Data Masters Podcast
November 15, 2023

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

Randy Bean
Founder and CEO of NewVantage Partners and Innovation Fellow at Wavestone

In this episode of the Data Masters podcast, we speak with Randy Bean, founder and CEO of NewVantage Partners and Innovation Fellow at Wavestone, following its acquisition of New Vantage Partners. We discuss various trending topics, including data-driven transformation, the challenges organizations encounter in becoming data-driven, generative AI, and the long-term outlook for AI and data-driven technologies. As a strategic adviser to Fortune 1000 leaders, Randy takes you into the boardroom to share what's top of mind for CEOs regarding AI, which is both mesmerizing and terrifying. We delve into how CEOs plan to establish guardrails, deal with ethical issues, and manage AI to ensure it doesn't create more risks than benefits. Randy also gives us his predictions for the CDO agenda 2024 and beyond. 

Tune in to this captivating discussion!

About NewVantage Partners (a Wavestone company): NewVantage has been at the forefront of data-driven transformation and innovation for nearly two decades as expert practitioners and C-executives who have led corporate transformation initiatives with a focus on the planning, design, execution, and implementation of data-driven business processes.

I'd rather read the transcript of this conversation please!

Intro - 00:00:02:

Datamasters 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 Datamasters podcast with your host Anthony Dayton, Data Products General Manager at Tamr.

Anthony Deighton - 00:00:39:

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 CDO-IQ program, shares his knowledge as a guest lecturer, and as a guest lecturer in this Chief Data Officer program at Carnegie Mellon 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.

Randy Bean - 00:01:39:

Hello Anthony, delighted to be here, nice to see you again as always.

Anthony Deighton - 00:01:43:

Yes, we wanted to talk about data and analytics trends for the coming years. And we will do that towards the end. I thought as a kind of foundation for the conversation, it might be fun to start by talking about your book, Fail Fast, Learn Faster. I can see a copy in the background. And 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, 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. What gets in the way?

Randy Bean - 00:02:33:

Absolutely, and thank you for having me today. There's no doubt about what the number one blocker is. You know, we've been conducting a survey of Fortune 1000 data leaders for a dozen years. This year will be the 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, and change of processes. Those things are never easy. Ninety percent 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 data-driven to leverage AI, they have to change a lot of their processes and they've been very successful serving the customers that they serve and producing the products that they deliver to the market. 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.

Anthony Deighton - 00:03:37:

So another big piece of the book, which will, I think, connect to a later conversation we can have around AI, was around ethics. And I thought it was really interesting that you even included ethics as a topic in the book. It was probably a bit ahead of its time in a way. But maybe, 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?

Randy Bean - 00:04:06:

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 operational efficiencies in their business. But they often don't give sufficient consideration to what are some of the ethical risks, and that includes everything from data privacy to built-in biases in AI algorithms and various types of algorithms. And so there's an increasing risk because of the information that's gathered on customers of an organization. One of the books that I mentioned in the AI chapter is called The Surveillance Economy. And the point is that the data collected on you is such that 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 cautious way with the data. Guideposts and standards and policies and one of the things that the survey shows each year is that Data leaders say that only 40% of the organizations have established any type of 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 working on an AI privacy and ethics structure for several years now. But for most companies, it presents risk and exposure. And that's one of the things that can hurt a company more than anything else.

Anthony Deighton - 00:05:49:

I think that's a really interesting point. Organizations often think about these linear measures like profitability or growth or cost structure which don't change very much if you're doing a world-class job. You're growing at 50 to 100 percent, but most organizations, especially the bigger ones, are growing in the 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 could go from being in business to out of business, or you could go from being a normal business to being at the sharp end of the U.S. Government in the course of a lawsuit. Meaning these are like big risks that could actually cause the end of your business.

Randy Bean - 00:06:35:

You can give you a classic example. 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 Wells Fargo Bank, unlike the New York banks, was situated in proximity to Silicon Valley. So a lot of mindsets and innovation filtered into Wells Fargo. I first started working with Wells Fargo about 2001 and they had basically, it was the internet and data group and it was really at the forefront. They were very good at bringing 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 the online channel. 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-sell ratios by far in the banking industry. As a result of that, for a period of time, with the largest, most highly capitalized in terms of market capitalization of major banks, so they grew substantially. What happened was that a few folks within the organization took it to the next level and they started signing people up for products without their permission to continue to boost those cross-sell ratios. As a result of that, there was a scandal and as a consequence of that, Wells Fargo has been under intensive regulation for the past decade. You can be 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 all down very quickly.

