Scott Taylor: [00:00:01] It should be on the results that are going to come out of those findings. What action do they want those executives to take? As proud as they might be about their algorithms and about all the mistakes they made, that they then fixed in all the fifteen iterations of what they did to get there. That executive team, I'm sorry, doesn't care about that.

Harpreet Sahota: [00:00:41] What's up, everyone? Welcome to another episode of the Artists of Data Science. Be sure to follow the show on Instagram @theartistsofdatascience and on Twitter @artistsofdata. I'll be sharing awesome tips and wisdom on data science, as well as clips from the show during the Free Open Mastermind Slack Channel by going to bitly.com/artistsofdatascience. We'll keep you updated on biweekly Open Office hours. I'll be hosting for the community. I'm your host, Harpreet Sahota. Let's ride this beat out into another awesome episode.

Harpreet Sahota: [00:01:20] Our guest today is an avid business evangelist, and original thinker who continually shares his passion for helping organizations navigate the wave of the big data revolution by expounding on the strategic value of master data.

Scott Taylor: [00:01:33] His methods of communications knows no bounds and has spread the gospel of digital transformation through public speaking engagements, blogs, videos, white papers, podcasts, puppet shows, cartoons and all forms of verbal and written communications. Who knows? Maybe after this meeting we'll get together and drop the master data and machine learning mixed tape. He's a firm believer in making your data do the work and has educated and enlightened countless business executives who care more about the strategic why rather than the technical how to the value of proper data management. He works his magic by focusing on strategic rationale. Business alignment rather than technical implementation and system integration. Over the last two decades, he's been solving master data challenges with a large global audience working with global enterprises and has an unwavering commitment to establishing lasting partnerships with enterprises and innovative data brands that want to change the nature of data management. So please help me in welcoming our guest today. The man who innovative tech brands like Nielsen, Microsoft and Cantor call when they need to comb their data down. The data whisperer himself, Scott Taylor. Scott, thank you so much for taking time out of your schedule to be here today. Appreciate you joining me, man.

Scott Taylor: [00:02:47] Thrilled to be here here. Now, this is great. Thank you. What a great introduction. I'm all inspired by my background here.

Harpreet Sahota: [00:02:54] You've got you've got quite the interesting professional journey. You've worked with a lot of awesome organizations. Wonder if you could just talk to us a bit about your professional journey, how you got involved in the data world. And what drew you to this field?

Scott Taylor: [00:03:09] Oh, sure, sure, sure. So I've been in the data business for, as you mentioned, a couple of decades now.

Scott Taylor: [00:03:13] And I think I was kind of hard wired for the master data taxonomy ontology space because my parents told me when I was a kid, instead of building with my Lego blocks, I sorted them. So I think if you sort your block, sort your toys, you're got a chance to be in the data business, so anybody out there listening to your kids, sort their toys, encourage that. It's not all about building. Sometimes it's about making sure things are structured and organized the right way. But my first data job was at a brand that's now part of Nielsen, where we had a standardized set of location data about supermarkets, and we started licensing that to the packaged goods companies who needed to put stuff in supermarkets. They had to kind of know where every store was. And this was a highly structured, well governed, expertly stewarded set of content that enabled a lot of these major manufacturers to outsource their master file maintenance. And once I saw what happens when multiple parties use the same data and the power of one data, I never looked back. It was it was it was great. And I continue to be inspired by how people can use this really foundational stuff every day when I talk to people.

Harpreet Sahota: [00:04:32] Awesome. So that's interesting. So you went from sorting blocks to sorting data.

Scott Taylor: [00:04:38] [Inaudible]

Harpreet Sahota: [00:04:40] So you mentioned that one of the gigs you had there at the grocery store, what what are some other early gigs that you've had in this space?

Scott Taylor: [00:04:48] Well, I've said Nielsen for about 15 years, so it's really the core of my initial career in the database. Before that, I sold advertising space.

Scott Taylor: [00:04:55] I was a consumer promotion. But every time I've done anything that wasn't data, I haven't been as happy. So I'm really sticking in the data side. Are a lot of independent consulting and I worked at a boutique consultancy that were really specialized on helping data owners capitalize and monetize and productize and all the other "ize's" around their core data assets. Again, taxonomy stuff, master data content. I also spent some time at Dun and Bradstreet helping them get their master data product properly positioned and help them with the strategy and the position around that. And I've worked for all kinds other companies all around. Again, helping people tell their data story is really the way I've landed on it today, because people who have data need to tell some sort of story about it to get either the support they need from their own management or if they're a service provider to make sure that their customer base understands the true value.

