:21,609 –> 00:22:25,129
anthony_algmin: in in that process, so I want. I want to think,

407
00:22:23,840 –> 00:22:27,760
doug_laney: Yeah, the, but there there’re scenarios like that in the beta B world,

408
00:22:27,429 –> 00:22:28,429
anthony_algmin: Oh, yeah,

409
00:22:28,000 –> 00:22:33,680
doug_laney: where Th. They’re t entirely missed right, Um, like we. we moved, Um, we moved

410
00:22:33,840 –> 00:22:37,680
doug_laney: homes a number of years ago, And when you move uh home, you, you put it. you

411
00:22:37,760 –> 00:22:40,000
doug_laney: list your home right. It goes into the m ▁l s

412
00:22:39,949 –> 00:22:40,949
anthony_algmin: right,

413
00:22:40,240 –> 00:22:43,920
doug_laney: database for those not in the U. U. S. It’s the multiple listing service. It’s

414
00:22:44,080 –> 00:22:48,240
doug_laney: basically a a big national database of homes that are on the market And so it’s

415
00:22:48,400 –> 00:22:53,760
doug_laney: basically public public knowledge, Um, uh, of public public data. And so the you

416
00:22:53,840 –> 00:22:56,720
doug_laney: know, everybody starts contacting you if you’ve put your home on the on. The

417
00:22:56,720 –> 00:23:01,040
doug_laney: market recently you get the the flyby night mortgage companies, and Um, people

418
00:23:01,280 –> 00:23:06,560
doug_laney: trying to sell you loans, and and uh, moving boxes and moving companies and

419
00:23:06,640 –> 00:23:10,000
doug_laney: landscapers and painters, and they all come out of the woodwork right. And so

420
00:23:10,080 –> 00:23:14,000
doug_laney: the only company that didn’t contact us with when we moved was our own bank. Uh,

421
00:23:14,320 –> 00:23:18,480
doug_laney: I banked with them for Uh, through a series of acquisitions for like forty

422
00:23:18,640 –> 00:23:23,840
doug_laney: years, and W. why? why didn’t they reach out to me And you know, I asked a bank

423
00:23:24,000 –> 00:23:27,120
doug_laney: of friend. He said well, they, they thought maybe that was creepy. I’m like I’ve

424
00:23:27,200 –> 00:23:30,560
doug_laney: got a forty year relationship with them. It’s public knowledge that I’m moving

425
00:23:30,720 –> 00:23:35,360
doug_laney: and there’s a sign in my yard. How creepy is it for them to reach out and say

426
00:23:35,440 –> 00:23:38,960
doug_laney: hey, We see you’re moving. Can we offer you a mortgage? Uh, home equity line? We

427
00:23:38,960 –> 00:23:42,480
doug_laney: print you new checks when we move the stuff in your safe deposit pox, Can we um,

428
00:23:42,800 –> 00:23:46,800
doug_laney: you know so on and so forth, and uh what? It’s entirely missed opportunity for

429
00:23:46,880 –> 00:23:51,440
doug_laney: them. You know the number one time when when people move banks change banks, as

430
00:23:51,520 –> 00:23:56,160
doug_laney: when they move or or get married, married or divorced. And what? it? what? What

431
00:23:56,240 –> 00:23:59,920
doug_laney: a tragic missed opportunity for for them to recognize that there was some lu

432
00:24:00,400 –> 00:24:03,840
doug_laney: evenent for one of their long term customers. M. maybe they won’t me as the

433
00:24:03,920 –> 00:24:08,960
doug_laney: customer anymore. I, I don’t know. Um, and uh, I was thinking how how simple

434
00:24:09,120 –> 00:24:13,040
doug_laney: would it have been for them to hire? You know some high school programmer. Give

435
00:24:13,120 –> 00:24:16,400
doug_laney: ’em a can of red bowll, and say you know, match the cusser, right, An app to

436
00:24:16,560 –> 00:24:21,520
doug_laney: match the customer database with Uh, with the M ▁l s database, and tell us whose

437
00:24:21,600 –> 00:24:24,800
doug_laney: mo which our customers are moving, so we can offer them a a mortgage right,

438
00:24:25,620 –> 00:24:26,620
doug_laney: So

439
00:24:27,520 –> 00:24:30,800
doug_laney: the tune turns out there, The mortgage got subsumed by them. They probably lost

440
00:24:30,960 –> 00:24:33,680
doug_laney: a you know, a tenth of a point or anythingth of a point on it. They could have

441
00:24:33,680 –> 00:24:34,880
doug_laney: gone direct to me. so

442
00:24:35,389 –> 00:24:36,389
anthony_algmin: yeah, yeah,

443
00:24:35,760 –> 00:24:40,160
doug_laney: anyway, Um, you didn missed a lot of missed opportunities there. data

444
00:24:40,240 –> 00:24:43,040
doug_laney: monetization doesn’t need to be rocket science, right,

445
00:24:43,529 –> 00:24:47,209
anthony_algmin: Well, and that that’s I think, a pattern that we see all over the place is

446
00:24:46,829 –> 00:24:47,829
anthony_algmin: that

447
00:24:47,140 –> 00:24:48,140
doug_laney: Mhm,

448
00:24:47,529 –> 00:24:50,489
anthony_algmin: people think that everything does need to be rocket science and they miss

449
00:24:50,649 –> 00:24:52,649
anthony_algmin: the stuff right in front of their faces to try

450
00:24:52,340 –> 00:24:53,340
doug_laney: Yeah,

451
00:24:52,729 –> 00:24:54,489
anthony_algmin: to do some fancy. Everybody wants machine

452
00:24:54,420 –> 00:24:55,420
doug_laney: Mhm,

453
00:24:54,649 –> 00:24:58,409
anthony_algmin: learning. We can’t even do basic arithmetic with data

454
00:24:58,100 –> 00:24:59,100
doug_laney: right,

455
00:24:58,809 –> 00:25:00,409
anthony_algmin: of the time, let alone machine

456
00:25:00,500 –> 00:25:01,500
doug_laney: mhm,

457
00:25:00,729 –> 00:25:02,089
anthony_algmin: learning. Let’s just find,

458
00:25:03,289 –> 00:25:05,929
anthony_algmin: Call the fruit on the ground. Let’s just pick the stuff up. We don’t even

459
00:25:06,089 –> 00:25:08,009
anthony_algmin: need to reach up and pull it off the tree. It’s

460
00:25:07,820 –> 00:25:08,820
doug_laney: right, right,

461
00:25:08,089 –> 00:25:09,289
anthony_algmin: already sitting there for us.

