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  • - Hello and welcome to Experion's Weekly Data Talk,

  • 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 Open Matters and a strategic advisor

  • for some of the biggest global brands

  • like Goldman Sachs, Microsoft, GE, and ESPN.

  • Barry is also a bestselling 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.

  • - Thanks for having me today.

  • - Barry I thought it'd be great if we can just kick this off

  • if you could share a little bit about yourself

  • and the work you're focused on right now.

  • - 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.

  • - Very, very cool.

  • I mean, 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 those articles about robots taking over

  • and definitely in the financial space

  • we're seeing signs of robo technology

  • and artificial intelligence.

  • In one of your latest articles

  • 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 could talk

  • a little about that

  • because that was a really great article you wrote.

  • - 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 it,

  • strategic consulting industry in the United States alone

  • is about a 70 billion dollar industry.

  • Think of them as doctors

  • that are doing business the old fashioned way,

  • which is a patient, a CEO,

  • comes into my old alumni firm, McKinsey, or Bane, 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 captured in a data structured way

  • that benefits in today's machine

  • learning artificial intelligence.

  • All our article was proving is 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 going apply to every single industry

  • where the services industry has had a huge growth cycle

  • in 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.

  • - 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?

  • - I did.

  • And that's happening around the world

  • in the financial services industry.

  • You look back awhile, 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, really 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 in there 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.

  • You've seen the explosive growth of not just ETFs

  • and passive managers, you're now seeing

  • what's called robo investing like Wealthfront and Betterment

  • begin to offer for you and me, the individual investor.

  • - 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 working with AI

  • despite the layoffs?

  • - Sure, the layoffs aren't that big, right?

  • In fact, they're not big at all.

  • Growth in the industry is outpacing layoffs by far.

  • So, there's a massive fear, I call it,

  • on layoffs by, you know, robots displacing you and me.

  • I don't believe that.

  • It's going to create new skills,

  • obviously we might argue that 100 years ago

  • machines displaced humans when we started building cars,

  • or 150 years ago, tractors started displacing farm workers

  • and 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 to your industry,

  • which is all the services industries,

  • not just the financial services industries,

  • the marketing service industry, the audit service industry,

  • the legal services industry.

  • We gave examples of every industry,

  • the medical service industry,

  • where robots and artificial and machine learning

  • are going to complement human intelligence

  • and human learning.

  • - Yeah, it's amazing.

  • It's amazing to see the growth

  • and I feel like just in the last year

  • just 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.

  • And I really like your positive outlook about,

  • just as we looked in the past,

  • how technology has displaced some workers,

  • there's just new jobs that were created from that.

  • So, I love your positive outlook.

  • I'm curious about some of the feedback you received,

  • because your article in Harvard Business Review

  • was very very popular, was shared all over the web.

  • I loved reading it.

  • I'm just kind of curious at the responses you've got.

  • - Well, surely there were both positive and negative

  • as you might imagine because we were taking a pot shot,

  • right, at the world's most elite.

  • My alumni firm, McKinsey, and Megan Beck who's my co-author

  • is an ex-Bain person.

  • So Bain-iac, right?

  • And my brother was at BCG,

  • you know, this our heritage from a lifetime ago.

  • It's hard for them to understand when they are reporting

  • all day long, McKinsey at their very best, is reporting

  • on the impact of AI in 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,

  • right, which what I think is coming anyway.

  • You and I will be getting my advice from what's sitting

  • right there on my desktop.

  • And, so, we had obviously had negative feedback.

  • Consultants saying, well what job will I have

  • and who's going to write your next article Barry?

  • - (laughing)

  • - According to your interview, a bot is going to have

  • some influence on that as well.

  • But there's negative and there was a lot more positive.

  • Which was 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, right, and likes.

  • It was amazing to me.

  • - Yeah.

  • - HBR, I think there was 400 or 350 comments.

  • It just blew my mind.

  • - Wow.

  • - People are interested, right,

  • in this article on this area.

  • - Yeah, like I said, it popped up on my radar,

  • I saw all the sharing.

  • People obviously loved it.

  • And yeah, so I was just curious to hear about,

  • obviously there's going to be people

  • that are kind of frustrated or upset,

  • especially in 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.

