Marc Vollenweider is the CEO of Evalueserve and has spent over 15 years guiding Evalueserve to become a global research, analytics and data management solutions provider. This is the second time Marc has appeared on the podcast; you can listen to his first interview here. Marc has recently written the book titled, Mind+Machine: A Decision Model for Optimizing and Implementing Analytics, which Mark and Marc cover on this week’s show. Some of the other topics covered in this interview are:
● Marc’s transition from being a McKinsey partner to founding a business employing over 3,500 people.
● The winner-takes-all characteristics of the markets Marc plays in, and his strategies to go after these markets, are detailed in his new book Mind+Machine.
● The counter-intuitive benefits arising from simplification and automation.
Mind+Machine Strategies That Enable Corporations to Develop New Innovation Capabilities, and Make Better Decisions Faster
This is Mark Bidwell. Welcome back to the Innovation Ecosystem. With me today is Marc Vollenweider, who is the founder of Evalueserve. Marc, you were on an earlier show several months ago which got extremely good ratings. So, welcome back to the show.
Thank you very much, Mark, for having me today.
When we last came together, we talked about your book, and that will have been released when this show goes out. But, before we get there, there’s a few things that some of our listeners were interested in finding out more about from our earlier conversation. What I’d love to do is a couple of things. Firstly, you were a partner at McKinsey, you were running their offshoring operation, I think, originally, in India, and then you’d made the transition to set up Evalueserve. That’s the right biography or the right timing, right?
That’s correct. I was a consulting partner in India and then also ran the McKinsey back office center, the so-called “knowledge center” in Delhi as a side responsibility and then fell in love with this idea, and then sort of left McKinsey and started Evalueserve from scratch as a Greenfield Venture.
That’s the bit that I wanted to start on because McKinsey, fantastic organization, great place to work, enormous learnings, and that model worked well and is proven, as is the back office, but as you stepped out into the big wide world of doing this on your own, I’m really interested in this journey from executive to entrepreneur. What were your key learnings as you went out to set up this business?
I always wanted to become an entrepreneur. So, leaving McKinsey at the partnership level is, of course, pretty tough because you’re on a cushy salary. It’s very interesting work, and it’s, of course, global in nature. So, there you go from the high sort of global brand to no brand startup, no office, no employees, no clients, and only your own money to invest in. That was, of course, a tough experience as you can imagine.
You had dependents as well. I think you’ve got quite a large family as well, haven’t you, Marc?
That’s correct. At the time, we had already three children of ages 6, 5, and 3, and one was in the pipeline, so it was not totally without risk. But, I think one of the biggest learnings in this was that my wife supported me all along, and this was probably one of the major sort of success factors in all of this. You can very well say that this was one of the key points.
Wonderful. I think one of the things about the story was how quickly you were able to scale the business, but also, I think you made some decisions earlier on around not taking on business until you had the processes in place, and then you were able to scale really quickly, which is very rare because it’s not just a software business. You use software a lot, but can you just explain what was your thinking, because it sounds like a very systematic, very analytical approach to building a business, which might have created some tension, given the emotional challenges or the stress with setting up your own business as well. How did you manage that?
Yes, in hindsight, it probably looks a little more professional than it was at the time. When we got into this, we thought that the world would come and ask for our services, which was unfortunately and totally surprisingly not the case, which was also one of the major learnings. I think even at McKinsey, you don’t know everything, so that’s really good. When we left McKinsey, we started out on the premise of what is called geographic cost arbitrage. You hire young, eager, and very smart people in India and they will do very smart work for Western companies, and of course, there’s a lot of cost differential. We did that, but then we learned very quickly that there was a so-called “double chasm”. It was like the model was completely new, so the clients weren’t quite sure of our abilities, so they would say, “What? You want me to outsource my strategic research work?” That was chasm #1, and then chasm #2 was, “Oh, you want to do that from India with these dial-up modems that, at the time, still made that strange noise when you are connected.” We had to educate the markets a lot, and initially, we would have almost gotten bust. We only became profitable after 14 months, and before that, had several moments where our cumulative cash flow curve, or the money in the bank was down to $50,000 down from our one million of invested own capital at the beginning, so it was close.
