How Artificial Intelligence is Changing the Financial Services Industry @BarryLibert (Episode 7) #DataTalk

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Every week, we talk about important data and analytics topics with data science leaders from around the world on Facebook Live.  You can subscribe to the DataTalk podcast on iTunes, Google Play, Stitcher, SoundCloud and Spotify.

In this #DataTalk, we had a chance to talk with Barry Libert about ways artificial intelligence is changing the financial services industry. Make sure to follow Barry Libert on Twitter and LinkedIn. Also learn more about him on his website: BarryLibert.com

This data science video and podcast series is part of Experian’s effort to help people understand how data-powered decisions can help organizations develop innovative solutions and drive more business. To suggest future data science topics or guests, please contact Mike Delgado.

Here is a full transcript of the interview:

Mike Delgado: Hello and welcome to Experian’s weekly DataTalk, a show featuring some of the smartest people working in data science, as well as thought leaders leading the industry. Today we’re very excited to feature Barry Libert. He’s the CEO of OpenMatters and a strategic advisor for some of the biggest global brands, like Goldman Sachs, Microsoft, GE and ESPN. Barry is also a best-selling author and has written for the New York Times, Wall Street Journal, Financial Times, Harvard Business Review and many others. Barry, it’s an honor to have you in our chat today.

Barry Libert: Thanks for having me today.

Mike Delgado: Barry, I thought it’d be great if we can just kick this off, if you can share a little bit about yourself and the work you’re focused on right now.

Barry Libert: Sure. So this work started a long time ago. It’s the basic thesis that, just like the human genome, there’s an economic genome, and you can use data science to basically find out what that economic genome is.

Mike Delgado: Very, very cool. It seems like every single day, I’m reading articles about artificial intelligence, how it’s changing the workforce. There’s a lot of fear I think sometimes, in those articles, about robots taking over. And definitely in the financial space, we’re seeing signs of robo-technology and artificial intelligence.

One of your latest articles that you wrote for Harvard Business Review, you wrote an article focused on how even top consultants in financial services may soon be replaced, and was really curious if you can kind of talk a little bit about that. Because that was a really great article you wrote.

Barry Libert: Sure. So, the article was more than just financial consultants. It was marketing consultants, strategic consultants, financial consultants, auditors, legal, doctors, all consultants. So, if you think about the consulting industry, just talk about one piece of the strategic consulting industry, the Unites States alone is about a $70 billion industry. And think of them as doctors.

They’re doing business the old-fashioned way which is a patient, a CEO, comes in to my old alumni firm, McKenzie or Bain or BCG, and they ask them a question, “What’s wrong with me?” And that doctor, that consultant, gives them a piece of advice based on their own experience. None of that experience is capture in a data-structured way that benefits from today’s machine learning, artificial intelligence.

All our article was proving was that it’s not just medicine that artificial intelligence or machine learning … or in financial services where artificial intelligence and machine learning are taking an impact. It’s gonna apply to every single industry, where the services industry has had a huge growth cycle for the last 50 years. And that means all the best consultants are going to have to deal with the fact that machines learn faster, are more scalable and more repeatable than you and me. Nothing more complex than that.

Mike Delgado: Yeah. I think I even read an article a few months ago, talking about Goldman Sachs had a layoff of a lot of their financial advisors. Did you happen to see that one?

Barry Libert: I did. And that’s happening around the world in the financial services industry. If you look back a while, which is decades now, the financial services industry went from what’s called active managers, which was financial advisors giving advice to you and I about what stocks or bonds to buy, based on what that manager had as his or her perspective on the financial markets.

A long time ago, John Bogle, who was the chairman, CEO of Vanguard, had a thesis that monkeys made better decision makers than financial advisors. That you could basically throw darts at the wall and get a better return. Now the truth of the matter is he was right. ETFs, or what’s called passive investing, have outperformed the smartest of all stock and bond managers, which means that the construct of actively advising is doing less well than passively investing. And you’ve seen the explosive growth of not just ETFs and passive managers, we’re now seeing what’s called robo-investing, like Wealthfront and Betterment, begin to offer it for you and me, the individual investor.

Mike Delgado: Yeah, and I think it seems that, from what I understand around Betterment and some of these robo-advisors, sometimes the combination of artificial intelligence choosing what stocks or indexes for people to invest in, but also there is some element of human management. Do you happen to know how humans are kind of working with AI, despite the layoffs?

