The B2B Revenue Executive Experience
The B2B Revenue Executive Experience

Episode · 5 months ago

Data Literacy: It’s Everyone’s Concern w/ Matt Cowell

ABOUT THIS EPISODE

You’re in college and (other than the vaguely threatening hairspray bills your Mötley Crüe tribute band has racked up) things are going pretty well. You passed that calculus class you’ve been worried about all semester — and managed to forget basic arithmetic before the celebration keg was even cracked. If forgetting is so much easier than learning, even in academia — how can you ever expect to upskill a whole organization?

You know you can’t just give up. Especially when it’s something everyone needs to know — like data.

When you want data literacy across a whole organization, you turn to today’s guest, Matt Cowell, CEO at QuantHub, an organization dedicated to helping you imbue every part of your own organization with the dark data arts.

In this episode, we discuss:

  • Why longform learning fails
  • The importance of data literacy in every facet of your business
  • Why AI and machine learning are useless without data literacy

Now that you know how to infuse data literacy into your team, are you ready to learn how to build trust and confidence with your content strategy or how to optimize your tech stack? Check out the full list of episodes: The B2B Revenue Executive Experience.

You're listening to the BDB revenue executive experience, a podcast dedicated to helping executives train their sales and marketing teams to optimize growth. Whether you're looking for techniques and strategies or tools and resources, you've come to the right place. Let's accelerate your growth in three, two, one. Welcome everyone to the BB revenue executive experience. I'm your host, Chad Sanderson. Today we're talking about the importance of data in all elements of business. We've all heard the discussions around big data or data analytics, but often this is followed by confusion on how to capture, analyze and apply it to the business. To help us, we have with US Matt cal CEO of Quat Hub. Matt, thank you so much for taking time and welcome to the show. Yeah, thanks, Chat. I appreciate you having me so we always like to ask kind of an offwall question, just our audience to get to know you a little bit better, and we like to start with you know something, if you think about the different personas that we have in life, I'm curious something you're passionate about that those that may only know you from a work perspective might be surprised to learn about you. I love well, I'm up pretty open book, so even the people that I work with usually know this about me. But I love cheesy s rock music and and so if video was on here, you'd see my guitars in the background. So I can't help it. I don't even care. It's all right. I love it. So all right, so now you're talking. You're hitting my sweet spot too, so let's talk. So which what's your favorite s rock? Well, I mean I like poison Bon Jovi, deaf Flippard, I certainly love guns and roses. I have an audibility. Maybe it's to sound like Axel rose. So I love doing Karaoke of like flucome jungle or sweet child of mine. So ridiculous stuff. You know. That's awesome. Yeah, Hey, I'mn I grew up on that too, and I'm not afraid to admit that I've actually been to some of those concerts way past the S. well, they're all coming back around and making a lot of money on rede and tour. Oh yeah, the deaf leppard molly crew thing. They got cancel. Oh yeah, that that's that's got to happen. That's just gonna sure happen. Awesome, all right. So thank you for sharing that. Let's kind of get in the topic of the day. And so when we think about, you know, big data and just ai around data, what do we mean by it being a critical skill set for the future of work and what does that translate to in different roles? Some of the pre material you sent over refer to it as that critical skill in the future work and I'd love just be able to provide the audience with some context on that perspective. Well, you know, so often people talk about Ai and you know it's become sort of Buzzword Bingo to hear all of the talk about Ai and all the potential and and the transformative natature of that technology. And you can see it all around you. You know, I don't want to say Alexa too loud here, but cons fond, but you know, I mean that type of thing is changing the way we live, the way we work, and and so those skills, you know, those highly technical skills where people are data scientistate engineers and they're dealing with all that data and they're doing the machine learning in that type of thing. Obviously that's one set of skills but that's not really I mean that's important, no question, but the reality is without all of us becoming more data literate, and and I can explain what that means, but become becoming more data literate, then a lot of the potential that exist in leveraging ai is it's just that it will stay as potential and out. So I'll give you an example of just kind of data literacy. So take a person that's in a in a sales roll and there may be an entry level sales person. They take a lot of notes on conversations. They enter those notes into the CM. Thus it becomes data. And so if they're if they're not taking good notes or they're putting in default values for fields, then there's not much a person can do downstream of that to actually, you know,...