Anthony Deighton - 00:08:13:

Exactly. And so accounting for those existential risks is something, I mean, people do it poorly in our personal lives, but businesses also do it very poorly. So over the course of the last 12, 18 months, your writing is increasingly focused around AI specifically versus data more generally. And exploring a lot of those sort of opportunities and risks that AI presents. 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 and analytics. So one thing I've been asking folks on this podcast is whether they think JNAI is a big deal or if it's sort of overblown. I think in this case, the answer speaks for itself. 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.

Randy Bean - 00:09:12:

Yeah, well, first and foremost, it's important for me to say that I tend to be a technology skeptic. In other words, 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're coming to wide mainstream adoption. And so, you know, when I first started hearing about generative AI, I was highly skeptical. That was the approach that I took even before that, a year or two ago, data products. When I first read that, I was like, oh, is it just data fabric, data mesh, you know, data democratization? Until I started hearing from it for a wide range of organizations and came to understand the power of it. So generative AI was, you know, I had the same mindset around that, but I can tell you this. Last week I went to the The Wall Street Journal tech live event in Laguna Beach. It was the second time I went a few years ago and it was all about AI and it completely changed my perspective. What I was telling people 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. And then they'd say a few things and I'd say, but 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 The Wall Street Journal tech live event last week. They had folks like Sam Altman from OpenAI, Vinod Khosla from Khosla Ventures, 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 general capability within the next two or three years. Some of the 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 extraordinary. Our quantum supremacy, this notion that AI can complete a calculation in seconds that would have taken the conventional computer 10,000 years. So the reality is that I went into the event last week as a skeptic. I came out kind of mesmerized and terrified because I think the reality is that in one form or another, it's going to come about very quickly, quicker than we expected. And that organizations need to be prepared at least in terms of... What that plan is, what their godrails are. Ethical issues, 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 to start thinking about it immediately.

Anthony Deighton - 00:12:02:

Yeah, and that very much connects to the prior conversation. Although, interestingly, I think there are risks on both sides of that ledger. On the one hand, there's an existential risk associated with over-adopting, if you want to say it that way, generic technologies and doing something that puts you at risk for customer backlash or a regulatory backlash. On the other hand, there's an equal risk that ignoring it and thinking it's not a big deal could put your business at risk of being disrupted and eliminate it very much. So as a CEO, you face risks on both sides of that ledger.

Randy Bean - 00:12:37:

And there's another aspect to that which I didn't mention, that is the impact on jobs. So for example, One of the questions that was asked at the event last week was, what if a large majority of white collar tasks can be performed more effectively using AI? And, you know, Sam Altman from OpenAI stated, every technology revolution affects the job market. That's the way of progress. We'll find new and better jobs. You know, Vinod Khosla said, AI will replace 80% of 80% of all jobs within 10 to 20 years. And they're really talking about what they call white collar cognitive manual labor.

Anthony Deighton - 00:13:16:

Yeah, and again, a trend or an experience that we've had as a society many times before some new technology comes along and changes the kinds of work that people do on a day-to-day basis. However, I think the interesting idea there is that... We're talking about white collar cognitive work and not manual labor. And maybe for the first time, we see the introduction of a technology which could affect the CEO themselves.

Randy Bean - 00:13:48:

Well, you know, that's very interesting because just after I got back from this event, I had lunch this week with Boston Hotel Year. So this person runs a series of hotels. And he was saying, and he's not a technical person, and I, by historic background, 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 automate so many of the back office functions. 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 labor, 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 at the The Wall Street Journal event, a robot going around after dinner with different types of popsicles for people to choose.

Anthony Deighton - 00:14:43:

So I was going to ask you a little bit about that as well. This distinction between, or a lot of your writing has been around how CEOs should think about generative AI 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. They're not the CEO yet. Perhaps they have ambitions of being the CEO. 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 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 just starting it up as a junior engineer at a tech company, how should they think differently about this?

Randy Bean - 00:15:34:

The young entrants actually are already so far ahead. As an example, this summer I had the privilege to go on the tall ship U.S. Coast Guard Cutter Eagle from Boston to Maine for three days. And the reason why I went was I had given a class for the chief data officer for the data and analytics organization. And as a reward for that, I guess they invited me to sail with them on the tall ship and in turn do a fireside chat with 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 U.S. Coast Guard Academy said, how many of you are using Chat GPT and virtually every hand on the call 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 because that, you know, there may be some CEOs that 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 start to focus their attention on it. So that's kind of part of the message. And this is not just some tool like social media or TikTok that young people are using, it can have a significant impact on your business and you need to start to become aware and set up groups within your organizations and policies and practices to determine what is the power and what is the risk and what are the activities within your organization that can be applied to realize the benefits that can be achieved and also mitigate the downside.