Harpreet Sahota: [00:05:54] Where do you see kind of the field of big data and digital transformation? Like what's this landscape going to look like in two to five years?

Scott Taylor: [00:06:04] Yeah, I think and I kind of take a sort of a dissenting view here a little bit because I don't think the core value of this foundational content.

Scott Taylor: [00:06:15] It's going to change to a point where it's unrecognizable. Kind of an awkward way to describe it there.

Scott Taylor: [00:06:20] But my point is, all these things happening, big data, I tend to think that's kind of a marketing term anyway, but it just means lots and lots of data cloud, hybrid, kubernetes, whatever you want to call, whatever the cool stuff is happening today, it all needs highly structured content to work. So whatever happens in B.I and analytics and machine learning and artificial intelligence in next two to five years, it is absolutely going to need this same kind of content, the kind of content I ran into 20 years ago. And so for me, I look across that through line and say, OK. Data management and master data in particular is macro trend agnostic. No matter where we've been, no matter where we are today, no matter where we're going. All those things are going to need this highly structured data and hardware comes and goes. Software comes and goes. Data always remains.

Harpreet Sahota: [00:07:17] Yeah, that's awesome. Awesome point. Because, I mean, even with with respect to machine learning, statistical analysis like linear algebra is linear algebra. Right. It still requires data to be structured and in a format this can be easily ingestible by whatever model it is that we're using. So we have the fundamental nature of having organized data is always going to be a driving force for any meaningful results to happen.

Harpreet Sahota: [00:07:40] OK, so...so in this vision of the future...,

Scott Taylor: [00:07:41] The one point that I want to make is I think the stakes are going to change. I think the stakes are getting higher.

Scott Taylor: [00:07:46] So the idea that you can avoid data management or that data management is kind of a project or that senior leadership doesn't need to understand why they need it. That's changing. And so that very much is a development, at least in the way people are operating around data. And I look at it go, if you if you're an enterprise, you got enterprise data. If you get enterprise data, you've got enterprise data problems. They've got to be fixed by the right kind of management and digital means data. There's a lot of business people actually don't see that link as clearly as the data folks do. But if you're doing anything digital means you're going to have data. And again, if you have data, you need data management.

Harpreet Sahota: [00:08:27] So so in what kind of way do you see the stakes changing?

Scott Taylor: [00:08:32] I mean, it's today could be live or die, right? So there's not a company out there. It's nobody out there going. Yeah. We don't really need data. We're fine. We don't need to change the way we work. We don't need to transform in anyway path. Everybody knows they've got that urgency. And if you look at the big disruptors, the classic disruptors now, Uber AirBnB, Amazon, they all came into the space, partly where they're part of the reason they were successful is that they were able to manage these these data assets and the data about the things they do and the people that they work with and the products they provide and services at scale in a way that the legacy providers could.

Scott Taylor: [00:09:15] And so part of how they ate the lunch and disrupted those those legacy providers was they worked with data in ways that people hadn't before. And so for me, that heightens the urgency, because if you're complacent in some kind of legacy space there, people are going to come at you from the data side. They're going to look for like the soft underbelly of whatever you do. Look at how they can manage and capture that data better and put it to use better and they can take you out. And I think people realized that that wasn't always the case. You could use brute force, but now data delivers that kind of magic where people can scale in ways they never saw before.

Harpreet Sahota: [00:09:56] So kind of in this vision of the future where the stakes are changing. What do you think it's to separate the good data professionals? I mean, I'm sorry, what do you think is separate the great data professionals from the merely good ones?

Scott Taylor: [00:10:08] I always come back to the same thing, which is the ones that build the foundation are going to be better equipped.

Scott Taylor: [00:10:13] So people tend to focus on the fancy, cool, sexy stuff.

Scott Taylor: [00:10:18] And if you build all that on a weak foundation, it's going to fall. And I keep using that word foundation because it's a great way to think about it. And it helps get the enterprise stakeholders, the business leadership who have to be engaged. Realize it's not all about the cool stuff. It's not all about the latest data science thing you're doing, because you can't do that at scale unless you've got great data. So what's going to separate the folks? You know, the good from the bad, the the good from the great is how well they manage and and govern those core data assets they have. And for me, it all centers back to those classic master data domains of customer, vendor, partner, prospect, products, service, asset offering, things you make, people you sell to, and buy from and partner with. You don't have that right. You don't have that relationship data. Right. You're not gonna have a business.

Harpreet Sahota: [00:11:17] That's awesome insight

Harpreet Sahota: [00:11:18] Can you talk to us a bit more, I know you've expounded on on master data how important it is, which you just my defining that for us.