...do a meaningful analysis on some of that data, because the date is not there. And so that's an example where you need more data literate people and they need to realize their role in this sort of data quality life cycle. And so that's where we all need those types of skills. We all need to recognize the data is critical and as a critical element of successful companies in this day and age and that's only going to continue to move more in that direction. Yeah, I couldn't agree more and I think it actually gives us an opportunity because anybody in sales knows, and having been a sales leader myself, you're always trying to get people to understand why they need to put data in the crm. Bright. Yeah, then when you get downstream you fired somebody new there like hey, the data, I have his crap us this like well, you know, it's kind of a cycle. You kind of need to me right to this. Yeah, and I've heard some people say, you know, they'll just kind of sit back and say, I just want to let the AI do the processing and I'll look at the results and okay, fine, but there's a need to understand. I think the source of that day to the cleanliness of the data. Is there a different perspective here we might be able to give people that. You need to be, you know, in sent them to be a little bit more literate and a little bit more involved and at least understand how that aim may be processing or presenting those results. What I think? So, I mean that part of it is people. People think that AI is like a silver bullet or and this is some sort of magic, but you know, you can actually just become a little bit more data driven and start getting value without ever having to give give any of the work to a data scientist or a data engineer. So when we talk about being more data driven, you know, if you're in sales and you're trying to figure out where your best prospects come from, and and and started to fit and you start analyzing where people are falling out of the funnel and you start looking at that those are questions that you're asking right and that in a more data driven approach, would be to then answer those questions with data and so look more deeply at where people are falling out of the the funnel and and look at the reasons why they're falling out and start doing some data analysis on that. You'll start uncovering insights by just by virtue of answering those questions with data and you'll start uncovering insights and now all of a sudden you can make decisions based on those insights that improve you know, could be improving conversion rates, you know, starting next month. And and so that's an example that it's not about data science right, it's about just being a little bit more data driven, at being a more data driven enterprise, and that I mean those are that's a sales example. That example, that type of example, crosses the entire the entire enterprise, whether it's in hr where you're recruiting or or you're in operations and you're trying to improve a process, or you're in marketing and your your you looking at the legend conversions or where your leads are coming from. ME, they're just so many different aspects of of that and it doesn't take data science or the magical, mythical ai to to solve all your problems. You know it's right there in front of you if you just ask some good questions and then answer those questions with data when I think it's I think it's a really important point when we talk about these roles that they don't think of themselves as data sciences. There needs to be a data awareness, a data mindset, because there is so much of it out there and there are so many ways that it can be leveraged and utilized. It's almost like I remember a most stick with sales, for example, but you almost have to have an understanding of how business is operate, especially if you're selling be to be, you have to understand kind of the basics in how businesses operate, how things get done inside of a business. That's one skill set that we've seen team struggle with. But now there's this other layer of this awareness that you're adding data and benefiting from the data. So making that part of the mindset I think, becomes a challenge for a lot of the organizations. And when you look at these organs that are out there, how have you seen organizations effectively approach upskilling, you...