However, we honed in on the key processes that worked. One was market research and business research, and the other one was intellectual property services, so patent searches. So, we started with these two and then got going and really sort of made sure that we had the processes in place, got our first success stories. Very small projects initially, but we got recommendations and then it grew very rapidly because we had already established the backbone, which could then be scaled rather quickly, and I think this is one of the main learnings that, in order to scale, one needs the processes and the backbone in place to really do that, otherwise you just multiply chaos, which is not necessarily conducive to good quality at the client’s end.
That led to a very fast growth curve of, sometimes, 100% per year. But, we had started from zero, so initially, these growth curves were, of course, rather easy to create. Later on, it became harder, but then we expanded into new types of services. So, for example, the whole financial services industry opened up to us with equities research, with investment banking, with index processing, and then on the other side, we had procurement-related services, like procurement intelligence, strategic intelligence, and increasingly, a lot of data analytics.
The growth happened, one, because of geographic expansion and new clients, but secondly, also because we had new service lines that we added. But, all of them had kind of a similar backbone, similar process in place. That’s how we grew.
Last time, Marc, we talked about a lovely example of how you helped to automate the pitch book development for some of the investment banks, and it struck me. You know, one of the big themes on the Innovation Ecosystem podcast is that in order to innovate, you’ve got to create space. You’ve got to actually simplify and get stuff out of the way so that you can actually begin to get into some of the bigger opportunities, or explore some of the bigger opportunities in front of you.
I’m really curious. The example that you gave a few months ago – of taking the pitch book development process off the plates of a lot of these investment banks, can you just give us an example of what were the banks able to do as a result of you taking that on and simplifying and standardizing. Because, obviously, it enabled them to generate or leverage new capabilities, essentially. Can you give us some examples of what they were able to do as a result of that space being created by you?
Yes. So, the banks had one fundamental problem that all the work was still manual. So, it was slow, it was error-prone, and it was complex when you look at how the managing director of the bank would interface with his or her associates who would then work together to create the pitch book that ultimately would lead to the client meeting. Of course, that would always also happen on Sundays, Saturdays and Sundays, because that’s normal working hours for the bankers. But, all of that, bringing everything together was very complex.
What we did was we tried to simplify the workflow, and that led to a series of effects. First, much higher productivity. So, the bankers, supported also by our teams, of course, could get the job done in much fewer hours of work. That’s point number 1.
Number 2, we were also able to speed up the process. If you look at our latest version, which we didn’t have at the time when we spoke last time, now it’s a proper platform called Cogito. So, Cogito is now a complete workflow system that doesn’t just do — sort of doesn’t just provide an interface to do the research, but it also organizes the whole workflow of a pitch book from A to Z, and it then also provides the knowledge management. The whole process of putting everything together becomes much easier. The MD can allocate work to the various associates. Work packages come together again, reviews can happen, and everything, therefore, becomes much more efficient.
A second point which I think is also important is that we have now created centralized assets, which are client independent. So, say we have an asset now of 40,000 clean logos that is always clean, always up-to-date with the latest changes in corporate identity and so on, then that leads to a much lower number of changes, or corrections, that need to be made in the final pack, so everything becomes faster. Also, things like APIs, or these are standard interfaces, the databases that go and fetch the data and pre populate the documents already, all of that leads to more speed, and then interestingly, we also hire quality. On top of it, it creates also new capabilities that the clients didn’t have before. For example, one surprising thing that happened was at the time when a few bankers, there was some fatal cases of being overworked in London —
Literally fatal, right?
Fatal. People died, yes, probably because they were overworked. But, that led to a shockwave in the whole industry, so Saturdays are now off work. In order to police that, the clients started using our workflow systems as a compliance tool, which was a totally surprising and unexpected side effect of things. So, I think there were business benefits of such because things were faster. They were faster to the client, the quality was better, but on the other hand, there were also side benefits in terms of overall management and better analytics of how the process works.
I would say the learning from that is it’s probably a little bit like what happened with the iPhone. It’s very hard to simplify, but once you do it, you address a completely new market and you create opportunities that you didn’t know of before. I would call this the option value of simplicity, which is very, often, counterintuitive, and even for us, very surprising.
This is fascinating, because one of the things — I haven’t created pitch book, but I’ve worked with people creating them, and I know it’s pretty painful work. So, you’re liberating them from the pain of the work, and also the time associated with it, but there is this option value of the simplicity, which means, I guess, that people are able to do something else with their time and come up with new ideas to progress the business of the bank. Any specific examples of how that’s worked in your client base?