Barry Libert: Sure. So, the layoffs aren’t that big. In fact, they’re not big at all. The growth of the industry is outpacing layoffs by far. So, there’s a massive fear, I call it, on layoffs by robots displacing you and me. I don’t believe that. We’re just gonna create new skills. Like, obviously, we might argue that 100 years ago, machines had displaced humans when we started building cars. Or 150 years ago, tractors started displacing farm workers, when we started automating farms.

This is just the next iteration of machines complementing human workers in ways that you and I don’t scale. And all our research has shown, and our article was about, was sorry for all of you people that don’t think it’s coming into your industry, which is all the services industries, not just the financial services industries, the marketing service industry, the audit services industry, you know, legal services. We gave examples of every industry, the medical service industry, where robots and artificial and machine learning are gonna complement human intelligence and human learning.

Mike Delgado: Yeah, it’s amazing to see the growth. And just in the last year, tremendous growth in people interested in machine learning, artificial intelligence. I mean, I was looking at some Google trends on the amount of traffic being generated around those terms. Part of it might be out of fear, but then to your point, there is going to be this strategic alignment between human and artificial intelligence working together. And I think it’s going to be a beautiful thing.

I really like your positive outlook —   just as we looked in the past how technology has displaced some workers, there are new jobs that were created from the new technology.

I’m curious about some of the feedback you received because your article on Harvard Business Review was very, very popular. It was shared all over the web. I loved reading it.

Barry Libert: Well, surely they were both positive and negative, as you might imagine. Because we were taking a potshot at the world’s most elite. My alumni firm, McKenzie, and Megan Beck was my co-author, is an ex Bain person and my brother was at BCG. This is our heritage from a lifetime ago. It’s hard for them to understand when they’re reporting all day long. McKenzie, at the very best, is reporting on the impact of AI on all other industries, but not their industry.

It’s like them suggesting, like the taxi industry wouldn’t be impacted by Uber, the consulting industry wouldn’t be impacted by AI or Alexa. Which is what I think is coming anyway. You and I will be getting my advice from what’s sitting right here on my desktop.

And so we had, honestly, negative feedback. Consultants say, “Well what job will I have? And who’s gonna write your next article, Barry?” To answer your question, reviewer, or to say, interviewer that you did is a bot, is gonna have some influence on that as well. But there’s negative, and there was a lot more positive, which is trying to understand, what does it mean to me. Just like you said, how do I think about it? We were surprised. On my own LinkedIn page, 2,500 reviewers and likes. It was amazing to me. And HBR, I think there were 400, 350 comments. It just blew my mind.

Mike Delgado: Wow.

Barry Libert: People were interested in this article, on this area.

Mike Delgado: Yeah, like I said, it popped up on my radar, I saw all of the sharing. People obviously loved it, and yeah, so it’s just curious to hear about … obviously there’s gonna be people that are kind of frustrated or upset, especially those in FinTech, who might be fearful of their jobs. But I think you definitely touched on a hot topic that everyone’s very, very concerned about, but also excited about too.

Barry Libert: Yep. I think that was our point. But the most important part of our point was to tell truth about something. Megan and I were both presenters at the annual MIT platform event. And it wasn’t just falsehood in that article. My co-presenter as a keynote speaker at the annual MIT platform event, was Alexa. We had to mic Alexa. And we literally asked the machine questions that no consultant could ever ask about global economic trends that were the synthesis of machine learning AI, looking at 40,000 companies across the world, across thousands of variables and tens of millions of words, over 40 years.

So it wasn’t … people thought we were kidding. But happy to share an image that shows me talking to Alexa on stage. Of course, I started with an Alexa knock-knock joke. But I took Alexa all the way to the end, asking board-level questions, of things they couldn’t get from their consultants, if they had to pay, you know, $10 million.

Mike Delgado: Wow. That is amazing. That is amazing. Just seeing the voice AI products being developed, Alexa, and what’s possible, like what you just shared, because of artificial intelligence, because of the amount of data that could be crawled and analyzed. And for Alexa to be able to return back to you smart answers very, very quickly, where it would take an advisor a lot more time to go do some digging. The ROI on that is just phenomenal.