...know, around this at scale, say whether the team's a hundred people, a thousand people or tens of thousands of people? How our organizations effectively kind of building this into their cultural approach, on boarding or whatever, to ensure that that data mindset becomes front and center? If I'm honest with you, I don't think they're doing it very effectively, and so I think the reason that that it's to a large degree so far been ineffective is is that a lot of people, when they think about, you know, a learning initiative and a need to up kill a lot of people, then they just go back to our sort of default learning approach. That we've we've done all our lives. You Mea we've all done this and that we went to school. We it was long form learning. So in school, obviously it was in certainly really long form learning. So it was courses of semester at a time. You would learn, learn, learn, take a test and then you'd forget it. And and we all have experienced this. I'm sure I can't see you, but I'm sure you're nodding your head, because those the way we've all gone through this. And then, unfortunately, you know, data literacy is a problem across an entire enterprise. Yet people keep doing the same thing. We will then buy licenses to, I don't know, Corse air or something like that and send out a data literacy training. That's long form. It's probably several hours. People don't have a lot of context as to why it matters. They go through a training and they take a probably take some sort of quiz at the end, and then they don't revisit it and they forget it. Yeah, and it's just fundamentally the way our brains work. So this is not a corse era problem, it's just the way our brains work. Is that right after we learned something, we forget it and and so there's a very steep forgetting curve right after you learn something and if you don't revisit it, it's gone. And so that's why things like spaced trout, you may have heard of space, to repetition, where you you see something soon after you learn it, and so their techniques to avoid that. But unfortunately, to the question. And when you've got a, you know, Tenzero Person Company and you have a five thousand of them, need to upskill and in this the default approach of sending out Fivezero licenses to a platform and saying, Hey, go search on data skills and go take a couple of courses. Yeah, and that's just not even remotely close to being something that would work for yeah, you leave it to them, Hey, go do this or do something else. A chance. Never, they're never going in there. That, I heard some becomes almost like information security training, like how much pain, how much tension do you pay to that Chad? Yeah, now you check in the box. It's your check in the box and that's not effective. And so this is too important for that. I mean, frankly, information security training is probably too important for that as well. But so you know, we actually think about this and much more of a micro learning, kind of continuous learning type approach, something that is consumable by each and each and every one of us. And so if you think about like the dual lingos of the world, that's how people are learning languages. They're learning it ten to fifteen minutes of time. They're learning a little bit every day, and that's actually there's learning science to back up that. That is the way to learn effectively and to remember even more importantly, and so that's kind of how we think about it and I think it's a great approach and it's one. Is it one of those things where you think that is something we've learned from Ai, or there are other things that people should take away from kind of the AI approaches to data and learning to enhance our own learning experiences? Is it that bite size kind of approach and that continual evolution? It's funny actually we are one of our co founders is our CTEO and he and I do a presentation. We call it human machine learning and we actually show people that there are a lot of core laries between the way machine learning works and the Algorithms and the way we as humans actually learn the most effectively. What's funny is people think it came in that order, like machine learning was around and then all of a sudden we learned how to...

...how to learn more effectively as humans. Well, that's not true. Of course, in the s there were actually was a lot of research around cognitive psychology and and you know how humans learn more effectively, and then you start getting research in machine learning. That actually happened in the S and which was kind of based on neural networks, is based on trying to approximate how humans learn and doing that with machines. And so it's really came in that order. But for some reason culturally we've always still taken the learn everything at one time, long form learning, take a test at the end and then forget everything. We've kind of always had that in our culture. So we've never really embraced the truly best way that we learn. I love it. And so there was a phrase in some of the preg material to that I wanted to dig into, and it's it was the cons of of the rise of the citizen. Data scientist. Help me. Help me unpack that and understand what we're talking about there. But I think it really does go back to what we were talking about earlier where and and you know that's that's a that is a movement where people call it a citizen data scientists. I think that continues to be in this misnomer and make people think that this the highly technical role. What that really means is that we're all responsible for data analysis. We're all responsible for doing some amount of analysis and analytics on data, even if we're not professional analytics employees. And so the data citizen or the citizen data scientists, the way I look at that as that's all of us and we're all responsible for varying aspects of you know, you talked about earlier week. Actually all create data. Now we don't realize it, but we all create data. We all do some amount of analysis. It doesn't matter what role we're in. We're making some decisions and we can do some analysis to support those decisions. And that's where that sort of movement to, where you don't just throw everything over the wall to your analytics team, you start realizing that each and every one of us have a role in that. Well, I mean it is and it's interesting. Anybody who paid attention to the Cambridge analytics stuff. For saw any of the shows where they talk about just how much data is being collected analyze? Yeah, we purpose. I mean I think back to the the social dilemma on Netflix. Care the crap out of me because you know, you have no idea just how much data just by carrying around, you know, our lovely little phones and how much data it's putting off. If you don't have a down the right way, we're contributing to it, whether we want to or not. Yeah, I mean it's not you can't even get away from it anymore. I mean it's it's just all around you, like right now, back to the Alexa thing. I it's hearing me my voice right now. Is Data. That's a great way to think of it. It is one hundred percent data and people have gotten I think there's a lot of conversation around, but people have gotten a lot more comfortable with parts of it then they were. Because I can remember, not that I want to date myself, but what I can remember going back when we were designing APPs on the phones for enterprises and we were like no, no, you want to turn on location services. They're like, I'm not sharing my location. I don't want the phone to know. And then fast forward and now they're like, they're irritated if the phone doesn't know where they are and is it's sourcing restaurants that are within a hundred foot, ratings kind of thing. Yeah, you know, we instantly want to give up the privacy for convenience. Until you know, so many shines a light on it, but it is part of that. We're all part of the data pool these days. Oh, absolutely, yeah, I mean, and that's not going anywhere. But but up, I mean the the rise of all of the devices that the nests of the world, all the devices that are turning on your you know, everything in your house from Your Voice. And you know, really that's just the beginning of that. What they really want to be doing is actually automatically turning those devices on and off in response to, you know, your schedule, movement, all these things. I mean, that's really the void. You ever even Amazon actually just said this recently, that the voice is just a stop gap. That's not really the endgame. The end game is that it's actually responsive to your habits, into your...