I’ll give you another example of our platform, Insight Bee. Insight Bee is essentially a pay-as-you-go on-demand research platform in the cloud. We’ve essentially packetized the size of a McKinsey knowledge center into a mobile phone, and you as the client can order research in a few seconds via an ordering interface that looks a bit like Amazon, and you can get customized research that specifically answers your question and your requests, and this in a pay-as-you-go fashion.
Now, we started out with a general market intelligence version of it, but now we can have many more, and one of the hottest ones right now is the procurement intelligence version. Now, what does it do? Indirect procurement managers need a lot of research for analyzing their suppliers and their risk profiles. So, by using partly artificial intelligence and APIs and big data tools, we can find and compress the data already very early on, and then the human would come and synthesize the findings into insights, and then push it out via a cloud-based interface to the clients.
For the clients, this means much lower infrastructure costs because they don’t need to keep this all in-house. The knowledge management happens automatically, so they find stuff much more quickly. Then, the alerting function is something completely new in research. So, people are getting alerted to changes, or flags, for example, when a supplier gets sued, when there is a major issue, for example. I mean, there will always be the scare stories of Volkswagen for example, but it would be smaller issues like storms in Thailand and damaging the supply chain, or hard disk production, for example. So, these are things that lead to capabilities at the clients’ end which they didn’t have before, so they can inform their supply chain much earlier that some things happen in their supply chain, and therefore, the risk of being out of stock for the sales force is much reduced.
There are new ways of doing things that, at the same time, lower the cost, but also increase the service levels. I think that is the learning we have. It doesn’t mean that increasing service level means more money to be paid. Actually, to the contrary, the simpler things are and the more targeted these analytic use cases become, the higher the utility and the lower the cost. So, again, this is a counterintuitive finding.
Interesting. This is such a big theme, this idea of simplification because it does seem like organizations have become, partly because of technology which has become overwhelmingly complex, and what you’re doing, essentially, is bringing technology, which is part of the problem actually, bringing it back into and making it part of the solution and creating this space for people to think differently about their business, and also, as you say, create this optionality.
The last time we talked, Marc, you talked about the enormous amount of regulatory challenges hitting the financial services market, and we talked a little bit about disruption and how, I think you said, it was one client that had more regulatory changes that needed to be implemented, then they had employees. Then, we got into the conversation about disruption in the banking industry and everyone running off to FinTech startups. You work in multiple verticals across many industries. Where else are these sort of seismic changes going on from a point of view of disruption or a point of view of market changes. Which other industries are you seeing? Professional services, for instance. Where are you seeing the big changes, but also the big opportunities. This is really addressed to people either who are in those industries or who are thinking of getting into those industries, who are listening now.
Yes. I think there were a series of areas, and my list is certainly not complete, but I’ll give you a few examples. Let’s pick professional services to start with. I think the consulting industry is undergoing quite a bit of change, and as an example, I would like to give the topic of data analytics. Many of the consulting firms, the very big strategy firms, but also niche players that, for example, work in the risk space, they have been doing engagement-based consulting for a long while. So, you have a three-month project solving a particular topic in risk. Say, an IFRS 9 implementation. But then, their cost structures are such that they can’t support the organizations on an ongoing basis going forward, and I think that is changing now. Many of the clients not only want the consulting. They also want support during the implementation, and not only that, they want continued support on a delivery basis, essentially forever.
What we are seeing happening is that many of the big consulting firms and these niche players are looking for delivery partners. That’s, for example, one area where we see a lot of traction, so we serve these consulting firms as a delivery partner, we put in technology, we build these use cases, we knowledge manage these use cases, and we can then deliver the ongoing service at a much lower costs. This is not, in any way, competitive. This is completely symbiotic, and I think that’s the interesting part. So, there’s, I wouldn’t call it necessarily disruption of the industry, but it’s new trends that open new possibilities because the client world is changing dramatically.
If you look at the revenue streams the client is spending or the cash-out for that kind of work, I guess, in the old days, it was very lumpy. You’d have your guys, McKinsey, for a period of time, and they’d go back to steady state. Am I right in thinking this is more of a software as a service sort of cost-basis?