462
00:25:09,060 –> 00:25:10,060
doug_laney: Mhm.

463
00:25:09,449 –> 00:25:11,609
anthony_algmin: It’s going to go bad if we don’t do something now,

464
00:25:10,800 –> 00:25:14,800
doug_laney: Yeah, and you know a lot of companies say. Well, you know, it’s not our

465
00:25:14,960 –> 00:25:19,440
doug_laney: business. You know. monetizing data. It’s not our thing. Um, it’s a little bit

466
00:25:19,840 –> 00:25:23,920
doug_laney: outside of our, or we, we can’t get the resources or the budget for it. Um. So

467
00:25:24,000 –> 00:25:26,960
doug_laney: one of the things we’re doing at Westmann Rose, we’ partnered with an investment

468
00:25:27,120 –> 00:25:31,760
doug_laney: bank and they will fund data monitization efforts for our clients, so we’ll go

469
00:25:31,840 –> 00:25:34,880
doug_laney: in and do the work. The investment bank will pay for the work, and then the

470
00:25:34,960 –> 00:25:38,880
doug_laney: owner of the data. The client will actually get a revenue stream from. You know,

471
00:25:39,040 –> 00:25:42,640
doug_laney: from however it’s it’s monetized and without having to do any work other than

472
00:25:42,720 –> 00:25:47,520
doug_laney: give us access to their their systems, the data goes into a trust, Um, where we

473
00:25:47,680 –> 00:25:52,080
doug_laney: have rights to it and then they get uh a revenue stream and the investors get

474
00:25:52,240 –> 00:25:54,640
doug_laney: get paid back on that as well. And so it’s a great model.

475
00:25:55,289 –> 00:25:58,649
anthony_algmin: and that’s actually in alignment with one of the the topics that’s been a

476
00:25:58,649 –> 00:26:02,649
anthony_algmin: common theme in recent episode. Des, which is this alignment of incentives

477
00:26:02,549 –> 00:26:03,549
anthony_algmin: and one of the things

478
00:26:03,060 –> 00:26:04,060
doug_laney: Mhm,

479
00:26:03,449 –> 00:26:07,449
anthony_algmin: that I’m always looking for with partners and business opportunities. Or

480
00:26:07,529 –> 00:26:11,369
anthony_algmin: what have you is when when both sides have perfectly aligned incentives, So

481
00:26:11,449 –> 00:26:15,849
anthony_algmin: if you can create monetiz monetization from data in a way that can benefit

482
00:26:14,780 –> 00:26:15,780
doug_laney: Mhm, yeah,

483
00:26:16,249 –> 00:26:18,409
anthony_algmin: everybody and grow that pie. Now

484
00:26:18,020 –> 00:26:19,020
doug_laney: mhm,

485
00:26:18,809 –> 00:26:23,609
anthony_algmin: everybody involved is motivated to do the things together to to grow the pie

486
00:26:23,769 –> 00:26:27,209
anthony_algmin: as much as possible when we can find those opportunities. It’s it’s the best

487
00:26:27,449 –> 00:26:29,129
anthony_algmin: way of business Y you can get,

488
00:26:30,880 –> 00:26:31,920
doug_laney: Yep, For sure,

489
00:26:32,169 –> 00:26:35,449
anthony_algmin: So I want to expand. So we’ve talked a little bit about data as an asset,

490
00:26:35,529 –> 00:26:38,729
anthony_algmin: but there is, and in the spirit of great transitions, talking about things

491
00:26:38,889 –> 00:26:43,049
anthony_algmin: that are are of uh, um, you know, top of mind and and popular

492
00:26:42,740 –> 00:26:43,740
doug_laney: Mhm,

493
00:26:43,449 –> 00:26:46,809
anthony_algmin: terms. These days we just did an episode on Buzzwords not too long ago, But

494
00:26:46,889 –> 00:26:50,249
anthony_algmin: this one is one that I think has been getting a lot of news lately is around

495
00:26:50,409 –> 00:26:54,489
anthony_algmin: digital currencies, and so data as an asset is one thing, data as a

496
00:26:54,569 –> 00:26:59,369
anthony_algmin: currency. Is that a whole nether thing, or is that just an evolution of the

497
00:26:59,369 –> 00:27:01,049
anthony_algmin: concept of data as an asset Because

498
00:27:00,820 –> 00:27:01,820
doug_laney: yeah,

499
00:27:01,289 –> 00:27:03,449
anthony_algmin: that does actually create now.

500
00:27:03,700 –> 00:27:04,700
doug_laney: mhm,

501
00:27:03,869 –> 00:27:04,869
anthony_algmin: a

502
00:27:05,769 –> 00:27:09,609
anthony_algmin: diminish like now. Data does have this kind of um

503
00:27:10,649 –> 00:27:15,289
anthony_algmin: scarcity where it it represents something of of tangible value. How does

504
00:27:15,449 –> 00:27:17,049
anthony_algmin: that imp? like? How does

505
00:27:16,660 –> 00:27:17,660
doug_laney: it,

506
00:27:17,209 –> 00:27:20,969
anthony_algmin: infoms feel about that? Does it throw monkey wrench at anything? Or or how

507
00:27:20,869 –> 00:27:21,869
anthony_algmin: do you explain it?