Harpreet Sahota: [00:11:25] And then take us through what you call the "eight 'Ates"

Scott Taylor: [00:11:29] Eight 'Ates going to run through all of them.

Scott Taylor: [00:11:32] So, master data, if you look at Wikipedia definition of master data, it's a common source of of basic business data that's used across multiple, applications, workflows, and processes. And I love that definition because I actually put it there. So I was able to put that in Wikipedia, it's been unedited and untouched for eight years. So don't anybody mess with it! But that for me, is a really accessible definition around what master data is. And I actually separated from MDM, Master Data Management, is the process to create that asset. But I like the focus on the tangible assets of the data itself and what it can do for it's organization. You know, when you think about the eight eight. That's kind of one of my play on words. I like to come up with these somewhat pithy ways to describe the master data space. It tends to be rather boring and technical. And as you mentioned in the introduction I'm much more the WHY than the WOw. And these eight 'Ates were kind of buckets, or use cases, or notions, or things I have seen that around what people do with data and especially the master data side. So kind of going in order. The first one is relate. You got to build a relationship. Want to create relationships. You want to develop relationships. And so every business wants to relate with those parties that they work with through the products and services.

Scott Taylor: [00:12:57] So that's the end result of all of it is relationships. So the first eight is relate before you get to relate. You've got to validate. Some piece of data comes in. You've got to validate it. Is it real? Is it right? Is it deceptive? Do I have it already right that I searched before I created that particular customer record? That happens a lot. And we will talk about garbage in, garbage out. A lot of garbage comes in when they get bad data from from a customer create process, let's say, or duplicate record or product information that's skewed. So you've got to validate first, then you want to integrate second, third 'Ate. I guess that is integrate where you take all these disparate data sources and you kind of pull them all together in some way or another. Again, I'm not going to get physical or design architecture here, but in some way to try to integrate all that stuff. Then you want to aggregate roll stuff up. A lot of reporting, most reporting, almost all executive reporting is some form of aggregation. Right. What are the markets we're in and who do we call on and who owns them? And what categories are best and how many how many customers do we have? All of that is aggregating stuff up. That's all rolling stuff up or some, you know, some combination of those dimensions.

Scott Taylor: [00:14:10] The next one is interoperate. And that allows systems to talk to each other. That allows processes to go at scale. So if things you drive interoperability and I'd say that that eight is probably the most important word of the century here, how things interoperate, how they connect the whole Internet of things is based on interoperability. Standards in different verticals are formed around the ability to enable interoperability. So it's all around that connection. And common data, standardized data, allows you to do that. So on the bottom, you have I'm saying the bottom, because I kind of draw this out and I'll give you some links to some of these videos I did around all the eight eighths and so on. But we kind of build this foundation around, validate, integrate, aggregate and then interoperate on top of that. Then you can evaluate. Then you can do A.I., then you can do B.I., then you can ML, then you can do analytics, and do all that great stuff that certainly the data scientists are focused on. How do we put this data into play? But it's well-structured and it works for the basic things. If you do evaluate later and then the next 'Ate is communicate. So once you've got that evaluation process done, you've got these metrics you want to be able to share those with another party. In a way they understand it.

Scott Taylor: [00:15:28] And a lot of the words in the master data space, a lot of the words and terminology and imagery around, are we on the same page? Do we speak the same language? Do we have a common understanding? Do we have a business glossary? And if that pinnacle is relate, I will tell you, if you want to have a good relationship, you've got to communicate. So ask anybody you love, they're going to tell you the same thing. If you don't tell me what the problem is, I can help you. So you got to communicate. So, that's actually seven 'Ates for those of you who are counting. And the last one is circulate. You've got to get that data to move. Data to have value has got to be in motion. It can't be stuck in some sort of silo. It can't be locked in a PDF. It can't be held by some person who's been there 30 years and know how to do that special report that nobody else knows how to do. Those days are already over. And that guy who can do that little special report, the only reason you can still do it now. Is this because he hasn't been caught yet, so will that get that data out of these silos and move across the organization. I'll take a breath here. But those are my eight 'Ates. That's the quick version of my eight 'Ates.

Harpreet Sahota: [00:16:28] It's awesome.

Harpreet Sahota: [00:16:29] Yeah, I know a lot of our listeners are going to gain a lot of value from that. That's very insightful. Thank you. As somebody who's been working with a lot of executives, you kind of know how to communicate with them. And, you know, a lot of data scientists there, they're quite technical they're in their own little world, in their own little bubbles. Sorry if I'm offending any data scientist out there. But in general, we tend we tend to just like to be off in the corner building our models. Let's say a data scientist somehow finds themselves in a room full of executives and has to present on their findings. What should the focus of their communication be on?

