What happens when everyone is a data worker? w/ Kelly Wright from Gong

About this episode

In a relatively short time, we’ve gone from collecting and neglecting data to managing, enriching, and learning from data. We are in the age of collective data empowerment, where user-friendly apps and data ubiquity mean almost anyone can answer complex business questions. So why does the “data-driven” enterprise still sound like a pipe dream?

Join Tim, Juan and special guest Kelly Wright, president and COO at Gong and former VP at Tableau, for a discussion on data and analytics past, present, and future.

Special Guests:

Kelly Wright

Kelly Wright

COO at Gong

This episode features
  • Ways organizations can maximize the value of data they are already collecting
  • How to address challenges with data and application sprawl 
  • The Gong Show was a popular TV talent show in the 70s. What was your favorite game show growing up?
Key takeaways
  • Understand the true reality! Everyone is a data worker!
  • Valuable assets in an org: people, people, people… and then data
  • When people are empowered to answer their own questions, faster.. they like their job more.

Episode Transcript

Tim Gasper:
Everyone, it’s that time once again. It’s time for Catalog & Cocktails. It’s your weekly honest, no-BS, non-salesy conversation about enterprise data management with tafety, tasty, tafety, tafety, tasty beverages in hand. My name is Tim Gasper. I’m a longtime data nerd and product guy at data.world. And joined by Juan Sequeda.

Juan Sequeda:
Hey, Tim. Here, I’m Juan Sequeda. I’m the principal scientist at data.world, and always excited to take the break middle of the week. And today, I’m actually right now in San Diego so it’s two o’clock, when it’s usually four o’clock I do this.

Tim:
Yeah, you’re West Coast this time.

Juan:
West Coast this week, but actually I’m going to do some work a little bit after this though. And today, we have a very special guest and it is a person who has really seen a lot in the data world. This is Kelly Wright, who is the president and COO at Gong, and is a former EVP of sales at Tableau. One of the things I really love about Gong, which is a tool that is a revenue intelligence platform that we use at data.world, is that they collect and they do so much stuff with data. You have to go read Gong’s blog to go see all the analysis that they do. I mean, you learn so much from that. And it’s just an example of how to go do things with data and how to take that to the next level. Kelly, it’s a pleasure to have you here. Thank you so much for joining us.

Tim:
Welcome.

Kelly Wright:
It’s so great to be here. Thank you, Tim and Juan, for having me and thanks for the commercial for Gong. It’s great that we have a raving band right here on the call.

Juan:
Oh, yeah. I mean, okay, we’re not supposed to be salesy, but Gong is a really cool tool. I’ll say that.

Tim:
It’s cool for the data.

Kelly:
All about the data, it is. We unlock reality in that data. We’re going to be talking about that data today, I understand.

Juan:
Yes, yes. So let’s start off with our tell and toast. So what are we drinking and what are we toasting for, Kelly?

Kelly:
Oh, well, let’s see. Mine is very exciting. I am in a hotel room in Napa at an offsite, and I am drinking water. It is hard and real, but here it is. Why water? It’s pretty early here on the West Coast set too, to start drinking a happy hour.

Tim:
The wine’s coming later?

Kelly:
Yes. Yes.

Juan:
How about you, Tim?

Tim:
I am drinking a little bit of Glen Moray 15, some scotch. So that’s what’s on the menu today. So not a cocktail, keeping it really simple, just some scotch.

Juan:
I think today is the first time which I’m not having a cocktail. I actually have a beer here with me, which was one of my favorite beers, Stella. Yeah, it’s 2:00 PM here on the West Coast, but I have to say I’m in San Diego and I can see this beach right in front of me over here. It’s beautiful. And I just feel like having a beer right now. And I’m toasting because I’m at a conference. I’m actually right now at DGIQ. And it’s just fascinating to be back in person. And I’m really excited, I’m going to shout out. I met a big fan, somebody who actually came up to us. So I met with people who actually listened to the podcast. And then I want to shout out to Cody Pastini. She was a super fan and has been watching all our episodes. So, hey, thank you so much. This is really cool to go meet people who actually listen to us and think we have cool things to say, so…

Tim:
Yeah. Thanks, Cody.

Juan:
So, all right. We got our, for warm up, funny question here. So, The Gong Show was a popular TV talent show in the 70s. What was your favorite game show growing up?

Kelly:
The Gong Show, did you bring that up because I work for Gong?

Juan:
Yeah. We always… So this is a-

Tim:
That’s a tie-in.

Juan:
Exactly. We have our producers who come in. They organize the whole abstract and put in the funny questions, so that’s where it comes from.

Kelly:
Oh, I got it. I think my favorite game show was Wheel of Fortune. I loved figuring out the little phrases all the time so…

Juan:
How about you, Jim? Jim, instead Tim.

Tim:
Tim. Yeah. I loved Wheel of Fortune a lot as well. And I also really loved Jeopardy!, and I feel like one was always after the other or whatever because it was like, “Okay, let’s watch him spin the wheel, then let’s do some trivia.” So, that was always a lot of fun growing up.

Kelly:
That was my toss up. Everyone in my house liked Jeopardy!, but I wasn’t nearly as good in Jeopardy! as I was at Wheel of Fortune, so…

Tim:
I wasn’t very good at answering the questions, but it was still fun.

Juan:
Well, as a kid-

Tim:
How about you, Juan?

Juan:
Nickelodeon. Nickelodeon had a bunch of these game shows, I mean, so many of them. I remember just… I mean, it’s a blur right now, but Nickelodeon.

Tim:
Yeah.

Juan:
And I do want to shout out-

Tim:
People running around and the slime comes down and all that kind of stuff.

Juan:
The Slime Cup, oh, yeah. And then there’s also the American Ninja Warrior, and it’s a shout to one of the founders of data.world, Matt Laessig, who was, I think, nine-time participant American Ninja Warrior. That’s another cool show to go see, so…

Tim:
Yeah.

Kelly:
That is.