508
00:27:22,320 –> 00:27:26,640
doug_laney: um, W. We make sure I know where you’re going, so Yes, companies will share in

509
00:27:26,880 –> 00:27:32,080
doug_laney: in trade data for goods and services or commercial. You know, Uh, favorable

510
00:27:32,180 –> 00:27:33,180
doug_laney: commercial terms, Right

511
00:27:32,749 –> 00:27:33,749
anthony_algmin: Mhm,

512
00:27:33,200 –> 00:27:35,200
doug_laney: And so that’s one way to monetize It’s one

513
00:27:34,749 –> 00:27:35,749
anthony_algmin: sure,

514
00:27:35,200 –> 00:27:38,640
doug_laney: of the you know eight or nine patterns of data monetization that we’ve

515
00:27:38,720 –> 00:27:44,320
doug_laney: identified. Um Is is uh, making data available in exchange for good the services

516
00:27:44,400 –> 00:27:46,640
doug_laney: or commercial, Uh, favorable commercial terms.

517
00:27:46,269 –> 00:27:47,269
anthony_algmin: yeah,

518
00:27:46,800 –> 00:27:50,480
doug_laney: But are you talking about digital currencies That? that’s not something? I’m I’m

519
00:27:50,660 –> 00:27:51,660
doug_laney: an expert in

520
00:27:52,009 –> 00:27:56,969
anthony_algmin: I’m just curious from a a perspective, looking through a data as an

521
00:27:56,740 –> 00:27:57,740
doug_laney: Mhm,

522
00:27:57,049 –> 00:27:58,329
anthony_algmin: asset lends. We

523
00:27:58,100 –> 00:27:59,100
doug_laney: right,

524
00:27:58,489 –> 00:28:02,249
anthony_algmin: have digital currencies, And, and it’s less about the mechanics of how the

525
00:28:02,329 –> 00:28:06,649
anthony_algmin: currcies work, or or, or what have you? But when we are thinking about the

526
00:28:06,669 –> 00:28:07,669
anthony_algmin: um,

527
00:28:08,329 –> 00:28:14,249
anthony_algmin: virtual side of a tangible asset, which I mean, Fiat curcies, as it is, have

528
00:28:14,969 –> 00:28:17,449
anthony_algmin: some representative function. But does

529
00:28:18,489 –> 00:28:23,289
anthony_algmin: it matter that this is represented purely by data? Is there a you know a a

530
00:28:24,009 –> 00:28:28,009
anthony_algmin: evolution of data as an asset, or does it really matter that it’s digital

531
00:28:28,249 –> 00:28:32,729
anthony_algmin: versus Uh, printed on paper? It’s still a currency. It’s still going to be.

532
00:28:33,049 –> 00:28:37,449
anthony_algmin: You know, track the same way we do dollars and cents, Uh, today, and

533
00:28:37,220 –> 00:28:38,220
doug_laney: Mhm.

534
00:28:37,609 –> 00:28:41,209
anthony_algmin: and that the data value of it is not substantially different that. I guess

535
00:28:41,189 –> 00:28:42,189
anthony_algmin: that’s my. my question.

536
00:28:43,440 –> 00:28:47,200
doug_laney: Yeah, well, the thing is, data can be duplicated. So unless you artificially

537
00:28:47,360 –> 00:28:52,880
doug_laney: restrict the the digital uh, instantiation of some tangible item, then

538
00:28:53,920 –> 00:28:58,080
doug_laney: uh, you know you’re not able to maintain that uh. that price point unless you

539
00:28:58,240 –> 00:29:02,000
doug_laney: artificially maintain scarcity, then you can’t maintain that price point. So one

540
00:29:02,080 –> 00:29:06,800
doug_laney: of the datavaluation models that that I developed, Um, the the market value of

541
00:29:06,880 –> 00:29:09,200
doug_laney: data. You’ looks at that, which is you know how many

542
00:29:10,720 –> 00:29:14,320
doug_laney: you have to kind of attenuate the price point versus the market size, right to

543
00:29:14,400 –> 00:29:20,880
doug_laney: optimize the revenue curve, and Um. it. And so because data is this non rivalr

544
00:29:21,200 –> 00:29:25,040
doug_laney: non depleting asset we can sell, we can create infinite copies of it, but

545
00:29:24,669 –> 00:29:25,669
anthony_algmin: Mhm,

546
00:29:25,120 –> 00:29:29,120
doug_laney: we don’t Because we created infinite copies of it ats price point, we wouldvve

547
00:29:29,280 –> 00:29:34,160
doug_laney: to ▁zero right, So as with the the, The revenue, so we want to maintain some

548
00:29:34,240 –> 00:29:38,880
doug_laney: kind of artificial um. scarcity for it. Uh, a great example is that of that is.

549
00:29:38,960 –> 00:29:43,200
doug_laney: Uh, where I worked at at a gardener, right, So Gardner, you know, sells research

550
00:29:43,680 –> 00:29:46,880
doug_laney: reports. Um, obviously see this subscriptions too, but you can buy individual

551
00:29:47,200 –> 00:29:49,520
doug_laney: research reports, but they maintain a particular price point

552
00:29:49,469 –> 00:29:50,469
anthony_algmin: right.

