The Future of AI and Data-Driven Technologies: Insights from Randy Bean, NewVantage Partners

data Masters is the go-to place for data enthusiasts we speak with data leaders from around the world about data analytics and the emerging Technologies and techniques data Savvy organizations are tapping into to gain a competitive Advantage our experts also share their opinions and perspectives about the hyped and overhyped Industry Trends we may all be geeking out over join the data Masters podcast with your host Anthony Dayton data products general manager at Tamer we're back with another episode of data Masters today we're thrilled to welcome Randy Bean an influential figure in the global data landscape he's the founder and CEO of new Vantage partners and has recently become an innovation fellow at wavestone after its acquisition of new Vantage Partners Randy is a recognized thought leader regularly contributing to a wide range of Publications including Forbes the Harvard Business Review MIT Sloan management review and CDO magazine he serves on the editorial board of the CD IQ program shares his knowledge as a guest lecturer and as a guest lecturer in this Chief data officer program at Carnegie melon University Randy specializes in providing strategic guidance to Fortune 1000 leaders helping them harness the power of data and analytics for business success his vast experience and expertise make him an exceptional guest for this episode welcome to data Masters Randy hello Anthony delighted to be here nice to see you again as always yes so I thought uh it would be fun to you we wanted to talk about data and analytics Trends uh for the coming years and and we will do that uh towards the end I thought as a kind of foundation for the conversation uh it might be fun to start by talking about your book uh fail fast learn faster I can see a copy in the background um and you know the book spends a lot of time talking about this link between organizations people process and data and data strategies and how organizations can really take advantage of data to drive their business strategy so I thought maybe if you could start us off maybe share a little bit uh if if you could share a little bit about what you view as maybe that number one blocker that number one inhibitor for organizations to be data driven so what gets in the way uh absolutely and thank you for having me today um there's no doubt about what the number one blocker is you know we've been conducting a survey of forun 1,000 data leaders for a dozen years this year will be the uh thir 13th year and we kick it off next month and what the survey consistently shows is it's not an absence or lack of Technology but rather it relates to people and cultural change and transformation organizational development change of processes those things are never easy you know 90% of Fortune 1000 companies are Legacy companies in other words they've existed for many generations in some cases over 150 years so those are not digitally native born companies so for them to become datadriven to leverage AI uh they have to change a lot of their processes and they've been very successful serving the customers that they that they serve and producing the products that they deliver to the market but they have to make this transformation and Leverage The Power of data and Leverage The Power of analytics and AI to continue to succeed in the coming years another big piece of the book um which is will I think connect to a later conversation we can have around AI uh was around ethics um and uh I thought it was really interesting that that you even included ethics as a topic in the book it was probably a bit ahead of its time in a way um but maybe uh can you share a little bit about your view of ethics in data and what a lot of organizations have gotten wrong as it relates to ethics around data yeah uh it it's an area that tends to be overlooked because many organizations are thinking about how they can leverage data to say increase personalization or improve the customer experience or generate oper operational efficiencies in their business but they often don't give sufficient consideration to what are some of the ethical risks uh and that includes everything from data privacy to uh built-in biases in um AI algorithms and various types of algorithms and so there's uh uh an increasing risk because of the information that's gathered on on uh customers of an organization one of the books that I mentioned in the AI chapter is called the surveillance economy and the point is is that the data collected on you is such that uh basically the party can know everything about you including where you are at any particular points in the day based upon the transactions security cameras things of that kind so if you have that information you need to use it in a very uh cautious uh way with uh guide posts and standards and policy and one of the things that the survey shows each year is that uh that data leaders say that only uh 40% of the organizations have established any type of um standards and policies or sufficient standards and policies around the management of data and establishing ethical standards and 80% felt that the industry wasn't doing enough there's a lot of initiatives in place such as the European Union is been working on an AI I um privacy and ethics structure for several years now but for for most companies it presents uh risk and exposure and that's one of the things that can hurt a company more than anything else yeah I think that's a a really interesting point um organizations often think about these linear measures like profitability or growth or cost structure uh which you know don't change very much you know if you're if you're doing a worldclass job you're growing at 50 to 100% but most organizations especially the bigger ones are growing in the single single digits or maybe low double digits but these are very linear measures I think the point you're making here is that these ethical considerations represent what I call existential risks so that you know you could go from being in business to out of business or you could be you could go from being a normal business to being at the sharp end of the the US government on on the course of a lawsuit meaning these are like big risks that could actually cause the end of your business I can give you a classic example uh it's actually an organization that worked with for many years and was one of my favorite clients in many respects and that's Wells