Latest / The Adults In The AI Room / Scott Taylor, the Data Whisperer - Episode #18

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Scott Taylor, the Data Whisperer - Episode #18 · The Adults In The AI Room

0:10
SPEAKER_00: And AI in the enterprise, as we move from the current state to the future of the cognitive assistance and in all aspects of our personal and professional lives.
0:22
SPEAKER_00: Thanks so much for listening.
0:23
SPEAKER_00: If this is your first time, we invite you to subscribe wherever you listen to podcasts.
0:28
SPEAKER_00: And now here are your hosts, Seth Early and Chris Featherstone.
0:36
SPEAKER_04: Welcome to today's podcast.
0:38
SPEAKER_04: I'm Seth Early.
0:39
SPEAKER_01: And I'm Chris Featherstone.
0:40
SPEAKER_04: Chris, what's going on?
0:41
SPEAKER_04: How's your week been?
0:42
SPEAKER_04: I know you've been pretty jammed these days.
0:44
SPEAKER_01: It was well, I mean, I thought I lived in paradise until forest fires came.
0:48
SPEAKER_01: And uh yeah, a couple weeks ago we had to get evacuated, which was super interesting.
0:53
SPEAKER_01: Yeah, but everything's good, you know.
0:55
SPEAKER_01: The uh the winds prevailed and took it a different way.
0:59
SPEAKER_01: So fortunate for us, unfortunate for others.
1:01
SPEAKER_01: But so this week I get to um chop wood and stack it all up, getting ready.
1:08
SPEAKER_01: I mean, you're you're on a you know out in the middle of of uh the forest too, right?
1:13
SPEAKER_01: So I've got to clean stuff up and get ready for winter.
1:16
SPEAKER_04: Well, so how far did the uh fire get to you?
1:18
SPEAKER_01: A mile and a half away.
1:20
SPEAKER_01: Yes.
1:20
SPEAKER_04: How long did you have to be evacuated for?
1:23
SPEAKER_01: It was uh it was actually on the in the main national news.
1:25
SPEAKER_01: It was the Elmo 2 fire in Montana and it burned about 90,000 acres.
1:31
SPEAKER_04: I'm sorry you had to deal with that.
1:33
SPEAKER_04: That's awful.
1:33
SPEAKER_04: Yeah, it's just another experience of life.
1:35
SPEAKER_04: Let's go ahead and jump into our podcast.
1:37
SPEAKER_04: Our guest today is a passionate self-proclaimed data evangelist.
1:42
SPEAKER_04: He's an author, a speaker, and master data manager storytelling advisor.
1:47
SPEAKER_04: If you are in the master data management space, you you have been amused and enlightened by his creative, humorous approach to educating companies about strategic value of master data management.
1:58
SPEAKER_04: Please welcome the data whisperer, Scott Taylor.
2:01
SPEAKER_03: Hi, everybody.
2:02
SPEAKER_03: Great to be here.
2:03
SPEAKER_03: Thanks for having me.
2:05
SPEAKER_01: Who gave you the title uh data whisperer?
2:07
SPEAKER_03: I did.
2:08
SPEAKER_03: I gave it to myself, inspired by the horse whisperer, right?
2:12
SPEAKER_03: They tame horses, they calm them down.
2:14
SPEAKER_03: I felt coming from the data management space, I helped tame and calm data down, was an effective moniker, and then I put it on a badge one day at a conference, and I just got so many positive reactions that it's that it's stuck.
2:29
SPEAKER_03: So I kept it.
2:31
SPEAKER_03: But we're all data whispers in the data management space.
2:33
SPEAKER_04: We all help calm data down, we all and data psychologists and data therapists, and uh, I think there's a lot of that in there, just helping people get over their fears and and concerns and anxieties around these types of initiatives kind of breaking.
2:48
SPEAKER_03: There's probably a whole practice area around data psychology that we could get into.
2:52
SPEAKER_03: Exactly.
2:53
SPEAKER_04: Exactly.
2:53
SPEAKER_04: So talk about tell me about your background, tell me about how you got to where you are.
2:57
SPEAKER_04: What the world according to Scott, what did what did you start with?
3:02
SPEAKER_04: And tell us about your education, your training, your your childhood, your hobbies, your your favorite uh uh animal.
3:12
SPEAKER_03: So you want my life story.
3:13
SPEAKER_03: There you go.
3:13
SPEAKER_03: All right, just sit back, guys, ready to roll.
3:16
SPEAKER_01: I think Scott, the the fun part is that you know, um, we get quite a few data folks on this, and it's an it's a it's a podcast that's focused on AI.
3:27
SPEAKER_01: And however, I love the fact that we have folks like yourself on here because it sets the stage well for what you know all these other things are going to do with the data.
3:38
SPEAKER_01: So it's a it is it it really is an interesting notion because we get a lot of folks that that will ask us, well, how do I get into that or what does that look like?
3:46
SPEAKER_01: And you know, and so just having the origin stories from all our guests is super important, but it's also it's also super interesting because a lot of people don't start out going into these areas of their career and yet they find themselves now as a data whisperer, right?
4:03
SPEAKER_01: So, how did you get there?
4:04
SPEAKER_01: You know what I mean?
4:05
SPEAKER_01: How do I get there?
4:06
SPEAKER_03: I'm I'm from the data management side of the space, so I kind of bifurcate the space for simplicity's sake between data management, right?
4:15
SPEAKER_03: Data governance, data stewardship, master data, reference data, metadata, RDM, MDM, PIM, DAM, all those foundational activities we need at an enterprise to curate and create that foundation versus the business intelligence, analytics, data science, AI, ML, visuals, you know, all the cool, sexy stuff.
4:39
SPEAKER_03: So I come from the data management side, and I think I got started in the space because my parents told me when I was a kid, instead of building with my Lego, I sorted it.
4:51
SPEAKER_03: Oh, that's funny.
4:52
SPEAKER_03: So that was immediate sort of taxonomy kind of structural.
4:58
SPEAKER_03: That's as far as I've ever gone, though, with data.
5:00
SPEAKER_03: So I don't I'm not a data practitioner.
5:04
SPEAKER_03: And so my first real data job was working for a company that is now part of Nielsen.
5:10
SPEAKER_03: We had a uh standardized list of every supermarket in the country, seems kind of dull, which it was, but uh we made it exciting.
5:19
SPEAKER_03: That's where my first storytelling in data came from, even though we didn't call it back that that back in the day, it was like marketing, that's what they call it.
5:29
SPEAKER_03: But we went to consumer package goods companies and we had a list of all these stores, and they were trying to keep the same list, and we had a better list, and it was kind of outsourcing customer master maintenance, and we repositioned this as uh there was a unique key on it that I branded, and we drove this idea of having a branded kind of integration key that went across all these different data, and this was pre-2K, so this was before you know data sharing was a big thing and all this other stuff.
5:59
SPEAKER_03: But we were still, as you guys know, people have been using data for a long time.
6:02
SPEAKER_03: I love when people say data is the new anything, it's like data's been around since the old data around before there was computer, there was data before there was electricity, so we know that those of us have been there, yeah.
6:14
SPEAKER_03: But that really opened my eyes to the power of things like highly structured, well-curated data and what it could do for an organization.
6:24
SPEAKER_03: I was at Nielsen for 15 years and built this great business.
6:28
SPEAKER_03: We built a whole network effect, and it was it was really fun.
6:31
SPEAKER_03: Then I went off and uh consulted with WPP and Cantor, another iconic data world-class data company, then spent some time at Dunham Brad Street.
6:41
SPEAKER_03: So I've always been on the supplier side, on the vendor side, always representing data actual data companies rather than software or services, and just dealt with every kind of enterprise at every level of data maturity and every category all over the world.
7:00
SPEAKER_03: So that limits my scope.
7:02
SPEAKER_04: What was your undergraduate in?
7:04
SPEAKER_04: Were you did you have a uh computer?
7:05
SPEAKER_04: Yeah, Berkeley.
7:06
SPEAKER_03: I went to Berkeley and studied the uh very important data preparation subjects like history and dramatic arts.