553
00:29:49,920 –> 00:29:54,560
doug_laney: for those to make sure that it’s Um. It’s something that’s scarce and it’s a

554
00:29:54,560 –> 00:29:58,720
doug_laney: premium for an organization to have access to that research report. You know,

555
00:29:58,880 –> 00:30:03,040
doug_laney: the newspapers are another store. They want to sell as many as possible. Um, but

556
00:30:03,120 –> 00:30:06,000
doug_laney: they still want to make you know revenue from them. So they sell them for a

557
00:30:06,000 –> 00:30:09,360
doug_laney: buck, fifty, or you know two bucks. Um. but that’s a different. you know, a

558
00:30:09,440 –> 00:30:14,160
doug_laney: whole different, similar similar model, but a different. Uh. a different curve,

559
00:30:14,180 –> 00:30:15,180
doug_laney: right,

560
00:30:14,889 –> 00:30:17,769
anthony_algmin: yeah, that’s that’s really interesting around

561
00:30:17,380 –> 00:30:18,380
doug_laney: yeah,

562
00:30:17,929 –> 00:30:19,049
anthony_algmin: the like. the

563
00:30:19,140 –> 00:30:20,140
doug_laney: mhm,

564
00:30:19,689 –> 00:30:22,569
anthony_algmin: forced scarcity of data assets that could

565
00:30:22,180 –> 00:30:23,180
doug_laney: mhm,

566
00:30:22,729 –> 00:30:24,169
anthony_algmin: be theoretically copied.

567
00:30:26,329 –> 00:30:31,129
anthony_algmin: You’re bringing me back to like microeconomics and by old economic courses,

568
00:30:31,529 –> 00:30:33,929
anthony_algmin: where where if you have infinite supply, the price

569
00:30:33,620 –> 00:30:34,620
doug_laney: right,

570
00:30:34,169 –> 00:30:36,089
anthony_algmin: is necessarily going to go to

571
00:30:35,700 –> 00:30:36,700
doug_laney: mhm,

572
00:30:36,089 –> 00:30:38,009
anthony_algmin: ▁zero. It’s going to get pressure all the way downward,

573
00:30:37,860 –> 00:30:38,860
doug_laney: yeah.

574
00:30:38,249 –> 00:30:41,369
anthony_algmin: but if you create scarcity then you will have

575
00:30:41,060 –> 00:30:42,060
doug_laney: right.

576
00:30:42,249 –> 00:30:44,249
anthony_algmin: a limitation there, so that makes a lot of sense.

577
00:30:43,520 –> 00:30:47,120
doug_laney: so a Anthony, My want has skipped to the last chapter in my book, which is about

578
00:30:47,360 –> 00:30:49,520
doug_laney: applying economic models to to data.

579
00:30:49,549 –> 00:30:50,549
anthony_algmin: Yeah,

580
00:30:50,240 –> 00:30:54,480
doug_laney: So it, it’s a. It was a bit, you know, theoretical, but there were some, uh,

581
00:30:54,880 –> 00:30:58,320
doug_laney: some prescriptive. you know, kind of recommendations That that came out of that

582
00:30:58,400 –> 00:31:03,360
doug_laney: work. Uh, I did with some. Some colleagues w, where we examined traditional

583
00:31:03,600 –> 00:31:07,440
doug_laney: economic models like supplying demand and productivity, frontiers, and marginal

584
00:31:07,600 –> 00:31:13,600
doug_laney: utility, and so forth, and said, Hey, you know, these models were all designed

585
00:31:13,680 –> 00:31:17,760
doug_laney: with traditional goods and services. In mind, you know, we go back to Econ

586
00:31:18,000 –> 00:31:19,440
doug_laney: class. We talk about guns and butter

587
00:31:19,149 –> 00:31:20,149
anthony_algmin: y.

588
00:31:19,600 –> 00:31:24,640
doug_laney: right? And so? Um, nobody had ever really thought about how do they apply to to

589
00:31:24,380 –> 00:31:25,380
doug_laney: data?

590
00:31:26,080 –> 00:31:30,240
doug_laney: and uh, so so we gave that some thought and came up with some really interesting

591
00:31:30,400 –> 00:31:33,840
doug_laney: things like. Um, you know marginal utility right, So we? S we as human fiel

592
00:31:34,000 –> 00:31:38,080
doug_laney: marginal utility, you know, I drink one beer, and you know that’s great. I drink

593
00:31:38,240 –> 00:31:41,040
doug_laney: the second one. No, it’s good, the third one man, Maybe not so much. After so

594
00:31:41,120 –> 00:31:42,720
doug_laney: many, I might start, have maybe negative

595
00:31:43,760 –> 00:31:49,680
doug_laney: negative utility right that we won’t picture that, But Um, do do systems and

596
00:31:49,840 –> 00:31:54,240
doug_laney: computers where the primary increasingly the primary consumers of data? Do they

597
00:31:54,320 –> 00:31:58,560
doug_laney: feel marginal utility the way humans do. No, No, they don’t. So should that

598
00:31:58,880 –> 00:32:03,840
doug_laney: alter the way that we as producers or publishers of data? Uh, architect these

599
00:32:04,000 –> 00:32:08,720
doug_laney: systems. Yeah, Probably yes, and so. Um. some of our recommendations were how to

600
00:32:09,200 –> 00:32:13,680
doug_laney: how how to change the way that we architect data production data collection, Um,

601
00:32:14,800 –> 00:32:19,440
doug_laney: dated distribution, kinds of kinds of systems, Um. And then I, I, I think I, I

602
00:32:19,520 –> 00:32:24,480
doug_laney: told you, Uh, before the show I, I also teach a courseson infoms at the

603
00:32:24,720 –> 00:32:30,160
doug_laney: University of Illinois Geese business school, and um, uh, In one of the

604
00:32:30,240 –> 00:32:35,520
doug_laney: assignments I give the the students is to uh, pick any economic model And you

605
00:32:35,600 –> 00:32:39,040
doug_laney: know, apply apply to data and does it break down? Does it still work? Is

606
00:32:39,280 –> 00:32:43,600
doug_laney: irrelevant? How would you alter it? And so I have dozens and dozens of really

607
00:32:43,920 –> 00:32:48,320
doug_laney: interesting papers from students on on this and I’m trying to get them them than

608
00:32:48,480 –> 00:32:52,000
doug_laney: published. I had. Uh, a dozen students in my Fi. the first time I taught the

609
00:32:52,080 –> 00:32:55,840
doug_laney: class. I had sixty last year, had four hundred this year in the class. so I’ve

610
00:32:56,000 –> 00:33:00,480
doug_laney: got a real, a real, real great body of Uh. of of papers from this with this one

611
00:33:00,640 –> 00:33:02,560
doug_laney: assignment that I’d love to love to publish.