Scott Taylor: [00:17:04] It should be on the results that are going to come out of those findings. What action do they want those executives to take?

Scott Taylor: [00:17:12] As proud as they might be about their algorithms and about all the mistakes they made that they then fixed in all the fifteen iterations of what they did to get there, that executive team I'm sorry, doesn't care about that. They care about and they actually don't even care so much about the result, that glorious metric that you pulled out or that insight unless you can tie it to some recommendation of action. So going into it, what do you want to come out of that meeting with? What would you like those people to feel do behave? What kind of thinking process do you want to take them through so they will then change the way they act afterward? You got to think about what action do you want to take and build backwards from there? Save your methodology. Save it for the chitchat later. Save it to wow them if they go "Wow, how did you do that?" But if you start off with a page, it says, let me take you through 150 steps. I just went through. You won't get the step four if you're with the CEO. They don't have time for that. So resist that, as proud as you might be of it and remind yourself. This is about moving my business forward. And how is my insight that I created or my opportunity that I discovered going to move the business forward?

Harpreet Sahota: [00:18:27] That's awesome. Awesome advice. Thank you so much. So I know a lot of my audience are working in or they might be going into businesses that are just starting on their digital transformation journey. So organizations that are kind of breaking from a legacy state to multiple silos towards integrated enterprise systems. You know...

Harpreet Sahota: [00:18:46] What are some challenges you see a data scientist facing in that scenario? And do you have any tips for them?

Scott Taylor: [00:18:54] Sure. I got this for everybody. So, you know, from that too when you look at these silos, you've got to recognize what they are.

Scott Taylor: [00:19:01] So silos exist in some cases for very good functional reasons. Right. You need to have different departments, do different things. But the data's got to cut across that. So find ways that you can help that data cut across those silos. I think of when I describe master data and structure, I try to use very simple imagery. So if you think about something as simple and as easily to understand as a table basic table, I know the graph people out there aren't going to like that, but let's just use it a basic cable. People are really good at adding columns, but they're not so good at aligning rows. And so the row part of the business is what's important. Those rows are where people are actually thinking about a row. Could be a relationship. It could be a product. It could be a market. It could be a category. What are the attributes and values you have across all those columns? But remember, those silos in a way, act as. As columns in an organization. So for master data or trying to come up with the kind of insight that can help the whole organization. How does that, as the data you're working on, really become that row? And other advice I might give people is learn the business.

Scott Taylor: [00:20:12] So I find that I'm trying to, you know, remind people as much as possible when I hear the data science side or the data side or especially the I.T. side was very guilty of this for a long time. It was, OK, we've got it. We're helping the business. We're separate from the business. We support the business. I would love to people to start to realize, you know, we're all part of the best. You are the business. If you're a data science, you are the business. How do you move that business forward? And it's impossible for you to learn too much about your own business. So one way to cut across silos to is make friends around the organization, find that salesperson who knows nothing about data science, but sure loves to increase customer relationships and show how your data and your data science activities can help them build a better relationship. You will make somebody a hero and you will be a hero. And those folks can certainly help you tell your story to because they're good.

Harpreet Sahota: [00:21:11] I always use this analogy when it comes to working with all these different bits of data and organizing them. It's Kind of like working with like a Rubik's cube, right. You've got to get...got all this mess going on. But it's your responsibility to to massage it. Put it together so that, you know, all the colors lined up on each side of the cube when it comes to master data. What are the similarities or differences in the challenges a legacy system organization faces versus a tech startup? And how can one navigate these waves?

Scott Taylor: [00:21:43] That's good. If you're a tech startup, you have the beauty of not having a bunch of legacy stuff by definition.