Tim:
All right. Well, let’s kick it off with our honest, no BS discussion here. So, when people say we want to be data-driven and they’re like, “Oh, I’m going to go buy all these apps, and now I’m data-driven.” It’s like, “No, that’s not really it.” So, when we talk about having a data-driven enterprise, why does it still sound like a pipe dream? You would imagine that we can actually be very data-driven right now, but it seems like we’re not. Or are we? You’ve seen this evolution of data for so long, where are we right now?

Kelly:
Yeah. Well, I think actually some people don’t think it’s a pipe dream. They actually think that they’re data-driven, their company’s data-driven, but there’s a difference between our companies being data-driven by just collecting and storing the data, or do they really have a data-driven culture and a data-driven enterprise. And those are two different things. And there’s been this huge evolution in the whole data ecosystem that we’ve seen over the last couple decades. It’s probably worth one chatting a bit about that. And I think the difference is, just because there’s so much data out there and your company is collecting it, doesn’t mean that you’re tapping into your data asset as much as you could. And where companies should be really be thinking is, what can they do to become not only a data-driven company, but to build and foster and continue to just promote a data-driven culture within their enterprise. And that’s, I think, the companies that are at the forefront and are in the leaders, they’re really thinking about data-driven culture, not just data in ecosystem. Does that make sense?

Juan:
No, it does. I really like how you make the distinction of one thing is collect data. “Oh, we need to go get more data. Let’s go catalog the data to see everything we have.” I mean, yes, you can go do that, but then it’s like, “So what?” I mean, at the end of the day, we need to understand the different problems you need to go solve, but that is around the culture. And I think that’s something that is, again, we talk about people, processes and tools, and you got catalogs to the tools that are needed to go collect data, but we really need to understand where the people in the process is coming around. I know we were having our previous conversations. I love how you were going through this evolution of data and analytics about before. And the before times, it was just, “Hey, just the strategic consultants will be the ones who are looking to the data.” Share us a little bit about how you’ve been living through that entire evolution.

Kelly:
Yeah. I mean, well, anyone that has met me knows I am super passionate about data. I’ve built my entire career on data and there has been an evolution of it. And so, if I think back early in my career, when I took my short fray out of sales and I went and did some strategic consulting at McKinsey and Bain, that was when there were these outside experts, these strategy consultants, consultants that you brought in to understand the data, because it was too complicated for people to do in-house. Now, those consultants are still doing a ton with data, but then you went through this whole thing of like, what should companies even think about with regards to data?

Kelly:
And so when I was early on, I was employee 10 at Tableau Software, and I was the first person in sales, and I remember when we started talking about data. Now, everyone says, “Well, should your company care about big data?” Back when I started at Tableau, people didn’t care about data, they didn’t even care about little data, much less big data. Data was just kind of an afterthought, but then what ended up happening is, people started collecting all this data and storing the data, but they didn’t know what to do with the data. So it started first of, no one cared about data, then it started with, “Well, we’re going to collect and store the data.” But if you collect and store the data and you don’t do anything with it, it’s not particularly useful. And so part of what our mission at Tableau was, what our mission was, we help people see and understand data. And so it was empowering people to be more self-sufficient. The whole data democratization of how do you empower people to be able to answer their own questions?

Kelly:
Because there was this challenge which was, there’s so much data, but there was a very finite group of people that understood how to power the systems that generated the answers. So you had to be an IT consultant, you had to be a DBA, you had to be a data scientists. And so then, you had all these people that were, what we called, data users, analysts, data enthusiasts, even just everyday business users, whether you were a marketer, a salesperson, a recruiter, even if you’re thinking about outside of the business world, someone that is a teacher or a principal or a doctor or nurse, people that had all this data, and they were the domain experts, and they didn’t know how to ask questions and answer questions. So that was the next stage. You went from collecting data and storing data, to then empowering people to be self-sufficient answering their own data.

Kelly:
And now a lot of companies have embraced that, empowering people to have access to the data and to ask and answer their own questions. But now we’re at the next stage of the evolution, and that next stage is, there’s so much data from so many diverse sources and data coming all the time from so many different places and it’s big data that now, people, even if they have the power to be able to have some system or tool, they don’t know what question to ask. So they end up going back to opinions and hunches because it’s just too complicated to figure out all the questions to ask. And so now it’s the next generation of the data evolution, collect, store, give access, empower people to ask and answer their own questions. But now, there’s this whole layer of AI on top of it, of how can you allow organizations to be able to be more autonomous and self guided and to have more guidance in how people even think about answering questions with their data.

Tim:
That is really cool how you’re painting sort of the evolution of how this has evolved. I think we’d love to zoom in a little bit on a couple of these sort of evolution points. And so, for example, companies are kind of stuck in this collection mode and they’re now trying to move from sort of collection and storage to self-service. What do you think are the big keys to success there? And then, similarly, I think we’re going to ask you about like, well, now, how do you go from self-service to more like this broader intelligence?

Kelly:
Well, self-service takes a couple things. One is, in order to enable people to ask and answer their own questions, they have to have access to the data. And there’s some organizations that lock down the data so much. Now, we have to be appropriate with the data. You can’t give all data to everyone, especially for those of us that are in public companies or affiliated with companies that are soon to be public. There’s all of this that you have to do to be thoughtful about who has your data because you can’t have too much inside of trading all that.

Kelly:
But some companies take that to too much of an extreme and then lock down so much of their data. And if we’re going to empower people to have a data-democratized environment or culture, then they have to have access to data. So I think number one is access to data. Once they have access, then are we giving them the tools to be able to interact with that data themselves and to be self-sufficient to have a conversation with that data? And that requires companies to think about tools, systems, processes to enable the everyday user who might not be so technical to be able to have that interactive conversation with their data.