612
00:33:03,049 –> 00:33:04,489
anthony_algmin: yeah. that’s amazing.

613
00:33:04,660 –> 00:33:05,660
doug_laney: Yeah,

614
00:33:06,329 –> 00:33:11,929
anthony_algmin: I. I think about you other representations of data like La, lately, uh, tic,

615
00:33:12,089 –> 00:33:15,689
anthony_algmin: to, has been going crazy with with how many people are out there. It seems

616
00:33:15,769 –> 00:33:20,249
anthony_algmin: like everyone today wants to be a content producer and want to have. Um. you

617
00:33:20,249 –> 00:33:21,529
anthony_algmin: know, just whatever they’re doing

618
00:33:21,300 –> 00:33:22,300
doug_laney: mhm,

619
00:33:21,689 –> 00:33:24,329
anthony_algmin: out there, and like we heard the same thing with Twitter back in the day,

620
00:33:24,229 –> 00:33:25,229
anthony_algmin: too. It’s like Does

621
00:33:24,980 –> 00:33:25,980
doug_laney: right,

622
00:33:25,129 –> 00:33:29,049
anthony_algmin: anybody really care what I had for lunch today? Apparently they do, and

623
00:33:28,740 –> 00:33:29,740
doug_laney: Mhm.

624
00:33:28,829 –> 00:33:29,829
anthony_algmin: so

625
00:33:30,409 –> 00:33:37,049
anthony_algmin: do we see that people as data, or or our entertainment is data. Um, Is that

626
00:33:37,289 –> 00:33:40,889
anthony_algmin: is that changing anything? I think. I feel like there’s so many things about

627
00:33:41,049 –> 00:33:46,969
anthony_algmin: data that you’ve captured like withs, but also just in our data space that

628
00:33:47,209 –> 00:33:51,049
anthony_algmin: aren’t fundamentally changing there. I’ve see very little fundamental change

629
00:33:51,289 –> 00:33:56,409
anthony_algmin: personally, but I see new manifestations of patterns that we’ve seen before.

630
00:33:56,749 –> 00:33:57,749
anthony_algmin: Do you see

631
00:33:57,140 –> 00:33:58,140
doug_laney: Okay,

632
00:33:57,629 –> 00:33:58,629
anthony_algmin: anything truly

633
00:33:58,500 –> 00:33:59,500
doug_laney: well, like

634
00:33:58,809 –> 00:34:02,009
anthony_algmin: novel today in how data is evolving?

635
00:34:03,920 –> 00:34:08,320
doug_laney: um again, I’m thinking about this more at an enterprise level, And

636
00:34:09,360 –> 00:34:14,000
doug_laney: you know most organizations have an entire department You dedicated to Uh,

637
00:34:14,160 –> 00:34:16,240
doug_laney: procuring office supplies. Right as

638
00:34:15,789 –> 00:34:16,789
anthony_algmin: Yeah,

639
00:34:16,320 –> 00:34:20,160
doug_laney: well, my Penns conference. I need to go to some more conferences. My pens are

640
00:34:19,900 –> 00:34:20,900
doug_laney: running out.

641
00:34:20,549 –> 00:34:21,549
anthony_algmin: No kidding,

642
00:34:20,720 –> 00:34:26,080
doug_laney: right, Um, but most companies don’t have a single person dedicated to procuring

643
00:34:26,240 –> 00:34:30,800
doug_laney: data supplies. There is a wealth of data out there. Most companies obviously

644
00:34:30,960 –> 00:34:34,880
doug_laney: have a hard time generating value from their own data, but maybe one of the ways

645
00:34:34,960 –> 00:34:38,960
doug_laney: they could do that is by integrating with external data sources. There are data

646
00:34:39,120 –> 00:34:43,600
doug_laney: sources from Uh, syndicated data providers like you know, like the Dunn, Brad

647
00:34:43,840 –> 00:34:47,120
doug_laney: Streets, and all down the line. there must be five thousand or maybe ten

648
00:34:47,280 –> 00:34:52,160
doug_laney: thousand now companies that are selling data as a primary offering. Um. there

649
00:34:52,480 –> 00:34:56,800
doug_laney: are billions of websites that can be harvested. Uh content can be harvested

650
00:34:57,200 –> 00:35:01,440
doug_laney: from. There are Uh, tens of millions of open data sets published by government

651
00:35:01,760 –> 00:35:08,720
doug_laney: organizations and and G, Os, and and others. Um. There is uh, um, a social media

652
00:35:09,040 –> 00:35:13,520
doug_laney: content right that can be harvested as well, and so to not have an individual in

653
00:35:13,600 –> 00:35:17,840
doug_laney: your organization who is aware of all of these data sources and their potential.

654
00:35:18,560 –> 00:35:21,040
doug_laney: Um. to drive value for your organization is.

655
00:35:20,749 –> 00:35:21,749
anthony_algmin: Mhm.

