Guest: David Goldstein, GCA Strategy & Insights, Johnson & Johnson

Today’s episode is hosted by Betsy Peters. She is talking with David Goldstein, GCA Strategy & Insights at Johnson & Johnson. Join us as they discuss the significance of trust when making data-driven decisions, how curiosity drives innovation, and how David’s experience analyzing data for political campaigns informs his decisions in the corporate world.

David Goldstein is a data-driven strategist with extensive experience in delivering game-changing results for clients from a range of verticals, including political and advocacy work. He is skilled in deep-diving on products/services, developing a media plan and creative brief focused on reaching and persuading the target, and keeping careful watch over meaningful KPIs to optimize the efficacy of his work. Goldstein has experience in using a range of tools, including 3rd-party data, surveys, focus groups, big data, social media analytics, creative direction, and vendor/team management. He has co-founded Tovo Labs, a company revolutionizing digital campaigns, and We Defend Truth, an organization dedicated to defending the foundations of democracy through groundbreaking work in the digital realm. Goldstein is currently working as a full-time strategist at GCA Strategy & Insights, part of Johnson & Johnson, where he has been since January 2023.

Subscribe and listen to the Rev-Tech Revolution podcast series on:

Riva Data Revolution podcast series on Spotify Riva Data Revolution podcast series on Apple Podcasts Riva Data Revolution podcast series on iHeart Radio
Spotify Apple Podcasts iHeart Radio

Prefer reading over listening? We got you covered! 

Announcer (00:04): Welcome to the Rev-Tech Revolution podcast. Today’s episode is hosted by Betsy Peters. She’s talking with David Goldstein, GCA strategy and insights at Johnson & Johnson. Join us as they discuss the significance of trust when making data driven decisions, how curiosity drives innovation, and how David’s experience analyzing data for political campaigns informs his decisions in the corporate world. All of this and more on the Rev-Tech Revolution podcast. 

Betsy Peters (00:36): Hello David, and welcome to the Rev-Tech Revolution. 

David Goldstein (00:39): Hi, Betsy. Thank you so much. I’m really excited to be here. 

Betsy Peters (00:42): You have had a really fascinating background that got you to your current role at J&J. Tell us a bit about where you developed your skills and how being involved with politics really sharpened your data-driven approach. 

David Goldstein (00:54): Yeah, that’s a great question. I really cut my teeth in the political world first and foremost, and then graduated from there into much more corporate strategy, which is obviously what I’m doing now. I think the advantage for doing a career that way, starting in politics moving into corporate is just the nature of the campaigns that are run in both in a political campaign, you have this really brutal binary where either you win or you lose on one day. With corporate campaigns, it’s just not. Either they stretch out over a very long period of time. There’s all these different elements. I think what that does is when you’re on a political campaign, everybody is attenuated to the numbers, the data, everybody wants to know what’s the current polling, what’s the current field data, what’s the current, and it’s not just the high level staff, it’s even down to the volunteers who are just obsessed with the numbers and if what is happening is actually working and if it’s not, what has to be pivoted on or switched to get those numbers to improve. 

I think what happens in a corporate campaign, because you don’t have that attenuation, is a lot of people take a bit of a step back and say, you know what? My little area is fine. It’s going well. I’m doing my part. And they’re not as holistic in their consideration for what’s happening. They’re not as obsessed with the details of not only what they’re doing but what the larger campaign is doing. So I think having somebody like me around and my orientation to things is to look at numbers every single day, multiple times and try to figure out very, very quickly what can be changed if the numbers need to be improved upon. If our numbers are good, what do we need to keep them staying high? 

Betsy Peters (02:56): That makes a lot of sense because there’s a lot of research about neurobiology saying that flow follows focus, and so just the ability to get into a flow and try and problem solve is probably so much more acute when you’re in a political environment than in something like a long-term campaign. Yeah, that makes a lot of sense. So how do you culturally overcome that? How do you build a data-driven culture in an organization where those decisions are based on longer term evidence than shorter term evidence or even intuition or opinion, which sometimes are our guests are dealing with? 

