Jonas Christensen 2:27
Dhiraj Rajaram, welcome to Leaders of Analytics. I am so excited to have you on the show today.
Dhiraj Rajaram 2:35
Thank you for having me Jonas.
Jonas Christensen 2:38
Yes, this is going to be a very, very interesting conversation. Because as I've already introduced the audience to you, you have a very interesting background. And also, you are someone with opinions on lots of things. So I have no doubt that we're going to be exploring lots of topics today in the world of Decision Sciences and data analytics in general. But let's hear straight from you Dhiraj. So in your own words, could you tell us a bit about you, who you are, what you do, and your career background?
Dhiraj Rajaram 3:08
Sure, I am the founder and CEO of Mu Sigma. That's what I identify with myself. Mostly, I started my life as a management consultant, initially at PricewaterhouseCoopers, and later at Booz Allen Hamilton. I never thought of myself as an entrepreneur, that happened by chance, the idea of Mu Sigma came to me when I was at Booz Allen Hamilton and I pitched this idea to them saying that, look, there is a newer way of thinking about decision making. And we should explore opportunities by manifesting it. And the best thing that happened to me was the idea got rejected. And that allowed me to see if I could build this. Microsoft became our first customer, and I will always be thankful to them for that. And we never looked back after that. Today, we work with over 140 fortune 500 clients, we've trained more than 14,500 people in data science, becoming decision science, we have a labs ecosystem that looks at the cutting edge of what AI machine learning algorithms, all of these things are involved in over the last two decades, we are coming close to generating approximately a billion dollars of profit for our company. To do that, in analytics and Decision Sciences, you need to make at least about 100 billion dollars of impact for your customers. Otherwise, they're not willing to share that with you. And that's something I'm very, very proud of and proud of my team, which has contributed to building this category as much as building this company. So that's Mu Sigma and my background quickly.
Jonas Christensen 4:47
Very good. And it is definitely something you can be proud of. Because when I was studying your company, it's fascinating to see the journey from starting it to I think 17 or 18 years later being quite the large organisation tht spans many continents. Now tell us a little bit about what the company offers in terms of products and solutions. Because you describe yourself as a decision sciences company, what does that entail, specifically?
Dhiraj Rajaram 5:15
See, the big D in our world is actually not data, its decisions. When you think about it from a purpose, it's all about helping either individuals or organisations make better decisions and faster decisions and more useful decisions. When you see it from that perspective, you will see that the world that we live in today is changing exponentially. We are moving from a bigger is better world to a faster, better world. We used to compete on economies of scale. Today we don't compete on that, we compete on economies of speed. We used to make products and services, today we make experiences. The science of experiences, and the science of interactions is one and the same. So one of the hypotheses that we had very early on, that problem solving must evolve from entities to interactions. So thinking about the problem as a whole becomes very, very interesting. Your world is not one or two big problems, but many, many, many small problems that are interacting with each other. If the problem space is interactions, then the solution space must be interactions too. The future of solutioning for problems is going to be interactions between math, business, technology, behavioural science, design thinking, and a whole host of new things, which we may not even know. Other things are going to come and we may not even know, but they have to be part of the interactions. So figuring out how to think about problem solving as interactions becomes very, very important. That's what Mu Sigma brings to the table. We are enabling organisations to scale analytics. We are enabling organisations to make it go faster, to speed it up. We enable organisations to make it sustainable, and not let it remain in a sandbox. And last but not the least, we enable organisations to have enough slack so that when a new question comes up, they can answer it feeling abundant, rather than feeling a sense of scarcity. For all of these things, you need a new way of thinking about problem solving. And that's what we bring to the table. How do you do more with less? And how do you do less to get more? So that's how we think about it. If I want to have a one line answer for you wish I could have started with that one line answer would be how do we improve the curiosity quotient of an organisation? Which means cost per question must go down. Cost in terms of time and cost in terms of money. If you can answer more questions, you are, as an organisation are more prepared for a world of uncertainty. In a world of uncertainty, you have to solve it through optionality. And to be a producer of optionality, you have to be very curious, you have to ask a lot of questions. And that's what we think of the curiosity quotient.
Jonas Christensen 8:20
There's a lot to explore in that Dhiraj, and we will do that, especially this curiosity quotient I think is very powerful. I wanted to take a step back and go right back to your sort of founding moment, if I call it that. So I'm sitting here imagining you as a young guy going into the boardroom at Booz Allen Hamilton with your idea ready to present and, and it gets knocked back in there, you have a choice in life, you can say, okay, no one likes my idea. I will keep going the way I am. Or you can actually say, Okay, this is a crossroads. And I have to go and follow this and make this happen myself. What went through your head at that time? And how did you decide to become an entrepreneur in that moment?