Yes. So, we should also look at the commercial models that are changing. Initially, one started out with the so-called FTE model on our end. So, you hire a team, say a team of five people, on a permanent basis for a certain period of time. Now, this is changing. We see new models emerging. One is pay-as-you-go. So, for example, in our Insight Bee platform, the clients pay by consumption. So, if they ask for additional research, they pay for it as you go on a fully variable basis, which of course, increases their flexibility a lot.
Then, on the other hand, you also have what we call pay per unit. So, say you’ll pay for a report, you’ll pay a fixed price. That, I think, has several advantages for the clients. I mean, it’s, of course, more flexible. But, on the other hand, it also incentivizes the supplier to reduce the per unit cost of whatever product, and therefore, they become more efficient at it, and therefore, they lower the price. So, by going into these kinds of models, I think it’s a win-win for both parties, but of course, and that’s clear, it will be a winner takes it all situation for several products. Because, once you cracked the machine learning on a certain analytic use case for a certain product, once you’ve cracked the neural network problem, and once you have all the APIs, it becomes a winner takes it all situation, because then you sit on a lot of intellectual property, and that then helps you protect your particular use case. But, as there are millions and millions of use cases, it’s not that bad. So, there are lots of opportunities for players to come up with these kinds of models.
That’s very interesting, the winner takes all, the platform play where the barriers to entry, or the moat that you’ve built around your business just gets bigger and bigger, and more and more unassailable, I guess, from the competition. If you look at the future of your businesses, where do you see the biggest opportunities? Where are the winner takes all kind of spaces that you’re going after?
Without giving away the strategy of Evalueserve too much – I mean, to a certain extent, it’s described in the book – but, basically, the point is that we believe that there are one billion analytic use cases out there. By segmenting these into the ones that have return on investment and the ones that don’t, we of course, focus on the ones that have return on investment, and we see a few mega trends affecting that.
For example, cloud and mobile is certainly going to stay for good. This is not a hype. This is reality, and we see that whichever use cases will deliver, will deliver them in the future over cloud and mobile so everything is automatically cloud and mobile enabled. Which means that we’ll have lots of synergies in various places, say on the marketing side, sales side, delivery, knowledge management, customer acquisition. We can address completely new segments of clients. For example, smaller clients or subsidiaries of companies that are in geographies we just simply could not access with our sales force. That leads to market expansions. That’s a mega trend.
Then, second, there is, of course, all these hardcore data analytics use cases around the current data sets, but also new data sets. So, when you look at, for example, IoT, Internet of Things, or Industry 4.0, of course, these are, of course, slogans by the marketing departments of the big players, but it’s reality. Now, lots of data sources that need to be analyzed, lots of use cases need to be knowledge managed, and nobody has cracked that yet. We believe we have a platform for managing and knowledge management of analytic use cases, that, for us is a very big push.
Then, we have, of course, the topics that we’ve talked about before. In financial services, there are a variety of areas like risk, like banking support, like cross-selling analytics. So, there’s a series of use cases over there. Then, on the innovation side, more on the patent/technology side, there is a lot of technology monitoring, innovation scouting going on, and we capture that. Then, there is, of course, the whole space of monitoring. So, I give you a very specific — monitoring and flagging and alerting, I would say.
I’ll give you a specific example. For Chinese luxury markets, we analyze language, Chinese language that is being used in e-commerce websites, or social media, or other types of sources, collect this information, draw conclusions from it, and then package it so that, say, the equities research analyst for LVMH in Paris would then have a view on the Chinese luxury goods market, even though he or she doesn’t speak Chinese and gets it delivered it to them on a more or less live basis.
These are new opportunities, and of course, the whole procurement intelligence space for us is a very important one. I think, currently, our procurement intelligence version on Insight Bee is cracking major clients almost every week, so that’s almost disruptive. As you can see, our future lies in very targeted point solution for relatively big problems or big analytic use cases, and then we can scale up. I think this will be supported, of course, with a slightly less specific group of research analysts who are going to support slightly less structured use cases. I think the mix between these point solutions, based on workflows, and AI, and so on, plus human talents sitting on top, and this on a global scale. That’s really what we’re looking for.
You touched on the book, because the book that you’ve written, “Mind+Machine”, which will be out by the time this show’s out. What was behind writing the book? Because, you alluded to one of the big challenges is that if you’re moving so quickly in a winner takes all market that you’re going after then, one wouldn’t want to put too many secrets out there. So, what was behind writing the book, and how have you managed that? Because, it’s also a complicated subject, but you’ve been focusing on trying to make it more accessible to executives and middle managers who don’t have the same technical knowledge base that you’ve got. How have you handled those two things, Marc?