Anthony Deighton - 00:17:14:

Right. So 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 technologies comes from these groups of people that have really burned it into their day-to-day work on a daily basis.

Randy Bean - 00:17:30:

Yeah. I mean, you know, 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 say, well, you know, maybe this is, you know, seen this movie a thousand times and this will peter out, but 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 then you're not just doing business as usual and living in the dark ages and refusing to adapt and evolve as an organization. But you need to be selective. You can't chase every new idea. But this is one that. 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 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, 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.

Anthony Deighton - 00:18:51:

Sure. I'm glad you brought this question up about training, because it brings me to maybe this feels like a very minor point. But I actually think this is one of the more interesting elements of this generative technology. Is that 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 results. We like to use ChatGPT, or use OpenAI's APIs, or Google Vertex AI. And we love the output, but we don't think about what went into training and what biases that training data had, where there's actually missing data. And to your point about CEOs 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 what 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 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?

Randy Bean - 00:19:57:

Well, I don't have the magic wand or the magic answer, but clearly, data quality is more critical than ever. So all of the practices and processes that you need to put in place to ensure data quality become even that more critical.

Anthony Deighton - 00:20:14:

And again, those ethical questions. So making sure you're looking at these sources of data and looking for bias is really important.

Randy Bean - 00:20:21:

Yeah, and you know, there's so many examples of those from real estate lending based in various communities and the history there. Identifying those biases, mitigating those biases, standardizing the data in ways that are normative and objective as opposed to reflecting those biases will go a long way.

Anthony Deighton - 00:20:45:

So maybe to take a contrarian view here, my view is this is actually a great opportunity. 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 less biased or look for sources of that bias and eliminate it. You think about, and you use the example of lending, lending adjudicator, people who make decisions about loans are, by their nature, very human and therefore can make very biased lending decisions, which we can all agree is not good from a society's perspective. It's also not good from a profit perspective, so you're missing a lot of opportunities. 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 view?

Randy Bean - 00:21:45:

There's a lot of obvious examples, and I don't think I'll touch those with a 10-foot pole on a recording, but they're so obvious. But Mustafa Suaman, who has written this book, The Coming Wave, and spoke at the event last week, 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 healthcare, increase education. And he pointed to, in spite of all of the problems in the world today, when you look at these statistics on a global basis, people are living longer, eating better, more educated, all of those things. But at the same time, he also cautions, quote unquote, well, he says, yeah, we're going to live in an epoch where the majority of our daily interactions are not with other people but with AI and that there's many unintended consequences with that. And he kind of closes with this quote, will AI unlock secrets of the universe or create systems beyond our control? So it's really incumbent upon us as human beings and business professionals and data professionals to, especially knowing what we know to do everything that we can to safeguard the uses of AI to ensure the quality integrity of the day that that goes into these models. Again, it's easier said than done. But over the past 20 years, there's been such an advance in data quality, data cleansing capabilities that hopefully we're moving in the right direction.

Anthony Deighton - 00:23:24:

Absolutely. So let's shift the conversation a little bit to your point about the future, and let's talk about the future. And you mentioned this at the beginning, that New Vantage Partners does an annual survey to senior data and analytics executives. You did this most recently in January of this year, 2023. So I wanted to talk about this in sort of three bits. First, you did the survey roughly a year ago. How'd you do? How are the predictions? 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 let's see. And then really think about the long-term, next five to 10 years. But let's start with 2023. You did the survey, you made a set of predictions. What did you get right? What did you get wrong? How'd you do?

