The B2B Revenue Executive Experience
The B2B Revenue Executive Experience

Episode · 1 month ago

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


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 executiveexperience, a podcast dedicated to helping executives train their sales and marketing teams tooptimize 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 elementsof 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 applyit to the business. To help us, we have with US Matt cal CEOof Quat Hub. Matt, thank you so much for taking time andwelcome to the show. Yeah, thanks, Chat. I appreciate you having meso we always like to ask kind of an offwall question, just ouraudience to get to know you a little bit better, and we like tostart with you know something, if you think about the different personas that wehave in life, I'm curious something you're passionate about that those that may onlyknow you from a work perspective might be surprised to learn about you. Ilove well, I'm up pretty open book, so even the people that I workwith usually know this about me. But I love cheesy s rock musicand 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 allright. 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 yourfavorite s rock? Well, I mean I like poison Bon Jovi, deafFlippard, I certainly love guns and roses. I have an audibility. Maybe it'sto sound like Axel rose. So I love doing Karaoke of like flucomejungle or sweet child of mine. So ridiculous stuff. You know. That'sawesome. Yeah, Hey, I'mn I grew up on that too, andI'm not afraid to admit that I've actually been to some of those concerts waypast the S. well, they're all coming back around and making a lotof money on rede and tour. Oh yeah, the deaf leppard molly crewthing. 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 forsharing that. Let's kind of get in the topic of the day.And so when we think about, you know, big data and just aiaround data, what do we mean by it being a critical skill set forthe future of work and what does that translate to in different roles? Someof the pre material you sent over refer to it as that critical skill inthe future work and I'd love just be able to provide the audience with somecontext on that perspective. Well, you know, so often people talk aboutAi and you know it's become sort of Buzzword Bingo to hear all of thetalk 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'twant to say Alexa too loud here, but cons fond, but you know, I mean that type of thing is changing the way we live, theway we work, and and so those skills, you know, those highlytechnical skills where people are data scientistate engineers and they're dealing with all that dataand they're doing the machine learning in that type of thing. Obviously that's oneset 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 andI can explain what that means, but become becoming more data literate, thena lot of the potential that exist in leveraging ai is it's just that itwill stay as potential and out. So I'll give you an example of justkind of data literacy. So take a person that's in a in a salesroll and there may be an entry level sales person. They take a lotof notes on conversations. They enter those notes into the CM. Thus itbecomes data. And so if they're if they're not taking good notes or they'reputting in default values for fields, then there's not much a person can dodownstream of that to actually, you know,... a meaningful analysis on some ofthat data, because the date is not there. And so that's anexample where you need more data literate people and they need to realize their rolein this sort of data quality life cycle. And so that's where we all needthose types of skills. We all need to recognize the data is criticaland as a critical element of successful companies in this day and age and that'sonly going to continue to move more in that direction. Yeah, I couldn'tagree more and I think it actually gives us an opportunity because anybody in salesknows, and having been a sales leader myself, you're always trying to getpeople 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 justkind of sit back and say, I just want to let the AI dothe processing and I'll look at the results and okay, fine, but there'sa need to understand. I think the source of that day to the cleanlinessof the data. Is there a different perspective here we might be able togive people that. You need to be, you know, in sent them tobe a little bit more literate and a little bit more involved and atleast understand how that aim may be processing or presenting those results. What Ithink? So, I mean that part of it is people. People thinkthat 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 datadriven and start getting value without ever having to give give any of the workto a data scientist or a data engineer. So when we talk about being moredata driven, you know, if you're in sales and you're trying tofigure out where your best prospects come from, and and and started to fit andyou start analyzing where people are falling out of the funnel and you startlooking at that those are questions that you're asking right and that in a moredata driven approach, would be to then answer those questions with data and solook more deeply at where people are falling out of the the funnel and andlook at the reasons why they're falling out and start doing some data analysis onthat. You'll start uncovering insights by just by virtue of answering those questions withdata and you'll start uncovering insights and now all of a sudden you can makedecisions 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 thatit's not about data science right, it's