7:14
SPEAKER_03: So and I pull on my dramatic arts training constantly, being uh doing events, doing podcasts like this, doing content and webinars and all of that.
7:24
SPEAKER_03: I like to say I have a fear of not public speaking.
7:27
SPEAKER_03: That's my so then just went out on my own after D and started doing all the stuff that I had the funnest time with when I was working at, you know, kind of a real job in corporations, doing events, doing presentations, doing all this kind of content stuff, and went out under this moniker, the data whisperer.
7:48
SPEAKER_03: I saw there were a whole bunch of other thought leaders, influencers, whatever you want to call them, who were making a go of it and actually making a living.
7:55
SPEAKER_03: And went, I've got a point of view.
7:57
SPEAKER_03: But my question was like, okay, do I have a point of view that anybody cares about if I'm not representing Nielsen?
8:01
SPEAKER_03: Could I build any kind of following?
8:03
SPEAKER_03: And then could I get brands to sponsor me?
8:05
SPEAKER_03: And it was like, yes, yes, yes, in pretty rapid succession.
8:08
SPEAKER_03: And the last couple of years it's just been a blast.
8:11
SPEAKER_03: So that's what I've been doing.
8:12
SPEAKER_03: But always focused on kind of the data.
8:15
SPEAKER_03: Where does data management fit in the broader scheme of things?
8:19
SPEAKER_03: Because it's not sexy, because it's not thought of as fun, because it's not new.
8:23
SPEAKER_03: And in my experience with all these enterprises, I met so many data leaders.
8:28
SPEAKER_03: Many of them in the early days were all in IT, but they exhibited two very similar emotions.
8:35
SPEAKER_03: One was this really incredible, determined passion around their commitment to make data work for their organization, especially kind of structured master data, reference data, metadata, and how they knew it was going to transform all kinds of problems.
8:51
SPEAKER_03: But that was generally coupled with this intense frustration because nobody would listen to them.
8:58
SPEAKER_03: And part of the reason nobody listened to them is because nobody understood them.
9:03
SPEAKER_03: And so I lived in this little niche that I'm very comfortable and happy in, which was in between kind of the IT slash data side and the business side and helping that data side explain the value to the business side.
9:21
SPEAKER_04: Right.
9:21
SPEAKER_04: So let me ask you this.
9:22
SPEAKER_04: So when you think about telling stories, like when people get into these presentations where they talk about all the wonderful motherhood and apple pie of data and master data and metadata and taxonomies and information architecture and all this, people's eyes glaze over, right?
9:36
SPEAKER_04: Because they're not, it's not resonating, but different stories will resonate with different people.
9:42
SPEAKER_04: And so when you start thinking about this, how do you kind of approach the storytelling piece of this?
9:48
SPEAKER_04: And can you give us some examples of things that really resonated or that really turned the light bulbs on in people's minds when you're talking to executives?
9:56
SPEAKER_04: Like, how do you translate that dry, you know, difficult, complex, abstract material into those stories?
10:04
SPEAKER_01: It's like you were talking about building and we want to talk about two by fours and trusses and drywall.
10:10
SPEAKER_01: Do you know what I mean?
10:11
SPEAKER_03: I mean, that's yeah, I know exactly what you mean.
10:13
SPEAKER_03: But then and part of the challenge that a lot of data leaders have is that they talk about the how rather than the why.
10:19
SPEAKER_03: So one thing that just sort of organic my background sales, marketing, strategy, natural communicator, storyteller.
10:26
SPEAKER_03: I love that storytelling is a thing now in the data space.
10:30
SPEAKER_01: It's it's definitely a hashtag now.
10:31
SPEAKER_01: You can throw in your LinkedIn attribute.
10:33
SPEAKER_03: Yeah, when it's always been like that's the way people communicate.
10:36
SPEAKER_03: But I'm glad it is.
10:38
SPEAKER_03: And and and when you kind of take it apart, data storytelling, I don't talk a lot about the data, but there's the story part, which is that narrative, then there's the telling part, which is the technique to communicate it.
10:50
SPEAKER_03: And that's the part that I tend to focus on.
10:52
SPEAKER_03: But the things I pull, you know, how simple can you make it?
10:56
SPEAKER_03: An exercise I do all the time.
10:57
SPEAKER_03: It's like, what's the fewest number of words I can use to articulate a concept?
11:04
SPEAKER_03: And can you make things relatable in some way in general?
11:08
SPEAKER_03: Do you use analogies and do you use metaphors?
11:11
SPEAKER_03: Do you tell kind of a narrative, or do you just come in with a hundred-page deck and expect to go through it all?
11:18
SPEAKER_03: And then the CEO goes, you know, what do you really want from us?
11:21
SPEAKER_03: And oh, I want to get through the next 95 pages.
11:23
SPEAKER_03: You know, that's not the way to do it.
11:25
SPEAKER_03: But coming up with these kind of pithy, like for instance, I I describe master data, which is my favorite kind of data, and I go out there and declare it's the most important data.
11:35
SPEAKER_03: And I haven't had anybody really challenging me on it, which is kind of fun, because uh my rationale is it's the data about the most important things in your organization, being your relationships and your brands, and you don't have good data about that, then the rest of stuff doesn't matter.
11:51
SPEAKER_03: But stra so when I talk about what's really critical, and you guys know this, right?
11:55
SPEAKER_03: Unique identifiers and standardized hierarchies and taxonomies and geographies.
12:01
SPEAKER_03: I kind of backed into this thing I call the four C's as an example, and just share with people look if you're structuring your data, and it's important to have structure, you need a code, you need a company, you need a category, and you need a country.
12:12
SPEAKER_03: So a simple way to describe it and then kind of go through what those things are.
12:16
SPEAKER_03: But in most cases, it is focusing on that why, focusing on why things like this really important structural data enables the strategic intention of whatever that enterprise is, and come from the sales and marketing side, you have to dive into some big global prospect or customer, figure out their business, and then apply why, in my case, you know, Nielsen or DB would help them.
12:42
SPEAKER_03: But it took a while, but it I realized, okay, you know, these problems these people have are pretty similar.
12:50
SPEAKER_03: And if they're not identical, they're more the same than they are different.
12:53
SPEAKER_03: So can you take their internal terminology about something like a relationship, right?
12:58
SPEAKER_03: Could be a customer, partner, prospect, citizen, patient, employee, consumer, right?
13:04
SPEAKER_03: Employee, but they're all relationships, right?
13:06
SPEAKER_03: So can you can you distill that down if you if you understand the value of relationships in an organization?
13:11
SPEAKER_03: And every organization has relationships, and if they don't have relationships, they don't have a business, so it's important everywhere.
13:18
SPEAKER_03: But speaking that internal terminology helps kind of cut through some of the chaff and and get to the point, and then figuring out how it aligns to the business objectives.
13:29
SPEAKER_03: And I learned it the hard way.
13:30
SPEAKER_03: I tell about it in my book, I talk about a really seminal moment in my career when I went into this company.
13:36
SPEAKER_03: I'm talking to the head, similar thing that I talked about, talking to IT.
13:40
SPEAKER_03: They love it, they get it.
13:41
SPEAKER_03: They're like, the business doesn't understand it.
13:43
SPEAKER_03: We're gonna set up this meeting with the head of sales.
13:46
SPEAKER_03: You come in and I explained it to him, and he kind of I'll never forget this.
13:51
SPEAKER_03: He kind of leaned back and he goes, I don't know what you're talking about.
13:54
SPEAKER_03: All I know is I gotta sell more bread.
13:56
SPEAKER_03: And this doesn't sound like it's gonna help me sell more bread.
13:59
SPEAKER_03: I forgot to mention he worked at a bakery, big bakery company.
14:03
SPEAKER_03: And it was like, that was a long ride back to the airport and realized I gotta come up with a better way to talk about this because it's not working.
14:11
SPEAKER_03: The terminology isn't working, the vocabulary is is too technical, it's focused on process rather than output.
14:17
SPEAKER_03: And so these things, you know, there's no single magic bullet, but the idea of not of speaking the language of the business and making sure the vocabulary you use is already something that people understand on the other side.
14:30
SPEAKER_03: You're gonna shortcut your process.