Randy Bean - 00:24:12:

First of all, going forward since last year into this year, now it's the Wavestone survey since Wavestone acquired and moved into partners. I wouldn't call it so much predictions as it is tracking the progress that's been made. And it's a mixed bag. So for example, One of the series of questions we ask is about the progress of data and analytics aspirations. So we asked, are you driving business innovation with data? This year, 59.5% said yes. But actually in 2019, 59.5% also said no. And then up and down a little fluctuated. And that's the best the results. 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 a decrease. Are you managing data as a business asset? This year, 39.5%. Five years ago, it was 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 a data culture? 20.6% said yes, down from 28.3% five 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. There's many people that are now responding to the survey that 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 chief data officer or a chief data analytics officer. And this year it was 86%. So... Many of the people that are answering the survey now, they're one year, two years, three years, four years into the role. So the point in all of that is that progress is slow, progress is gradual. It may be disappointing to some, but the great news as illustrated by the appointments of chief data officers, the need for data and AI leadership is only growing. That's not going away. It's only increasing. And you think about it, this is a relatively nascent job. 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 if you go back a generation 35, 40 years, when the chief information officer role was first established, the inside joke was CIO stood for career resolver. So even though there's short tenures, 24 to 30 months, and some would say they're shrinking, this is all to be expected. And over the long term, I think that organizations are going to become more data-driven. They are going to leverage AI in their business. These things will become second nature. And it's worth mentioning that the digitally native companies, the Apple, the Amazons, the Googles, Facebook, whatever they're called these days, these organizations don't have chief data officers. 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 from the processes and ways of doing business that they've operated with for decades and generations and trying to be more agile in terms of their use of data and AI capabilities in their businesses.

Anthony Deighton - 00:27:36:

Got it. So if we cast our eye forward to next year, and to your point, progress is slow and steady and sometimes even backwards. Are there a couple of things you expect to change next year? Are there sort of big shifts that are right on the horizon that listeners should be kind of keeping in the front of their head? Like these are things, you know, the big signposts that don't miss this one, the things you're tracking, they people really should watch out for next year?

Randy Bean - 00:28:05:

Yeah, I'd say two things. One thing which has manifested itself this year is really a change in expectations of what the chief data and analytics officers should deliver. So because of economic uncertainty, which could continue, there's been a huge focus on delivering measurable business value. And as a consequence, close to half of the Fortune 1000 chief data officers that I knew at the beginning of the year are no longer in their roles for a variety of reasons. So there's 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 officer 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 for panelists. 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. Again, these roles are fluid and evolving, but I expect they'll become even more fluid and evolving over the next 12 to 24 months, and that's not a bad thing.

Anthony Deighton - 00:29:37:

Yeah, that's great. So if I just sort of. Summarize that a little bit, CDOs need to get closer to the business and away from IT and really think about their frame their job in the context of business value and away from technical implementation. And number two is that organizations must have some strategy for locating decision making authority around gen AI, whether that's with CDO or not. Maybe we might, I don't know, I don't wanna put words in your mouth, my bias would probably be towards the CDO since it's largely a data-driven challenge.

Randy Bean - 00:30:13:

Yeah, it's great to build and create capabilities and those are often necessary for the long term but at some point you need to show me the money as they say. You need to show the value. You can't say, you know, we're in year 10 of building a data warehouse except for now it's called a data lake and now it's called a data fabric. You know, that's so often from the business leaders I hear, oh, not another data project. And often when I meet with the CDOs or the CIOs, they talk about the capabilities, the engineering, the architecture. 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 organization like Jamie Dimon at J.P. 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 give them all a round of applause as opposed to the CDOs that I hear from that say, you know, we've created all those great capabilities and nobody appreciates us. You really have to have that length, that sponsorship, the relationships with the business to have that credibility. And as you build upon that credibility, you can start to establish some momentum and some meaningful breakthroughs.

Anthony Deighton - 00:31:33:

Yeah, I think that's really sage advice. So maybe to bring us home, as they say, casting your eye forward a long way into the future, five, 10 years, which admittedly, totally unfair question, but here we go. Any predictions for where you see data analytics, big data, Gen. AI, general artificial intelligence going? Is there something that you want to put a stake in the ground for where we are five to 10 years out?

Randy Bean - 00:32:05:

Well, if you listen to the folks at the The Wall Street Journal event, basically you'll be kicking back in your armchair, you'll have the robots bring you all of the data information, most of the rope tasks will be solved and your job will just be to do the big thinking, the creative ideation. So that's what they're predicting. So we'll see what happens.

Anthony Deighton - 00:32:28:

Yeah, well look, that's actually not such a bad vision, because it puts listeners in charge of creative tasks over boring ones. And presumably the kinds of things which bug us on a day-to-day basis that are boring and repetitive are gone away, and leaving us more room for... Either enjoyment and relaxing activities or creative activities. Will all be little CEOs. Not a bad vision or future. Hey. Randy, thank you so much for joining us on Data Masters. It's been a pleasure.

Randy Bean - 00:33:00:

It's always a pleasure, Anthony. Nice speaking with you today. 

Outro - 00:33:10:

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