Indeed. I was approached by Wiley, a big publisher in the U.S. a year ago, and they were looking for somebody to write a book about Mind+Machine. Now, as it happens Mind+Machine is our company strategy, and their research department found us, so they felt there was a need for evoking the space that would simplify or demystify the world of analytics to somebody who either needs to make decisions on analytics, or needs to benefit from analytics, or is maybe the part of creating analytics. I then started writing this book in March of this year, 2016, and it became clear very quickly that I needed to simplify a relatively complex topic.
I think the key point there is that, currently, there is so much noise out there regarding big data, artificial intelligence, and basically everybody is saying, “Oh, IBM Watson is going to run away with all the work and the humans won’t have any work left,” and I think what we see in our daily work is that this is not the case. In fact, we see it’s still very much the mind, the trained mind, who is able to come up with so-whats and insights, and support it by the machine, which is very important. But, the support of the machine happens more at the data level at the simple data layer.
Already, a little bit more complex topic, I’d say, the information layer, or the insight level, there the machines fail rather miserably at this stage, at least. I’m not sure how that’s going to look like in 20 years from now, but I felt it was important to let people know, who are not specialists in the field, that they are not wimps just because they are not using AI every morning. Sorry for the language, but I think it is important to understand that many people, many managers, feel overwhelmed by this marketing language, and one of the findings from the book is that only 5% of all analytic use cases are actually about big data. Everything else is small data, and that’s why I coined the term, or the sentence, “Small data is beautiful too,” which usually gets me a chuckle when people hear this.
Also, in artificial intelligence, we tested 25 artificial intelligence engines, and only one of them, which was Squirro of Switzerland, was good enough to help us improve our process. I think I’m slightly more skeptical on how quickly artificial intelligence is going to change the world at the insight level. Of course, at the data level, like Google has shown, it works very well. Overall, I think what they try to achieve with the book is to write almost a guide for the generalist business manager who needs to understand this vast environment of analytics. Mind+Machine needs to make decisions, but in a language that is not driven by statistical terms, or everything focusing on big data. It’s more around how do you manage analytics in your company? How do you create ROI from it? How do you manage the complexity of all these analytic use cases by managing portfolios of analytic use cases, and I give a methodology on how to do that.
What resonates most with people who are kind of fresh to the subject, if you’d like?
Yes. I think, first, they love that not everything is big data and AI. They love that. The second one is they also see the organizational aspects of the whole topic. In fact, there is a chapter in the book that people really seem to like, which talks about the psychology of analytics, because everybody thinks that analytics is very rational. But, in fact, when you look at what’s happening in organizations, it’s everything but rational.
It’s about power, as usual, I guess.
Exactly, and they’re the nerds and the anti-nerds who can talk to each other, and their return on investment of analytic use cases doesn’t work out, and workflows systems are not in place, so the user interfaces are bad, so therefore, the end user who actually needs to make a decision based on some information coming from analytics doesn’t have the right information at the right time in the right place in the right format. I think this was the first sort of feedback that I got that people said, “Oh yeah, I can see that every day in my company.”
The second feedback was, I think they appreciate that the analysis of the trends that affect the whole area and that they are very good news for them. For example, cloud and mobile, or Internet of Things, or user interface design or workflows. On the other hand, I think highlighting some of the risks, like data confidentiality, is just experienced again by the IoT denial of service attack that happened two weeks ago when Netflix went down. I mean, there are risks, and people, I believe, are not aware of the very significant risks they are getting into. So, I think raising the awareness of what’s going out there and what are the risks is important, especially for the non-specialists.
A banker, for example, whose junior ED, or engagement director, is dealing with a neural network for algorithmic trading, if you don’t understand what this neural network does, and there is, of course, no audit trail for most of this, then you just simply don’t know what happened when the trading results went sour and you lost $10 million on the account on the trading desks. So, I think raising the level of awareness is important. That’s appreciated.
The third thing that people came back with and was saying, the third part of the book, which essentially describes the methodology is a very prescriptive rule-based approach where people can use it as a reference guide to tell their people, “Have you checked for user interface design? Have you checked for these risks? Have you done this? Have you done that.” So, it’s basically a very useful, almost textbook approach for how to do it, and not to miss out on too many things.