David Goldstein (03:40): Yeah. I think that the big thing is there has to be a tremendous amount of leadership from on high. The CEO, the C-suite, they have to make it clear that the old way of doing things is not acceptable with the massive amounts of data that are coming in. It needs to be made actionable and strategic and everybody has to get on this boat. If there are exceptions to that rule, if there are teams or divisions who are allowed out of that, it really undercuts the message and it sends the message to teams that they don’t really have to do this as long as overall performance or something like that is good. And the dangerous thing about that is it’s really short term thinking. We’ve seen time and time again, teams get obsessed with very short term metrics and totally drop the wall when it comes to the long term.

And I’ve always said what with respect to the hand that rocks the cradle, it’s really the fingers that are on the purse strings that really can rule the world. So I think making it clear to people that budgets are going to be cut if key metrics aren’t hit, can be the stick and the carrot can simply be, you guys can do better performance, you guys can be a better team, and you can get much, much more optimal results by having this database strategy than not having it in place. So I think both those crushers should align to force teams to basically up their game when it comes to data and tech. 

Betsy Peters (05:26): Yeah. Good points. Have those two things been sufficient for you to get people to move from that longer term thinking around corporate campaigns, to something shorter term, like checking data on a daily basis to see which way the wind’s blowing type of thing? How do you think about that orientation to time in addition? 

David Goldstein (05:52): Yeah. I think the other thing that can really help is starting to a certain extent small and then letting that expand on its own outward. And what I mean by that is the C-suite, the CEO should always have an idea of which teams tend to be a little bit more innovative, a little bit more tech savvy. And what the CEO can do is basically use those teams to get the engine started on moving to a database culture simply by socializing their results better and their behaviors better. If other teams start to see that, then they can really start to understand why that pressure is coming down from on high, why all these new resources are being made available to them and should create a certain amount of jealousy and possibly even worry that they’re getting ahead of us when there’s no reason for them to be. 

So I think there’s a lot of fear with managers a lot of times in admitting that they’re a little bit scared of a new approach. Big data just by itself can be a fear invoking term, but I think if you see it in your peer set and you can get a very up close and very results that you can believe in because you know that team, you know what they do, I think that makes it very easier for you to accept and for you to ask the questions necessary in order to get those practices into your own team and really start to up-level what you’re doing. 

Betsy Peters (07:37): So is there an example in your background of this type of cultural challenge that you can share with us, even if names are redacted to protect the innocent? 

David Goldstein (07:46): Yeah, exactly. There was one time I was working with a major nonprofit organization and they had a bit of an insight that maybe they should worry about their brand health just like any corporation does. And what was extraordinary is we introduced brand health as a metric and started to talk to the teams about it. And one of their top people who stood up and basically said, “I will never be held to this metric ever. I don’t want anything to do with it. I respect the fact that’s where this organization is going, but I’m not going to be held to a brand health metric.” And what was scary to me was after the meeting, I spoke with my contact and I was like, “So who was that guy?” And he’s like, “Well, if we’re going to be involved in brand health, he’s the guy who’s going to be in charge of our brand health.” So I was like, “God, shoot me.” 

And what had happened when we went to go speak to him is that he thought his performance and his team were going to be evaluated against the company’s brand health. And from his ears, he was like, “That’s crazy. I don’t know what I do that feeds brand health. So last thing in the world is I want some ma of metric coming from an outside consultant to determine the size of my team, the salaries and bonuses, all the rest.” So we had to have a bit of a come to Jesus moment, but we also had to explain to him very clearly ran health in and of itself wouldn’t decide his job. What was important to the executive director and the board of the organization was we simply start to pay attention to it. And when I talked to him and showed him the materials and everything that was going into brand health, I was like, what this is going to give us is a number and we are going to understand what is feeding that number and what is not. 

And the things that you can control, the things that you can impact are going to be handed to you for you to develop a much bigger strategic vision than you’ve ever had access to before. This is not, and at no point are we going to say your experience doesn’t matter, or this number doesn’t go up by five points, you’re fired. What we are going to say is, “This is what’s working, this is what’s not, so let’s go with what’s working and drop what’s not. And we’re going to do that in consultation with you.” So I think in that situation, I was called the John Snow Test, and not to give any spoilers away for Game of Thrones, but one of the biggest mistakes in that shit show in books John Snow makes is he makes this huge organization changing decision and doesn’t really bother to tell anybody in his organization. So they kill him. 