about just being a little bit moredata driven, at being a more data driven enterprise, and that I meanthose 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 oror you're in operations and you're trying to improve a process, or you're inmarketing and your your you looking at the legend conversions or where your leads arecoming from. ME, they're just so many different aspects of of that andit doesn't take data science or the magical, mythical ai to to solve all yourproblems. You know it's right there in front of you if you justask some good questions and then answer those questions with data when I think it'sI think it's a really important point when we talk about these roles that theydon'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 andthere are so many ways that it can be leveraged and utilized. It's almostlike I remember a most stick with sales, for example, but you almost haveto have an understanding of how business is operate, especially if you're sellingbe to be, you have to understand kind of the basics in how businessesoperate, how things get done inside of a business. That's one skill setthat we've seen team struggle with. But now there's this other layer of thisawareness that you're adding data and benefiting from the data. So making that partof 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 haveyou 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 thousandsof 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 andcenter? If I'm honest with you, I don't think they're doing it veryeffectively, and so I think the reason that that it's to a large degreeso far been ineffective is is that a lot of people, when they thinkabout, you know, a learning initiative and a need to up kill alot of people, then they just go back to our sort of default learningapproach. That we've we've done all our lives. You Mea we've all donethis and that we went to school. We it was long form learning.So in school, obviously it was in certainly really long form learning. Soit was courses of semester at a time. You would learn, learn, learn, take a test and then you'd forget it. And and we allhave experienced this. I'm sure I can't see you, but I'm sure you'renodding your head, because those the way we've all gone through this. Andthen, unfortunately, you know, data literacy is a problem across an entireenterprise. Yet people keep doing the same thing. We will then buy licensesto, I don't know, Corse air or something like that and send outa data literacy training. That's long form. It's probably several hours. People don'thave a lot of context as to why it matters. They go througha 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, andit's just fundamentally the way our brains work. So this is not a corse eraproblem, it's just the way our brains work. Is that right afterwe learned something, we forget it and and so there's a very steep forgettingcurve 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 ofspace, 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 youhave a five thousand of them, need to upskill and in this the defaultapproach of sending out Fivezero licenses to a platform and saying, Hey, gosearch on data skills and go take a couple of courses. Yeah, andthat'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, Iheard some becomes almost like information security training, like how much pain, how muchtension do you pay to that Chad? Yeah, now you check in thebox. It's your check in the box and that's not effective. Andso this is too important for that. I mean, frankly, information securitytraining 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 ofcontinuous learning type approach, something that is consumable by each and each and everyone of us. And so if you think about like the dual lingos ofthe world, that's how people are learning languages. They're learning it ten tofifteen minutes of time. They're learning a little bit every day, and that'sactually there's learning science to back up that. That is the way to learn effectivelyand to remember even more importantly, and so that's kind of how wethink about it and I think it's a great approach and it's one. Isit 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 theAI approaches to data and learning to enhance our own learning experiences? Is itthat bite size kind of approach and that continual evolution? It's funny actually weare one of our co founders is our CTEO and he and I do apresentation. We call it human machine learning and we actually show people that thereare a lot of core laries between the way machine learning works and the Algorithmsand the way we as humans actually learn the most effectively. What's funny ispeople think it came in that order, like machine learning was around and thenall of a sudden we learned how to... to learn more effectively as humans. Well, that's not true. Of course, in the s there wereactually was a lot of research around cognitive psychology and and you know how humanslearn more effectively, and then you start getting research in machine learning. Thatactually 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 alwaysstill taken the learn everything at one time, long form learning, takea test at the end and then forget everything. We've kind of always hadthat in our culture. So we've never really embraced the truly best way thatwe learn. I love it. And so there was a phrase in someof the preg material to that I wanted to dig into, and it's itwas the cons of of the rise of the citizen. Data scientist. Helpme. Help me unpack that and understand what we're talking about there. ButI think it really does go back to what we were talking about earlier whereand