It’s actually relevant — what’s the expression? It doesn’t require you to be working on a big data or a small data project either, actually. It’s kind of you can use it on any kind of intervention in an organization to change things, right? I mean, that’s what I find quite compelling about the methodology.
Correct, yes. It’s independent of what kind of data you’re talking about, whether it’s big or small, whether it’s qualitative or quantitative, whether it’s workflow-based or not, it doesn’t matter. The methodology was kind of all-embracing and focuses on the individual analytic use cases and its ROI. So, it ultimately always comes down to ROI and how it produces it either by increasing productivity, by shortening throughput times or improving time to market by improving quality, or by giving new capabilities. So, all analytic use cases are measured along this, and with 39 examples in it, it kind of gives a good overview of what kind of analytic use cases that are out there.
One of the challenges with putting this down in the traditional book format is obviously by the time you’ve written it, it might be out of date. It’s a very rich book, and as you say, a very broad range of case studies. The area is moving so quickly that I presume that there will be some new revisions coming out pretty soon as the frontier evolves more aggressively, right?
Well, it depends. It’s almost a little bit like math. How has math changed during the last 100 years? For the normal user, math is still the same. I’m not talking about the super specialist user who knows about Riemann spheres and things like that. So, I try to write the book in a way that it looks at the logic of everything rather than looking at the leading edge analytic use case that is out there, but is probably not relevant for 99.99% of our population. So, in that sense, the problems described in there are generic in nature. The trends that are in there, like cloud and mobile, Internet of Things, all of that, I think they’re going to be around for quite some time to come, and the methodology described is independent of any fad, latest fad or fashion in, say, predictive analytics, because you can exchange one tool for the other, and still, the same logic still holds.
I would say it’s probably going to survive. However, what we already see is that we might need to write additions for specific industries, because we’ve already heard, for example, from financial services, or from pharmaceuticals, or from industry that they would like to have specific sort of chapters on each of their industries and see what’s going on in there. While right now, everybody’s happy with sort of getting the overview across all industries.
I think it would be also interesting to see what — I mean, a lot of your clients, you come from large clients, Global 1,000 Companies who you work with, but as you mentioned early on, Insight Bee is targeting smaller SMEs and startups, and far larger client base. It would be really interesting to see what companies have been able to do with those technologies to really accelerate their growth as well.
Correct. In the book, I also allude to the democratization of analytics, which basically cloud and mobile brings along. By having cloud and mobile, there is a level playing field for small and big companies, and in many cases, I would argue that small companies have a big advantage because they’re smaller, more nimble, and more flexible, and by leveraging these technologies, they have the same access to the end clients, and very big companies, and I think this is a big opportunity for the smaller companies to play in this game.
Actually, I think as we mentioned in the last show, the company that I’m involved in, a genomic software company, we use the technology and it saved us a huge amount of time, but it also enabled us to do slightly different things with that data. So, coming back to the whole innovation drive and the ability to simplify to create the space to innovate, irrespective of whether the listener is sitting in a large organization dealing with some big questions around how to manage the data pools that they’ve got, even smaller organizations can benefit from this capability that you’re making available across these different platforms.
Absolutely, and I think this is really good news for everybody because it gives them the flexibility to be able to come up with new products, new solutions very quickly. If anything, the big players need to be, let’s not called it afraid, but at least aware of the smart and young companies around them, and maybe create ecosystems around themselves, like GE is trying to do, for example. The big companies, the smart big companies are probably going to try and create standards and ecosystems around themselves so that innovation can bubble up, but still be sort of in their sphere of influence by having control of the standards.
That’s one of the big themes. We’ve talked about it before. The concept of the Innovation Ecosystem is ultimately the — you know, very few companies have access to all the resources. In fact, none of them have access to all the resources they’ll need to move forward, so being able to connect and collaborate up and down the value chain in a pre-competitive environment as well as head to head with your competition, these are all emerging characteristics, I think, that companies need to get good at. So, it’s almost like the 21st century version of matrix management, is ecosystem management.
Absolutely, and just the existence of your channel, your company that is conducting all these interviews and just trying to bring innovators together is a form of innovation ecosystem, and I think the more we can have these, the better. Procter & Gamble is trying to create that through their Connect organization and does, I think, a very good job of that, and other companies will certainly try to do that as well.