And it’s that a attitude of I want to change this organization, but the organization is people and people make up that organization. And if you’re not bringing them along and fulfilling their emotional needs and addressing their very, very basic values and concerns, they’re never going to join in with you and it’s going to be a horrible experience for everyone and it will fail. So John Snow honestly didn’t know anything- 

Betsy Peters (11:19): I was just trying to think of an elegant witness segue with John Snow, so I’m glad you brought it up again. No, it’s good and it’s memorable. Trust is everything in an organization, especially when you have to move as fast as we all do these days. So instead of going John Snow, I’m going to segue down on trust. Sure. And ask you to get a little more granular because of course, data, driven decision making relies on that. We’re moving from this world where you had to trust the highest paid person in the room or the person with who is closest to the customer about their intuition. And now we’re moving towards data in a system, but you have to have trust in the data in the system. So I wanted to go there for a little bit and talk about, we all know that bad data exists in our systems. How do you manage the effect of that as you’re establishing this type of culture? And are there different types of approaches that you take to that transition? 

David Goldstein (12:16): Yeah, no, that’s a great question. This is going to sound a bit odd. I’m not sure I’ve ever actually seen bad data. What I have come across a lot is misaligned data. A person has a vision, they want to put a teamwork organization on a mission, and they just assume that the engineers and the other folks out there who are going to be working on pulling in the data understand that vision as well as the person themselves do. And that’s a massive mistake. And the reason it’s such a mistake is because people don’t understand. When they say unstructured data that is very literal. And if you imagine an unstructured building, it would just be a pile of bricks and steel and all the rest of it. It’s a big VAD of goo when you’re actually staring across how that data sits in nature. 

So what you have to school your engineers on and work with them very, very, very closely on is when you go in and start to put those structures on that goo and start to pull it out and analyze it and action on it, and implement the strategy based upon that at every single step, the data that’s coming in is the data that you actually need. Otherwise, it’s not necessarily a matter of garbage in, garbage out, but misaligned resources in. And there’s absolutely nothing you can build with that. 

So I think it’s fundamental. I love working with data scientists so very close with folks who code and engineers, and I think that’s really helped me be just as crystal clear as I can on exactly what’s needed because understanding that from our point of view is not really what their job is, and I don’t think it should be. I should be a matter of teamwork coming together, somebody with a vision, somebody with the hands to reach in and make that vision possible, always aligned at working together so that it actually works. I think anything else, you’re putting yourself in a very, very dangerous slot situation. 

Betsy Peters (14:40): Yeah, it’s a great answer. And you went in a different direction than I thought you might, so I appreciate that because I think from a design perspective and an architectural perspective, 100% what you said is correct. I guess being pragmatic, is it true that you’ve never really walked into a situation where there was no bad data because you weren’t at the other end of that approach? 

David Goldstein (15:04): No. 

Betsy Peters (15:05): Architecture and design

David Goldstein (15:06): I’ve walked into situations where the data was not what they told me it would be because they had made assumptions and they hadn’t spoken to their engineers. So once upon a time, I was working with a major hotel casino and I was asking them about guest data, and they’re like, “We have all that.” And I was like, “Okay. Well, what do you have?” And they’re like voter databases, but with our guests, we’ve got all kinds of data points, all kinds of blah, blah, blah, and we survey them all the time, so we know them really intimately. And that just seemed remarkable to me because honestly, the marketing was trash and it wasn’t effective at all. So when I went and took a look at their database, what I immediately saw some point, the survey responses, and these are surveys on their guests were pushed off and were ending up in another database, they lost the matching markers between those two. 

And then furthermore, somebody had the bright idea that for storage issues, they needed to reduce the amount they were holding on each guest. So a lot of times it was just a name and maybe some additional data points like a credit card number or an email address. But it’s a profound example to me of just how disconnected leaders can be from the fundamental things they need to be successful. So that was a situation where it was misalignment. If I had just needed people’s names, that was a great dataset. But it was very, very- 

Betsy Peters (16:53): It goes back to what you were saying at the very beginning where especially in the corporate world where you have long-term campaigns, everybody gets their own little vision of what their touchpoint in the customer journey is or their part of the campaign is, and they’re executing on that. And the dots that connect get thought about afterwards not as you’re designing that piece. As you go into environments like that, what’s your thought about man versus machine much, and when do you rely on the human to put the data in and connect the dots versus how much do you do in the background automating when it’s an ongoing process versus a batch process, for example? 