Fargo bank and Well Fargo Bank uh unlike the New York Banks was situated in proximity to Silicon Valley so a lot of the mindsets and Innovation uh filtered into Wells Fargo and I first started working with Wells Fargo about 200 one and they had uh basically it was the um internet and data group and it was really at the Forefront and they were very good at bringing uh their customers onto the online Channel I think when I first started working with them it was about 10% and within a few years it was 50% of their customers were on on the online Channel and so they were gathering more and more information they were very sophisticated in this regard but one of the things that happened was that through the things that they were doing well they achieved the highest cross cell ratios by far in the banking industry and as a result of that uh for a period of time with the largest most highly capitalized in terms of market capitalization of major Banks so they grew substantially but what happened was that uh a few folks within the organization took it to the next level and they started uh signing people up for products without their permission to continue to boost those cross cell ratios so as a result of that there was a scandal and as a consequence of that uh Wells Fargo's been under intensive regulation for the past decade so you can be uh hitting a home run you can be at the Forefront you can be the most Innovative company in your industry but the smallest type of abuse can come back to bring it you know bring it all down very quickly exactly and so you know accounting for those existential risks is something I mean people do it poorly in our personal lives but businesses in also uh do it very poorly so over the course of the last you know 12 18 months uh your writing is increasingly focused around AI specifically versus data more generally and exploring a lot of the sort of opportunities and risks that AI presents um and I think it's also fair to say you've had a specific emphasis on gen Ai and the implications that this new set of Technology have in data uh and analytics um so one thing I've been asking folks on this podcast uh is whether they think jna is a big deal or if it's sort of overblown um I think in this case the the answer speaks for itself like I think it's clear you think this is a huge deal but first of all let me validate that conclusion and then maybe share your view on why you think it's such a big deal first and foremost it's important for me to say that I tend to be a technology skeptic in other words I I don't trace I don't chase the latest technology Trends and in that regard I view myself as kind of a barometer for Fortune 1000 organizations because Fortune 1000 organizations really shouldn't be chasing things until they come into wide wide uh mainstream adoption and so you know when I first started hearing about generative AI I I was highly skeptical that was the approach that I took uh even before that a year or two ago data products when I first heard that I was like oh is this just data fabric data mesh you know data democratization until I I started hearing from it from a wide range of organizations and came to understand the power of it so generative AI was in that same uh you know I had the same mindset around that but I can tell you this um last week I went to the Wall Street Journal Tech live event in lagona Beach uh it was the second time I went went a few years ago and it was all about Ai and it completely changed my perspective uh you know what I was telling people uh up until a few weeks ago people would say can you come on our podcast and speak to us about Ai and I said you know I'm really not an expert in AI um and then they'd say a few things and I'd say but yeah I can't do that and I said yeah but you said you weren't an expert well in any event I went to this Wall Street Journal Tech live event last week they had folks like Sam Alman from open AI coala from uh Coastal Adventures among others and they weren't just talking about generative AI they were talking about something called artificial general intelligence AGI which they defined as the state where AI can perform all human cognitive tasks better than the smartest human and they saw this coming into uh General capability within uh the next two or three years you know some of the uh things that were mentioned during the event 18 million gigabytes of data are added to the global sum every single minute of every day that's uh extraordinary a Quantum Supremacy you know this notion that AI can complete a calculation in seconds that would have taken the conventional computer 10,000 years so the reality is is that um I went into the event last week as a skeptic I came out uh kind of mesmerized and terrified because I think the reality is that in one form or another it's going to come about uh very quickly quicker than we expected and that organizations need to be prepared at least in terms of U what their plan is what their guard rails are you know the ethical issues um how they're going to manage this so that it doesn't create more risk than benefits they really need to think through the use cases they need to approach this in a systematic fashion but they need need to start thinking about it immediately yeah and that very much connects to the the prior conversation although interestingly I think there are risks in on both sides of that ledger on the one hand there's a existential risk associated with you know you know over adopting if you want to say it that way gen Technologies and you know doing something that puts you uh at risk for customer backlash or regulatory backlash on the other hand there's an equal risk that you know ignoring it thinking it's not a big deal could put your business at risk of being disrupted and eliminated very much so you really you know as a CEO you face risks on both sides of that of that ledger and there's another aspect to that which I didn't mention and that is the impact on uh jobs so for example uh one of the questions that was asked at the event last week was what if a large majority of white calla tasks can be performed more effectively using Ai and and uh you know Sam malman from open AI stated you know every technology Revolution affects the job market that's the way of progress we find new and better jobs you know vard kosa said AI will replace 80% of 80% of all jobs within 10 to 20 years and they're really talking about you know what they call uh you know white collar cognitive manual