...movements and these types of things, so you don't even have to tell it what to do. Yeah, and that's a little we'rewelling in for. Yeah, right, I mean that's spooking, right, it is. It is spooky. But I also can see the flip side of it where you know, okay, if you got nothing to hide and no big deal, and what's you know, hey, the house is smart enough that it knows you're be home about this time, it can heat it up, it knows you're going to typically, you know, turn on the oven to cook something, so I can start one with the oven. I mean there's there's some definite benefits from a convenience standpoint. Yeah, the people, I think, just going to have to get comfortable understanding that in order for that stuff to work really well, they have to share a lot of the data and the insights into what their habits are. That's exactly right. Yep, and I mean we've already come along way. Like you said, Your Privacy, Oh, you should have privacy, or our comfort level with certain different aspects of privacy, has changed dramatically over I mean even maybe the last five years. Oh yeah, and when, and even to the point, we go back to the devices in the house, I start thinking about it, it's like, okay, I got an asked in here. I got TV's that I'm fairly certain are listening to me. If I'm not, if they're not watching me, I don't have I'm looking around. I think Alexe's in the other room. Alexe is in the other room, but I mean there's all these things where it's just this constant stream of data. People used to think about, you know, emf waves and all the ways going through the air. Just think about all the data that's been transmitted. I mean it's like saying how much is out there and what could be done with it if analyzed appropriately? Yeah, and that I mean, and that's really that's really the key, is that you know there's so much. It goes back to the first thing we talked about, which is the potential for ai to be transformative in business and life in general. I mean, it's already that's already happening. And with data, with the proliferant proliferation of data and devices that measure things that we weren't measuring before. You know that the amount of data out there just it continues to increase exponentially and with it then you know the potential for new ways to use it in harness it yeah, absolutely. All right. So let's pivot here a little bit talk about quant hub help our audience understanding kind of what you all do there and what was your journey to arrive there? Yeah, we're an early stage company. We've been around for almost four years now and we're a data scale platform. So data is very, very important to us and you're in Dat real hearts and so we have an assessment platform that measures data skills of like data scientists, date engineers, and we've got enterprise customers all over the world using us in their hiring process. So if they're, you know, trying to hire a data scientists, you usually get a lot of candidates and don't really have a strong ability to tell them apart on skill, and so that's what our assessments do. We also have an upscaling platform and that's more kind of broad day to literacy, like we were talking about earlier, and it's a micro learning platform, so it's a little bit every day, kind of fits into the white space of your day. It's something that the scalability aspect that we were talking about earlier is really, really an important aspect of being able to change the culture is because it's a it's sort of an every employee type of problem. And and so, you know, are big focus is coming up with something that that people can adopt, that and that you can roll out across, you know, a majority of the enterprise, which is this off the shelf solutions that are out there. You just can't. They just don't scale to fifty percent of the employees and a company or more. Yeah, and so, okay, when you think about kind of what you're doing at quantum, what's on the horizon, like what's on the future and coming a around the corner, whether it be in terms of aim, machine learning or even just ways that you plan on internalizing it at quand up, what are you the most excited about? If you're looking, I mean, nobody likes to look twelve months out anymore because nobody saw the pandemic coming. But if we're looking like six, twelve months out, yeah, what do you see on the horizon that you're the most excited about? Well, I mean, honestly, I get fired up about being I mean I think the position that we're sitting in is and you know, naturally startups have have lofty ambitions. I we are going to be the...