Barry Libert: Yeah, my always message is, I have a simple thesis in life, I want to replace Barry call, a call to Barry, with a call to Alexa. I’m tired of getting calls. They ask the exact question of me every day, I’m gonna become a platform network and AI company for the last 20 years, from AT&T’s who was my first client a long time ago. And I just want to a call answering service, they can call right into Alexa, and they can say I’m calling Barry, and Alexa will answer the phone.

Mike Delgado: Yeah. I think there’s gonna be a Barry bot.

Barry Libert: There you go. I’ll hook it up to some of your other interviewees and people that you’ve had on your show, so I can figure out how to do that next.

Mike Delgado: Yeah. It’s so funny because, when Alexa first came out, I was thinking, “Oh, so it’s just something you can ask about the weather.” You know, I had a very small knowledge of what capability of some of these voice AI systems were doing. Like oh, weather, shopping, basic things like that.

But yeah, when you’re talking about being able to use it for strategic business intelligence, being able to crunch numbers and return back to you the data that you want, that is outstanding.

Barry, what does the C-suite think about all this? What is their pulse?

Barry Libert: Oh they’re nowhere. They are stuck in Excel land, which is about forty-year-old strategy, technology. Getting annual reports put up in board packs, technologies that place board packs on their iPads. They’re nowhere. In every board meeting I attend or participate with or the executive teams, I say it’ll finally be somewhere when the chief data scientist, not the chief data officer, the chief data scientist is sitting next to the chief financial officer, and he or she is reporting the status of the business, because there’s more non-financial data out there that provides the vitality of the company, than there is financial data.

So they’re nowhere. They would have literally no insights about what is possible today.

Mike Delgado: So with that lack of knowledge, because part of it is just the tremendous growth in AI, in big data, that’s been happening, and it’s just skyrocketing right now. So I’m kind of curious, for those that work in data science, thinking about data labs, data science teams, how do they, and how do you recommend that they approach senior leaders, maybe it’s the chief data officer, or the CTO, how do they make a clear business case for using AI and doing business in this way?

Barry Libert: So it’s really important, you know, when in Rome, speak Roman, things like that. Or when here, speak English. Whatever the words of the language, when in China, speak Chinese. When in business, speak business-speak. And my data scientists don’t know how to speak business-speak. Even my chief digital officers don’t know how to speak business-speak. There are no shortage of digital guys who report to me through their companies, they tell me, I’m the tech guy or the tech woman. I go, don’t speak that language. Even if I get it, they’re not gonna get it.

So data scientists now have to learn the science of business models as well. Which is, today’s business models are powered by AI. I mean, think about the fact that Facebook is spending two billion a quarter in AI and data science. Data scientists inside incumbent organizations have to explain, in economic terms, what are the revenue opportunities, what are the cost-saving opportunities, what is the shareholder value opportunities, and if they’re a social enterprise, what is the impact opportunities that you can create as a data scientist that will impact the agenda of the organization? Otherwise you’ll be reporting to somebody in the tech department.

Mike Delgado: So for a data science team, who should they be reporting to? Because sometimes some companies don’t have a chief data officer. So in those types of companies, where should data science report to and where would they make that business case to?

Barry Libert: Well, you know the CFO, otherwise known as the chief financial officer, I call them the chief no officer, the CNO. They have to go report to the CNO and explain to the CNO why data science is today’s oil rush, it’s today’s gold. It’s the insights that power, they would argue tomorrow organizations, but in fact it’s today’s what I call the trillion-dollar behemoths, the Apples, the Amazons, the Alphabets, the Facebooks, the Microsofts.

They need to go to the CNO and say, “Look, those companies are worth almost a trillion dollars, they’ll be our first trillion-dollar organizations of all time, because they understand at the base and the foundation of those organizations are data, platform and network. And it’s that Venn diagram that creates all the economics.”

The CNO, if he or she is any good, will understand, “Wow, that’s something I’ve never heard before. Let me see what I can do to achieve that goal.” So if I were them, I’d report to the CNO.

Mike Delgado: And for those companies that are looking to hire a chief data officer, the CDO is kind of a new role, emerging role, that we’re seeing more companies bring on. What skill set should that CDO have?

Barry Libert: So currently, I think CDO means chief digital officer, right? Which is an important distinction from data science. Digital officers, in my view, are really important in an organization. They’re the intersection of information technology and business. They’re the people who can speak the business language and make a business case for technology as the core of the organization. They are often not data scientists, at least I’ve not seen. They come out of the technology realm.