and you know that's that's a that is a movement where people call ita citizen data scientists. I think that continues to be in this misnomer andmake people think that this the highly technical role. What that really means isthat we're all responsible for data analysis. We're all responsible for doing some amountof 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 lookat that as that's all of us and we're all responsible for varying aspects ofyou know, you talked about earlier week. Actually all create data. Now wedon't realize it, but we all create data. We all do someamount of analysis. It doesn't matter what role we're in. We're making somedecisions and we can do some analysis to support those decisions. And that's wherethat sort of movement to, where you don't just throw everything over the wallto your analytics team, you start realizing that each and every one of ushave 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 ofthe 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 dilemmaon Netflix. Care the crap out of me because you know, you haveno idea just how much data just by carrying around, you know, ourlovely little phones and how much data it's putting off. If you don't havea down the right way, we're contributing to it, whether we want toor not. Yeah, I mean it's not you can't even get away fromit 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 isone hundred percent data and people have gotten I think there's a lot of conversationaround, but people have gotten a lot more comfortable with parts of it thenthey 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 thephones for enterprises and we were like no, no, you want to turn onlocation services. They're like, I'm not sharing my location. I don'twant 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 restaurantsthat are within a hundred foot, ratings kind of thing. Yeah, youknow, we instantly want to give up the privacy for convenience. Until youknow, so many shines a light on it, but it is part ofthat. 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 ofthe world, all the devices that are turning on your you know, everythingin your house from Your Voice. And you know, really that's just thebeginning of that. What they really want to be doing is actually automatically turningthose devices on and off in response to, you know, your schedule, movement, all these things. I mean, that's really the void. You evereven Amazon actually just said this recently, that the voice is just a stopgap. That's not really the endgame. The end game is that it's actuallyresponsive to your habits, into your...

...movements and these types of things,so you don't even have to tell it what to do. Yeah, andthat's a little we'rewelling in for. Yeah, right, I mean that's spooking,right, it is. It is spooky. But I also can seethe flip side of it where you know, okay, if you got nothing tohide and no big deal, and what's you know, hey, thehouse is smart enough that it knows you're be home about this time, itcan 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 theoven. 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 understandingthat in order for that stuff to work really well, they have to sharea lot of the data and the insights into what their habits are. That'sexactly right. Yep, and I mean we've already come along way. Likeyou said, Your Privacy, Oh, you should have privacy, or ourcomfort level with certain different aspects of privacy, has changed dramatically over I mean evenmaybe the last five years. Oh yeah, and when, and evento the point, we go back to the devices in the house, Istart thinking about it, it's like, okay, I got an asked inhere. I got TV's that I'm fairly certain are listening to me. IfI'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 ofdata. People used to think about, you know, emf waves and allthe ways going through the air. Just think about all the data that's beentransmitted. I mean it's like saying how much is out there and what couldbe 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 thepotential 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 proliferationof data and devices that measure things that we weren't measuring before. You knowthat the amount of data out there just it continues to increase exponentially and withit then you know the potential for new ways to use it in harness ityeah, absolutely. All right. So let's pivot here a little bit talkabout quant hub help our audience understanding kind of what you all do there andwhat 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 realhearts and so we have an assessment platform that measures data skills of like datascientists, date engineers, and we've got enterprise customers all over the world usingus in their hiring process. So if they're, you know, trying tohire a data scientists, you usually get a lot of candidates and don't reallyhave a strong ability to tell them apart on skill, and so that's whatour assessments do. We also have an upscaling platform and that's more kind ofbroad day to literacy, like we were talking about earlier, and it's amicro learning platform, so it's a little bit every day, kind of fitsinto the white space of your day. It's something that the scalability aspect thatwe were talking about earlier is really, really an important aspect of being ableto change the culture is because it's a it's sort of an every employee typeof problem. And and so, you know, are big focus is comingup with something that that people can adopt, that and that you can roll outacross, you know, a majority of the enterprise, which is thisoff the shelf solutions that are out there. You just can't. They just