14:33
SPEAKER_04: Yeah.
14:33
SPEAKER_04: So did you come up with a story about bread or how did the how did this kind of evolve for you?
14:38
SPEAKER_04: So give us an example of something that's really resonated, you know, where an executive hasn't understood something and an analogy or a metaphor help them understand that because I do want to extend this to kind of the things that are first and foremost to organizations today, which is AI, machine learning.
14:55
SPEAKER_04: They think they can do it automatically.
14:57
SPEAKER_04: They don't necessarily think they need to worry about data architecture or reference data, taxonomies, metadata.
15:04
SPEAKER_04: The machine's going to do it all magically.
15:05
SPEAKER_04: But talk about how you've kind of translated that into you know a story that has kind of changed people's thinking.
15:11
SPEAKER_04: Can you give us an example of one of those?
15:14
SPEAKER_03: Speaking of bread, and I just realized there's this connection here, this motif in my career.
15:18
SPEAKER_03: So that was decades ago that I talked to this one major bakery.
15:22
SPEAKER_03: Last year I went into another, I was brought in.
15:25
SPEAKER_03: Part of what I do for a living is work with enterprises and kind of do inspirational, data-oriented conversations with, let's say, their teams or their business leaders and that kind of stuff.
15:36
SPEAKER_03: So this company, which was another global bakery company, one of the largest on earth, they they were putting in this new sales system.
15:45
SPEAKER_03: And they knew everybody understood sort of the value of the outputs and the end result.
15:49
SPEAKER_03: But these folks had seen me at another conference, a data management conference, and they were struggling with reinforcing the value of making sure that the standardized data and the stewardship and the governance that they were putting in place was adhered to and connecting those.
16:04
SPEAKER_03: So I actually used a bread example and I had cartoon level graphics where I started off with a sandwich, and that it was so at this point it was almost symbolic, and said, okay, this sandwich represents your objectives.
16:18
SPEAKER_03: You want this tasty sandwich, which is all the benefits of this new system that you're putting together.
16:23
SPEAKER_03: The next graphic was a cartoon of a bunch of people baking bread and say, Well, you know that there's a process here, and those are all your system symptoms, and those are all our your systems and your processes to put in place and all the software that you're going to use.
16:36
SPEAKER_03: But and then I had a bunch of ingredients flour, yeast, water.
16:41
SPEAKER_03: But if you don't have those proper ingredients going in there, you're not going to get that bread, and you're certainly not going to make that tasty sandwich.
16:48
SPEAKER_03: So just another variation of G-I-G-O, but you've got to make it more resonant to resonate with the audience.
16:56
SPEAKER_03: But that kind of almost symbolic way to share it through metaphor, through that kind of storytelling.
17:02
SPEAKER_03: In that case, they were like, Yeah, this is great.
17:04
SPEAKER_03: We get it.
17:04
SPEAKER_03: She's like, Oh, we're using this graphic all the time.
17:06
SPEAKER_03: Explain, you know, when we bring new people in, this is what we're doing.
17:09
SPEAKER_03: We're, you know, the objective is a sandwich, but we got to make sure we got the right ingredients going in for the bread, which is simple stuff.
17:15
SPEAKER_04: Yeah, and for the ingredients, what was that representing in terms of data?
17:19
SPEAKER_03: That was representing the data.
17:21
SPEAKER_03: So that was representing okay, we need the right data, it needs to be properly, you know, created and stored and managed, and the ingredients have to be of high quality, even though I like to not use the word quality in these discussions because I think it's just too much for the data, but there's still like a like a and to me, there's still like a little bit of a I guess, I guess if you could accept that metaphor and just say, okay, I'm always the one saying, Well, all right, how do we get to that next level and the detail and so on?
17:50
SPEAKER_04: But but I think for your purposes, that was sufficient to help them understand that the data is one of the ingredients, yeah.
17:57
SPEAKER_03: And that and that man, you know, and that good data is gonna have that sandwich taste better, right?
18:03
SPEAKER_03: You know, good ingredients are gonna make sure that that bread is is no matter how good your bakers are, right?
18:09
SPEAKER_03: You don't have you're not gonna, yeah, and I and I plan that all the time.
18:13
SPEAKER_03: You know, we talk about AI.
18:14
SPEAKER_03: If you put bad data into AI, you get AS, artificial stupidity.
18:18
SPEAKER_03: You don't put the right stuff in, it doesn't come out.
18:21
SPEAKER_03: We play on this, you know, we learn this on the first day of data, we learn garbage in, garbage out, but that you know, that hasn't really that doesn't drive any action, it's just like a you know, a snarky comment that the analysts made the back of the room, well, garbage in, garbage out, that's why it didn't work.
18:38
SPEAKER_03: It's like, let's do something about that.
18:40
SPEAKER_01: I thought I thought you were going someplace completely different with AS.
18:42
SPEAKER_01: I was hoping it was some you know artificial shit, but you know, that's generally what you get with stupidity.
18:50
SPEAKER_03: This is a family podcast, Chris.
18:52
SPEAKER_03: Really?
18:52
SPEAKER_03: This is we'll bleep him out.
18:56
SPEAKER_03: If you put if you put bad data into BI, you get BS.
18:59
SPEAKER_01: So yeah, see, it works, it works.
19:02
SPEAKER_01: That's that's AS or BS.
19:04
SPEAKER_01: Yeah.
19:05
SPEAKER_04: So how are you conveying this to executives these days in terms of you know, like I always find you you talk about the value of data, and people will nod their heads vigorously, yeah, data is important, there's a lot of value in it.
19:18
SPEAKER_04: Yes, we need to be data driven.
19:19
SPEAKER_04: And then when you start looking at what it's gonna take in terms of investment and in terms of process change and organizational change and you know, and technology uh remediation, and they see the price, they go, whoa, it's not that important, right?
19:33
SPEAKER_04: It's not that valuable.
19:34
SPEAKER_04: So how do you and and then there's always a well, we need an ROI, we need an ROI, we need an ROI.
19:39
SPEAKER_04: But many times when you're doing these things that are very foundational, you know, you can't, you are not showing a direct ROI, right?
19:46
SPEAKER_04: Because it's an enabler.
19:47
SPEAKER_04: And then what I also find is as soon as there's an ROI, everybody jumps onto that.
19:52
SPEAKER_04: Oh, it's my 360 program, oh, it's my you know, user experience program, oh, it's my data remediate, you know, it's this, it's this.
19:59
SPEAKER_04: So everybody kind of piles onto that, but really none of it will work without that foundation and having that linkage is what's so critical.
20:07
SPEAKER_04: So, how do you kind of convey that to executives these days, especially when you know a vendor is whispering in their ear that the you know the software whisperers, the then vendor whispers, when they are selling aspirational capabilities?
20:21
SPEAKER_04: Remember, I'm not telling saying that vendors lie, saying they're sending selling aspirational capabilities, meaning they lie.
20:29
SPEAKER_03: But when they're doing that, we might no more than a you know, no more than an airline that shows people who are, you know, having a wonderful time on vacation that because they've taken that trip, they can't make sure you have a wonderful vacation, they just get you there.
20:45
SPEAKER_03: But that aspirational piece is okay, I think.
20:48
SPEAKER_03: But yes, they they tend to, you know, an airline wouldn't say, all right, we're gonna make sure you have a nice, what, nice weather on the beach.
20:55
SPEAKER_03: While some of these software vendors will suggest all you need, that's all you need.
20:59
SPEAKER_03: All you need is this new platform, and boom, it happens, and then they forget about or don't mention the data part of it, and it you know happens constantly.
21:08
SPEAKER_01: The worst word in in software sales is absolutely right.
21:13
SPEAKER_01: Can it do this?
21:14
SPEAKER_01: Absolutely.
21:15
SPEAKER_01: Is it is it gonna do this?
21:17
SPEAKER_01: Absolutely, right?
21:18
SPEAKER_01: You hear that all the time, and yet the folks that are talking about it, you know, and and to their credit, they just don't know.
21:24
SPEAKER_01: They don't know what the the you know, because there's the problem is, and we get into this a lot, is is data that's been predefined and that actually needs to be refined in order to actually find something insightful for a semantic business that can't you can't, you know, apply this pre definition.