And I try explaining the difference between a software architect and a data scientist are like the difference between a plumber and an electrician, or a neurosurgeon and a cardiologist. They’re not from the same -ology. They’re called data scientists and technologists, right? They’re not, they come from a similar lineage, but a different lineage. So data science has to find its own voice. And I think it’s okay to be into this traditional CDO, the chief digital officer. But I think data scientists today need to make a business case to be right to the CEO.

Mike Delgado: Okay. Yeah, I like that. I like that. But like you said, it needs to be done in business speak.

Barry Libert: Correct.

Mike Delgado: Speaking the language.

Barry Libert: Otherwise they’ll be underfunded and too late to the party. This stuff, like TensorFlow is a free orphan since November from Google. So you get these spectacular open source environments like Linux now, in the AI world. And they’re moving so fast, if you’re not in the world of being there all the time as an organization, forget about as a data scientist, you’re just gonna miss the whole game. Because by the time you get into the game, it’ll be generations further. It’s already free.

So the question is, it’s free between … basically it’s free with AWS, it’s so inexpensive, and Google offerings and Microsoft offerings. It might as well just be free. The question is its application and its economic impact on an organization.

Mike Delgado: Barry, I wonder if you can share some of the ways or stories that you see consulting firms or businesses using AI right now, to improve their business decision.

Barry Libert: Sure. Let’s start with the big one that was announced a few months ago, which is Blackwall, Blackstone rather, which is a five trillion dollar investment management organization, publicly traded, made an announcement that they’re going to improve their returns using robots, quote unquote robots, AI machine learning robots. Because they realized that at scale, they needed to use that five trillion dollars at scale. They need to be able to use large amounts of data that historically, viewers could watch all those screens, you know, you watch these traders are watching a hundred screens or four screens.

It’s impossible to look at the level of data that we’re consuming and absorbing in our data science group. It’s impossible to see it all, and to make sense of the pattern recognition. So I think Blackstone is a really good example of that. From there, I think that … I’m sorry, I meant BlackRock, not Blackstone. Sorry, which is another investment … so BlackRock is a really good example of that.

You’re now seeing it, obviously in the healthcare world. You see now, the original Craig Venter experience of doing the genome. You’re now seeing AI use the human genome to create something called CRISPR, which is gene editing, which is an amazing thesis. Last week alone was the first time ever … I saw this article, or this news announcement, last week, for the first time ever, a human embryo was genetically modified using CRISPR, which is an AI-based tool, to alter the genetics of the DNA of that human. They didn’t let that embryo live, but that was last week. So CRISPR, an AI-based tool, for genomic editing. Just like we’re suggesting you could do economic editing.

Second example. But you’re now seeing it in the mining industry, something you’d never imagine, which is you see it in the gold and oil industry, where they’re trying to understand the data from below the level of the ground and the water, to do that as well. So this is at the tip of the iceberg of where large amounts of data, which historically had been unmined, are gonna be organized, structured, create data lakes, and on top of those large, expansive data lakes, create machine learning capabilities that will produce insights for humans to learn from, humans and machine to work together.

Mike Delgado: Yeah, I think I saw that article. And then I saw a similar article about some scientists that were able to inject animated gifs, or like video, into human cells. And it’s just mind-boggling to see the amount of work that’s being done in the human body to help make human life better for health reasons, etc.

Barry Libert: Correct. So you can imagine, just like BlackRock is gonna try to make investment decisions better, and you can imagine how we’re hoping to try to make business decisions better, you can imagine how the healthcare industry is gonna use it to make better healthcare decisions. Now they have a cultural decide, because you could decide you want to have long hair and blue eyes, and do some gene editing, Michael. I could decide that I want to be completely a different human, right?

Well that’s gonna be some ethical questions about, you know, should that really be possible. Should I be able to have … you know, my wife and I aren’t gonna have any more children, but maybe children were gonna decide they want to have specifically engineered children using AI. Those are gonna be probable questions for us to answer.

Mike Delgado: Yeah, I mean all the ethics involved, it’s fascinating. Especially as things are moving so quickly. I’m kind of curious, Barry, are there any other, as you look into the future, especially in terms of financial services, do you see anything upcoming, as far as things that will be AI-based, to help improve those that are looking to invest or manage their finances? I’m just really curious.