Dhiraj Rajaram 9:07
See to be fair to Booz Allen Hamilton, they liked my idea. They just felt that I was too young and I should make partner before executing on it. So they just wanted to give me about five to seven years, which I was not ready for because I thought the time was now. So I did get positive validation from them on the thought that the idea was good. And I was not surprised by that because the idea was based on sound fundamentals and, you know, all my thinking has come from physics, you know, if there is one thing I cannot stop talking about, it's about physics. And and a second order things would be movies and cricket. But physics is number one, I would say. And in physics, you know, something that has shaped my thinking, in many ways is the second law of thermodynamics, or otherwise called Boltzmann's law, which says that entropy in the World can only increase, which means that the world will have more uncertainty in the future, the world will have more volatility and ambiguity and the world will have more complexity. So that world would mean that decision making in the future will only have more noise. And therefore, the ability to remove noise and get the signal is going to be very, very, very important. And that requires a new way of thinking about problem solving. And so that was based on that fundamental appreciation for the fact that entropy, or the lack of order will only increase. What I saw in my management consulting experience was that organisations were struggling to deal with entropy, let me make it a little easier for you to understand what is the purpose of an organisation the purpose of an organisation is to solve problems. And when you see those problems, they manifest as content initially, it's an idea in somebody's mind, and then it becomes conversations between people. And then it becomes computation where it goes into a machine, which will enable everything to get speeded up. And last, but not the least, if one of these happened, well, then it commercialises really, really well. This journey from content to commercialization is severely hampered because of unaddressed complexity in the organisation. And because of that, these organisations don't thrive in uncertainty, they feel like a deer in front of headlights, they get stuck. And eventually they get disrupted. So that thought stayed with me. And I felt that large Fortune 500 companies, which are going to create more and more data, are going to need a path towards changing the way they think about problem solving. And allow that journey from data to decisions. The way I see it is data to dialogue and dialogue to decisions happen. That was the thinking behind how Mu Sigma got started.
Jonas Christensen 12:09
So then you have this idea and you say, "Okay, I'm gonna go and, and start up my own company". From then to actually getting customers because there is no company really without customers and setting all that up and then growing is quite a journey. How did you convince your customers your first customers, you mentioned that one of your first ones was Microsoft, which is a pretty big ticket to land right off the bat. How did you convince them that that you were someone to bet on because for all intents and purposes, here is a new guy with a new company. And what did you have as opposed to either way you presented yourself or your toolkits was enough for companies to sign up to?
Dhiraj Rajaram 12:51
See people think of companies as either B2B or B2C. I think of it as H2H. It's always human to human. And when you see yourself operating on a human to human level, neither do you feel abundance, nor do you feel scarcity, it is all of us are human beings, and all of us have an idea. And you have to be authentic about that idea. I would talk about the concept of Mu Sigma, continuously with as many people as possible. And keep getting feedback on the idea. Keep getting feedback. On the words I'm using, keep getting feedback at an individual level, you are GPT you are JonasGPT, I am DhirajGPT. And our ability to learn quickly is going to be tested by life. And from that perspective, I was practising every day I was a single founder with no employees for about eight months. And at that point in time it was extremely frustrating, because the demand was not there, there was nobody willing to join me. And there was every reason to doubt but the ability to keep her going, keeping the conversations open with whoever would be willing to speak to me was a constant way to improve the conceptualization and conceptualization becomes very, very important before content, I would say concept. So and conceptualization is an iterative process. While doing that, I met with a Jim Minamino, who was the head of consumer research and market insights at Microsoft. And Jim was a very senior person very, very, I was a 28 year old kid and he was maybe a year away from retirement or something like that. And he was passionate about math and he liked the concept. The concept was what he liked. And he knew that I was young. And this concept was coming from a young and belligerent mind. And he was willing to give this kid a chance. And I would I am indebted to him forever, for the fact that he gave me an opportunity to take a pilot and execute that for Microsoft and we never looked back after that. Our journey was one where after Microsoft, we had other cloud customers across 10 different industry verticals slowly come in. And slowly, the feedback started improving. The market started recognising who Mu Sigma was investors started seeking us out, we took in about $14 million of primary capital, and very recently returned about $900 million to our investors, $45 million to employees. And that was a that is very satisfying. And we also built the category. Many companies came out of Mu Sigma, where they pretty much took the same model, and some of my own employees, and built copycats, which is not which is the best thank you that they could give me because there is no better your appreciation for you other than people saying that, "hey, what you're doing makes sense. I will also do it".