labor yeah and you know again a trend or a an experience that we've had a society many times before some new technology comes along uh and changes the the kinds of work that people do on a day-to-day basis however I think the your point the interesting idea idea there is that we're talking about white collar cognitive work and not Manual Labor uh and and maybe the for the first time uh we see the introduction of a technology which could could affect uh the CEO themselves well you know that's very interesting cuz I had uh just after I got back from this event I had launched this week with a Boston Hotel year so so this person runs a series of hotels um and he was saying and he's not a technical person and I by historic background wasn't wasn't technical until I became Technical and he said that he had started using chat GPT and he found that chat GPT he could use to um automate so many of the back office functions uh and so he was really interested in what he'd be able to do with this in his business but he said but we'll always need people to set the tables and I said oh no that's what the robots are going to do so in terms of that manual labber uh you know there's a lot of talk at the event too in terms of what robotics can do and their capabilities they even had um at the Wall Street Journal event a robot going around after dinner with different types of popsicles for people to uh choose so I was going to ask you a little bit about that as well uh this distinction between or a lot of your writing has been around how CEOs should think about generative AI uh and and think about the effect that it has at a corporate strategy level but I was wondering if you'd be willing to comment I think a lot of listeners to this podcast maybe are earlier in their career uh they're not the CEO yet Perhaps they have Ambitions of being the CEO um but they've just started in a new job or in a new role is there a difference between how you think CEOs should consider generative AI Technologies or even General AI intelligence as the even the next step versus how somebody who's just getting started in a job in consulting or a job in banking or they're you know or or just starting at a as a junior engineer at a tech company how should how should they think differently about this well the the Young entrance actually are already so far ahead as an example uh this summer I had the privilege to go on the tall ship US Coast Guard Eagle uh from uh Boston to to Maine for three days and the reason why I went was um I had given a class for the chief data officer for the data and analytics organization and um as a reward for that I guess they invited me to sail with them on the tall ship and in turn uh do a fireside chat the chief data officer so you know I mentioned that I was a slow adopter of Technology well the chief data officer asked of the 220 Cadets from the Coast Guard Academy said how many of you are using uh chat GPT and virtually every hand on the VA went up and I said well that's very interesting because I'm here as the expert and I've actually never used it so you know that's the situation with CEOs um because you know there may be some CEOs that you know have used it but it's something new it's something a little too new you know it gains some type of critical mass before they're going to you know start to focus their attention on it so that that's kind of part of the message and this is not just some tool like uh social media or Tik Tok that young people are using it can have a a significant impact on your business and you need to start to uh become aware and set up um groups within your organization and policies and practices to determine what is the power and what is the risk and what are the activities within your organization that it can be applied to to uh realize the benefits that can be achieved and also mitigate the the downside right so it it would be fair to say that as the CEO of a Fortune 500 organization perhaps your best knowledge and information about the potential for these technology comes from these people this group of people that have really burn burned it into their day-to-day work uh on a daily basis this is what I say now that I've had an entire career you know the only benefit of being old of being around for a long period of time is you have the benefit of experience and perspective so you can kind of see things and you can kind of uh say well you know maybe this is you know seen this movie a thousand times and this will Peter out but there there's also those things that are different and unique and breakthrough and so you bring that experience and judgment but you also have to be open and receptive to new ideas and you have to be ever Vigilant so that you're um you know so then you're not just doing business as usual and living in the Dark Ages and uh refusing to uh adapt and evolve as an organization but you need to be selective you you can't chase every new idea but this is one that um is going to have an impact and it's just basically there's a lot more data and there's a lot more computing power in terms of massively parallel processing and you take these things and you put them together along with the um training that computers have basically been trained so that they're not dependent upon human beings to figure out how to do tasks so it's just this um it's a problem that organizations have been working on for 50 or 60 years now but it's really reached that critical mass in terms of potentially Universal applicability I'm glad you brought this uh question up about uh training uh because it brings me to uh maybe this feels like a a very minor point but I I actually think this is one of the more interesting elements of this generative technology is that the important the output of these generative models is a function of the input of data that was used to train them and we like to look at the result we like to use chbt or use open AI apis or vertex Google vertex Ai and we love the output but we don't think about what went into training it what biases that training data had what where there's actually missing data or and to your point about uh CEOs or or anyone in an organization using historical data to make predictions about the future if something didn't exist in the past you wouldn't use it to would have it available to you to make a prediction in the future so how are you thinking about advising CEOs and organizations around this question of sort of you know the the common term is garbage in garbage