Scott Taylor: [00:21:48] And there's a point where there's an early stage place where you don't need a big MDM system. You don't need the, quote, master data when you become an enterprise. That's the way I like to define it. The bottom level of that, you know, maybe it's a certain revenue, maybe it's a certain amount of employees. But what starts to happen is you start to get multiple systems. So you've got two systems that have customer information. Then you have five systems that have customer information. Then you're getting more successful when you're in multiple regions and they've all put in ERP and so on. And so that's where this legacy state starts to happen. You just buy more and more software systems to manage what will look like similar domain. And that's where the challenges come in. But if you're a startup, you know, just think about that going forward and think about, OK, if we're going to scale, every startup wants to be, you know, Fortune 1000, no matter what, those are their aspirations. Just think about, OK. Everything should be done as one offs. How would you start to manage this if you were managing this broadly across a wider organization? And at some point you're gonna reach a point where, OK, we need to have data governance, we need to have formal data management. We need that stewardship. Two folks in a garage, there's not a lot of master data to master there. But as it starts to build, you've got to look for those opportunities to share information. So if you've got a set of data that you're working with that's become in some way the standard for your organization. Make sure you share that you don't need another data scientist opening up, you know, a system and just start with a blank screen again and starting over. Try to form some kind of consensus around some form of standards as early as you can. Don't go too nuts, but at least have that notion in mind because it's going to help you a lot down the road.

Harpreet Sahota: [00:23:40] I love the bits you do on the Master Data Blender's and wonders. So what would you say is the biggest data blunder in the last year or two?

Scott Taylor: [00:23:54] Well, data blunders and I cannot talk about them not, you know - a data breach wouldn't be one. But there's actually one. I think that's more common. I don't know, it's it's not a huge one, but it's kind of a common thing that happens. And it's where I help people understand the value of master data. If you go into a hotel, you've got your little key card, you put it in the door slot and the light is red. Can't get it in, right. The hardware works because the light turned red. The software works because it read the card. But it's a data problem. It is a master data blunder. You gotta go back downstairs. You've got to talk to the front desk person. They've got to change some sort of attribute in their file to say, yes, this is an active guest or whatever that is, and then the door works. That's how it works. So a blunder that's consistent is not being able to get into a hotel room or calling as somebody you work with, somebody who served you in some way and they don't realize that you're already a customer or you're in a division of one company. And that service provider doesn't realize that your sister division is also a customer or the reverse happens to. Right. You're buying from a vendor and you're buying from another part of that vendor and they don't treat you holistically as a customer. So there's a billion of these sort of master data blunders all over the place.

Harpreet Sahota: [00:25:12] So what about some data wonders?

Scott Taylor: [00:25:14] Data wonder for me and I use this again all the time to is just walk into a supermarket. You will see master data at work, not even behind the scenes - standing at the checkout counter. So every time you take any bottle, can, bag, tube, box of anything out of your cart. You put it on the belt and the checkout clerk takes it and scans it across the little scanner and you hear "beep". That little beep is master data at work. It is the universal product code, the UPC with a standardized set of reference data that's universally recognized, that works across thousands of manufacturers, for thousands of products, in thousands of places, with dozens and hundreds of different technologies. They all work because of this uniform, highly structured, reference and master data that's represented by that little code, that little barcode on every can and bottle you pick up. And for me, that's just, you know, a continuing wonder because that's lasted for decades. You can continue to build on that foundation. And imagine if you went to a supermarket and everybody had to key, still key in the price. So that's the kind of data that comes out of that, too, is is has transformed that whole business in terms of insight, and consumer demand, and planogramming, and logistics. But it starts with that little teeny, tiny, little code and that little "beep" that is master data at work.

Harpreet Sahota: [00:26:37] That was really awesome. I never even thought of either of those situations in that way.

Scott Taylor: [00:26:41] So I've tried bringing on a lot of people to tell you all about kind of all these sort of reference things and internal stuff and architecture stuff. And so, again, for me, I kind of pride myself, and that's my place in this.

Scott Taylor: [00:26:53] That's my part in this whole space. You got a minute with your CEO? How do you explain it? And I've gone. I have friends of mine who go. OK. Do you just set a timer and wait for somebody that you're talking to to just finally go, oh, I get it. And it's like, yeah, you've got to bring people from I have no idea what you're talking about. How do we live without this? And that comes from telling a good story. And I've learned how to tell stories. And the core part of my career, which was selling. So every salesperson is a good storyteller. So let them market.

Harpreet Sahota: [00:27:26] What's up, artists?

Harpreet Sahota: [00:27:28] Be sure to join the free open mastermind slack community by going to bitly.com/artistsofdatascience, It's a great environment for us to talk all things data science, to learn together, to grow together. And I'll also keep you updated on the open biweekly office I'll be hosting for our community. Check out the show on Instagram @theartistsofdatascience. Follow us on Twitter @ArtistsOfData. Look forward to seeing you all there.

Harpreet Sahota: [00:27:56] That's awesome, actually. That brings me to my next question. Here's the most up and coming data. Scientists tend to focus primarily on these hard technical skills. They think that that's what's going to separate them from the rest of the crowd. What are some of the soft skills that that candidates are missing that are really going to separate them from the competition?