Jonas Christensen 15:56
And today you have - I won't quote it, actually - I think I found a number on the internet. But how many employees does the company have today, approximately?
Dhiraj Rajaram 16:04
By June we'll be about 4500 employees.
Jonas Christensen 16:07
Yeah, so that's a fantastic growth rate. And it must feel so pleasing, having spent that eight months on your own, working out what the concept actually is and to today. What an amazing journey. Now Dhiraj, let's dig into the magic that's made all this happen, the stuff that happens at Mu Sigma, because you talk about the ability for organisations to make better decisions, faster decisions and actually feel comfortable in uncertainty, which I think are sort of three very powerful dimensions. And that is something that we can aspire to. But reality is, this is at least my experience, When you walk into businesses, it sounds good, but no one is really wanting to rattle the trees. No one's really wanting to take big risks, because there is organisational risk, there is also personal career risk involved and so on. Tell us about how you actually move these organisations along to change the mindset because it's almost a mindset shift before the technology solution. How do you do that? And maybe you could get some examples of how you've been able to move organisations along.
Dhiraj Rajaram 17:17
Sure, sure, sure. So, clarity reduces the cost of taking risk. And unless you have clarity, you cannot take risk. When you have a very complex situation in a large company it obfuscates the details. And as it does that, it introduces fear into the organisation and all the organisms inside the organisation. I actually believe that even the organisation is an organism. And it experiences that fear. And fear is all the negative things that could happen to one in the future. And hope is all the positive things that could happen to one in the future. But how do you convert fear into hope, mathematically speaking, you have to have a modulus operator for create converting fear into hope. And that modulus operator modulating your fear, in other words, comes through the mind space, a mindset, which orient yourself towards learning more than knowing. And if that's your "why" learning over knowing your "what" has to orient itself towards experimentation over experts. And your "How" happens by not keeping secrets, the new IP is not intellectual property, but actually interaction property. All of these three things, learning over knowing extreme experimentation and interaction property orient the organisation to a mind space of abundance, and not a mind space of scarcity. But to do this, you can't just say it, and they will take it, you have to show it to them, you have to create processes for that you have to create platforms that enable that. So that's what we did. We went about building talent with this mindset. We went about hiring very, very young people in India, and putting them through a very rigorous interview process to get people with the right mindset. It was not about, literally about their grades or about how smart they were, but orienting it towards one thing and one thing only: canthey learn, do they have an intention to learn. That became very, very important. And once they had that, right, we put them through a training ecosystem. Mu Sigma operates as a university in a world where many businesses many universities have become businesses. We are a business that runs as a university. Right? So the I heard somewhere that the opposite of diversity is university. So the The thinking this a good university needs actually diversity. Right? It needs diversity in thought and needs diversity in actions. It needs diversity in all its inputs, it needs diversity in all its outputs and need diversity in all its outcomes. So when you see it from that perspective, what we did was operated ourselves, our people, our customers, and our investors, as a university, as a learning environment where we are constantly using what I would call the OODA loop, where you observe, you orient, you decide and you act, and then you feed back loop, observe, orient, decide act feedback. So that perspective of creating the OODA Loop became very, very important for us as a business. And through that we started not just making new kinds of food that will enable organisations to go through transformation, but also build a kitchen for them, that will enable them to make food by themselves. So making the kitchen and making food, that's what we were about, and really, really moving from being a reliable vendor to a trusted partner to a trusted adviser in the space of Decision Sciences.
Jonas Christensen 21:14
So if I paraphrase what you're saying, really, the core of it, you have built a company by creating a whole bunch of people who have the right predisposition to be trained in in your specific model to basically adhere to this way of thinking, and then they go in and implement that across organisations. And that's, that's it?
Dhiraj Rajaram 21:39
it's not just training our people, I would say, Jonas, it's also having a perspective to help our customers learn, our customers are also learning and we are also learning from our customers. So the true learning happens when all stakeholders are learning from each other. And you create an environment of learning, doing and teaching. So learning by doing doing and teaching, teaching, by learning, you know, all three, all these three things are constantly interacting with each other. So our customer environment, our Mu Sigma, internally, Musikmesse, employees, all three, all of the stakeholders, and maybe even our investors, our word learning, everybody was learning. And we were authentic, and open about our ignorance, and comfortable in the discovery of ignorance and the journey of the discovery of ignorance. And that authenticity allowed us to build something which is very, very valuable.