656
00:35:21,440 –> 00:35:26,160
doug_laney: uh. I would say, irresponsible of any organization today did not have a full

657
00:35:26,320 –> 00:35:27,920
doug_laney: time data data curator.

658
00:35:28,589 –> 00:35:29,589
anthony_algmin: Yeah, yeah,

659
00:35:30,169 –> 00:35:32,569
anthony_algmin: I think I think the the point is well made, and I would imagine this

660
00:35:32,649 –> 00:35:36,649
anthony_algmin: audience of ours Dayta leadership lessons would would be nodding their head.

661
00:35:36,749 –> 00:35:37,749
anthony_algmin: Uh,

662
00:35:36,980 –> 00:35:37,980
doug_laney: Yeah, yeah,

663
00:35:38,009 –> 00:35:39,369
anthony_algmin: you know, quite. uh,

664
00:35:39,860 –> 00:35:40,860
doug_laney: mhm,

665
00:35:40,249 –> 00:35:45,529
anthony_algmin: quite extremely right now. Um, so I do want in the in a few minutes that we

666
00:35:45,609 –> 00:35:48,729
anthony_algmin: have left, and I know a lot of your your strategic work with Westman Roe.

667
00:35:48,969 –> 00:35:51,929
anthony_algmin: You’re going to be working with organizations of some size and and some

668
00:35:52,409 –> 00:35:55,849
anthony_algmin: resources to be doing. You know pretty big things that that impact their

669
00:35:55,540 –> 00:35:56,540
doug_laney: mhm,

670
00:35:55,929 –> 00:35:59,849
anthony_algmin: organizations. For those uh out there that are in smaller organizations,

671
00:36:00,169 –> 00:36:01,609
anthony_algmin: Maybe entrepreneurs, Um.

672
00:36:01,340 –> 00:36:02,340
doug_laney: Mm,

673
00:36:01,849 –> 00:36:06,409
anthony_algmin: do you have any advice for them on things they could be doing to make the

674
00:36:06,489 –> 00:36:10,729
anthony_algmin: most of their data assets, or some practices that would help them? Um,

675
00:36:11,849 –> 00:36:16,329
anthony_algmin: continue to be good stewards of the information that they have, so that they

676
00:36:15,949 –> 00:36:16,949
anthony_algmin: can

677
00:36:16,180 –> 00:36:17,180
doug_laney: Yeah,

678
00:36:16,649 –> 00:36:19,689
anthony_algmin: capitalize on it when they have an ability to.

679
00:36:20,400 –> 00:36:23,920
doug_laney: You know almost any start up today is is capturing. You know, some source of

680
00:36:24,000 –> 00:36:29,120
doug_laney: data from the transactions or the observations or or their own activity. Um, and

681
00:36:29,280 –> 00:36:30,960
doug_laney: so, I would encourage them to

682
00:36:32,300 –> 00:36:33,300
doug_laney: consider the

683
00:36:33,940 –> 00:36:34,940
doug_laney: Uh,

684
00:36:35,680 –> 00:36:40,560
doug_laney: the range of ways that that data could be generating value for them, and bake

685
00:36:40,720 –> 00:36:44,080
doug_laney: that into their business model. so, even if it isn’t really a core part of their

686
00:36:44,160 –> 00:36:48,880
doug_laney: business model, make it so. and um. there there are examples of companies that.

687
00:36:49,120 –> 00:36:53,120
doug_laney: Oh, there’s one company I worked with that was building a an analytic database.

688
00:36:53,760 –> 00:36:57,040
doug_laney: Uh, and uh. they. They briefed me while I was a gardener, and I said. Well, this

689
00:36:57,120 –> 00:37:00,480
doug_laney: is great. Everybody else and their brothers building a a a bigger, better,

690
00:37:00,640 –> 00:37:03,840
doug_laney: faster, analytic database. and they said Whys is great. I said. Well, how do you

691
00:37:03,840 –> 00:37:09,040
doug_laney: know how great it is And they, they said Well because we’ve we’ve ingestted, Uh,

692
00:37:09,120 –> 00:37:13,200
doug_laney: we ingest bit torent traffic. I said well, uh, wonderful. I said. How much bit

693
00:37:13,280 –> 00:37:17,280
doug_laney: Tor traffic do you inrest? They said all of it I said. W. What said you? You

694
00:37:17,360 –> 00:37:21,200
doug_laney: know, you have data about every application and software and

695
00:37:22,640 –> 00:37:27,120
doug_laney: music. You know track that’s being shared illicitly over over a bit torn it, and

696
00:37:27,120 –> 00:37:29,600
doug_laney: they’re like, Yeah, we sure do. I said. You know that data would be more

697
00:37:29,760 –> 00:37:35,120
doug_laney: valuable to the producers of that content then your, your, your little database

698
00:37:35,280 –> 00:37:38,480
doug_laney: would would be, And they, they actually pivoted. It’s a company called True

699
00:37:38,640 –> 00:37:43,440
doug_laney: Optic. They’re not one of the uh, top over the top, o, t, t, um data providers,

700
00:37:44,080 –> 00:37:48,480
doug_laney: and Um, you know with clients like Disney and C, B, S. and and others Now who

701
00:37:48,560 –> 00:37:53,120
doug_laney: who are saying that we want to know when people are illegally trading Uh copies

702
00:37:53,360 –> 00:37:57,360
doug_laney: of our our Tv shows and movies and where they are and where they’re moving from.

703
00:37:57,520 –> 00:38:01,360
doug_laney: Not not specifically which individuals, but they want to know where there’s

704
00:38:01,600 –> 00:38:05,760
doug_laney: unmonetized demand for their products and services. So um, you know, think about

705
00:38:05,840 –> 00:38:07,520
doug_laney: the ways that you can use data. Um

706
00:38:08,560 –> 00:38:12,400
doug_laney: in in alternative ways. If you need help, reach out to. you know. a consultant

707
00:38:12,480 –> 00:38:16,720
doug_laney: like me is to just to I date for a little bit. Um. I run workshops on this stuff

708
00:38:16,880 –> 00:38:18,720
doug_laney: and and bake it into your business model.