Juan:
Very important key point here is, having a conversation with the data. My aha moment right now is, it’s not just about we want to have a bunch of data, give the answers to questions I have because yeah, I mean, that should be a given, we should be able to go do this right now, but there’s just so much stuff out there that when you start thinking about that we don’t even know what those questions could be. I’ve been having actually a couple of hallway conversations here in this conference which is about that, about data-driven hypothesis generation. It’s like at this point, we have a bunch of data we are collecting. It’s like, what are even the things that we could be asking? What are the things that we don’t know that we should be asking about, that we should have?

Juan:
This is where the machine should be able to come in and say, “Hey, I got a bunch of opinions and thoughts,” and the machine should come in, saying, “Yeah, these are good ones and here’s the answers. But, actually, you should be thinking about it this way, or not you should be, like here are these other possibilities.” Right? So now you really step out of the box and kind of like, “Oh, wow. I didn’t know this was possible.” And this is just having a conversation with the data. I’m just kind of connecting some dots that I have in my head here, past conversations. And I really like this about having conversations with the data.

Kelly:
Yeah. Oh, well, Juan, I think three different things just popped up when you talked about that. So let’s just go walk through what the three different ones on. One is having a conversation, an interactive conversation with the data. Many companies think about themselves as being data-driven when they have someone in some group and some business intelligence group or some analytics group goes and creates a bunch of executive dashboards, a bunch of production dashboard, a series of reports that are generated and sent via email or Slack every day, and then there’s a bunch of static reports. So, that’s useful. We’re looking at the data. There are certain stats and metrics that you want to look at every single day.

Kelly:
The challenge is, that doesn’t engage with an interactive conversation with the data, because if people are really being data centric, what happens when you see a dashboard? When I see a dashboard or a KPI, I look at it, but then what happens? I look at it and then I have a whole bunch of questions around why that data is showing me that one specific number or that series of numbers. And so then, it surfaces a whole bunch of additional questions. So to be data-centric, it’s not just about serving up the data in some kind of a static report. Even if it’s an interactive report that has a few predefined drill pass, it’s then empowering people to say, “Hey, this is surfacing some information. And now, I want to be able to answer an additional follow-on question and then an additional follow-on question to get to that questioning and interrogation type of mode.” So, that is what I mean, Juan, with conversation at the data.

Kelly:
The second piece, Juan, which you touched on is, how can you have this kind of people don’t really depend on the hunches? They have an idea, and then how do you actually go drill into it? And this is what is the exciting part of where we are today with data, is once a company does get to be self-sufficient with the data, then they can say, “Hunches are good. Opinions are good. We’re hiring people in our organization that actually have really good intuition. But we don’t want to just act on that intuition alone, we want to be able to have the hunch, but then empower us to be able to ask the questions that we can go validate our hunches with facts.” So, that’s the second piece. The third piece is, when we talk about the future of where we’re going with being more autonomous and guided, and maybe that’s something that we could go to later in the conversation.

Tim:
Well, this autonomous and guided aspects, actually, I think something that’s really interesting and kind of connects to the next step of this whole evolution. You’re talking about getting to self-service and having this conversation around data, having access to the data, then you want to be able to get more autonomous and guided around the data. A question I would have for you around this, Kelly, is, is the fact that we’re having trouble getting data literacy and having these conversations around data, a big driver of the need for autonomous and guided approaches? Maybe I’ll just start with that.

Kelly:
Yeah. I think it’s a little bit that way, but I would probably frame it differently. I would frame it that there’s so much data. There’s diversity of data because you have data coming from your transactional systems, from what people are doing with social media, with all the different touch points that you have with your customers, with all of your employee data, all your financial data, and you go on and on and on, marketing data. There’s so, so much data that are coming from all these different apps, all these different systems. So yes, there is something of, there’s so much data. It’s big, it’s complex, so what do you do with it?

Kelly:
I think the other piece though is, because there’s so many systems, there’s also certain systems that are dependent on human beings actually entering the data into the system. So for instance, let’s take CRM or any CRM system that a company uses. The CRM is supposed to be the single source of truth of all of your customer data, but it’s heavily dependent on what the reps put into the system. And the reps are so busy, they don’t have time to put everything in. So if they’re not going to enter in all their emails, enter in all their calls, enter in their notes from every Zoom interaction that they had or every single web conference that they had, then you don’t have a complete view of the data in those systems. So we’re collecting a whole bunch of data, but then some data’s dependent on what you’re putting in. So even though you think you have a lot of data, the data’s often incomplete.

Kelly:
And so part of the way to think about the AI is, how can you have a holistic system where it’s going through all of your interactions and it’s pulling all of those datas to really create a true version of reality? And this is one of the reasons I joined Gong. I’m so excited about what we’re doing here, because whether or not you’re interacting with your customer on a web conferencing platform or on email or on a call or in Slack or whatever it might be, text, we’re collecting all of that data. So you actually have clear visibility on that data. So the first is collecting the real single source of truth, where it is full visibility. And then next, well, now that you have it, how can we layer autonomous guidance on top of it to be able to help people to ask those questions or get directions on what they should do with the data, where it might be something they didn’t even know to ask?

Tim:
So that’s really interesting and compelling, Kelly, because it sounds like when you talk about this sort of autonomous and guided layer, it’s not just an add-on at the end of this. Right? It’s not just like, “Okay, collection, storage, self-service, now let’s pop our autonomous and sort of guided intelligence on top of that.” It sounds like you’re talking about how that can impact actually all parts of this, even all the way to collection. Right? If the CRM data collection is not effective right now, garbage in, garbage out, actually that autonomy and guided approach can affect that aspect of the platform as well.

Kelly:
I think when we’re thinking about the evolution, everyone first was, “Okay, we’re going to manually go put all the data in. We’re going to tie all our data together, we’re going to use all these different systems to put data in, and we’re going to assume all the data’s there. And now we’re going to empower people to have conversation with their data.”

Tim:
Mm-hmm (affirmative).