out but how to train these models or improve these models and think about what's going into them versus what's coming out well I don't have the magic wand or the magic answer but um clearly you know data quality is uh more critical than ever um so all of the practices and processes that you need need to put in place to ensure data quality become even that more critical and again those ethical questions making sure you're looking at these sources of data and looking for bias in them uh is uh is really important yeah and you know there's so many examples of those from uh you know real estate lending uh based in various communities and the history there so identifying those biases uh mitigating those biases uh standardizing the data in ways that are um normative and uh objective as opposed to reflecting those biases uh will will go a long way so maybe to take a contrarian view here my view is this is actually a great opportunity uh we talk about making computers more like humans to let them have general intelligence but humans in many cases are the root of a lot of these biases and the opportunity I think when we think about these generative Technologies is to design uh less biased or look for sources of that bias and eliminated um you know you think about and you use the example of lending you know uh lending adjudicators people who make decisions about loans are by their nature very human and therefore make uh very can make very biased lending decisions with which we can all agree is uh not good from a society's perspective it's also not good from a profit perspective because you're missing a lot of opportunity to this point if you actually have better training data you could have a generative model do a much better job of predicting and making decisions on loans what's your what's your view there's a lot of obvious examples and I don't think I'll touch those with a 10- foot pole on I'm recording but there you know there're so obvious but um Mustafa sulan who has wrote written this book The coming wave and spoke at the event last week you know his view was that the benefits of AI would outweigh the risks and he talked about the ability of AI to help grow food detect natural disasters increase the standard of living improve the quality and affordability of healthc care increase education and he pointed to in spite of all of the problems in the world today when you look at these uh statistics on a global basis you know people are living longer eating better more educated all of those things but at the same time he also cautions quote unquote yeah we're going to live in an epic with the majority of our daily interactions and not with other people but with AI um and that there's many unintended consequences with that and he he kind of closes with this um quote will AI unlock secrets of the universe or create systems beyond our control so it's really incumbent upon us as um human beings and uh business professionals and data professionals to uh especially knowing what we know to do everything that we can to um safegard the uses of AI to ensure the quality Integrity of the day that that goes into these models um again it's easier said than done but over the past 20 years there's been such a an advance in data quality data cleansing um capabilities that you know hopefully were moving in the right direction absolutely so let's um shift the conversation a little bit to your point about the future and and let's talk about the future uh and you mentioned this at the beginning that new Vantage Partners does an annual survey to senior data and analytics uh Executives you did this most recently in January of this year of 2023 so I wanted to talk about this in sort of three bits so first you did the survey roughly year ago uh H how'd you do how are the predictions uh then we could talk a little bit about next year you haven't done the survey yet so it's totally an unfair question but you know let's see and then really think about the long term next five to 10 years but let's start with 2023 like you did the survey uh you made a set of predictions what did you get right what did you get wrong uh how'd you do well first of all going forward since last year or into this year is now it's the wavestone survey since you know W Stone acquired new manage partners and it's I I I wouldn't call it so much predictions as it is uh tracking the progress that's been made and and it's a mixed bag so for example uh one of the series of questions we ask is about the progress of data and analytics aspirations so we ask are you driving business Innovation with data this year 59.5% said yes but actually in 2019 59.5 all also said yes and the number went up and down a little so it fluctuated and that's the best result so we said are you competing in data and analytics in 2023 40.8% said they were actually the number in 2019 was 47.6% so it decreased are you managing data as a business asset this year 39.5% 5 years ago is 46.9% have you created a data driven

culture 23.9% said yes the others all said no in 2019 it was 31 % said yes and have you established uh uh have you established a data culture 20.6% said yes down from 28.3% 5 years ago so you know why have these numbers gone down in part because the problem keeps getting harder there's more and more data uh you know there there's many people that are now responding to the survey that uh weren't five years ago so for example one of the things we ask a lot about is the role of the chief data officer so in 2012 only 12% of the organizations reported having a cheap data officer a cheap data and analytics officer and this year was 86% so many of the people that are answering the survey now they're one year two year three years four years into the role um so the point in all of that is that um progress is slow progress is gradual it may be disappointing to some but the great news as um illustrated by the appointments of Chief data officers the need need for data and AI leadership is only growing you know that's not going away it's only increasing and you think about it this is a relatively nent job you know it's really been 15 years since the first cdos were appointed after the financial crisis in that case it was with major Banks and you know if you go back in generation 35 40 years when the Chief Information officer was first established the inside joke with CIO stood for career is over so even though there's short 10 years you know 24 to 30 months and some would say that shrinking you know this is