Jonas Christensen 22:42
Yeah. So you're not the firm that that goes in and says, "Let us come and help you we know the answer". We'll come instead and help you find the answer. We don't know what would be know how to find it with you, which is a beautiful subtlety.
Dhiraj Rajaram 22:54
You got to be precise, we are not a know-it-all company. In fact, we are a learn-it-all company. So we believe in learning it all. And that's how we think about Mu Sigma.
Jonas Christensen 23:06
So you have what I said, products and you have services. So you have software solutions that come with the package. And then you also have your people that go and teach the are almost call it the gospel of the company, the way to think about making decisions, how do these things go hand in hand? And I suppose what what is the suite of products and services that is on offer from Mu Sigma?
Dhiraj Rajaram 23:30
Sure. That's a very important question, and I have to choose my words very carefully, as I speak about it. See, what we see is just so that we understand the difference between products and services, right, we see a world where if a problem changes quite often, then it's very difficult to productize it. Right. If on the other hand, if the problem stays constant, it's easier to take, critique the function, functional specifications, make technical specifications, write a product, and then use that product. And it will constantly be evolving. Because of the world that's changing constantly, you cannot have a pure product because the world is changing. On the other hand, the world does not give you so much time that you can do everything by hand. So you need accelerators. So our model was not the Superman model of hiring one special person who has all this knowledge and ability in him. It was not a robot, it was not Superman, it was not a robot, it was more Iron Man, right, where the man and the machine are interacting with each other and you will see the word interactions being used again and again and again. So if you have hydrogen and you have oxygen, the interactions between them when it is positive creates was it water or life giving. And when it's negative, it can be hydrogen peroxide and not so friendly to life. So the perspective is getting the right interaction between man and machines become very, very important. So there is problem definition, there is solution creation, and then the solution implementation, what we see is that the problem definition space has to have design thinking and empathy creation and that orient itself very well for services to solution creation phase needs accelerators, and needs software. And that's where the software comes in handy, really. And then the solution implementation, again, requires a services perspective, because you're landing it in a world which is, which is foreign to the solution. If the solution feels comfortable in a problem space, it is not even needed, you have to enter a foreign land, you have to be an immigrant, the solution is an immigrant that enters the nation of problem spaces, right. And having a having a good ecosystem that welcomes the solution immigrants into the problem spaces becomes very, very important. And that also needs to be serviced. So you have service software service. So it's a service as a software as a services and software as a service as a software ecosystem. I call it the SaSaSa model, but not software as a service. In this case, service comes first, and then software, and then services and then software. It's a constant feedback loop between these two things that manifests itself. So that's how we think about it. And that's what we implemented and why they implemented this, we not only built solutions for our customers, we think of it as new kinds of food. But we also built a new kind of kitchen for them so that it becomes sustainable for them in the future. So having the approach of the kitchen and the food coming together and interacting with each other on a constant basis becomes very, very important.
Jonas Christensen 26:46
Yeah, fascinating. I'm sitting here, as you should talk, reflecting back on your three core beliefs. I'll quote them again, just so we have them there, which is "learning over knowing", "extreme experimentation" and what you called "the new IP".
Dhiraj Rajaram 27:02
So the extreme experimentation over experts, it is important to state the other side of an idea just for more clarity. And the new IP already has the perspective of the fact that there is an old IP interaction property over intellectual property, so that the aspect of learning via negative is very, very important for clarity.
Jonas Christensen 27:24
Yeah, absolutely. And when I hear this, Dhiraj, I'm thinking that you have a challenge that most listeners would also have, which is, they would like to do more experimentation in their organisations, they'd like to challenge the status quo, which is really what data scientists need to do to actually change the way that things work now, because the ruling paradigm is not necessarily what we're describing here, which is a world of increasing entropy and all this stuff. But a world where people would like things to be the way they are and stay stable, you're coming in to say you actually have to experiment you have to sort of test the limits of your organisation in a controlled way, nevertheless, but you have to do that. That's a challenge that most listeners of this show would actually also have myself included. When you meet that resistance at the senior decision making level, how do you break through that? What is your formulas for success in organisations in that regard?