Scott Taylor: [00:28:14] Awesome. Awesome question there. So obviously, you need to know, you know, the hard skills stuff you've got if you want to be a data scientist. You got to know a lot of things that I can't even touch. Right.

Scott Taylor: [00:28:23] So you got to know how to how to make all that stuff work. But those soft skills, and I'm much more of a soft skill guy, are around communication, are around listening, are around being able to sense what a need might be in the other party you want to talk to and realizing that those people have to be taken through some sort of story, some sort of journey to get them to where you are. That happens. I mean, I have lots of ideas like people, lots of ideas. You get really excited. You you see the end result. This is it. Well, some people get you know, they're not there yet. Get them to my point earlier. OK, what's it going to do for them? But you've got to have that soft skill listening, you know, take up sales training course not because you're gonna go out and carry a bag and have a quota. But the structure of selling is really important in storytelling. And people have to be able to tell those stories with empathy, with emotion, with emphasis to get people to to really get excited about what you're trying to what you're trying to bring them.

Harpreet Sahota: [00:29:29] It's an excellent point because, you know, human beings love story. Its stories are as old as language is. So being able to communicate your findings in a story, it's definitely an important skill. I really like that. There's a lot of people out there who are trying to break into the data space and maybe they feel like they don't belong there or know enough for they aren't smart enough. Do you have any words of encouragement for them?

Scott Taylor: [00:29:54] You know, [inaudible] I mean, the data space, I think, is the hottest space out there.

Scott Taylor: [00:29:58] That's not an original opinion. And part of the reason I think it's so valuable to business and that's the context I'm talking about all of this is business, is that data can change the nature of a business in ways that other stuff that has existed can't. And so it becomes that fuel that can power all kinds of different opportunities at an organization. And it has this multiplier effect of I talk about businesses want to do basically three things. They want to grow. They want to improve. They want to protect rights. They got to grow your business, get more customers, expand. That's everybody's favorite bucket. You want to improve. You want to be more operationally efficient. You want to find those opportunities to save money, to save time, to scale. And then the bigger you get, the more risk you have to mitigate. That's the least fun. But it's really important because I could pick a whole business down if you don't mitigate that risk. Master data in specific and data in general can do all three of those things. Marketing, basically grows business sales growth, a business legal, protects the business operations, improve a business hard to find another department, another topic area that can do all three. So know that and realize, you know, my tips to getting in the data business. Find your passion in there. Just like anything if you. Love it. You got to love it, find peers who like it, find these groups.

Scott Taylor: [00:31:20] Go LinkedIn, listen to podcasts like this, hear the passion out there. And everybody will tell you when you're working with something that really taps into your passion. Taps into that emotional side of you. You're going to do a whole lot better job and you're going to enjoy a lot more. So finding that niche for yourself. I did. I found a way to be in the data business and in my case, without ever getting literally physical with data. I don't design stuff. I don't touch anything. I don't know coding. I think Kafka wrote Metamorphosis. You know, that's about as far as  - I don't even know what kubernetes is. I do know that a pirate's favorite programming languages is R (Arrrr) so that much, I know. But I'm able to do it because I understood there's got somebody going to tell the story. That's my part. So you don't have to do all of it. In some cases, you might want to find someone like me to kind of help bring your voice. You got the how? I've got the why. That's a great partnership. You can you know, you cut the stake. I can be the sizzle. There's a lot of partnerships out there. So I don't think you need to do all this, but find your niche and find your expertise and then run with it.

Harpreet Sahota: [00:32:27] Love it, man.

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Harpreet Sahota: [00:33:02] One last question before we jump into our lightning round here. What's the one thing you want people to learn from your story?

Scott Taylor: [00:33:08] To remember the value, the foundational importance, the critical nature of master data. Say it 100 times. I'll say it a million times. It is the most important data any organization has. And if you're a data scientist and you spend, what, 50 to 90 percent of your time preparing to do the work you want to do because the data isn't clean enough, it's not organized enough. What do they call it? Data munging? That gets solved in part by leveraging the master data resources you might already have. So if you're in an organization and you're data scientist and you're not buddying up with the data management, the data governance folks, you are missing a huge opportunity. And that data management group, they're the ones who really own things like the definition, or manage the definition of a customer, of a product, of a category. Again, a lot of those rows that you want to put a lot of those columns in, go find that first. So I want to remind people, especially data science folks out there, don't start with a blank screen. Don't come up with your own category. Definitions, love, understand and welcome the opportunity to leverage your own master data.