David Goldstein (17:34): Yeah, no. And I think that question applies to a lot of what we’re particularly seeing now. I think one of our basic misunderstandings is that data can be objective. That’s what I always hear. Well, what’s the data? The data’s objective, and anybody who spend time in data collection knows it’s the furthest thing from it. We’re making very subjective assumptions of what data we need, what type of data, how to collect it, how to analyze it, and each step of the way, gigantic subjective guesses are being made. 

Betsy Peters (18:13): And it’s all in a certain context that you may not see when you’re pulling it out of context. Yeah, absolutely. 

David Goldstein (18:18): Exactly. But if you take a step back from it, you realize it’s fine. But what’s fine and has to be fine is that the person making those subjective decisions genuinely understands holistically and operationally and functionally what needs to be done in order to have it inform the larger picture, the larger project, whatever it might be. So when it comes to automation, you can recognize a repetitive task, you can recognize something that does need to be automated, but I never suggest anybody do that without consulting the internal experts they have on why maybe that hasn’t been automated yet and why it’s still done that way. Then there’s hosts of great reasons, and they can be anything from like, well, our computer doesn’t actually have the software to do that to something else. Like, “Well, we just don’t understand the nature of automation.” 

So for me, the biggest thing with man versus machine is to get rid of the verses and just have people and machines working as closely together as possible, because these are force amplifiers. Humans can make the machines incredibly more useful, and the machines when used correctly help us to do work we could never even dream of doing in the past. 

Betsy Peters (19:45): Yeah. It’s like Steve Jobs quote about the computer being the bicycle for the mind type of thing. 

David Goldstein (19:53): Exactly. Yeah. And it can be a bicycle for the organization and the minds of a lot of people in the organization who haven’t really understood it up to that point and just need a bit of education and time spent with them. Sorry, here’s the kitten. 

Betsy Peters (20:11): Buddy. All right. 

David Goldstein (20:18): Yeah. And just to riff on this a little bit more, you see that a lot with the current AI situation. AI gets talked about in both anti and pro contexts as if there’s no humans involved at all except at the very end and it’s totally wrong because there’s humans involved at every single step. I mean all of that data, everything going into it, all the engineering, it’s human, human, human, human, human. And when I think about the dangers of AI such that exist, I always come back to, “Well, it’s the person wielding it who’s the danger not the thing in of itself.” 

Past use of AI look like audiences and platforms like Facebook where we could upload 10,000 names and then Facebook would find us a million people who looked like those 10,000. That’s enormously powerful for a small business. Where it was damaging is when people took the membership of an extreme far right party in Europe, put that into the machine, and all of a sudden found a million more people who would love that party if they heard about them. That was the basis for a lot of the destabilizing measures that we’ve seen in digital and social over these past few years. But it wasn’t intrinsic in the AI tech then, and it’s not now. It’s all going to be who’s using it and for what purpose. 

Betsy Peters (21:54): I’m going to segue away from AI for a little bit and go into… Let’s talk a little bit about using customer data for R&D and insights and strategy in your current job. So how do you use customer data to drive strategy and how do you use it to understand customer needs or unmet markets, that kind of thing? Underserved markets rather? 

David Goldstein (22:22): No, it’s a huge problem. Because there’s so much customer data, you can genuinely get lost in it, and I’ve seen that happen a lot where people, the fact that we could tap into thousands of data points on individuals is just overwhelming. And I think what’s important there is a good strategy should be based on customer data, but you have to be really aggressive on the type of data collection that you’re using. Is it surveys? Is it focus groups? Is it big data analysis? What statistical approaches are they using in order to extract those insights that feed strategy really well? One of the problems we always have, it goes back to that whole thing, fake laugh tracks that they use on sitcoms. And we all hear, and we all supposedly hate, have consistently been found to increase people’s enjoyment of those shows. But you talk to them and they’re like, “No, I hate that. I want you to get rid of it.” 