Kelly:
And that was super important, but now we have to all recognize data is growing and changing and evolving so quickly. It’s never going to work that we are going to manually, or even systematically by just using APIs and add-ons, put all the data there. So this is a way that AI can really help, to serve up the true visibility and the reality of what’s happening with all that data. So that’s the first way to use AI.

Kelly:
Another way to use AI is, well, is there some regular pattern matching that I can just serve up the same thing all the time? For instance, in sales, I know reps are always going to want to know what’s going on with this coaching, what is coming next in terms of their sales playbook. You can say the same thing for marketing or product. So certain predefined questions that you always want to ask, AI helps with that too.

Kelly:
And then the third piece is, how do we help to serve up insights from that data, that are from questions, that people may not even know how to ask? Or insights from the data where someone has an idea of, they want to look at this category or this topic but they don’t actually even know how to evaluate it. And that’s where we’re going to see the future of data going.

Juan:
I appreciate how you’re being very concrete on this, and let me go repeat this to see if I got this. We just ended up now talking about AI, which is really exciting. So I see this as two parts. One is about helping build and create the data, and all the other part is making use of it, right? Kind of how do we maximize the use of the data? So on that first part in creating the data, I mean, we’re always going to have so many disparate systems and silos and all that stuff. And even if we think that this is going to be the CRM or whatever is going to be our master system, there’s always going to be some other stuff that is not going to get in there. So, it’s never truly going to be one single source of truth or whatever, because there’s other sources that go compliment that. And it’s going to be growing. Like today, we can decide that we’re going to go connect all these stuff together, but tomorrow there’s going to be new stuff in there. Right?

Juan:
And somehow we want to be able to go create some automation. This is where AI can go help. I think something that Gong does really cool is that it does all this, again, not to get too salesy but, again, I’m a fan of Gong, is let me go do all the analysis on the transcripts on how people are… You can go see the sentiment of thought. You can go see what people are talking about. You start learning so much, not about the customer, but also then about the employees, and learning for coaching. So there’s just so much stuff to go do there. So, this whole first part is about how can we truly start creating that X 360, X, call it the customer, the employee. Right? And we need to have AI to bring all that data together. So, that’s that first part.

Juan:
And then, on the second, it’s how do we use AI to help us get more value out of the data? And the two ways of doing this is, one is, “Hey, there’s this stuff that we ask over and over again, basically kind of predefined questions, and I don’t want to have to manually go ask these questions. You should automate this for me. This is something that I already know that you’re already expecting me to go. You’re expecting me to go ask this question, just give it to me.” And the other part to that is, “I don’t even know what to ask.” And it goes back to my previous point of, some data-driven hypothesis generation, or just generating the questions that, “Hey, you should be asking me. You didn’t know that you should be asking me here. Go ask these questions. And I gave you a bunch of them, now which ones do you actually care about and let’s go solve them?” I just ranted a lot. But I mean, I understood this from the AI part that you were talking about. Any-

Kelly:
Yeah. I think I’d agree with all three of those. I nuanced the middle one a little bit, because the way you said it is, “Hey, when people know that there’s questions, just serving up the answers,” that to me sounds like a predefined KPI or a dashboard. I’d say that that is something that’s important, but that was something that Tableau or other BI systems could help with. Now, what’s happening is, it’s less about just having the predefined metric. It’s more of, if there’s a category of questioning that you know that you want to have, having AI, kind of put a wrapper around that. So it helps just serve up to people what they should do without them even having to lift a finger. So, for instance, in Gong, there’s certain things around coaching or deal management that we just know that all of our customers are asking for, so you can kind of serve it up.

Kelly:
The third category is, well, I don’t know what your company, exactly what your strategic priority is, but now if you tell me it’s really important, you want to understand what’s going on with… You have a strong competitor, you want to understand what’s happening all the time when your customers are talking about that competitor. Or you just did a price change, and you want to know how that’s landing with your customers and your prospects. Then, you can actually put in some kind of word of like, “I’m tracking what is happening when this competitor name’s coming up or when someone’s talking about a price differential,” and then you use AI to say, “Well, what are all the different words that would be price? And how do I actually serve up some insights of like a tracker of what’s happening around that universe and that category of insights?” So those are how I kind of tailor down those left two buckets a little bit more.

Tim:
Well, I have a related question to this automation kind of conversation. And I’m curious, Kelly, what your take is, just based on what you’ve said about AI and automation and how you’re leveraging it at Gong, but how you’ve seen it evolve. How much are you seeing that this application of AI, can be sort of general versus sort of domain specific? Right? So in the case of Gong, the most common use case being around sales. Right? For example, you mentioned the sales playbook and stuff like that. That’s sales peak? Right? Versus company specific, like “I’m a snowflake. I have to configure everything because it’s just me.” Right? Just kind of curious on how you feel about that spectrum and how automation and analytics can apply to that.

Kelly:
Mm-hmm (affirmative). Okay. Well, this is a broad sentiment of what you just said. Sometimes people are thinking about data in one bucket. What are we doing with data? What are we doing with our company’s information? How can we empower people to be more self-sufficient with the data? How can we think about data democratization? It’s over here in one bucket. And then they’re having a whole different conversation of, “Huh? The future of AI, what can we do with machine learning? What can we do with AI, any ways that we can be automating things, helping people to be more productive?” And it’s over here in a different bucket. And there is such tremendous power when you think about merging those two worlds. When you think about, “Hey, you have so much data,” and… I mean, I always talk about the top asset in an organization, is their people, of course, people, people, people do everything. After people, of course you have your customers, you have all these other things that are super important, but data is such a key asset.

Kelly:
And what happens is, many companies are thinking about how can they make the most out of their information and their data. And that’s an important question to ask, but sometimes they’re doing it too much in a vacuum without thinking about, just the same way people are saying, “How can I empower my people to be more productive, my systems, my supply chain, whatever, to be more effective with AI?” We should be asking the question of, how can we get more out of this super robust data asset by leveraging what our strategy is as a company with machine learning and AI, and combining that with data to be able to empower people, not only to answer their own questions with data, but to help tell them what to do when, based on all this data that we’ve collected over the history of time? Does that make sense, Tim?