  • - Yep, I think that's 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 the keynote speaker

  • at the annual MIT Platform Event was Alexa.

  • We had to mike 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.

  • - Oh, wow.

  • - It wasn't that we were kidding.

  • But, we're happy to share an image

  • that shows me talking to Alexa on stage.

  • I mean, of course I started with a silly quote,

  • 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 10 million dollars.

  • - Wow, that is amazing, that is amazing.

  • Just seeing the voice, AI, products being developed,

  • Alexa, Coursera, and what's possible.

  • And 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.

  • I mean, the ROI on that is just phenomenal.

  • - Yep, my always message is I have a simple thesis in life.

  • I wanna replace a Barry call, a call to Barry,

  • with a call to Alexa.

  • I'm tired of getting calls

  • that ask the exact same question to me every day,

  • I don't wanna become a platform network and AI company

  • for the last 20 years when AT&T was my first client

  • a long time ago.

  • And I said to them, I'll have a call answering service,

  • they can call right to Alexa and they can say

  • I'm calling Barry and Alexa will answer the phone.

  • - Yeah.

  • I think there's gonna be a Barry bot.

  • - There you go.

  • I'll hook it up so you can answer

  • some of your other interviewees

  • and people that you've had on your show

  • so I can figure out how to do that next.

  • - Yeah. (laughs)

  • It's so funny because when I, 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 capabilities

  • 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.

  • I mean, I'm really curious about, you know,

  • with all of these developments Barry, and especially too,

  • the article you've just written,

  • where are the boards and the C suite?

  • Where is their pulse?

  • How are they feeling about these different issues?

  • - Oh, they're nowhere.

  • They are stuck in Excel land,

  • which is about 40-year-old strategy technology,

  • getting annual reports put up in board packs, right?

  • You know, technology's in place

  • in board packs on their iPads.

  • They're nowhere.

  • I mean, every board meeting I attend or participate with

  • on executive teams, I say, it'll finally be somewhere

  • when the data, chief data scientists,

  • not the chief data officers, 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 literally have no insights

  • about what is possible today.

  • - So like, with that lack of knowledge,

  • because part of it is just like,

  • 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 you know, using AI and doing business in this way?

  • - So it's really important, you know,

  • when in Rome, you know, speak Roman, you know,

  • things like that, or when here speak English,

  • whatever the words or language.

  • When you're 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 that they tell me,

  • I'm the tech guy, I'm the tech woman.

  • They go, I 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 science 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 there's 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 know, you'll be reporting

  • to somebody in the tech department.

  • - Where, I mean, so for a data science team,

  • who, like in the, 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?

  • - Well, you know, the CFO, otherwise known

  • as the chief financial officer,

  • I call him 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 tomorrow,

  • they would argue, tomorrow's organizations,

  • but in fact, it's today's five,

  • 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,

  • it'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 three, 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 would report to the CNO.

  • - And for those companies that are looking to hire

  • a chief data officer and the CDO was kind of a new role,

  • emerging role that we're seeing more companies bring on,

  • what skillset should that CDO have?

  • - So currently I think CDO means chief digital officer,

  • right, which is an important distinction

  • from data science, right?

  • 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're often not data scientists, at least I've not seen.

  • They come out of the technology realm.

  • And I try to explain the difference

  • between a software architect and a data scientist,

  • are like the difference between a plumber

  • and electrician, right?

  • Or a neurosurgeon and a cardiologist.

  • They're not from the same -ology.

  • They're called data scientists and technologists, right?

  • They come from a similar lineage but a different lineage.

  • And so data science has to find its own voice.

  • And I think it's okay to be into the traditional CDO,

  • the chief digital officer,

  • but I think data scientists need to make a business case

  • to be right to the CEO.

  • - Okay.

  • Yeah, I like that, I like that.

  • But like you said, it needs to be done in business speak,

  • in the language.

  • - Correct.

  • Otherwise it'll be underfunded and too late to the party.

  • I mean, this stuff, like tensorflow is a free offering

  • since November from Google, right?

  • So you get these spectacular open source environments

  • like Linux now and Aioworld, and they're moving so fast.

  • If you're not in the world of being there all the time

  • as an organization, forget about some data scientists,

  • 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 base needs, it's free

  • with AWS or so inexpensive.