all to be expected and over the long term um I I think that organizations are going to become more datadriven they are going to leverage AI in their business and um these things will become uh second nature and it's worth mentioning that the digitally native Native companies the apples the Amazon the Googles uh um Facebook meta whatever they're called These Days these organizations don't have Chief data officers you know it's embedded in everybody it's part of the culture it's part of what's a given for them so it's really Legacy companies that are making that transition for from the processes and ways of doing business that they've operated with for uh decades and generations and trying to be more agile in terms of their use of data and ai ai capabilities in their businesses got it so if we cast our eye forward to next year and you know to your point uh progress is slow and steady and sometimes even backwards are there a couple things you expect uh to change next year the sort of big shifts that are right on the horizon that listeners should be kind of keeping in the in the front of their head like these are things the big signposts that don't miss this one the things you're tracking that that they people really should watch out for next year yeah I'd say two things one thing which uh has manifested itself this year is really a change in expectations of what the chief data and analytics officer should deliver so because of economic uncertainty which could continue there's been a huge uh focus on delivering measurable business value and as a consequence uh close to half of the 41,000 Chief data officers that I knew at the beginning of the year are no longer in their roles for for iy of reasons so there's a a real shift to moving from the technology side of being under the CIO to being under Business Leaders and being able to quantify the results of data and analytics investments in terms of customer Improvement Revenue growth increased profitability activities of that kind so I expect that to continue and in conjunction with that the role of the chief data office so will really continue to evolve significantly probably the biggest question that many organizations are asking today is whether AI should be the responsibility of the chief data officer or not and I hosted a panel in Boston last month and I had four panelists and I posed that question to the panelists and two of them said it should absolutely be the responsibility of the chief data officer and the other two said it should absolutely not be the responsibility of the chief data officer so you know again these roles are fluid and evolving but I expect they'll become even more more fluid and evolving over the next um 12 to 24 months and that's not a bad thing summarize that a little bit uh cdos need to get closer to the business and away from it and really think about frame their job in the context of business value and away from uh technical implementation and and number two is that organizations must have some strategy for locating uh decision-making Authority around gen whether that's with CDO or not and you know maybe we might I don't know I don't want to put words in your mouth but I my bias would probably be towards the CDO since it's largely a data driven challenge yeah it's great to build and create capabilities and those are often necessary for for the long term but at some point you need to uh show me the money as they say you know you you you need to show the value you can't say you know we're in the year 10 of building a data warehouse except for now it's called a data l and now it's called the data fabric you know that's uh you know so often from The Business Leaders I hear oh not another data project and you know often when I meet with the cdos or the cios they talk about the capabilities the engineering The Architects and then when you meet with the Business Leaders they say Hey you know we don't trust the data we're not getting the data we need to make the decisions we need to make in a timely fashion so the most successful cdos I see are the ones where the CEO of the organiz ation like Jamie Diamond at JP Morgan gets up in the annual meeting and said we couldn't have achieved these results if it wasn't for our data and analytics and the data and analytics organization let's all give them let's give them all a round of applause as opposed to the cdos that I hear that I hear from that say you know we've created all those great capabilities and nobody appreciates us you know you really have to have that link that sponsorship the relationships with the business to uh have that credibility and as you build upon that credibility you can EST start to establish some momentum and some meaningful breakthroughs yeah I think that's really Sage uh Sage advice so maybe to um bring us home as they say casting your eye forward uh a long way into the future five 10 years which admittedly totally unfair question but here we go uh any predictions for where you see data analytics big data gen uh General uh artificial intelligence uh uh going like is there something that you you want to put a stake in the ground for where we are five to 10 years out well you know if you listen to the folks at the Wall Street Journal event uh basically you'll be kicking back in your armchair you'll have the the robots bring you all of the data information most of the rot tasks will be solved and your job will just be to to do the big thinking the creative ideation so that's what they're predicting so um you know we we we'll see what happens yeah well look that's actually not such a bad a bad Vision because it puts uh listeners in charge of creative tasks over uh boring ones um and you know presumably the kinds of things which you know bug us on a day-to-day basis that are boring and repetitive are gone away and leaving us more room for enjoyment relaxing activities or creative activities yeah we'll all be little CEOs not a bad not a bad Vision or future hey Randy H thank you so much for joining us uh on data Masters it's been a pleasure it's always a pleasure Anthony nice speaking with you today data Masters is brought to you by Tamer the leader in data products visit tam.com to learn how Tamer helps data teams quickly improve the quality and accuracy of their customer and Company data be sure to click subscribe so you don't miss any future episodes on behalf of the team here at Tamer thanks for listening

2023-12-07 06:46

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