Barry Libert: Absolutely. So we’re working on risk AI projects, which is really cool. Right now Fitch and Moody’s, S&P provide debt rating, you know, all these funny-looking letters. We’re doing risk AI products now, not just strategy AI products. And that is really critical, because now you can cross these non-financial variables, like customer retention, customer intimacy and human capital, you know, engagement, and beginning to see ways that risks are created from what used to be called intangibles, which is you and me, and create these extraordinary risks for organizations.

Even organizations that have quote unquote assets, those assets become liabilities because you and I change a platform overnight. We decide we don’t want to have that asset anymore, and all of a sudden, those physical assets become liabilities.

So these new insights are being generated by massive social media pipes, and now soon, like human engagement pipes, like glassdoor.com. You’re gonna see us grab and take that stuff, not just with Bureau of Labor and Statistics, and being able to basically mash it up, which is what we’re doing, to give new insights to risk-based metrics for organizations and investors. Huge issues. And same thing with governments, I mean right now, government risk is the ability to pay. Well, what happens if Trump does something we don’t like? It’s gonna create some economic risks that are knowable from the language that we’re already using. So that stuff’s all available today.

Mike Delgado: You know, you talked just briefly about the big shakeup over at BlackRock, and how they’re shifting over to less human involvement in investing, and more AI approaches. I’m curious about, do you see any drawbacks from removing people from these investment strategy roles?

Barry Libert: So you know my comment on that. I don’t think humans will be removed. I think they’ll be changed. Just like I think farmers and mechanics have changed. Those people had to evolve in the world. I don’t know what the future looks like in the financial services world. I know they lead the rest of the world in terms of industries. Not necessarily, believe it or not, they don’t change themselves, which is the funniest thing. They change the products they offer to us, but they don’t change their business model. Which is quite ridiculous.

They’re still selling financial services. Insurance services are the same thing. The whole financial services industry is broken. But they have these amazing capabilities for product-centric capabilities to do something different. So the question is, how will their industries change to begin to look more like real-time, Alexa-driven, Siri-driven, Google Home-driven, whatever I want, when I want stuff? I mean, it’s gonna be an interesting question.

Mike Delgado: Yeah. I’m curious about, for those financial companies that have not adopted AI, maybe they want to, I’m kind of curious about what are some initial challenges you’ve seen for companies that begin to adopt AI? And maybe some advice you could give them, as they move in that direction.

Barry Libert: Well have you seen the boards of most financial services firms? Do you have any idea what most of them look like? They look like me, right? Old guys, old white guys, right? I mean, the statistics are clear. More than 90% of them are men and over age 60. I’m 63. Can’t believe I’m 63.

But the point is, they need reverse mentoring. They need to be mentored by my kids, who understand these constructs and live it every day. It’s not just about social media anymore, being reverse mentored, what is Twitter, what is a tweet, what is Facebook and how do you do Facebook Live? I mean, that was last generation, a decade ago. Now, these are the new technologies that sit on top of these experiences. They need reverse mentoring, so that they can bring into their board, not just social media people like Starbucks did, they need to bring into their board, young AI data scientists, so that every board meeting has, as a part of its journey, a daily dialogue, because then you come up to speed on this conversation, and they can part of it, not resistant to its reality.

So I really believe in reverse mentoring for these boards. And the infusion of new competencies. And the same thing into management teams.

Mike Delgado: You know, when I was reading your article, you wrote something that I would love for you to elaborate on. You said that, “Tomorrow’s elite consultants already sit on your wrist.” And you said Siri. “On your kitchen counter with Alexa, or in your living room, Google Home.” And we’re wondering if you can kind of elaborate on that.

Barry Libert: Sure. So you think about it, you know, Amazon purchases Whole Foods at a fairly reasonable price, to get access to more customers and to sell more food. It’s a big business. But the consulting industry, globally, is like massive. I think it’s in the hundreds of billions to a trillion dollars. And I’m talking about all services. Now you know they’re sitting on data regarding customer data, marketing data, data analytics on product data. You just know they’re sitting on all this data. All the data that the consultants have to work really hard, they’re called bespoke, which is, they’re like the old tailors in London, they’re bespoke tailors. Thinking that they’re basically somehow immune from this movement of data-driven decisions, which is outcome-driven decisions like in health care.