21:43
SPEAKER_01: It was like, why, you know, we actually sort of proliferate outside of, like we talked about, the BI notion and or you know, just SQL in general is ETL had an inherent flaw of it was you know already predefined as it got loaded.
22:00
SPEAKER_01: It in as opposed to, you know, the inverse of that is let's slam and cram it in and then figure out how to refine it after the fact.
22:06
SPEAKER_01: And so many people go into that with this notion of all my data is predefined and it's all going to go into this system that's managing everything.
22:15
SPEAKER_01: It's the source of truth and it's the line of business app and it's blah, blah, blah, blah, blah.
22:20
SPEAKER_01: And so will it do this?
22:21
SPEAKER_01: Absolutely.
22:22
SPEAKER_01: But they can't distinguish between pre-definition and the semantics.
22:27
SPEAKER_03: Everything demos beautiful.
22:29
SPEAKER_01: Right.
22:29
SPEAKER_03: That's where everything demos because the data in the demos, great.
22:33
unknown: Yeah.
22:34
unknown: Yeah.
22:34
SPEAKER_03: It's like, oh, this drop down.
22:35
SPEAKER_03: And when I see demos, that's how I get into conversation.
22:38
SPEAKER_03: I hate I don't do demos.
22:40
SPEAKER_03: I try not to watch that because I don't get any capabilities that got a feature and functionality.
22:43
SPEAKER_03: But when I'm forced to watch it, I will go like so.
22:46
SPEAKER_03: That drop down, where'd you get that?
22:48
SPEAKER_03: Did you get that?
22:48
SPEAKER_03: Oh, you can do anything you want.
22:50
SPEAKER_03: Well, that's the wrong answer.
22:51
SPEAKER_03: You know, it's it does.
22:54
SPEAKER_03: And so and so part of what you try to do is, you know, how do you get the data people in the room when they're making these big enterprise software decisions to make sure they're there going, okay, we need support to make sure the data will work in this new system.
23:08
SPEAKER_03: Because the system itself, and that's the fallacy that most folks are working under, which is I bought a new frying pan.
23:15
SPEAKER_03: Why doesn't this food taste any good?
23:18
SPEAKER_03: Because the ingredients suck and you don't know how to cook.
23:20
SPEAKER_03: But the frying pan on the commercial looked like it was, you know, and it's not yeah.
23:28
SPEAKER_03: So uh, you know, my new ninja foodie.
23:31
SPEAKER_03: It's it it's these things are are are rampant in our space, as we know.
23:37
SPEAKER_03: But there's always somebody when I worked with all these enterprises, they're the they're those data folks who know.
23:42
SPEAKER_03: And again, that's that frustration stuff I try to tap into.
23:44
SPEAKER_03: It's okay, how do you get in that room?
23:46
SPEAKER_03: Make sure, you know, don't just bitch about it or complain about it.
23:49
SPEAKER_03: You gotta get ahead of this.
23:51
SPEAKER_03: Yep.
23:51
SPEAKER_03: And so the part of the impetus to create this little niche of data storytelling that I've kind of carved in, which was data storytelling for data management versus data storytelling, which should which is the more prevalent one out there, data storytelling for analytics, or should be analytics storytelling, really, because it's all about BI and putting analytics in a business context to drive an action versus telling the story of why managing your data is important in the first place.
24:22
SPEAKER_03: And they're both.
24:23
SPEAKER_03: So my feeling is there's two kinds of data storytelling out there.
24:26
SPEAKER_03: There's the kind that is with data for analytics, then there's the kind about data, which is for business intelligence.
24:36
SPEAKER_02: Yeah.
24:36
SPEAKER_03: And we need both.
24:37
SPEAKER_03: You need it's not Sophie's choice here.
24:39
SPEAKER_03: You got to tell both, but that, but but they are distinctly different because there are many reasons why, but one of them is the analytics side tends to ignore the data management side, doesn't support it, sometimes gets competitive, sometimes gets adversarial and almost demeaning about it.
24:57
SPEAKER_03: I don't know how many times you guys have heard some big data leader, data leader who's really an analytics leader, stand up and say, well, data without analytics is worthless, or you know, data's just a cost center.
25:08
SPEAKER_03: That's all that is, until I do my, you know, business intelligence magic.
25:13
SPEAKER_03: And I hate that.
25:14
SPEAKER_03: I hate when they say that.
25:16
SPEAKER_03: I heckle them that way.
25:18
SPEAKER_03: And back to my bread analogy, I guess is the motif.
25:22
SPEAKER_03: You never heard a baker talk about how flour is worthless until I turned it into bread because they respect the ingredients.
25:29
SPEAKER_03: And that's my appeal to the analytics data science community, which is stop being adversarial, stop being, and it's it's still out there.
25:38
SPEAKER_03: And I think that that confuses the business side who just hears us bicker and goes, see, these guys can't even agree.
25:45
SPEAKER_04: You know, why are we fighting about which tech, you know, which part of it is better or more important than when you look at this space and you think about the fact that uh, you know, it is so hard, you know, executives been burned, you know, they've been made, they've made lots of investments and master data and data initiatives, and they haven't uh, you know, produced the value.
26:06
SPEAKER_04: But but we still have to focus on the blocking attack, and we still have to focus on the basics.
26:10
SPEAKER_04: How do you get uh why number one, why is it so hard to get them to do that?
26:14
SPEAKER_04: And and what are the things that you have to say to get them to focus on the basic blocking attack?
26:20
SPEAKER_03: What are the things that resonate?
26:22
SPEAKER_03: I I I think what I I think part of the problem is we use a lot of stuff that doesn't resonate, like data quality, like you know, we need a data quality initiative, we need a data hygiene initiative.
26:32
SPEAKER_03: You want to clear a room with stakeholders, start talking about a hygiene program.
26:35
SPEAKER_03: Nobody wants that.
26:36
SPEAKER_04: Talking about governance.
26:39
SPEAKER_04: Uh wait a minute.
26:40
SPEAKER_04: I'm uh I'm open never.
26:41
SPEAKER_04: How does never work?
26:42
SPEAKER_04: Yeah, yeah.
26:42
SPEAKER_03: How does never work for you?
26:44
SPEAKER_03: Yeah, but they're all you know, emotional, ad hoc, tactical soundings.
26:48
SPEAKER_03: It's like, why do I bother?
26:50
SPEAKER_03: So flipping it around understanding as a data leader, I think it's incumbent on data leaders to understand their business as deeply as possible before they start adding data to it.
27:04
SPEAKER_03: You know, what do we do?
27:05
SPEAKER_03: Why do we do it?
27:06
SPEAKER_03: Where do we bring value to our relationships?
27:10
SPEAKER_03: What's our brand like?
27:12
SPEAKER_03: What's that experience like?
27:14
SPEAKER_03: And then map that to the data you need to get there.
27:20
SPEAKER_03: I have a fairly simple definition of digital transformation, which I define as providing value to your relationships through your brands at scale.
27:29
SPEAKER_01: Yeah, at scale is key.
27:30
SPEAKER_03: Yeah, at scale is what makes it transformational, right?
27:33
SPEAKER_03: So it's providing value to relationships through your brands been that's what business is.
27:37
SPEAKER_02: Yeah, right.
27:38
SPEAKER_03: But at scale, you go, okay, at scale means technology, technology means hardware, software, data.
27:43
SPEAKER_03: If you have data, you need you've got data management problems or needs.
27:47
SPEAKER_04: By the way, I do want to uh let people know about halfway through our our uh uh time today, uh, and I wanted to remind folks, uh our listeners, that we are talking with Scott Taylor, the data whisperer.
27:59
SPEAKER_04: We'll be right back.
28:01
SPEAKER_04: We'd like to thank Marketing Artificial Intelligence Institute for their support of this podcast.
28:07
SPEAKER_04: Marketing AI Institute is a media, event, and online education business that makes AI approachable, actionable, and accessible to marketing leaders around the world.
28:16
SPEAKER_04: They run the world's leading marketing AI conference, MACOM, which brings together hundreds of marketing and business leaders to better understand, pilot, and adopt AI.