So that is a continual threat to the health of our customer, of basically extracting insights from our customer data is that they’re saying something when it’s actually a deeper something else. And I think for those reasons, you have to involve experts and you have to involve people who have an understanding of those possible failure points in order to push forward. And when I really try and push people now on is the fact that we can track how those strategies are doing on a daily basis when they get in front of real actual people, and there’s no reason why we can’t start pulling that data in order to iterate and optimize as the strategy continues forward. It’s an incredible approach to me because it tells you very quickly that’s something that was based on someone’s gut or a customer data insight that maybe was true two years ago, but isn’t true today, whether it’s actually working or not, and can save you tremendous amounts of money and time and resources and vastly improve the efficiency of your spend. 

Betsy Peters (24:51): So you used a really interesting word at the very beginning of that, which was aggressive. Is that a prioritization word or what do you mean by aggressive? When you’re saying that? You’re right, there’s a million data points that you could choose at any moment to formulate strategy, but picking the right approach and almost approaching it like a cultural anthropologist. Going back to people, they’ll tell you something different than what they mean often. So yeah, tell me what aggressive meant in that context. 

David Goldstein (25:23): I think it means being willing to challenge assumptions. I always say the most terrifying data point I see is something that reaffirms something I previously believed because your ego is always in danger of taking over. So seeing something justify what you hoped would be true is a great way to get led off of what is actually going to work. So I think that’s where that aggression comes in and just like, how do we know this? Why do we know it? Why are we saying it? What proof do we have? Push and push and push and push and push till you get to a point where the success is clear and then even at that point pushing harder to make sure you build on that and sustain it. 

Betsy Peters (26:08): Yeah. And I think it’s interesting, your political campaign background probably sharpens that because you had such a fixed endpoint of winner or loss, like you said, as a fixed binary. Because that platform for experimentation, at least in big enterprises, the way you talk about it, which is hypothesis and then test and iterate as frequently as you can until you’ve got a scale signal, that’s rare. We’re getting there, but it’s still rare. 

David Goldstein (26:40): Yeah, absolutely. And that that’s always been one of the ongoing things I’ve been interested in is the ability to be disruptive as a small entity simply because you can move faster than the bigger one that you’re challenging. 

Betsy Peters (26:55): Yeah. Interesting. Because the business J&J is in, are there any particular privacy or regulatory concerns when it comes to joining data that really block you from a strategic standpoint? 

David Goldstein (27:13): I would honestly say no. But that’s because I fundamentally believe in data privacy and I’ve seen so many campaigns work that didn’t have personally identifiable information, and so it’s no trip to me to have to abide by HIPAA and to abide by anonymization of data. In fact, I’m a very deep passionate believer if that’s what you’re depending on to do, well, I mean you’re terrible at what you do, there’s just no reason for it. So I am actually a very enthusiastic backer of GDPR and of all the rules and regulations that are put in place to protect data privacy simply because I think the downside is tremendous for us as individuals and as a society, and the upside is questionable at best. 

Betsy Peters (28:14): Yeah. I was going to say, is there any case where you’ve used data responsibly via redaction or whatever the case may be, that actually gave you an edge or you haven’t even gone down that path because you don’t need it? 

David Goldstein (28:27): Yeah. To be honest, there’s always these random court cases, somebody’s getting sued and you have to disclose a full dataset and you know, just have to remember and trust yourself, they’ve always been an ethical researcher and so they’re not going to find anything. So there’s just that continued career success. But then there’s also, whenever somebody says to me, well, I think this is one of those times where really have to push the boundaries on PII, because honestly it is terribly regulated in terms of actual enforcement. It’s more one of those things that comes down to the individual’s own ethics morals to do it. And somebody says something like that to me off the record. I’m like, well, let’s just try it with the anonymous data and see what happens. And it’s always worked out fine. I’ve never been at a point in 17 years of doing this work where I was like, “If we only had their names and their personal addresses and everything else.” 