Yeah, absolutely. Well, much of what we’re doing is in direct response to people in the marketplace saying, “This is the need. Can you help us?” So, Marc, just beginning to wrap this up. My final question before I come to the three questions I sent out in advance, we touched a little bit about your life earlier on in terms of your family life, to what extent do you actually sort of simplify and automate your personal work and your personal work life? I’m interested. How do you bring your Mind+Machine together in the Vollenweider household, for instance?
I would like to bring two examples here. The first one I have been working from home for the last 16 years. With four children, and especially during puberty, this is quite valuable, because especially during puberty, you are there when the children come home from school, and this would not have been possible without the use of machines. Having video conferencing, we started using Skype in 2003. Very early on, we were one of the first users, and since then, have of course, grown with it. That was one of the major points of using machines in the household, at least, for the combination of lifestyle and work.
The second one I’m learning from my son, he played one of these distributed computer games, internet games, where you build villages and the villages then stock up resources and then start attacking each other, and you basically try to win as much land as you possibly can. Now, of course, you wonder whether this might be the right type of game, but anyway, given nevertheless, that they’re playing the game, they were not fast enough in coming up the ranks, the global rankings quickly enough, so my son sort of figured out that he was largely playing people who had lots of time, so either people who didn’t go to school anymore, or who were jobless. So, he said he can’t do that. “I have to go to school. I have to work, I have to sleep, I have to eat.” He started building bots that could play the game and then he started building collaborating bots that could play the game together or together with his friends. So, they shot up the rankings and then really were in number 1 position in that they figured all of that out while, of course, going to school. The only thing they had to do was to give strategy instructions in the mornings. But, I think the learning from that is that you can achieve a lot by leveraging machines, even at home, and that frees up time to do more value-adding things together with your family or your friends. Of course, I’m trying to copy my son’s skills now, but he’s, of course, far more advanced than I could ever be.
That’s a great description of what a CEO does, right? Give strategy instructions at the beginning of the day. It’s a very nice analogy.
Yeah, and the other thing is I think I invited my son to come to strategy sessions of Evalueserve for the Insight Bee product because I soon realized after about 20 minutes into these meetings that he was having user interface discussions with the programmers that I couldn’t follow anymore. I think he did a lot to teach us how the modern world looks like. So, including your youngsters in strategy discussions in your business is actually something that adds quite a bit of value.
As it happens, I’m interviewing people to help me on my business at the moment, and so far, most of them seem to be at least half my age, which is quite sobering. But, anyway, that’s the new world, right?
And as you say, they bring completely different perspectives, which are vital for what we’re trying to do. So, Marc, just beginning to wrap this up. Three questions: what have you changed your mind about recently?
I’ve realized that I used to go to lots of meetings thinking that my value was actually in being in these meetings, and that got me to travel 300,000 air miles a year and exhausted me quite a bit. I think what I’ve realized and changed now is that maybe my value is more in the looking after innovation, looking after a few key clients, and then writing the book for Evalueserve, so that’s really what I’ve changed. I think everybody’s happy. Our management team are happy because they don’t have anybody messing around with their business units anymore. Second, they see a lot more traction coming in on the innovation, so I hope that’s good for everyone.
It’s a really nice example, because I use the language, “As a leader, do you create space or do you take up space?” and many people, as leaders, think that they need to run around and be in all the meetings, but actually, all they’re doing is taking up space versus the new model, which is emerging, which you articulated beautifully there, which is actually about creating space for people who you’ve hired to get on and do this stuff, right?
Second question: what do you do to remain creative and innovative?
I tend to be most creative when I’m doing something that has nothing to do with work. I have a couple of ideas for new books, I have also an idea for some interesting venture that I would like to start at some point. I think the idea is really that, while jogging, while running, while hiking in the mountains or skiing in the mountains, I have my best ideas, and I think I’m going to continue that. It’s a bit of a structured way of thinking, but sometimes it’s pure serendipity, and for some reason, these ideas just pop up. But, again, it means, coming back to your point you just made before, you kind of need to free up your mind in order to be able to do that. It doesn’t always work, but when it works, it’s actually pretty cool and you get lots of new insights.
I’m exactly the same. I can fill a page of notes when I come back from a run, just because for whatever reason, you’re not explicitly thinking about things, but it just flows naturally and it’s creative, and it’s a key source of insight for me, or inspiration for me anyway.