709
00:38:19,229 –> 00:38:20,229
anthony_algmin: Yeah,

710
00:38:19,540 –> 00:38:20,540
doug_laney: Yeah,

711
00:38:19,929 –> 00:38:23,529
anthony_algmin: well, and that’s that gets the wheels turning, because it is

712
00:38:23,140 –> 00:38:24,140
doug_laney: Mhm,

713
00:38:23,769 –> 00:38:27,769
anthony_algmin: something where the data that you encounter in whatever you started your

714
00:38:27,849 –> 00:38:33,049
anthony_algmin: business to do could lead to entirely new business opportunities that nobody

715
00:38:32,820 –> 00:38:33,820
doug_laney: Yeah,

716
00:38:33,209 –> 00:38:36,809
anthony_algmin: else might see because of your unique vantage point. So that’s great advice.

717
00:38:36,320 –> 00:38:42,960
doug_laney: yeah, right, we’re working with a Um, a pharmaceutical wholesaler, and uh, we,

718
00:38:42,880 –> 00:38:46,480
doug_laney: we ran through, some did some workshops with them, and generated a few dozen

719
00:38:46,640 –> 00:38:50,640
doug_laney: ideas on how to better leverage their data through the top. I. D. we run them

720
00:38:50,720 –> 00:38:54,240
doug_laney: through kind of a feasibility analysis, economic feasibility, legal, ethical,

721
00:38:54,720 –> 00:38:59,600
doug_laney: operational, technical, and determine that the three top ideas, Um, if they

722
00:38:59,680 –> 00:39:03,440
doug_laney: implemented them would generate a revenue stream of Uh, seventy five to a

723
00:39:03,520 –> 00:39:06,160
doug_laney: hundred million dollars a year. This is something that’s validated by their

724
00:39:06,640 –> 00:39:12,640
doug_laney: their c, f, O. So there, there’s uh lot of untapped ways to leverage your your

725
00:39:12,800 –> 00:39:15,920
doug_laney: data. Um, a lot of patterns that companies aren’t considering. They don’t even

726
00:39:16,000 –> 00:39:21,040
doug_laney: have a Dta monetization function in their organization. Um. And so that’s you

727
00:39:21,040 –> 00:39:23,280
doug_laney: know. That’s why I. I enjoy helping helping companies.

728
00:39:24,249 –> 00:39:27,529
anthony_algmin: Absolutely, and so I, one more question for you

729
00:39:27,860 –> 00:39:28,860
doug_laney: Sure,

730
00:39:28,249 –> 00:39:32,249
anthony_algmin: to kind of flip the other side of the coin from strategic opportunities

731
00:39:32,020 –> 00:39:33,020
doug_laney: Mhm.

732
00:39:32,489 –> 00:39:36,249
anthony_algmin: to take a business for those folks out there in the audience that, maybe

733
00:39:36,489 –> 00:39:40,809
anthony_algmin: earlier in their career, or trying to figure out how they create a career in

734
00:39:40,969 –> 00:39:45,209
anthony_algmin: data. What are the kinds of gaps as you look at across the entirety of

735
00:39:45,289 –> 00:39:48,889
anthony_algmin: enterprises out there? What what kind of patters do you see in terms of

736
00:39:48,969 –> 00:39:53,209
anthony_algmin: where are those gaps? Where are the needs for those businesses in terms of

737
00:39:53,369 –> 00:39:58,969
anthony_algmin: capable people to deliver these big ideas that that are going to continue. I

738
00:39:58,969 –> 00:40:02,969
anthony_algmin: think to transform how our organizations work and and drive value from data.

739
00:40:03,600 –> 00:40:07,360
doug_laney: Yeah, I, I think. obviously, data science is a big big area, but data scientists

740
00:40:07,440 –> 00:40:10,720
doug_laney: aren’t always the most you know. Creative ones are coming up with those ideas.

741
00:40:10,800 –> 00:40:14,160
doug_laney: You know they’ll Def. definitely execute on them, but I wouldn’t count on your

742
00:40:14,400 –> 00:40:18,160
doug_laney: on most data scientists to be the ones who are who are I dating around around?

743
00:40:18,220 –> 00:40:19,220
doug_laney: data? Um,

744
00:40:20,480 –> 00:40:23,200
doug_laney: it takes somebody with an understanding of the the marketplace. The industry,

745
00:40:23,520 –> 00:40:27,120
doug_laney: maybe other industries? No, I often get asked by by clients. You know, what are

746
00:40:27,200 –> 00:40:30,960
doug_laney: others in our our industry doing, and I you my flippant responses. Why do you

747
00:40:30,960 –> 00:40:34,000
doug_laney: want to be in second place and why not look at what others and other industries

748
00:40:33,900 –> 00:40:34,900
doug_laney: are doing, and

749
00:40:34,429 –> 00:40:35,429
anthony_algmin: Yeah,

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00:40:34,800 –> 00:40:38,800
doug_laney: adapt those ideas to your own. Your your own business, Um, but yeah, having

751
00:40:38,960 –> 00:40:43,920
doug_laney: somebody who’s creative heads up, Um, innovative, Uh is is really really

752
00:40:44,160 –> 00:40:47,680
doug_laney: important. having somebody who’s thinking about the economics of data and and

753
00:40:47,760 –> 00:40:52,240
doug_laney: data as a as an asset. What does that mean to the organization? Um? you know, I

754
00:40:52,400 –> 00:40:56,560
doug_laney: call all my graduate, all my graduating students. We call them uh, Um economists