28:26
SPEAKER_04: Today's podcast is brought to you by Simpler Media Group, owner of the Customer Experience Decoded Podcast, covering the latest in customer experience and marketing.
28:34
SPEAKER_04: CX Decoded is a semi-monthly podcast designed to deliver insight from leaders driving the evolution of customer experience with an emphasis on digital touch points, customer journeys, respectful data management, and voice of the customer.
28:53
SPEAKER_01: Early information science is a professional service firm with proven methodologies designed specifically to address product data, content assets, customer data, and corporate knowledge bases.
29:02
SPEAKER_01: Their information architecture expertise has allowed clients from all over the world to efficiently organize, access, and utilize their data, ultimately resulting in better business outcomes.
29:11
SPEAKER_01: They deliver customer-driven solutions that scale and adapt to the needs of a business as it evolves.
29:17
SPEAKER_01: Founded in 1994, Early Information Science has proudly delivered result-driven solutions for more than 100 brands across the top industries.
29:25
SPEAKER_01: You can get more information about Early Information Science at early.com, E-A-R-L-E-Y.com.
29:34
SPEAKER_01: Working with customers in AI, the number one question that I get usually is where do I start?
29:40
SPEAKER_01: What data do I need?
29:41
SPEAKER_01: How do I gather it?
29:42
SPEAKER_01: And it's great questions.
29:44
SPEAKER_01: Well, if you want to catch the wave of AI, not be crushed by it, you really do need to read a book written by Seth Early called The AI Powered Enterprise.
29:52
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29:59
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30:12
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30:20
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30:27
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30:33
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30:35
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30:39
SPEAKER_01: Out on Amazon.com today.
30:41
SPEAKER_01: And now back to the show.
30:44
SPEAKER_01: You certainly are.
30:45
SPEAKER_03: That would be me.
30:47
SPEAKER_03: Thank you.
30:48
SPEAKER_03: Station identification.
30:49
SPEAKER_03: That's our station break.
30:50
SPEAKER_03: Yeah.
30:50
SPEAKER_03: There we go.
30:51
SPEAKER_03: And we're back.
30:51
SPEAKER_03: All right.
30:52
SPEAKER_03: Let our affiliates know we're going to run long today for sure.
30:55
SPEAKER_03: Yeah.
30:56
SPEAKER_03: So it's so it's making that real for that particular organization.
31:00
SPEAKER_03: So I can talk about this very generic on purpose.
31:03
SPEAKER_03: And the work I do is try how to make these kind of statements and ideas as extensible as possible across every kind of business, as long as it's an enterprise.
31:12
SPEAKER_03: That's sort of the scope I focus on.
31:15
SPEAKER_03: If you're an enterprise, you've got these similar challenges.
31:19
SPEAKER_04: And showing that linkage.
31:21
SPEAKER_04: What you just said before is how do you produce that value?
31:24
SPEAKER_04: And then when you start looking at those different components for producing that value, what is the data that has to support that value?
31:32
SPEAKER_04: So it's it's understanding those processes, it's understanding who's involved.
31:37
SPEAKER_04: And then what are the data sources that you need?
31:40
SPEAKER_04: And how are those data sources uh managed, curated, or produced, or optimized, or a lot, whatever it is that needs to happen.
31:48
SPEAKER_03: Or how do they look today?
31:49
SPEAKER_03: How's that actually so another technique?
31:51
SPEAKER_03: And I've got in my book, I use it all the time.
31:53
SPEAKER_03: We would go in, I mean, at Nielsen and Dun and Brad Street, we would help people manage their data.
31:57
SPEAKER_03: So part of the approach, we did a lot more at Nielsen because DMB is a little not quite as uh um into that, unfortunately.
32:06
SPEAKER_03: I don't know how to articulate it, but at Nielsen, we were pretty overt about a process that I affectionately refer to internally as showing somebody their baby's ugly.
32:15
SPEAKER_03: And you'd have to just like show here's the here's the here it is, here's the shame report, here's the 57 ways you spell 7-Eleven, here's the hierarchies you you know, that are a mess.
32:24
SPEAKER_03: Here we did a we did a pie chart of your customer vertical classifications, and the biggest slice is other.
32:32
SPEAKER_03: Yeah, oh yeah.
32:33
SPEAKER_03: You know, and you just and then you and then you so part of it is sort of taking that data reality, a horror story, and mapping it to executive leadership who's talking about the importance of their relationships, right?
32:48
SPEAKER_03: Yeah, and say, how can you manage these global relationships?
32:51
SPEAKER_03: And again, the bigger they are, the bigger the problems.
32:53
SPEAKER_03: It's wonderful.
32:54
SPEAKER_03: How do you start to manage these relationships in any possible systematic and repeatable way when the data you've got is crap?
33:01
SPEAKER_03: And let me prove it to you.
33:02
SPEAKER_03: You know, if you've got a third of your files and they're that are duplicates, you can't talk your way out of that's a process problem you got to fix.
33:09
SPEAKER_03: You're never gonna, you know, software isn't gonna do it for you.
33:12
SPEAKER_03: You've got to fix it, you got to keep that garbage from coming in.
33:15
SPEAKER_03: You got to like, you know, how about search before you create?
33:18
SPEAKER_03: How about trying that once in a while?
33:20
SPEAKER_03: But these things, so tying those together, the really high-level strategic, visionary stuff that the company's trying to do, and smacking them in the face with the data reality and saying, you know, where you said we were gonna go in our investment day presentation about scaling the bit, you you don't have the data to get there, right?
33:38
SPEAKER_03: Our data situation, and it doesn't mean we need more data scientists, we need more data stewards, we need more data management to build that and showing those examples, I think, are very powerful.
33:49
SPEAKER_04: You know, when you show executives, here's what happens when I search, or here's what happens when I try to do a report, or here's what happens when you know, when we try to show uh this customer experience, you know, these are the things that the customer sees, or these are the things that you know your your your leaders are trying to make decisions on, but there's just not enough granularity or detail, or you have too many of those other miscellaneous general, not otherwise categorized categories.
34:19
SPEAKER_03: Yeah, why do you have you know other other was the favorite?
34:22
SPEAKER_03: Yeah, right.
34:24
SPEAKER_03: I saw one, you may have picked up this one.
34:26
SPEAKER_03: There was one that had an attribute value DK, and we asked them what that meant, and they said it stands for don't know.
34:32
SPEAKER_04: Wow, okay, that's great.
34:35
SPEAKER_01: So another form of you know, define irony, it's probably their most important data that they had is don't know, right?
34:41
SPEAKER_03: It's yeah, it's certainly one of the biggest categories, one of our biggest growing categories is don't know.
34:46
SPEAKER_03: But they said, But if you can find those tangible examples, you don't need a lot of them.
34:50
SPEAKER_03: I mean, I have story on story on that.
34:52
SPEAKER_03: One was going into this rather large company, talking to the COO.
34:57
SPEAKER_03: When I was at Nielsen, we had excellent hierarchies, retailer hierarchies.
35:01
SPEAKER_03: And as the exercise, we took their retailer hierarchy examples and put them against ours.
35:06
SPEAKER_03: And it started off really simply like, you know, okay, you've got this many stores in this market, we've got this many.
35:11
SPEAKER_03: You're missing like nine.
35:12
SPEAKER_03: All right, big deal.
35:14
SPEAKER_03: Or this, but and it just got I had the story we put together was increasing in increasing order of horrificness on purpose, and so we got to halfway through, and the COO, but it's one of the best business moments of my life, puts his head in his hands and he's like, enough, enough for it.
35:35
SPEAKER_03: Talk to Janet.
35:36
SPEAKER_03: Janet was our contact, she's just like beaming, like, we're gonna get this done.
35:39
SPEAKER_03: You know, the the the skilled part of me, the seasoned part of me didn't say, Oh, I've got nine more examples to show you.
35:46
SPEAKER_03: It was okay, we're done, let's move on.
35:48
SPEAKER_03: But sometimes that's what you got to do.
35:50
SPEAKER_03: You got to rub somebody's nose in it, you got to show them that, you know, yeah, you've got these great aspirations, but you don't have the goods.