don'tscale to fifty percent of the employees and a company or more. Yeah, and so, okay, when you think about kind of what you're doingat quantum, what's on the horizon, like what's on the future and cominga around the corner, whether it be in terms of aim, machine learningor even just ways that you plan on internalizing it at quand up, whatare you the most excited about? If you're looking, I mean, nobodylikes to look twelve months out anymore because nobody saw the pandemic coming. Butif we're looking like six, twelve months out, yeah, what do yousee 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 positionthat we're sitting in is and you know, naturally startups have have lofty ambitions.I we are going to be the... I believe this in myheart that we're going to be the company that everybody points to after this bigskill transformation over the next ten years. We're going to be they that theypoint to and say Quan hub was the biggest enabler of that. And sothat's that's where I believe we fit, because I think we have the onlyscalable and effective approach to do that. So that's where I that's our visionas a company. I mean that's literally written and our vision as a companyover the next six to twelve months. You know, I'm excited to seemore and more enterprises come on and and and, you know, use ourplatform 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 nowai out there that, so we are content is we write assessment questions andthen we curate learning materials, and so what's unbelievable is there's Ai out therenow that actually their algorithms and models that can literally, from scratch, readarticles, pick out the keywords of those articles and then find definitions of thosekeywords and right assessment questions covering the concepts covered. In that article and itcould do all that from scratch. WHOA. Yeah, I mean it's it's Iit sounds like science fiction and I really thought it was until our CTEOshowed it to me and I it's just it's just mind blowing stuff. Ithe same types of models. Literally you can actually give it a problem andsay right, code to solve this problem and you see on the screen itstarts writing code. mean, it's just if you love the you're probably we'reprobably similar age. So you probably love terminator to it starts feeling a littleterminator to like. I love it. I love it all right. Solet's Change Direction here a little bit. We ask all of our guests tostandard questions towards the end of each interviewer. The first is simply, as anexecutive, that makes you prospect or a target for a lot of peoplethat are out there and I'm always curious to understand when somebody doesn't have atrusted referral into what works best for you, when somebody's trying to capture your attentionand earn the right to time on your calendar. HMM, that isa 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'malmost unresponsive to cold outreach unless it's just unbelievable luck on the on the timingof the salesperson and that I'm thinking about something, we have a problem andthen they they reach out at exactly that moment, which is, you know, generally speaking unlikely. And so, but what I've definitely more responsive tois seeing people on Linkedin as part of a conversation about problems they have andthen 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 arepart 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 toyou, would believe would help them hit their targets or exceed and what wouldit be and why? HMM, well, I'm I'm from the I'm from theMidwest, and our chief revenue officer says I like to go all MidwesternMatt 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 salestechniques. What I like to do is I like to work with relatable peoplethat don't have a giant ego. And so, you know, I thinktry to be relatable, try to listen first. You know, one ofour core values is around listening through, you know, seeing the world throughthe lens of your customer or your prospect or your potential investor or or ateam member, and so seeing, seeing...

...the world through their eyes and andthen making sure when you're when you're speaking to them, say things in theway that not the way you want to say them, but the way youwant it to be received, and focus more on that. Mean that endsup putting yourself in a seat where you're thinking through your thinking through there you'reseeing the world through their eyes, and then that, I think that helpsyou then present things in a way that they receive it the way you wantthem to receive it, which in sales, you know, you have a goalright, right, how you want them to receive things in a certainway, and it doesn't matter to the way you want to say it mattersthe way they're going to receive it, and oftentimes people don't think about itthat way. They're they're selfconsumed with what they want to say as opposed tothe outcome they want to achieve by saying I think that is amazingly insightful adviceand 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 youwant us to send people? Yeah, I mean I'm at Matt at QuandHubcom. I'd love connecting with people. I'm passionate about this topic. Soif 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 andconnecting with people is one of the best ways to grow, and so I'malways 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'tthank 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 tobe REV exactcom leave us review on itunes and until next time, we valueselling associates with you all nothing but the greatest success. You've been listening tothe 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 somuch for listening. Until next time.

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