Tim:
I think so. I think so. And it sounds like you’re kind of proposing a different way to sort of frame the situation here, that it’s less about sort of general versus industry versus company specific, and it’s more about sort of driving towards action and leveraging your data and your people to do that.

Kelly:
I mean, if I think about what we’re doing, for instance, at Gong, is we want to understand what is the true reality, and then being able to empower people to be more successful, whether it is it within their sales and revenue organization, whether it’s within their company, whether it’s in whatever group, product, marketing, sales, wherever. And then when you’re looking at that data, you’re looking at data, not only your internal data, not only your sales data, but your market data, your company data. So I think it’s all kind of blends together. I don’t think you can really say, “Well, how should we be thinking about just our company data or our functional data, and then our company data, and then our market data,” because this is where companies kind of getting to… That’s a challenge. If they go and silo those data too much, then you’re not getting all of the benefit to be able to look at that whole data asset together.

Tim:
That makes sense. And then I guess just one more double click on this topic before we do another one is, I guess the last thing that I’m trying to figure out is… Let’s pick on sales for a second. Right? And so you’ve got one company calls it a deal, they call it an opportunity. This other group calls it an engagement. And I guess that’s just a very specific example of where different companies might have a different way to approach it. And I’ve always kind of thought as some of these semantics issues and some of these sort of choices as things that can be a barrier to sort of automation and AI to be effective.

Tim:
And I was kind of curious, is part of the reason, companies like Gong, for example, can address that is because they are actually involved in the collection of the data? And so you can actually kind of unify it around a sort of a consistent approach. Whereas, if somebody was like, “You know what? I’m just going to recreate Gong with Tableau. And I’m just going to make it myself on top of my data warehouse or something like that,” not only are you losing the intelligence that comes with smart collection and things like that, but also, you’re not benefiting with all these three different paths around AI that you’re putting around that common set of semantics.

Kelly:
Yeah.

Tim:
Does that make sense? Hopefully that’s not a too weird of a question.

Kelly:
Yeah. I think I understand where you’re going with that. I think that when I talk about the data evolution, so much of it has really been focused on serving up what the view of reality is. So, what is the reality, surface up visibility? We’ve already talked about, with all the rapidly changing pace of data, there is a challenge with how clear and how complete is that version of reality.

Tim:
Mm-hmm (affirmative).

Kelly:
And this is of one of the first things that Gong does really well, is because we can go grab all of the information without anyone having to do anything. We just go collect it, and it’s all there. It’s actually a very comprehensive, complete set of the data to make it show that it’s clear visibility, it provides clarity, and it is the entire view of the reality. So that’s the first piece where companies are struggling with when things are changing so quickly. This is what happened back in the days when I was at Tableau.

Kelly:
Sometimes we’d go have a conversation with someone, and they’d say, “Hey, we’d love to use Tableau, but we’re not quite ready. It’s going to take us three years to put together our data warehouse.” And we’d say, “Okay, it’s going to take you three years to put down your data warehouse with what you have now. But in three years, you’re going to have hundreds, thousands, millions times more data than you have today. And so, then, everything that you’re putting in place now for three years later, it’s going to be obsolete then.” And that’s what companies want to make sure. Are they looking at the full version of the truth? So that’s what I’m talking about with visibility.

Kelly:
On the autonomous side, it can get confusing. Think about how many times people have said they get to analysis paralysis. They want to go ask every question. They want to go look at everything. And they don’t, one, know how to ask the question; two, everyone’s talking about the dirty data. Well, this data and that data, you mentioned it, there’s sales, there’s deal, there’s opportunity. If you leverage and tap into AI, you can actually train the system that all three of those things, your deal and your opportunity, those are the same words. And now, you can bring that together in a much faster way than you’d be able to go, build it manually in something, like a data warehouse or a Tableau.

Juan:
Wow, my mind’s growing. So much stuff here.

Tim:
Yeah, I’m taking a bunch of notes just based on processing this all. This is awesome.

Juan:
I wrote down this phrase here, is you want to understand what is a true reality so you can empower users so they can be successful. They need to understand what this reality is. I think it’s not just about… We always talk about the single source of truth and everything is, but there is a reality out there that we may agree. I mean, you can disagree on it, we just need to know what that is. And then, once we put all that data together, but based on kind of that knowledge. Right? So if we talk about a customer, we know that these things happen around a customer. We talk about a deal, we know these things happen around a deal, and a customer’s related to a deal. So this is kind of those high-level categories that you start seeing. And I want to understand all this data that’s around these particular topics. So this is something that… I’m thinking a lot about this.

Juan:
One thing I do want to touch is, is about maximizing the value of this data. I always do the magic wand exercise. We’re in a position of time right now, where we have this true reality of the data. We want to empower people to be more successful. What does that actually look like? I want to ground that a little bit more into kind of reality. Can you give us some examples that you’ve seen in your experience of how people are actually maximizing their value once they have all this data really well connected?

Kelly:
Yeah. Well, I can give some examples to just what we’re doing here. The way we talk about it, is we talk about it here at Gong of unlock reality. And if you think about it, oftentimes, reality is very locked. It’s like there’s data somewhere, people don’t know what to do with it. So what do we mean by this tangibly? Okay. I’m a salesperson so I’ll give sales examples. So, there’s a whole slew of sales people and sales reps, and they’re going and having a whole bunch of conversations. Now, I can look in my CRM and I can see what are in this stage, what are close to closing, what are actually close won, what are close lost? And if a deal works well, or if one team’s doing better than another team, I can go and ask and say, “Well, what happened?” And then people give me a whole bunch of explanations.