Betsy Peters (29:36): Yeah. I’d like to go back and revisit your point about architecture and data silos, especially in big organizations. So we recently had a webinar with Salesforce where there was this really telling slide that was entitled Decades of System Complexity Limit Digital Transformation. So the punchline of the slide was there’s just a ton of data silos across a customer’s journey that really impede a business’s ability to understand and serve them well. So how do you ensure that data flows seamlessly and securely? I mean, I think you gave that brilliant analogy about the unstructured data being the bricks and mortar, but is there anything else that you can add to that that might help listeners? 

David Goldstein (30:26): So this is genuinely not necessarily a political statement, it’s a factual one. When Donald Trump was elected president, I was very fortunate to be having a breakfast with somebody who had been in DC for 40 years. And I was curious. I was like, “Okay. Well. Obviously the Democrats are going to be opposing him and their interest groups, but what do you think might stop him from doing something too radical?” And my friend was very honest. He was like the bureaucracy. It was like if the individual federal employees of which there’s over a million of them don’t want to put one of his policies into place, they have a billion different ways of destroying it that he really can’t do anything about. And this is one of those hard truths that a lot of times business leaders, especially in this context, need to accept. It’s one of those silos. It might have been siloed by accident because somebody wasn’t thinking, but oftentimes it’s siloed because somebody wants to protect it. 

It’s very close like they don’t want it getting out. They don’t want somebody else using their data, presenting it in a way that data didn’t approve of maybe judging their team by it. So how do you change that is such a tough question because that person in charge of that, just like the bureaucrats, they have a billion different ways of slow walking your change so that it never happens and it absolutely undoes you. So the only way to overcome those situations is to set strict timelines that everybody is going to be hitting their changes by. And if they’re not, you don’t necessarily have to go in there and beat up the team. Sometimes it really is useful simply to set up new data flows, and that can be mind-boggling for people because they’re like, “Well, I have 30 years of data here. Why do I have to start a new one?” 

It’s because, well, it’s 30 years, but we can’t extract it, we can’t combine it, we can’t link it, we can’t push it together with other more useful data to do anything with it. So at least starting from scratch, it’s bigger investment, it’s a little more time, but it’ll actually get us to where we need to go. So I think in those situations, leadership just has to be very, very honest with themselves and can’t get tied up in this notion that simply believing in digital transformation, believing in unsiloing the data and putting consultants in place to make it happen is going to work. That there’s going to be so many obstacles that they never even dreamed of. So I always expect there to be tight timelines and I always expect there to be a backup plan just in case that data for whatever reason, can’t be extracted from that silo. 

Betsy Peters (33:20): It’s interesting that the second answer is about organizational culture. The first is about design and architecture, and the second is about culture, because especially in big organizations, that’s what it’s all about. In terms of organizational structure. Are you seeing the rise of revenue ops teams in the nomenclature? Is it moving from IT into something that is a little bit one foot in IT and one foot in the go-to-market team area? 

David Goldstein (33:54): I think it’s more innovative organizations. They’re recognizing the power of IT, a bit going far beyond simply getting PowerPoint on somebody’s computer or something like that. And understanding that the technology is fundamental to their success and bringing it into play, especially in such a fundamental area such as revenue operations, is simply going to be tremendously helpful for the entire business. I think once again, our habits take over and we have just terrible habits. So it’s one of those things where to get out of them examples have to be set, things have to be socialized, and leaders have to be shown a better way that by bringing these two together, you can achieve exponentially improved performance. 

Betsy Peters (34:48): So what’s the persona that is taking those roles on? Is it really IT folks who are migrating more into the ops side and into the go to market side, or is it all sorts that the more technically minded go-to-market folks are migrating into the system side? 

David Goldstein (35:14): I would say it’s a bit of a combination of both. One of those biggest questions across I think a lot of human endeavors is what is innovation? And I’m not even sure I’m as curious about that as I’m like, what does an innovator look like? So what I see with these organizations who are making that shift, they have an innovative person who has an expertise in one of those areas and a tremendous amount of respect and curiosity for the expertise in the other area. So if they’re an IT person, they’re really fascinated by what makes revenue and ops systems better. If they’re systems person, they’re really curious about IT, infrastructure and all of that. Those people tend to start the conversations that create the internal organizational knowledge and culture that allows those to migrate together and start to perform together. 