35:56
SPEAKER_04: Yeah, here's the ugliness.
35:58
SPEAKER_04: Yeah, here's the ugliness on the ground.
36:00
SPEAKER_04: Yeah, yeah.
36:00
SPEAKER_04: No, those tangible examples.
36:02
SPEAKER_04: And I did one way back from Motorola, and I was doing a search for mobile office, and I would get in Latin America, I'd get a picture of a woman's torso in a bikini with holding a phone, you know, like that was mobile office, right?
36:17
SPEAKER_04: That was how that was one that I showed them.
36:19
SPEAKER_04: They're like, yeah, yeah.
36:21
SPEAKER_03: Yeah, you can't argue with that.
36:22
SPEAKER_03: I mean, it's hard that you know the data doesn't lie.
36:25
SPEAKER_03: It's like the data tells its own story when you got all these problems.
36:29
SPEAKER_03: You have to interpret that, yeah.
36:32
SPEAKER_01: So most most people do they find you on social media or by word of mouth, or how do they how do you, you know?
36:39
SPEAKER_01: I mean, obviously, you you know, you're in the in you know, these these areas and stuff, but you know, the the produced version of you, you know, doing doing some of those things as well as the consulting version, how do we usually get you know people calling you in and bringing in?
36:53
SPEAKER_03: Now it's almost all inbound from LinkedIn, which is so wonderful, or word of mouth, or I'll do, you know, but it took some time to build that, yeah.
37:01
SPEAKER_03: Of course, and which I'm particularly proud of, but I've gotten a lot of recognition, which is swell because I started from like nothing, right?
37:08
SPEAKER_03: So I came out of D and B and Nielsen, and it's like like I said, do I have a point of view anybody cares about it?
37:13
SPEAKER_04: How long has it been since D?
37:14
SPEAKER_03: How long has it been since uh I started off on my own as the data whisper and you know MetaMeta Consulting in 2019?
37:23
SPEAKER_03: Uh COVID actually was was really so well.
37:28
SPEAKER_03: I I felt like too it was like there's all I took a was fairly strategic about approaching it and went, okay, can this even be a business?
37:35
SPEAKER_03: But there was a dis as usual, there was a disproportionate number of voices on the analytics and data science side.
37:43
SPEAKER_03: And so I kind of wormed my way in of like, let me be the data management voice in this data science agenda or this e-commerce agenda, or I just did a a gig a couple months ago in France on inbound marketing, it was all marketing, and I was the only data person there.
38:02
SPEAKER_03: So imagine you know what you get to teach people that when you're the only data person.
38:06
SPEAKER_03: Um it's really fun.
38:08
SPEAKER_03: But people see me on LinkedIn, I got a really decent following.
38:11
SPEAKER_03: I keep going, I'm out there, I've got stuff on, and the book helped a lot too.
38:15
SPEAKER_03: I realized if I was going to be serious about doing this, you gotta write a book.
38:20
SPEAKER_03: Yeah, and so that that was a compendium of a lot of my experience.
38:24
SPEAKER_01: And give a quick plug on the book that you know, the title, where people can find it.
38:29
SPEAKER_03: So my book is Telling Your Data Story.
38:32
SPEAKER_03: Data Storytelling for Data Management.
38:34
SPEAKER_03: I know this is only audio, but on the front of the book, there's a uh little label that says 99 buzzword free.
38:42
SPEAKER_03: That was important.
38:44
SPEAKER_03: I I didn't want to overpromise.
38:46
SPEAKER_01: It's I haven't I haven't read through the the entire thing, but what I have, you know, just in the first couple chapters it's it's a really great start, right?
38:54
SPEAKER_01: To people who want to understand how to get going.
38:56
SPEAKER_01: And you know, when people, when somebody says, Hey, you want let me show you something super cool.
39:01
SPEAKER_01: No, no, no, no, it's not cool, it's it's valuable.
39:05
SPEAKER_01: Let me show you something valuable.
39:06
SPEAKER_01: You know what I mean?
39:08
SPEAKER_01: And that's that's what I love about it is it's practical and it's not.
39:12
SPEAKER_03: I try to just lay it out.
39:13
SPEAKER_03: You know, there's not a lot of sort of market research I did.
39:16
SPEAKER_03: It came from knowing that I'm was effective in explaining the value of data management, yeah, taking those themes.
39:24
SPEAKER_03: Part of what I think I do is give people a little solace to realize that they're not alone, if you will.
39:30
SPEAKER_03: So the frustration that a lot of these data leaders from the data management side had, some of it came from like, well, you know, our organization is so messed up.
39:38
SPEAKER_03: It's like, guess what?
39:40
SPEAKER_03: You're an enterprise.
39:42
SPEAKER_03: This is the nature of enterprises.
39:44
SPEAKER_03: It's it's not your fault.
39:46
SPEAKER_03: It's okay.
39:47
SPEAKER_03: But you know, you got to fix it.
39:49
SPEAKER_03: And then to be a little bit, I don't know if it's controversial, but to be very pointed around the things that have been out trying to help data management for so long that haven't worked, like picking on the word quality, like picking on how like picking on that vocabulary and saying, even though it's really important, it doesn't sell well.
40:10
SPEAKER_03: And even getting down to the point of saying, okay, the the story you are gonna tell in most cases about data management isn't some epic journey.
40:20
SPEAKER_03: It's not a suspense story, it's a pitch.
40:22
SPEAKER_03: You got to sell this in.
40:24
SPEAKER_03: And whether you're when you're talking to your executives, you want them to take action on what you are explaining to them.
40:32
SPEAKER_03: And that is, at its essence, a sales process to get somebody to commit to take action on the benefits that you have to offer.
40:40
SPEAKER_03: That's selling more than anything else.
40:42
SPEAKER_03: So it's not like you got a quota, but those techniques I tried to put in the book a little bit.
40:48
SPEAKER_01: You know, we have all these people that that basically like, hey, I want to challenge the status quo, but they don't look in the mirror and ask that same question.
40:56
SPEAKER_01: And that's the problem, that's one of the big problems.
40:59
SPEAKER_01: Like, yeah, all of that stuff out there, yeah.
41:01
SPEAKER_01: Let them let them disrupt the hell out of the market and everything else instead of looking and saying, Well, let's disrupt our own status quo and ask hard questions of ourselves, which it's you know, it's it's the um the classic um, you know, lung doctor that smokes cigarettes.
41:18
SPEAKER_01: You know, it's like there should not be a gotcha here, guys.
41:23
SPEAKER_01: You know, but you know, being vocally and you know, just being self-critical in general is not a strong suit of humans, you know, at large, but you know, we'll say even at scale, you know, especially in these organizations.
41:36
SPEAKER_01: So yeah, it's but that's that's what I you know, I I I thought the first the first part of I'm still digging through it, but the first part of it, I loved it because it was just like you said, super practical.
41:45
SPEAKER_03: I just no thank you.
41:47
SPEAKER_03: That's a lot of the feedback I've been getting, which has been really gratifying.
41:50
SPEAKER_03: Yeah, and it's on Amazon, it's on Technics Pub.
41:53
SPEAKER_03: We'll give you guys a coupon code if you want to put it in the show notes, yeah.
41:56
SPEAKER_03: But it's been selling well, it's still and and helped with the validation of me as a voice in the community.
42:04
SPEAKER_03: And I draft, I you know, admittedly drafted off of that of the bigger buzz around data storytelling in general, yeah.
42:14
SPEAKER_03: But as usual, it wasn't talking about data management, it was talking about analytics.
42:20
SPEAKER_02: Yep.
42:20
SPEAKER_03: So it's like, where's the voice of data in data storytelling?
42:24
SPEAKER_03: Where's the voice of data management?
42:25
SPEAKER_03: I I don't hear it.
42:26
SPEAKER_01: I don't hear it.
42:27
SPEAKER_01: Let me help let me help you guys out by doing a double click into what is the facade, into actually the real tangible meat of what you need to be able to think about, look at, and question in order to get to this your facade, whatever that is, right?
42:43
SPEAKER_01: So yeah, it was good.