...company. I believe this in my heart that we're going to be the company that everybody points to after this big skill transformation over the next ten years. We're going to be they that they point to and say Quan hub was the biggest enabler of that. And so that's that's where I believe we fit, because I think we have the only scalable and effective approach to do that. So that's where I that's our vision as a company. I mean that's literally written and our vision as a company over the next six to twelve months. You know, I'm excited to see more and more enterprises come on and and and, you know, use our platform to transform. From a technology perspective, I'll say some really cool things, since we were talking about just unbelievably cool technological things. Is there's now ai out there that, so we are content is we write assessment questions and then we curate learning materials, and so what's unbelievable is there's Ai out there now that actually their algorithms and models that can literally, from scratch, read articles, pick out the keywords of those articles and then find definitions of those keywords and right assessment questions covering the concepts covered. In that article and it could do all that from scratch. WHOA. Yeah, I mean it's it's I it sounds like science fiction and I really thought it was until our CTEO showed it to me and I it's just it's just mind blowing stuff. I the same types of models. Literally you can actually give it a problem and say right, code to solve this problem and you see on the screen it starts writing code. mean, it's just if you love the you're probably we're probably similar age. So you probably love terminator to it starts feeling a little terminator to like. I love it. I love it all right. So let's Change Direction here a little bit. We ask all of our guests to standard questions towards the end of each interviewer. The first is simply, as an executive, that makes you prospect or a target for a lot of people that are out there and I'm always curious to understand when somebody doesn't have a trusted referral into what works best for you, when somebody's trying to capture your attention and earn the right to time on your calendar. HMM, that is a good question. I'm not very responsive to outreach. I will say this. I I'm much more responsive to seeing people that are part of a conversation. So whether that's on Linkedin then I am to just cold outreach. I'm almost unresponsive to cold outreach unless it's just unbelievable luck on the on the timing of the salesperson and that I'm thinking about something, we have a problem and then they they reach out at exactly that moment, which is, you know, generally speaking unlikely. And so, but what I've definitely more responsive to is seeing people on Linkedin as part of a conversation about problems they have and then potential solutions, and so that's not necessarily being a referral into me. Obviously I pay attention to those, but you know, seeing people that are part of a conversation and they have solved solutions two problems. I'm you know, I would be more responsive to that if if I saw something there, love it all right. So last question. We call it our acceleration inside. There was one thing you could tell sales, marketing or professional services people, one piece of advice you would give them that, if they listen to you, would believe would help them hit their targets or exceed and what would it be and why? HMM, well, I'm I'm from the I'm from the Midwest, and our chief revenue officer says I like to go all Midwestern Matt on people and and so, honestly, I I'm not going to you. You probably have lots of people on here that are given giving like sales techniques. What I like to do is I like to work with relatable people that don't have a giant ego. And so, you know, I think try to be relatable, try to listen first. You know, one of our core values is around listening through, you know, seeing the world through the lens of your customer or your prospect or your potential investor or or a team member, and so seeing, seeing...

...the world through their eyes and and then making sure when you're when you're speaking to them, say things in the way that not the way you want to say them, but the way you want it to be received, and focus more on that. Mean that ends up putting yourself in a seat where you're thinking through your thinking through there you're seeing the world through their eyes, and then that, I think that helps you then present things in a way that they receive it the way you want them to receive it, which in sales, you know, you have a goal right, right, how you want them to receive things in a certain way, and it doesn't matter to the way you want to say it matters the way they're going to receive it, and oftentimes people don't think about it that way. They're they're selfconsumed with what they want to say as opposed to the outcome they want to achieve by saying I think that is amazingly insightful advice and I hope a lot more people out there will listen to it. Matt, if a listeners interested in talking more about the topics we've touched on today, what's the best way to get in contact with you? Where do you want us to send people? Yeah, I mean I'm at Matt at Quand Hubcom. I'd love connecting with people. I'm passionate about this topic. So if if anybody wants to talk more about data skills or sales and marketing, I'm happy to happy to talk. We're you know, I found networking and connecting with people is one of the best ways to grow, and so I'm always open, always open to that. So that's one way. Of course, Quan hubcom is is a way to learn more about us. But yeah, I'm always happy to talk to people individually. All right, I can't thank you enough for being on the show. It's been an absolute pleasure my friend. Yeah, chat, I appreciate you. have me all right, everybody that does it for this episode. You know, the drill be to be REV exactcom leave us review on itunes and until next time, we value selling associates with you all nothing but the greatest success. You've been listening to the BB revenue executive experience. To ensure that you never miss an episode, subscribe to the show and Itunes or your favorite podcast player. Thank you so much for listening. Until next time.

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