  • And Google offerings and Microsoft offerings.

  • It might as well just be free.

  • The question is its application and its economical impact

  • on an organization.

  • - Barry, I wonder if you could share some of the ways

  • or stories that you see consulting firms or businesses

  • using AI right now to improve their business decisions?

  • - Sure, let's start with the big one that was announced

  • a few months ago, which was Blackwall, Blackstone rather.

  • Which is a five trillion dollar

  • investment management organization publicly traded,

  • made an announcement that

  • they're going to approve their returns using robots.

  • Quote unquote robots, AI machine learning robots.

  • Because they realized at a scale they needed to use,

  • at five trillion dollars at scale,

  • they need to be able to use large amounts of data

  • that historically viewers could watch, you know,

  • all of those screens, watch these traders

  • or watch 100 screens or four screens while you did it.

  • It's impossible to look at the amount of data

  • that we're consuming and absorbing

  • in our data science group.

  • It's not possible to see it all, right,

  • and to make sense of the pattern recognition.

  • So I think Blackstone is a really good example of that.

  • From there, I think that's, 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,

  • right, you see now, you know,

  • the original Craig Venter experience of doing the genome.

  • You're now seeing AI use the human genome

  • to create something called Crisper, which is g editing,

  • which is an amazing thesis.

  • Last week alone was the first time ever, said this article,

  • or this news announcement, last week,

  • for the first time ever, a human embryo

  • was genetically modified using Crisper,

  • 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.

  • - Wow.

  • - So Crisper, an AI-based tool for genomic editing,

  • just like we're suggesting

  • you can do economic editing, right?

  • A second example.

  • But you're now seeing it in the mining industry.

  • Something you'd never imagine.

  • Which is, we see it in the gold and oil industry

  • where they're trying to understand the data

  • from below the level of the ground in the water,

  • to do that as well.

  • So this is at the tip of the iceberg

  • of where large amounts of data,

  • which historically have been unmined,

  • are gonna be organized, structured, create data links

  • and on top of those large expansive data links,

  • create machine learning capabilities

  • that will produce insights for humans to learn from

  • and humans and machines to work together.

  • - Yeah, I think I saw that article

  • and then I saw a similar article about some scientists

  • that were able to inject like, animated gifs or 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, you know,

  • human life better for health reasons, etc.

  • - Correct.

  • So you can imagine just like Blackrock

  • is gonna try to make investment decisions better,

  • and you can imagine how we're helping

  • make business decisions better,

  • you can imagine how the healthcare industry is gonna use it

  • to make better healthcare decisions.

  • Now they have a culture to decide,

  • because you could decide you wanna have blonde hair

  • and blue eyes, right, and do some gene editing.

  • If I'm in Poland, maybe being you know, from there.

  • I could decide I wanna be

  • completely a different human, right?

  • Well that's gonna be some ethical questions that are,

  • you know, should that really be possible, right?

  • Should I be able to have, I can't get,

  • my wife and I couldn't have any more children, you know,

  • but maybe my children are gonna decide

  • they wanna have specifically engineered children using AI.

  • Those are gonna be awesome questions for us to answer.

  • - Yeah, 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'll be AI-based to help improve those

  • that are looking to invest or manage their finances?

  • I'm just really curious.

  • - Absolutely.

  • So we're working on risk AI products, which is really cool.

  • Right now Finch and Moody's S&P provide debt rating,

  • you know, all these funny looking letters.

  • But we're doing risk AI products now,

  • not just strategy AI products.

  • And that is really critical, because now you can look across

  • these non-financial variables like customer retention,

  • customer intimacy, human capital you know, engagement,

  • and begin 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, right?

  • We decide we don't wanna have that asset anymore

  • and all of a sudden

  • those physical assets become liabilities.

  • So these new insights that 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 Bureau of Labor statistics,

  • and being able to basically match it up

  • which is what we're doing, to give new insights

  • to risk-based metrics for organizations and investors.

  • Huge issues, and same for governments.

  • I mean, right now government risk is the ability to pay.

  • Well, you know, what happens

  • if Trump decides to do something we don't like,

  • it's gonna create some economic risks

  • that are knowable from the language that we're using.

  • So that stuff's all available today.

  • - You know, you talked just briefly

  • about the big shakeup over 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?