755
00:40:56,880 –> 00:41:00,480
doug_laney: instead of economists, Right, And so you know, folks like that can really help

756
00:41:00,640 –> 00:41:04,960
doug_laney: an organization think through the variety of ways to leverage data innovatively,

757
00:41:05,120 –> 00:41:08,480
doug_laney: but also Um architect appropriately for for the future,

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00:41:09,769 –> 00:41:13,049
anthony_algmin: fantastic, and I think that’s that’s great advice for anybody who out there

759
00:41:13,129 –> 00:41:17,129
anthony_algmin: and if you don’t already have a copy of Ins, you should definitely uh, pick

760
00:41:17,369 –> 00:41:21,369
anthony_algmin: one up. Um. but it’s uh, you know it. It’s something that will help

761
00:41:21,140 –> 00:41:22,140
doug_laney: Yeah, yeah,

762
00:41:21,529 –> 00:41:26,089
anthony_algmin: you reframe how you think about data. and and and plugs in a lot. It fills

763
00:41:26,169 –> 00:41:27,689
anthony_algmin: in a lot of the connective, tissue. The

764
00:41:27,380 –> 00:41:28,380
doug_laney: right.

765
00:41:27,689 –> 00:41:31,129
anthony_algmin: gap between where a lot of deep uh study is, And and I think that’s you

766
00:41:30,669 –> 00:41:31,669
anthony_algmin: know,

767
00:41:30,860 –> 00:41:31,860
doug_laney: yep,

768
00:41:31,369 –> 00:41:33,529
anthony_algmin: part of the reason, uh why it’s been so successful.

769
00:41:33,300 –> 00:41:34,300
doug_laney: thanks.

770
00:41:33,929 –> 00:41:35,449
anthony_algmin: And and you? you dug before

771
00:41:35,060 –> 00:41:36,060
doug_laney: Yeah,

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00:41:35,769 –> 00:41:38,969
anthony_algmin: Before we go. We are are just about out of time. but you know, I just want

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00:41:39,049 –> 00:41:43,209
anthony_algmin: to thank you too from a fellow data management practitioner for the

774
00:41:43,289 –> 00:41:45,609
anthony_algmin: contributions that you’ve made over many years to the

775
00:41:45,540 –> 00:41:46,540
doug_laney: and you as well.

776
00:41:45,769 –> 00:41:49,449
anthony_algmin: space and it’s been. It’s been awesome. Be getting to know you and and

777
00:41:49,609 –> 00:41:53,689
anthony_algmin: talking with you and Um again, you know, say hi to to the The gang over at

778
00:41:53,660 –> 00:41:54,660
doug_laney: well there.

779
00:41:53,689 –> 00:41:57,849
anthony_algmin: Westman Road, but I, I definitely appreciate how you’re continuing to try to

780
00:41:57,929 –> 00:42:02,009
anthony_algmin: push forward. Uh change in this industry. Help you’ll bring folks. I, I’m so

781
00:42:02,089 –> 00:42:06,809
anthony_algmin: happy for you on on the the Um Uov, Um, educational site. I think that’s

782
00:42:06,589 –> 00:42:07,589
anthony_algmin: something that

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00:42:06,980 –> 00:42:07,980
doug_laney: Yeah,

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00:42:07,449 –> 00:42:09,529
anthony_algmin: is is really important. so I appreciate that

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00:42:08,640 –> 00:42:11,920
doug_laney: I really appreciate that, Anthony. thanks and and I was going to offer ten of

786
00:42:12,000 –> 00:42:15,680
doug_laney: your Um. executive listeners who are interested in copy the book. They can reach

787
00:42:15,840 –> 00:42:20,880
doug_laney: out to me and Um. We have we make copies of available to Chief Date officers and

788
00:42:20,800 –> 00:42:22,480
doug_laney: and folks like that, So um,

789
00:42:23,620 –> 00:42:24,620
doug_laney: right,

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00:42:23,929 –> 00:42:28,009
anthony_algmin: fantastic and and we will certainly um, include the the relevant links Uh,

791
00:42:23,929 –> 00:42:28,009
anthony_algmin: fantastic and and we will certainly um, include the the relevant links Uh,

792
00:42:28,169 –> 00:42:32,409
anthony_algmin: in the show notes. And so um, you know, Doug. thanks again. Thank you for

793
00:42:28,169 –> 00:42:32,409
anthony_algmin: in the show notes. And so um, you know, Doug. thanks again. Thank you for

794
00:42:32,489 –> 00:42:35,209
anthony_algmin: being on the show and uh, I really really appreciate it.

795
00:42:32,489 –> 00:42:35,209
anthony_algmin: being on the show and uh, I really really appreciate it.

796
00:42:34,240 –> 00:42:37,680
doug_laney: I pleasure, like to have me Anthony, take care by everyone.

797
00:42:37,769 –> 00:42:41,049
anthony_algmin: thanks and thank you all for joining us today. You’ll find more information

798
00:42:41,369 –> 00:42:44,649
anthony_algmin: and links in the show notes. Dive deeper with my book at Data Leadership

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00:42:44,809 –> 00:42:48,489
anthony_algmin: Book Dot Com and use Promocod Aldman D. ▁el at the Di Diversity Online

800
00:42:48,729 –> 00:42:51,689
anthony_algmin: Trainding center for twenty percent off your first purchase, please remember

801
00:42:51,849 –> 00:42:54,809
anthony_algmin: to follow Data leadership lessons on Youtube or wherever you get your

802
00:42:54,889 –> 00:42:58,169
anthony_algmin: podcasts, and if you enjoy the show, please rate and review and help others

803
00:42:58,329 –> 00:43:01,849
anthony_algmin: find us Stay safe during these unusual times and go make an impact.

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