Kelly:
Now, I can see what’s in the system, what is the reason that the rep wrote in, that the deal came out. I can hear what they’re saying, is what happened with the conversation with the customer. I can maybe even go listen to a customer conversation. But if I want to see pattern matching of what is actually happening, it’s hard for me to understand. And if I want to unlock reality, now think about a system where I have all of those calls, all of those emails, all of the Slack, all of the text, all of the Zoom video calls, all the WebEx, and taking all of those customer conversations, pulling it into a system where now it actually tells me everything that’s happening in all of my customer conversations.

Kelly:
And I want to understand, well, what is happening for those deals, where they’re winning versus those deals that they’re losing. And maybe, I can look at what’s happening with the certain competitive track. What’s happening with the pricing? Is the rep actually talking about our playbook? An interesting thing companies can say, “Hey, we just launched a new playbook.” And for those reps who are using the playbook, are they selling more than those reps who are not? So rather than just going with the hunch, it’s actually pure data, which you wouldn’t be able to do if you didn’t wrap AI on top of it.

Juan:
You’ve painted a true picture of future, but I know that this is not future. I know this is reality with you guys, and I love your whole term of unlock reality about this.

Tim:
Yeah, that’s a phrase that I’m thinking a lot about as well. And I like that our conversation has painted sort of AI less of this thing that you’re adding on top. People always tend to talk about AI, at least it’s the fun thing to do. It’s like rocket science that goes on the end of it and just makes everything really exciting. And when you ground it in unlock reality, it’s about like, we’re just trying to get to the truth of the matter, and then take action on it. And it’s a much more practical perspective I feel like.

Kelly:
Well, Tim, I think there’s one more thing to talk about. So we’ve talked out how you unlock reality and it shows the whole version of, it gives visibility and clarity on what is truly happening. We’ve talked about the autonomous piece too. The third piece that is really important if we go back onto your initial question about data-driven cultures, is companies are trying to drive alignment in their organization. You have to have alignment. You have to have, the sales team is thinking the same thing as marketing, products thinking the same as go-to market, all of your executives are aligned. People are on the same page. And what happens is, when people are not clear on what is the true visibility and you can’t unlock reality, then everyone kind of makes their own hunches and they go their own ways. And then it gets to be, people are operating with different versions of the truth, and then it drives misalignment, and it creates more silos and people go in different ways.

Kelly:
So the other thing that people often aren’t thinking about when considering the importance of data-driven culture, one, we’ve talked about tapping into the asset. Two, we’ve talked about empowering people to be more self-sufficient and productive in their work so that they cannot only have that interactive conversation with the data, but they can actually tap into the data to serve up the right information at the right time to make them more productive and efficient. The third is, how do you actually drive improved communication, collaboration and alignment, which is some people don’t realize, is so tightly dependent on having good cohesive data strategy and a data-driven culture?

Juan:
So talking about culture here for a second, and this is always a hot topic right now, I’m always curious about… One of the conversations we were having a lot is about the decentralization, centralization of data, of the people who do the data work and move that down closer to kind of the domains of sales and finance and marketing. How have you been seeing kind of the cultures evolving? And what are best practices? What are dos and don’ts that you’ve seen over your career when it comes to establishing data cultures?

Kelly:
Well, the first is being thoughtful about access and being… Although we have to lock down some of the data, how can you actually empower people to have enough access to datas to be able to do their job? I think that’s the first piece. The second is, oftentimes, people think that, or I would say, now it’s changing, but if people aren’t technical, that they aren’t interested in interacting with their data. And it’s just not true. Everyone’s interested in interacting with their data. Whatever domain you are, whatever function you are, people can work much faster. For instance, if you’re a recruiter, I need to know how many people have I talked to? What are the different salaries that I’m giving people? Who’s accepting, who’s not? I need to be able to know them on the sales side. If I’m on the product, I need to have data to understand what are my customers’ saying, what’s happening.

Kelly:
Even if you’re not in business, think about even someone that is like a little league coach for their kids, they want to look at the data and all the stats. So I think it is a mental shift to believe that everyone actually is a data user. Everyone is a knowledge worker and everyone should care about data and information. And how are you empowering those people that have questions to do their work? How are you empowering them to get that work done without having to depend and rely on someone that’s highly specialized in a different group? So I think that’s the second piece.

Kelly:
I think the third piece is, how are we continuing to use our data to help people be more effective and more productive so that they can work faster. And that’s where a lot of, for instance, what Gong is doing, is think about onboarding. People are onboarded. They go through two weeks of training. Are they actually benefiting for that training? How are managers helping them to ramp faster? Everyone’s always talking about they watch me, it’s faster. Well, what if you had a system that now every manager could go in and hear what people are saying, see what they’re doing, and then you can be faster? It’s just making everyone be much more productive.

Kelly:
And I think the fourth, which some people don’t even think about, is when people are empowered to answer their own questions and to work faster and more effectively, not only are they more productive, they like their job better. They enjoy what they’re doing. They’re more passionate about being able to get their job done. I’ll give you two stories. For instance, at Tableau, I remember some of the most compelling stories weren’t when people said, “Hey, I was able to actually see this story or build this dashboard that helped me be more productive at work.” No, what people would say is, “I was having so much trouble getting my job done. It would have to take me 10 hours a day, every day to tap into this. And then I used Tableau and I could do what took me 40 hours in literally 10 minutes. And now it freed up my time and I could go have dinner with my kids every night. And what this empowerment of data, allowed me to get my life back and to spend more time with my family.” And that was very inspiring.

Kelly:
Another story you have here at Gong is, someone will say, “I’m a manager, sales manager, and I just really wanted to be able to ramp all of my people faster and know I was doing everything possible to make them more successful at their jobs. And I’ve tried everything. And now that I’ve used Gong, I love my job, and it’s empowering me as a manager to have a growth mindset and for me to get better and for me to onboard everyone on my team better. It makes me so much better, and it makes me passionate that I can make everyone on my team thrive in a way that they couldn’t before.” And both of those are really personal stories. They’re not all this ROI stuff, which is important too, of course. But data and unlocking reality empowers people to be more inspired and more passionate and have more fun at what they’re doing every day. And even if all the other reasons didn’t matter, companies should have a data-driven culture just because of that.