My view is, since we’re already doing it, it’s already within Amazon, not that they’re picking it off now, or Apple’s or Google’s or Microsoft’s capability to displace their services partners. Now they don’t do that yet, because they have other, more attractive, low-hanging fruit. But they’re not stupid. As they … just like Apple did, when they put these funny-looking devices in our hands, right? These? They now provide a lot more than music. They provide almost everything to me, from education to healthcare, you can do a pin prick here and get your blood type. These are biometric devices.

It’s only a matter of time til the big five say, “Hey, would you like some advice?” And so my view is these guys, these five big … I call them the fab five … are just sitting in prey, ready for when they think this … the services industry is what they’re going after next. And they’re in our bedrooms, they’re in our kitchens, they’re in our living rooms, they’re in my office.

Mike Delgado: Exactly.

Barry Libert: They’re right here. I don’t have to call McKenzie to be right in here. I just have to call, hey Alexa, hey Siri, you know, give me an in.

Mike Delgado: Yeah.

Barry Libert: A lot less hassle.

Mike Delgado: I think about all the data when I’m walking around, when I go hiking with my family and I’m using apps to track where I’m at in the trail. Just the amount of data going into the health section of my Apple product, and data there that’s being tracked all around me, elevation gains, etc. So it’s just fascinating to think about, yeah, our phones are massive sensors, collecting so much data about us.

Barry Libert: Right. And that’s about us, which is only part of the story. About us. Which is every one of us. And they’re watching every one of us … I think the numbers, I’ve gotta, I could be wrong, something like 2,000 times a day, that I’m being hit on my phone. This is a lot of times. McKenzie’s not hitting me 2,000 times a day, which is my alumni firm. They’re not even hitting me at all. They don’t even know what I’m doing right now.

So if you think about it, it’s not to suggest that the bespoke consulting firms go away. It’s just like the Uber world, we’re gonna Uber-ize … you know, we’re gonna make stuff. Google’s taught us this. I want it when I want it, and just as I want it, right here, right now, in my home. And Amazon delivers it within 24 hours.

And think about that. It wasn’t like that when I grew up. It used to take a week or two to get a stock to myself and then to return it. Now, gosh, I don’t even know what a stock looks like.

Mike Delgado: Well Barry, I want to thank you. We’ve just come up on the hour, and want to thank you so much for being part of DataTalk, for sharing your insights with our community. I have certainly learned a lot from you, and I can’t wait to re-listen to this broadcast again.

I want to let everyone know that you can learn more about Barry Libert over at openmatters.com. I have the URL on the screen. Barry, is there anything else you’d like to share about how people can get in contact with you?

Barry Libert: Sure. You can find me on my Twitter handle, it’s @barrylibert B-A-R-R-Y L-I-B-E-R-T. My personal website is barrylibert.com, and we’re a machine learning data science company that examines the economics of the world.

Mike Delgado: That’s awesome. Thank you so much Barry. For those viewers who are new to DataTalk, you can find out more about our weekly data science video chats by going to experian.com/datatalk, and our next chat will be with Dr. Alberto Cairo, as he’ll share with us about misleading data and how to avoid creating the wrong data visualizations. You can find more about that in the about section of this video.

Thank you all for watching today’s chat, and we’ll see you all next week. Barry, thank you again, for sharing your insights with our community.

Barry Libert: Thanks for having me today. I really appreciate it.

Mike Delgado: Thank you. Take care.

Barry Libert: Bye bye.

About Barry Libert

Barry Libert is a digital board member, strategic advisor, angel investor and author. His portfolio of investments includes companies (past and present) that manage more than corporate social networks (customer and employee) for 150 leading brands, with 350,000 experts, and 40M members. He has also advised companies such as Microsoft, GE Healthcare, SunLife, Deloitte, ESPN and the US Army on how to improve their business models by using today’s digital technologies and networks.

Barry has also authored five (5) books and 20 e-books on the value of digital technologies and organizational networks. He has written for NYT, WSJ, Barrons, Harvard Business Review, Businessweek, Institutional Investor, Financial Times as well as other leading periodicals. He has also been on CNBC, CNN and Bloomberg TV. He has spoken at over 500 events to more than 30,000 people.

Make sure to follow Barry Libert on Twitter and LinkedIn. Also learn more about him on his website: BarryLibert.com

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