  • - 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.

  • - Gotcha.

  • - 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, 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, right?

  • They're still selling financial services, right?

  • Insurance services are the same thing, right?

  • 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 realtime, Alexa-driven,

  • Siri-driven, Google Home-driven whatever I want

  • when I want stuff?

  • I mean, it's gonna be an interesting question.

  • - 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?

  • - 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?

  • The statistics are clear,

  • more than 90 percent of them are men and over age 60, 63.

  • Can't believe I'm 63, but the point is

  • they need reverse mentoring, right?

  • They need to be mentored by my kids who, you know,

  • understand these constructs and they live it every day.

  • It's not just about social media anymore

  • being reverse mentored, what is Twitter and what is a tweet?

  • What is Facebook and how do you do Facebook Live?

  • 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 has as a part of its journey

  • a daily dialogue, because they can come up to speed

  • on this conversation and they could be 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 into management teams.

  • - 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.

  • I'm wondering if you could elaborate on that.

  • - 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,

  • right, it's a big business.

  • So the consulting industry globally is massive, right?

  • It's in the hundreds of billions if not trillion dollars.

  • And I'm talking about all services, right?

  • Now you know they're sitting on data

  • regarding customer data, marketing data,

  • data analytics on product data, right?

  • You just know they're sitting on all this data.

  • All the data that consultants have to work really hard,

  • they're called bespoke,

  • which is like they're the old tailors of London,

  • the bespoke tailors, thinking that they're basically,

  • you know, somehow immune from this movement

  • of data-driven decisions, which is outcome driven decisions

  • like in the healthcare industry.

  • 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, right?

  • Now they don't do that yet

  • because they have other more attractive low-hanging fruit.

  • But they're not stupid, right?

  • As they, just like Apple did

  • when it put these funny looking devices in our hands, right,

  • these music things, they now provide

  • a lot more than music, right?

  • They provide almost everything to me,

  • from education to healthcare.

  • You can do a pinprick here and get your, you know,

  • your blood type and these are biometric devices.

  • It's only a matter of time til the big five say,

  • hey, would you like some advice?

  • So my view is these guys, these five big,

  • I call them the fab five, are just sitting and praying,

  • ready for you know, when they think this is,

  • 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.

  • - Exactly.

  • - They're right here.

  • I don't have to call McKinsey, they're right in here.

  • I don't have to call.

  • Hey Alexa, Hey Siri, you know, give me an in.

  • A lot of things for a lot less hassle.

  • - 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 on the trail,

  • just the amount of data

  • going into the health section of my Apple product

  • and data that's being tracked all around me,

  • so elevation gains, etc.

  • So it's just fascinating to think about, yeah,

  • our phones are massive sensors

  • collecting so much data about us.

  • - Right, and that's about us,

  • which is only part of the story about us, right,

  • which is every one of us.

  • And they're watching every one of us, I think,

  • I think in numbers I've gotta come in small,

  • something like 2,000 times a day, right, you know,

  • I'm being hit on my phone, right?

  • Which is a lot of times.

  • McKinsey'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

  • when I'm talking to you.

  • So if you think about it,

  • it's not to suggest that bespoke consulting firms go away,

  • it's just that, it's just like the Google world will just,

  • we're gonna organize, we're gonna make stuff.

  • I mean, Google's taught us this.

  • I want it when I want it just as I want it, right here,

  • right now in my home.

  • Amazon delivers it within 24 hours.

  • They have their widgets over within an hour at my house.

  • And the thing about, it wasn't when I grew up,

  • it used to take a week or two to get stock to my house

  • and then to return it, right?

  • Now you know, gosh, I don't even know

  • what a stock looks like.

  • - Well Barry, I wanna thank you.

  • We've just come upon the hour

  • and I wanna thank you so much

  • for you being part of Data Talk,

  • 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 wanna 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?

  • - Sure.

  • You can find me at 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.

  • - That's awesome.

  • Thank you so much Barry.

  • Barry, for those viewers who are new to Data Talk,

  • you can find out more

  • about our weekly data science video chats

  • by going to Experion.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.

  • - Thanks for having me today, I really appreciate it.

  • - Thank you, take care.

  • - Bye bye.

- Hello and welcome to Experion's Weekly Data Talk,

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