Juan:
This is brilliant. I mean, this is the three-minute snippet that everybody should be listening to over and over again. I just love what you just said here. And in particular, I’ve had this conversation with other people about, when you’re hiring people and you think about people coming out right now into the workforce, it’s like, “Where would you like to go work at? I’m going to work at a place where everything is locked down. I have everything. It’s just spreadsheets being emailed around. Or I want to work at a place where they have a fantastic data infrastructure. They know that people who own stuff. I know who to go ask questions and stuff like that. Where would you want to go work?” I mean, I think people are going to get more excited to go work at a place where they have a really great culture around when it comes to data stuff.

Juan:
So, having your data in order is not just a technical problem you need to go solve right now. It is going to be part of how you’re going to go hire the best people, how you’re going to retain the best people with an organization. And yes, we need to be customer-centric and customer-obsessed, but I would actually argue that I want to be more employee-obsessed because we need to keep the employees super happy and to be loving the product, loving the company, because they’re going to be happy, they’re going to be delivering amazing things for the customers and so forth. So, I love how the culture is not just… I mean, yeah, we got to think about the ROI and the money and everything, but it’s a personal stories too.

Juan:
I talk to people, it’s like, “So what keeps you up at night? What’s the true pain point?” And sometimes, they’re just like, “I have to do all this stuff. It is so boring. It takes me forever. And I want to just come earlier and spend more time with my family. And that keeps me up at night because I can’t spend time with my family.” I mean, it kind of sounds cheesy, but it’s not. I mean, it’s not cheesy. This is the reality. Goes back to your thing, this is the true reality. And we go through, it’s not just about data and technology, there’s a whole people and a culture stuff. Anyway, you got me so excited about this. I really loved your comment here.

Kelly:
Well, you know what? I think the interesting part is, the people and empowering people to answer their own questions and to do their own work and to work faster, all of that is so inspiring for our teams in terms of hiring and retaining our talent. At the same time, it’s super compelling for our customers too. Because if you think about our customers, the worst thing that can happen… You know when you have an issue at some vendor, maybe it’s whoever provides cell phone or something that goes on with your cable, and you call up and they ask you a whole bunch of questions and then you get disconnected and then you call back and then you have to explain it all again, and it’s so frustrating. It’s like this vendor doesn’t even understand who I am. They don’t even understand who their customer is.

Kelly:
And the thing is, it’s empowering for our talent, but it’s super empowering and create these raving fans for our customers too. When we talk to our customers, our customers want to know we understand them. We care about them. We have done the research. We invest. And some companies, because they don’t have this data-driven culture, they wouldn’t even know where to start. They don’t even have all the information in one place to serve it up to whoever’s talking to the customer. They have all these different systems, they don’t blend, nothing’s serving up the insights.

Kelly:
So again, a story that happens, for instance, at Gong is, sometimes we’ll have our customers where someone’s gone, and they’ve listened to the calls. They’ve run through all the trackers. They’ve listened to what other people who have interacted with the customers, what they’ve said in their customer conversations. And so when they interact with the customer, the customer will say, “Wow, I’ve never even talked to you before. You know everything about me. You truly understand what my issues are and how you can help.” And that helps to create loyalty with our customers. It helps to create raving fans. And it helps go back, Juan, to what you talked about, is it’s people to people. And it’s so much easier to forge those ties, people to people, if you actually understand what’s happening and you have clear visibility into what the reality of the situation is. And so, it’s true everywhere we go, culture internally and culture externally.

Juan:
Yeah. Oh, we can keep talking for hours and hours about this. I’m so excited. And I love your energy. And I have to say, I really love how you organize your thoughts and you’re very, very kind of punctual in telling how things are. So thank you so much about this. It’s an hour. It’s almost flying by here. We want to go to our lightning round session here.

Kelly:
All right, lightning round.

Juan:
So I got a of couple questions. All right. So I’ll go first. Yes or no, is the future of AI for analytics vertical, such as for sales, for customer success, et cetera?

Kelly:
It’s for everyone. It’s not just vertical.

Juan:
Okay.

Tim:
So the second question: As part of self-service data being realized, will everyone in a company have access to a BI tool?

Kelly:
I think it’s hard to say BI. Business intelligence is kind of like an antiquated term in a way. I think if it is empowering your people with systems and tools where everyone can actually answer their own questions and tap into that data in one way or another, yes, everyone should have that. Will everyone have a tool that is in some kind of category categorized as BI? I think that’s too narrow of a question.

Tim:
I like your nuance there.

Juan:
All right. Third question: Can technology help a company improve their data culture?

Kelly:
Yes. Yes!

Tim:
Good.

Kelly:
We just talked about it with Tableau and Gong, of course. But I think that’s a little bit of a lay up. I work at data software companies, so of course the answer is yes.

Tim:
Well, yeah, but some people are so adamantly against that concept in the sense that they’re like, “No, it’s a people on a culture problem.” Right? And it’s like, “Oh, that’s crap.”

Kelly:
Okay, well here’s the thing.

Tim:
Well, we’re in a technology business, but-

Kelly:
You know what? If you can solve your data problem just with people, without any systems or tools, that would be very interesting. I’d love to see it. Maybe people keep it all up here in their head.

Tim:
There’s just a mind shift change.

Juan:
Talk about silos, right?

Tim:
All right. Final question for you in the lightning round here, will everyone become a data worker? Will we actually achieve all companies getting to this, everyone’s a data worker vision?

Kelly:
I think already everyone is a data worker, and companies just haven’t embraced that yet. Everyone is a data worker. And if you think about it, it’s not only true just in work. I mean, I think about it with everyone that I interact with. I think about it for my kids. If my kids want something, they want a new pair of shoes or they want to justify a vacation, they come up with a whole bunch of data. They’ve done their benchmarking and they’ve served up the Pricing. So everyone has a data culture. If you think about every function in a company, sales, marketing, product, HR, finance, recruiting, everyone’s looking at data.