42:44
SPEAKER_03: Maybe reinforce on, you know, don't talk about how you're doing it, talk about why it's important.
42:48
SPEAKER_03: Exactly.
42:48
SPEAKER_03: These are not, I didn't invent that, but you gotta somebody's gotta remind people of that.
42:52
SPEAKER_02: Yeah, yeah.
42:53
SPEAKER_03: And and try to be a little bit inspirational about yeah, the work you're doing is really critical to your organization.
43:00
SPEAKER_03: Don't forget that.
43:02
SPEAKER_03: But if you don't talk about it the right way, everybody else is gonna forget about it.
43:06
SPEAKER_03: For sure.
43:06
SPEAKER_01: You said you you oh go ahead, go ahead.
43:09
SPEAKER_01: Um keep going.
43:11
SPEAKER_01: Say you, you know, you said um you started leaning into you know a little bit.
43:16
SPEAKER_01: Did COVID help accelerate where you're at today, or did it Yeah, it did.
43:20
SPEAKER_03: So I it was, I mean, it initially it was a big hit, so all my events canceled, but it made me go through my own personal digital transformation.
43:29
SPEAKER_03: First of all, I wrote the book, so I sat down and went, All right, I've been talking about doing a book forever, and everybody done, you know, I gotta write this book.
43:36
SPEAKER_03: And I at least had the presence of mind, even though nobody knew how long it was gonna go.
43:40
SPEAKER_03: I felt like, okay, this time, this kind of time isn't gonna come around again.
43:45
SPEAKER_03: Have this much free time.
43:47
SPEAKER_03: So this is my internal dialogue, but it was write the book now or shut up forever.
43:52
SPEAKER_03: So hunkered down and wrote the book.
43:54
SPEAKER_03: I also got a whole lot better and really practiced and went very aggressively on.
44:00
SPEAKER_03: The video and virtual meeting kind of techniques.
44:04
SPEAKER_03: I think I'm really good on stage, but camera and lighting and sound, you know, all those things.
44:11
SPEAKER_03: Part of what I did was uh, and then when after I wrote the book, I did a virtual world book tour where I went to I presented to 35 of the Dama chapters.
44:22
SPEAKER_03: There's 50 of them.
44:24
SPEAKER_03: So that was a blast.
44:25
SPEAKER_03: And that helped me hone a whole other set of content, got me really well practiced on kind of doing the one hour webinar with questions and answers, and yeah, and and uh and it got me exposure all over the world, which before the only way to do that was to physically go.
44:41
SPEAKER_03: Right, right, right.
44:42
SPEAKER_03: And so got I did a lot of webinars for brands and then started to pick up and people saw me, and that and that kind of work is really I still love doing physical events, but it's pretty sweet to just kind of wake up and you know dress from the waist up and do your thing and uh in uh an event for somebody and and still be at home.
45:01
SPEAKER_03: So I was able to at least branch my business out.
45:04
SPEAKER_03: Right, right.
45:06
SPEAKER_04: So are your engagements mostly speaking and workshops and facilitation?
45:11
SPEAKER_04: Is that uh the bulk of the consultation?
45:13
SPEAKER_03: Yeah, not and and and when it and Phil, I'm really light.
45:16
SPEAKER_03: So I used to, and when I started when COVID hit, about half my business was actual consulting, consulting, and I would do product consulting for data organizations that were trying to productize their master and reference data assets.
45:33
SPEAKER_03: So very similar to the products I represented at Nielsen and DB, but also an extremely small niche.
45:40
SPEAKER_03: And I just started to get and then I was doing more and more events and more and more of this content stuff and working with more brands who were like, Oh, how about you work with us and we'll run excerpts of your book on our website, and then you can speak to our sales team.
45:52
SPEAKER_03: It's like this stuff is so much more fun.
45:54
SPEAKER_03: And you know, if you do consulting, you guys have this experience, right?
45:58
SPEAKER_03: Half the time, if not more, they don't listen to you anyway.
46:00
SPEAKER_03: So it's not very satisfying.
46:02
SPEAKER_03: And you get into the politics and you gotta learn somebody's business.
46:05
SPEAKER_03: So it was like this this kind of work is is way more fun, way more uh exhilarating.
46:12
SPEAKER_03: So I've stopped doing any of the kind of hardcore consulting as I would define it.
46:16
SPEAKER_03: I was never and just do, and most of the work is with other brands, so software companies, service companies that are trying and help amplify their message, support their prep because I talk at a very high level.
46:28
SPEAKER_04: Right.
46:29
SPEAKER_03: I'm very thin, but I'm very broad, and I'm kind of in a marketing sense, I'm top of the funnel.
46:35
SPEAKER_03: Let me get your attention, let me get you in the room, right?
46:38
SPEAKER_04: Right.
46:38
SPEAKER_03: So give you the big picture, and you know, here's why everything that any company's trying to do doesn't work unless you've got this stuff.
46:48
SPEAKER_03: And now take it away, do the demo because the premises are there, not their particular services.
46:53
SPEAKER_03: But if you I'm trying to get them to realize look, data management is macro trend agnostic.
46:59
SPEAKER_03: No matter what's going on, you're still gonna need it.
47:02
SPEAKER_03: And then I play a parlor game, new stuff comes out, and I go, all right, let me figure out where the master data reference data metadata story is in this, because it's there, right?
47:11
SPEAKER_03: It's going to be there.
47:13
SPEAKER_03: There's nothing in technology that doesn't need it.
47:16
SPEAKER_03: Give me an example or the I when the IoT came out, it was like, all right, what's the IoT?
47:22
SPEAKER_03: What's that work?
47:23
SPEAKER_03: Right, Internet of Things.
47:25
SPEAKER_03: And then very quickly, it was like, okay, everything needs to connect to everything else when it should.
47:31
SPEAKER_03: And you need the should part means you need identification, you need validation of entities, you need hierarchy structures, you need categorization, you need geographies.
47:41
SPEAKER_03: You back right into those four C's of like code company, category, country.
47:45
SPEAKER_03: You know, the metaverse, you know, how's that going to work unless you've got unique identifiers, unless you've got you know, if brands are going to participate in there, then these debranded entities and validation and you know, transactions only work if you've got standardized data.
47:59
SPEAKER_03: Right.
48:00
SPEAKER_03: So it's kind of peeling that around and just finding that incredible.
48:04
SPEAKER_04: And one of the things that that I found is you kind of it's kind of constantly swimming against this tide of vendors saying you don't need these things because we've already made those decisions.
48:15
SPEAKER_04: And I read an article, There's no AI without IA a few years ago that talked about the fact that when vendors say, Oh, it's in there, we've done it, that's because they have made the decisions about classification and data standards and data architecture before they've sold you this tool.
48:29
SPEAKER_04: If they say you don't have to do it, it's because they did it.
48:31
SPEAKER_04: And is that what's going to really differentiate your business?
48:34
SPEAKER_03: Yeah, and do you want the way they do it?
48:35
SPEAKER_03: Probably the way they do it.
48:36
SPEAKER_04: Is that going to treat your process?
48:38
SPEAKER_01: That's why it always it always absolutely works, right?
48:42
SPEAKER_01: Yeah, yeah, it's a demo.
48:44
SPEAKER_03: That's why it's everything.
48:45
SPEAKER_01: Absolute pre-definition works absolutely.
48:47
SPEAKER_01: How's that?
48:48
SPEAKER_03: Everything everything demos beautifully.
48:51
SPEAKER_01: Hey, so Scott, what's what's uh what can we look forward to from you?
48:56
SPEAKER_01: Um, because I do want to tie into your I think is your grandbaby, right?
48:59
SPEAKER_01: On the uh on the videos, but what what can we look forward to in terms of of what you got coming up this year?
49:07
SPEAKER_03: More content coming.
49:08
SPEAKER_03: I'm doing a couple of events, which are really fun.
49:10
SPEAKER_03: I'm event chair at this big data conference in Vancouver, that kind of stuff I just live for.
49:16
SPEAKER_03: It's really fun work for me.
49:19
SPEAKER_03: So I'll be the MC and kind of in between the this and and what I like is kind of making sure the energy is always there, that there's a connection.