Harpreet Sahota: [00:34:21] Awesome advice. Thank you. So jumping into our lightning round here. What's the number one book, fiction or nonfiction you'd recommend our audience read. And what was your most impactful takeaway from it?

Scott Taylor: [00:34:37] There's one, I don't know if it's number one. But you know you gave me some of some of the questions in advance.

Scott Taylor: [00:34:41] So I thought about it. There's a great book called Big Data, Big Dupe by a guy named Stephen Few. It's not that thick. It's a quick read. But what I love about it is he popped the balloon and bubble around the whole mystique of big data. And he actually validated for me a lot of thoughts I already had. So when big data came out, it was of a sudden there isn't just regular data anymore. Everything is big data. And we all know what that journey that the whole marketplace went through there. And, you know, the 3V's became the 42V's,  I don't know any other topic out there where people are only allowed to describe it by using words that begin with the same letter. Right. That's never happened before. But he just took it and he ripped to shreds. Now, you may not believe everything he said, and he's way over on one side, but it's a great perspective to kind of bring some reality because there's a lot of buzz. There's a lot of harm. There's a lot of fluff.

Scott Taylor: [00:35:40] There's a lot of unnecessary chatter. And what I like to call bad poetry in the technology space that takes people is, you know, off the topic.

Scott Taylor: [00:35:52] They should really be on. And that's why I play so much and rag so much around a lot of the terminology, because it doesn't mean as much as it should. But big data, big dupe. I don't know the guy. I'd love to sit and have coffee with him. I'm sure we could both go on a rant together. But it was a great book and it really, again, kind of helped validate for me. Yeah, I thought there was something weird about all that.

Harpreet Sahota: [00:36:12] All right. I gotta check that one out. Big data, big dupe, by James Few?

Scott Taylor: [00:36:14]  Stephen. Stephen Few.

Scott Taylor: [00:36:17] Stephen Few. Yeah. He starts off and he goes, okay. I just decided I'm not gonna call myself Stephen anymore.

Scott Taylor: [00:36:23] I'm going to call myself Big Steven. And you have to pay more to deal with me because I call myself Big Steven. It's like, yeah, that's it. I like this guy.

Harpreet Sahota: [00:36:31] I like him already, I like him already. So what do you say when you're whispering to the data?

Scott Taylor: [00:36:38] Just calm down, really. Just get structured, baby. That's what you gotta do. You just gotta get aligned, get standardised. I'll give you a little reference. I give you a little meta but you just gotta...You got to chill there. So doesn't have to be so wild. Does have to be so big or corrupt, or whatever. You just got to calm it down. And if you can calm data down, that's what I say. Data whispering is we're all data whispers in one way or another.

Scott Taylor: [00:37:04] If we help manage data, if we can get the insight out of it, if we get it to a place where it really can start to show the value.

Harpreet Sahota: [00:37:12] I love it. If we could somehow get a magical telephone that allowed you to contact 18 year old Scott. What would you tell him?

Scott Taylor: [00:37:22] Hi, buddy. How's it going? How? I would tell him I've had a really fortunate life. I love it. I've had a great life. I've had a great time.

Scott Taylor: [00:37:32] So I don't have a point where I go. OK. It's going to get better or because I had a great teenage life to my parents, wonderful, I was brought up at probably at overprivileged childhood. I know I'm extremely lucky, but I would probably tell them, look, it's going to, you know, this get a lot more focused. Well, we had a little challenge with that. I get, you know, really, I got 20 ideas and I got to nail down a couple of them to really focus on. I think for me, I would I would tell that 18 year old Scott, look, get a little more focused and also recognize where your true strengths are. It took me a long time to realize, OK, what am I really, really good at and how do I really get focus on that? Not to not learn other stuff, but I think I have some talents. I think everybody can point to the couple of things that really make them shine. And if you can take those up a couple of notches, that's going to bring you way above the pack in a way that doing a million things is itself.

Scott Taylor: [00:38:30] And tell him, you know, you're never gonna believe it, but you can end up in the data business. That's what you're never gonna believe.

Harpreet Sahota: [00:38:35] So what's the best advice you've received?

Scott Taylor: [00:38:38] What's the best advice? I don't know if I can sum up, like, sort of want advice, but I got a lot of advice from my father, which is a good place to get your advice. When I was working for him and I was a salesperson, he just helped me really understand how to manage focus. Probably one of the best things he ever said to me was he sold magazines face with magazine publisher. And I went on, I'd learned how to sell magazines space with him. And one of the first things he told me and I sort of turned it into a metaphorical thing was when you walk into a sales call, never give them the magazine. And what he meant by that was if you hand them the magazine, they want to look at it. This dates it because magazines search, you know, only one piece of media these days. But when it was like a dominant part of the media space, he said they're not doing two things. They're not really looking at the magazine and they're certainly not listening. So it was this idea of focus, again, probably a motif in all this conversation. And that was really important in that can that can exhibit itself and a lot of different ways, but was never given the magazine.