Kelly:
And this is I think part of what we’re talking about, going back to Gong. When we talk about unlock reality, and the question you just asked me of, is it verticalized? Some people would say, “Oh, maybe you just do that for sales.” But if you’re going to do it for sales and it drives empowerment, it helps provide clarity, it drives alignment, if you do it in sales, why shouldn’t we be doing that in marketing? Why shouldn’t we be doing that in customer success? Why shouldn’t we be doing it in tech support? Why shouldn’t we be doing it in product? Why shouldn’t we be doing in HR? And the list goes on and on and on. And so, everyone in a company is a data worker. Everyone is using and has access to data at all times. And people want to be able to be empowered to answer their own questions and to work more productively if they can. And so, everyone’s a data worker in my book.

Tim:
I love that as a big takeaway from this. Right? Everyone is a data worker, and if you’re not thinking that way, you should start thinking that way.

Kelly:
Yeah. If you believed that everyone was a data worker, are you doing what you need to do in your company to empower everyone to actually behave as a data worker? And if your answer is no, then that probably means that you can continue on your journey of being a data-driven culture.

Tim:
Yeah. This is your test for you and your organization. Are you meeting the bar?

Kelly:
Exactly. And even if you are meeting the bar, the world is changing so quickly, we’re never done. [inaudible 00:54:45]

Tim:
Don’t rest.

Kelly:
Yeah.

Tim:
There’s more to do.

Kelly:
More to do always.

Juan:
Well, this is a great way to go to our TTT, Tim takes it away with takeaways. Go first, Tim.

Tim:
Yeah.

Juan:
Yeah. Well, we have a bunch of notes here.

Tim:
So much good stuff here. So we’ll try to be brief. I’ll make three takeaway points and then pass it to you, Juan. So first of all, we talked about how AI can help around analytics, around your data, and you mentioned sort of three key ways it can. One was creating a holistic approach to creating a true version of reality. The second was, can I automate things in a way that helps people answer questions? That isn’t sort of a predefined approach, it’s taking more of a categorical approach. And then, also, can it help serve up insights that people didn’t even know that they needed, if we can actually get in front of these key decisions? And I love the way that you kind of broke down some of the different ways that AI can help. You talked a lot about true reality and how we can unlock reality and do so in as autonomous away as possible.

Tim:
And then, finally, you talked a bunch about culture, being able to empower people with access, being able to know that creating a great data culture doesn’t mean just empowering the technical people, it means empowering the less technical folks as well and turning everybody into a data worker. Right? I think about that phrase, knowledge worker that we use all the time. Data worker, knowledge worker, it’s really kind of related, right? If we want to empower everyone to actually become knowledgeable, to be actionable, we need to empower everyone to be an effective data worker. So what about you, Juan?

Juan:
Well, the main theme here is about, I think true reality, understand true reality, and everyone’s a data worker. I love that we’re going through as evolution. Right? At the beginning, we didn’t even care about data. It was an afterthought, then we started collecting and storing data, but we weren’t doing much with that data. Then, we started empowering being self-sufficient, things like Tableau and data democratization come along. But then, we just now have so much data, we don’t even know all the questions that we can ask. And right now, we’re in this position where AI can come in and help us figure out what are those questions we should be asking.

Juan:
And I truly love this whole conversation we had about conversations with the data. Right? Our initial version of being data-driven was just having a static report, and you can go in, maybe could drill down a little bit, but then you’d have all these follow-up questions. So you want to be very self-sufficient to be able to go answer those new questions, hypothesis you have, and validate them. And the future is, having this all autonomous and guided.

Kelly:
That’s a lot there.

Juan:
That’s a lot that we discussed here. Kelly, let me throw it back to you. Two questions: One, what’s your advice about life, about data, open-ended? And second, who should we invite next?

Kelly:
Yes. Well, my advice often in these, we talk about all these things, data, reality, autonomous AI, sales, all these different things. But I think a lot of it does come back to one thing I mentioned earlier, is just how are we thinking about our people as our greatest asset, and then empowering our people to be able to thrive, hiring the best people, allowing them to do their best work, bring their authentic self.

Kelly:
And everyone’s always focusing so much on operational efficiencies. How are we thinking about our people? And as we think about our people, so much of helping our people be empowered and thrive is about allowing them to get those tools to help them be better, to work on their growth mindset, to continue to grow and develop, to be able to have access to the right data. And data really unlocks all of that. And so there’s lots of other things you can do to really serve, to be able to be great culture companies for your people, but data is definitely a core one of them. So that, that’s what I’d say all about. The people and data actually helps build that culture.

Kelly:
The second, in terms of who should you have next, we had our annual celebrate event last week, and one of my favorite parts that we had brought in from the outside, was we had Dan Pink on as a speaker there. I don’t know if you could get him on the Catalog & Cocktails podcast, but it was so refreshing to hear about this whole body of work, which really is all centered around data. It’s all centered around drive, motivation, timing, and all the research with data that shows how you can actually optimize all these things around people and motivation, and how you get your most out of everyone there, all driven in data. And that’s what we’re all talking about. It was super interesting.

Tim:
I love that suggestion. I’m a huge Dan Pink fan. I’ve loved the whole mastery, autonomy, purpose that he kind of really became famous for, and then all his books that have come out since then. So great, great suggestion.

Juan:
Kelly, thank you so much. This has been a fascinating discussion, and I look forward to meeting you in person sooner and later, and continue having these conversations. Thank you for all the very valuable insights that you’ve shared with us today.

Kelly:
Well, thank you, Juan and Tim. And you know what? It’s been a pleasure over the years to work with data.world, and I’ve loved every part of the interaction. So thank you for inviting me here today and thank you, listeners, for tuning in.

Juan:
Awesome. Cheers. Have a great one.

Tim:
Cheers, Kelly.

Kelly:
Cheers.

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