49:28
SPEAKER_03: If there's something funny, spontaneous, I love that kind of challenge of being on stage and like coming up with some bit or a comment that relates to what just happened that illuminates it.
49:39
SPEAKER_03: And that for me intellectually is really satisfying.
49:42
SPEAKER_03: And so I'm doing more events.
49:44
SPEAKER_03: Um, there will be more puppets.
49:45
SPEAKER_03: If you haven't seen my puppet show, too much tech talk, which is about uh the CDO, the chief dog officer, and the ITB who speaks only in buzzwords.
49:56
SPEAKER_03: They're gonna meet up soon with a cat sultan from Meow Kinsey who will be dealing with them.
50:03
SPEAKER_03: They're gonna look at a couple of different uh opportunities.
50:07
SPEAKER_03: So I'm I'm I've been working on this for a while, just throwing all these kind of bits and jokes.
50:12
SPEAKER_03: And there's an antelist from Gardner, so he'll have his.
50:16
SPEAKER_03: And what I found is sort of the corny puns play globally.
50:20
SPEAKER_03: Yeah, they do, so it's as as much of a dad joke approach I have.
50:26
SPEAKER_03: It works.
50:26
SPEAKER_03: People think it's fun.
50:28
SPEAKER_03: So I definitely want to do more of those.
50:30
SPEAKER_03: I've got a whole series kind of planned here, so rather ambitious, but it uh should be fun.
50:35
SPEAKER_03: And yeah, more videos.
50:36
SPEAKER_03: I got another book.
50:36
SPEAKER_03: I'm gonna read to my grandson if you saw the little red data hand.
50:39
SPEAKER_02: Oh, yeah, yeah.
50:41
SPEAKER_03: Uh, there's a couple we're working on.
50:43
SPEAKER_03: One is uh the Emperor's New Dashboard.
50:46
SPEAKER_03: That one kind of writes that writes itself.
50:49
SPEAKER_03: Wonderful.
50:50
SPEAKER_03: Uh the three little data scientists in the big bag data, they all kind of work their mind.
50:56
SPEAKER_01: I mean, I know we we we joke about it and you know do it in jest.
51:00
SPEAKER_01: However, there's there's something there.
51:03
SPEAKER_01: And I had I just had a good friend of mine uh who's been on the podcast, a guy named Paul Zhao, who wrote a phenomenal children's book for his kids, and it was all around the solar system.
51:15
SPEAKER_01: And it was it was these these fictional characters, of course, in cartoon land that that you know, this seal that wanted to go to space.
51:23
SPEAKER_01: And he goes and he talks to his friends, who's one's scientist, one's just a creative, you know, all these different kinds of things to talk to him about it.
51:31
SPEAKER_01: And they all have their inputs.
51:33
SPEAKER_01: And I I you know, I think there's something to be said about you know, about teaching kids, because there really is nothing now, but teaching kids the and please feel free to take this and do this, where there's a structure of kids' books that talk about, like you said, and it goes back to, you know, why do kids take Legos and put them in the same form, in the same color, in the same structure, in the same size, so that they can build things appropriately depending on what the outcome that they're wanting to build.
52:06
SPEAKER_01: And they'll put all those things together first and organize it.
52:10
SPEAKER_01: I see my kids doing it with Skittles, right?
52:12
SPEAKER_01: Like, how do they want to eat them first?
52:14
SPEAKER_01: Which is the best, or do they're gonna be in the data business, that's for sure.
52:17
SPEAKER_03: Yeah.
52:18
SPEAKER_01: Well, but to your to your point, you're you're getting into your you're time, you're pulling on this really neat thread of there may be some scenarios where putting some things centered around kids' books that starts that deep subliminal struct, you know, pieces of data structure that helps them formulate what this means.
52:36
SPEAKER_01: Because you know, this, I mean, it's crazy.
52:38
SPEAKER_01: We talk about it at a um professional level, but there's a huge gap between my kids that want to understand how to develop something, what goes on and taught in school, to the time they get out of school and go into you know their secondary education, and that's where they get into hopefully, you know, somebody who's not self-important in terms of the instructor, actually helping them understand what this looks like.
53:04
SPEAKER_01: But if we can help them understand that, hey, you guys putting and organizing you know your blocks and your your your um your money or your your things into logical segmentation is absolutely healthy.
53:18
SPEAKER_01: And it's what we're doing is we're doing it because you obviously want to drive some kind of an outcome or you want to be able to do something with it in a specific way.
53:26
SPEAKER_01: So putting that stuff together could be some phenomenal children's books, right?
53:32
SPEAKER_01: And get them thinking in that so that they're they they actually associate later with some of this in terms of oh wow, yeah, I actually know exactly what they're talking about with you know with data management.
53:42
SPEAKER_04: I did this with the future data managers of America, yeah.
53:46
SPEAKER_03: Yeah, my my favorite part of that that doing the uh the video with my grandson, which everybody should watch, who's now affectionately known in LinkedIn as Data Whisperer Jr.
53:56
SPEAKER_02: So I love that exactly right.
53:58
SPEAKER_03: Where where the ad libs he had, so he was really he got really into the book, even though the book was a goof.
54:03
SPEAKER_03: And he's like, What's a silo?
54:05
SPEAKER_03: You know, why why do they do that?
54:08
SPEAKER_03: And so I and I had to answer, you know, kind of in givea keep it going.
54:12
SPEAKER_03: But for that was it was really fun.
54:13
SPEAKER_03: So, you know, overall, what you're gonna see from me is sort of more content, leaning more toward the entertaining side rather than the deeply informative.
54:21
SPEAKER_03: There's room for all of us, and just continue to that's great, help people spread the word about the importance and and and the strategic need for proper data management.
54:33
SPEAKER_03: I stay true to that focus and just figure out how many other fun ways I can talk about it and help people talk about it.
54:40
SPEAKER_04: Well, let's stick to uh label on your on your I was gonna say we are out of time.
54:46
SPEAKER_04: Uh a pleasure, Scott.
54:48
SPEAKER_04: Did you want to uh have some finishing, closing remarks there?
54:51
SPEAKER_03: Oh no, this has been great.
54:52
SPEAKER_03: I've you know enjoyed talking to you.
54:54
SPEAKER_03: I can't believe that it went by so fast.
54:55
SPEAKER_03: Anybody wants to reach me?
54:56
SPEAKER_03: I'm in in LinkedIn, the Data Whisperer and YouTube and my website, but uh happy to help and talk to anybody about talking about data.
55:05
SPEAKER_01: Um Scott, pleasure.
55:07
SPEAKER_01: Thank you so much for coming on.
55:08
SPEAKER_01: Let's let's collaborate on some kids' books.
55:12
SPEAKER_01: Sure.
55:13
SPEAKER_01: And take all your uh all your um good artists borrow and great artists deal.
55:17
SPEAKER_01: So let's take all your your good um characters that you've already developed.
55:21
SPEAKER_01: Let's get them personified into uh into a book and as they go through life and see what this can comes of it.
55:28
SPEAKER_04: Scott, it's been great.
55:29
SPEAKER_04: Thank you again.
55:30
SPEAKER_04: Uh this has been a pleasure.
55:31
SPEAKER_04: And uh the show notes will contain links to your your uh your your videos and uh your book.
55:38
SPEAKER_04: Uh and again, they're very, very entertaining and they're great fun.
55:42
SPEAKER_04: So uh keep up that wonderful work and uh uh looking forward to uh continuing to to communicate and collaborate with you.
55:50
SPEAKER_04: So thank you.
55:51
SPEAKER_01: Yeah, thank you so much.
55:52
SPEAKER_01: Have a great weekend, everybody.
55:57
SPEAKER_00: Thanks for listening to the Early AI Podcast with your hosts Seth Early and Chris Featherstone.
56:04
SPEAKER_00: We hope you enjoyed the program and took away some ideas that you can start to implement in your organization right now.
56:10
SPEAKER_00: If you're stuck and need help, check out www.early.com for information about early information science consulting services.
56:18
SPEAKER_00: And of course, check the show notes for links to any resources we mentioned today.
56:23
SPEAKER_00: That's all for now.
56:24
SPEAKER_00: See you next time.