Exactly. I’ll give you a specific example. I recently bought a 3D fractal from Swarovski, and it’s a fractal consisting of 4,423 little cubes, Swarovski cubes, that are glued together in a fractal structure. When I saw the lights going through and breaking into various other colors, I just had a couple of really cool ideas for some business ideas. It has nothing to do with each other, but I don’t know how the brain works. There are specialists for that, but it seems to be interesting.
Absolutely. Final question. To what do you attribute your success in life? Are there any specific skills or habits or mindsets that you’ve mastered that you think have really made a difference?
Yes. I think there are a few sort of things that you can control, and there are, of course, a few things you cannot control. The ones you can control, for me, are fairly simple. So, first, hard work is hard work. I mean, there’s nothing. That’s it. That is required.
Second is clearly creativity. Taking the time to be creative and thinking ahead of the game, because that avoids fights later down the road. So, I’ve seen that several times. By being just simply faster and more creative, you’ve already sort of resolved the issue before it could even arise. That’s the second one.
The third one is a simple saying that I have, which says, “Ethics will prevail.” It’s not completely used everywhere in the world at all times, but I just stick with it, because I feel that, ultimately, ethics is what is required, and I think in the long term, one is better off following ethics, because then you have friends. If you always go for the win and you need to deploy ways that are not so nice, at some point, it will come back to you.
Then, the fourth one, and I think that’s very important is being open to serendipity and options that you create. Had I not gone to India, I would have never founded Evalueserve, for example. It was risky, everybody said it’s end of career but you know. So, these are the four simple sort of rules that I apply, and maybe it has helped a bit.
Lovely. Excellent. Marc, it’s been a great pleasure having you back on the show. A lot of really rich content, not just in the conversation, but also in the book, and we’ll include the details of the book and where people can get in touch with you in the show notes, but many, many thanks for your time. We’ll look forward to keeping in touch and putting up, also, any reviews of the books on the show notes, as well, as they start coming out once it’s released on the bookshelves.
Great. Thank you very much, Mark, for having me in your show.
Okay, have a great day.
Thank you very much.
What Was Covered
- 04:30 – When Marc transition from executive to entrepreneur, what did he learn the most during that journey?
- 06:05 – How did Marc grow his business so rapidly?
- 09:50 – A couple of months ago, Marc helped automate a key process for a lot of investment banks. Fast-forward to today, what results has Marc seen from that work?
- 18:15 – Technology can get very complex quite quickly, but Marc is able to simplify these processes and leverage what technology is supposed to do in the first place: work efficiently and effectively.
- 21:15 – Marc doesn’t believe he’s disrupting the industry. He believes he’s exposing new trends, which then lead to new possibilities.
- 23:55 – When looking at the future of businesses, where does Marc see the biggest opportunities?
- 29:00 – Why did Marc write the book Mind+Machine?
- 33:05 – What kind of topics in Marc’s book resonate the most with readers who are fresh to the subject?
- 40:25 – In a lot of ways, small companies have a bigger advantage when it comes to disruption.
- 43:20 – How does Marc simplify his personal life?
- 47:00 – What has Marc changed his mind about recently?
- 48:20 – What does Marc do to remain creative and innovative?
- 50:15 – What does Marc attibutue his success to in life?
[Tweet “One of main learnings is that to scale, you need the processes and the backbone in place, otherwise you just multiply chaos”]
[Tweet “It’s very hard to simplify, but once you do it, you create opportunities that you didn’t know of before. I call this the option value of simplicity”]
[Tweet “The simpler things are, the more targeted these analytic use cases become, the higher the utility and the lower the cost”]
[Tweet “Once you’ve cracked the neural network problem and have all the APIs, it becomes a winner takes it all situation; you have intellectual property to protect your use case”]
[Tweet “Smart companies ike GE creating standards, ecosystems around themselves so that innovation can bubble up in their sphere of influence”]
[Tweet “Only 5% of all analytic use cases are actually about big data. Everything else is small data. Small data is beautiful too.”]
[Tweet “It always comes down to ROI from increasing productivity, improving time to market, or by giving new capabilities”]
[Tweet “The democratisation of analytics: with cloud and mobile, small companies have big advantage because they’re more nimble, flexible, with same access to end clients”]
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