I always hire based upon how flexible I think a person can be, and one of the fundamental questions I always ask in an interview is, tell me a time you were curious about something and what that looked like, because you just need people who have that, well, why does that work? Why don’t I just go talk to that person and see what they did and see if I can do anything with it? And maybe there’s something that we can collaborate on. Those are really the people that are making the world run, and I think they’re really the people who are building the future for us too. 

Betsy Peters (36:57): It’s an excellent answer. That curiosity drives the innovation that is bringing these organizational structures together to support each other because you have to have one foot in both worlds anyway. 

David Goldstein (37:09): Exactly. 

Betsy Peters (37:11): Is there a piece of advice you’d give a career changer or someone just beginning their career who might be interested in this space? 

David Goldstein (37:20): Yeah. I love the fundamental thing of just looking at successful people and finding out how they did it. So I always enjoy when a junior person or I teach occasionally, a college is a student, comes up to me because the answers are so basic, they can be a bit heartbreaking. It’s just like data’s not scary. So just start talking to people who understand it, understand how to use it. You don’t have to be ever a data scientist or an engineer to understand what those systems are capable and what they’re not, and have the confidence to understand that people like me are really worried about getting old and no longer being relevant. So having a young person come up to me and ask me for a bit of advice, I’m always thrilled to go into that with them and identify for them where I see the current gaps and where my concerns are of what’s keeping me up at night. 

And almost always a person who starts investing their time in those is somebody who’s going to be spending their time very wisely. One of my mentors always said, the tech changes, the humans don’t. So there’s fundamental things about us and how we interact with this tech that if you focus on and you study and you get used to are really going to just be a huge boost to a career and to your life in general. I’ve always found this work incredibly rewarding, and part of that is I get to help my friends too. Data affects my creative freelancer friends, just like it affects the CEOs who I work with. So just being able to help them out and understanding how to use Google Analytics, how to do SEO on their sites, how to actually employ social, all those are data informed things that get to help my buddies with simply because I invested my time years and years ago in understanding the host and getting to use them. 

Betsy Peters (39:29): That’s great. Good answer. Last one would be is for folks who are already in the position, do you have some resource or set of resources that you’d point them to keep sharpening their own saw? 

David Goldstein (39:46): This is one of the more difficult questions I think for me to take because I honestly have not seen a lot of work that I consider to be that impressive when it comes to things like organizational change. It can be a very gooey, mushy, almost like kumbaya type area, and I think it really has to be very hardnosed and it has to match the intensity a political campaign has in terms of that binary success or failure. Gregory Satell, S-A-T-E-L-L, I’ve always found him to be a really interesting thought leader in this, and I’ve continually gone to his resources and spoken to him a few times. 

There is a company I know of out there called Indigo Metrics, which is doing phenomenal work in terms of anonymized peer-to-peer evaluations, which I love because nobody knows better how you’re doing than the person sitting next to you a lot of times. And what Indigo is focused on is creating like a safe space for people to talk about themselves and their coworkers, rate them, and provide that feed that data back into a system so managers can really operationalize and start to see the shifts that are taking place and focus in on the areas that are weak and address them as quickly as possible. 

Betsy Peters (41:20): Those are two really good ones we haven’t heard before, so thank you for that. 

David Goldstein (41:22): Of course. 

Betsy Peters (41:24): 

And again, this has just been a terrific conversation, David. I don’t know if you know Chip Conley, the Airbnb board member, but he has this term called the modern elder who is as wise as he is curious, and I think that applies to you tremendously. 

David Goldstein (41:42): Well, thank you. Appreciate that. 

Betsy Peters (41:42): Wonderful conversation. I really appreciate all of the insights you gave us, and it was really fun to hear your perspective. 

David Goldstein (41:48): Thank you so much, Betsy. This has been a great interview and I really appreciate your thoughtful questions and time. Really enjoy those. 

Announcer (41:57): Thank you for tuning into the Rev-Tech Revolution podcast. If you enjoyed this episode, please don’t forget to rate, review, and share this with colleagues who would benefit from it. If you’d like to learn more about how Riva can help you improve your customer data operations, check out 

Related Podcast

Meet Team Riva at Salesforce Education Summit in San Diego, May 13 – 15!
This is default text for notification bar