Scott Taylor: [00:39:41] So that I always remember that when my father tells me. 

Harpreet Sahota: [00:39:44] What motivates you?

Scott Taylor: [00:39:45] Me? I just love I love doing fun stuff. So if you can hear my voice, I mean, I'm at a point in my career you mentioned the puppet shows and such a ball doing the puppet shows.

Scott Taylor: [00:39:54] What motivates me is the reaction I get from people for some of that crazy stuff. So we'll put a link or however you want to drive people to that piece of content. But I did this puppet show with all these different puppets. There's a CDO, which is the chief dog officer I've got a Bee, That's the IPB. I've got a guy from the business who's a monkey. So he's monkey business. It's pretty corny stuff, but it's got to it's got to play globally. And they're all chattering and arguing with each other and they're just spewing out all these buzzwords. And at one point, one of them just says, well, what about the data? Kind of stops the conversation. Like, yeah. We're talking about all the stuff. What about the data? But what inspires me absolutely was the reaction I got. And what I did not expect were the comments around. This is my life. I just got out of this meeting. You've just, you know... Were you in my last business meeting? Did you see our I.T. department? This is who - So the idea that, you know, this dog, and monkey, and bee puppet were able to reach people in this really bizarre way was absolutely inspiring. So it was really fun. And I got more I got more episodes to come there and to be able to do. You know, you mentioned in the intro there, I do white papers, but I also do puppet shows. Guess which one gets more engagement? And it's really fun to be able to do this really crazy creative stuff and get this nice response from it.

Harpreet Sahota: [00:41:18] I've checked out a couple of them - they are pretty funny. I like them. So can you describe your morning routine?

Scott Taylor: [00:41:24] My morning. I kind of get up. Coffee. Got to have coffee. Kind of buzzed through the iPad. Check on the headlines.

Scott Taylor: [00:41:31] Figure out what I got to do and then just kind of get into it. And for me, it's kind of different every day. So I'm spending a lot of time on the content side of it. I decided about a year and a half ago that I would this independent content creator and I'm still talking to my 18 year old self going - Okay, Which of these 12 ideas you want to do? And, you know, sometimes you figure a way to lunch and you haven't done any of them because you're doing like a little piece of all of them. But my routine is is especially before all this, you know, lockdown was different every day. I got a whole line of phone calls or I might be doing some interviews. I've been on both sides of the mic if you're well on your side and I'm on now and it's just kind of sort all that out. I wouldn't say the only the only absolute regular part is I got to have those two cups of coffee. Otherwise, it's a whole different kind of day going.

Harpreet Sahota: [00:42:20] I feel you, man. I do. I do an entire French press that's like massive too much coffee. But I love it. So. So how how how can people connect with you? Where can they find you? Where can they find your content.

[00:42:33] I'm mostly on LinkedIn. So you can find me on LinkedIn, Scott Taylor - The data whisperer. Look for me there. I'm happy to accept invitations or follow me.

Scott Taylor: [00:42:40] And I've got all kinds of content there also on YouTube. So I've got a YouTube channel where everything sits, most of my exposures on LinkedIn. But it's hard to keep that stuff still because the feeds change so much. So I also put everything on YouTube. But I have at least 50 videos. They're now kind of a whole library of stuff. Also my Web site, which is going to get better, MeteMeta Consulting MetaMeta - we're about what it's about. So again, that's sort of met a part that we talk about, talking about stuff, helping people tell their data story. And I'm particularly delighted that if you if you Google Scott Taylor, the data whisperer, I come on the first page. I'm always on the first page of the video parts of I don't if data whisper, I don't reach above the fold yet. I'm moving myself up there. Certainly if you hit the video tab, you'll see all kinds of pictures of me and my content there, too. So that's nice. Excited to get that organic reach after all this work.

Harpreet Sahota: [00:43:35] Yeah. Awesome, man.

Harpreet Sahota: [00:43:36] Go ahead. No, a link on this to the show notes. Scott, thank you so much for being so generous with your time. We had a really great conversation. I enjoyed having you on the show. Thank you so much for being here.

Scott Taylor: [00:43:46] Oh, sure. It was a blast. Thank you so much. We're done already. I'd go for another two hours, if you want. Thank you. Loved to be here.