Dhiraj Rajaram 28:25
I think most senior leaders today, in large fortune 500 companies realise that it is inevitable that their world is changing really, really fast. They wouldn't have any role to play in the organisation if their worlds were not changing. So I think that's not lost on them. They're very good at it. And so what we see is, many of these larger organisations want to do it, but they are, they are a deer in front, the organisation as a whole is a deer in front of headlights because of fear, right? The name, Mu and Sigma comes from the fact that to make a good decision, you need to know the answer, and you need to know how confident you are about the answer. The answer comes from Mu or expectation. And Sigma gives you the confidence level around what your answer could be. That's the first order what's level thinking about the name, but when you see the second order, second level, thinking about the name, if you will notice the past, we all ways human beings have craved for certainty because it gives us comfort. Right? And the word Mu stands for the average based on what the past is. And that is the expectation and anything around that gives us comfort because that's the past and we are following the past and therefore there is less discomfort, which is natural. Now before all the game of industrialising, execution was all about new shunting sigma, right, where the factory model or the software development lifecycle all of this was about being predictable and not having any disappointment and surprises, right. That's how you thought about it. That's why it was new shunting signal. But now we are entering a new world, which is changing so fast that you have to be constantly exploring, you are not driving on a straight road where you can put yourself on cruise control, and you can ride the car without having your hand on the wheel, when your hand is constantly on the wheel, the accelerator and the brake, and you're turning up and down and sideways. Which means that you are, the amount of time you're spending on exploration related to execution is significant now, and it's only going to increase so you have to industrialise your exploration process, your exploration platforms, your explore people, your people who explored so that industrialization would mean that you would need a Mu seeking Sigma, you have to be a Sigmaxer, you have to orient yourself to a world where you're not afraid of sigma, but you love Sigma. For the organisations to have that perspective. It's not just a skill set or a tool set, but it's also a mindset. So what we do is, first thing we do is make the ease of concept discovery, through articulation through words through examples, through you know, other people doing it in front of senior leaders, once they buy into it, they are like saying, Hey, this is fantastic. I wish my organisation was like this. And at that point of time, we go with middle management, and help them through workshops, and tools and processes. Give them enough confidence. One of our tools, we have two tools that we bring to the table in our software, one is called "The Enablers of Confidence", the other is called "Art of Problem Solving". The enablers of confidence allows the middle management to feel confident enough to make the moon the art of problem solving is the discovery of ignorance. And through this, these two tools manifest two kinds of engines in the organisation one is the signal engine, which is there is tonnes and tonnes of data you want to remove noise and out comes, answers or signals. That's the signal engine. But that's not enough to make a good decision. Good decisions are made through the interactions between questions and answers. So you'll also need an inquiry engine. So we build the manifestation that comes out of the enablers of confidence tool. And the Art of Problem Solving. Two is the inquiry engine and the signal engine. And these two engines interact with each other and provide insights and eventually those insights result in decisions and outcomes, and so on and so forth. So, and then there's a feedback loop, which allows the organisation to keep doing that. So we think that that's the way we have gone and done it and one of the things we saw in organisations is unaddressed complexity comes in the way of the organisation dealing with fear. So the ability to aknowledge complexity, address complexity, use things like network theory and complexity science, to be able to see how various problems are connected to each other becomes very, very important. The future of work is that it is a network. So the ability to see the network of problems in the organisation see the Knowledge Graph manifests that knowledge graph and say that I am going to dream about the problem space as a network as a whole. I am going to detail the problem space at a very, very granular level, I'm going to describe the problem through outcomes rather than to outputs or inputs. And eventually, I'm going to see this completely as a decision supply chain that becomes really, really important. Seeing it as a decision supply chain a journey of flow of data to dialoguing to decisions, that's the way you ought to see it. And that's something we enabled for enable for, for organisations in our interactions with me.
Jonas Christensen 34:13
Very interesting. So what I'm hearing is you actually have to make people comfortable to even step into this new type of problem space before you can challenge them to think differently, that they want to do it but they are feeling that there's a risk in doing that in the first place. I'll direct risk or just the I suppose the the risks that lots of humans have in organisations of making the wrong decision of losing face or just not fitting in all the human subliminal messages that are flying around in an organisation in human interaction. So I think that we can take a lot from that and we can learn a lot from that approach and because I think I've generalised here a lot but I think still there's there's something to it. A lot of people who are coming in with technical solutions and people working in data sciences are problem first and, and the solution is to them obvious. So why don't we just do it? But there's this winning of hearts and minds that has to come first. Is that fair?
Dhiraj Rajaram 35:12
Absolutely. Absolutely. Because, you know, like I said, the journey the organisation exists to solve for problems and solving the problem all the way from concept and content to conversation and computation and then eventually commercialization. So when you see all these scenes here, it's a journey and the more value to move through that journey. You need the pipes to be unclogged at all points, and complexity clogs it. So unaddressed complexity, not addressed complexity. Addressed complexity speeds it up. And unaddressed complexity clogs it. So you are to unclog unaddressed complexity. Every idea, at one point of time was complex, and it's in its journey to become ordered. So you know, the complex unordered disordered thing becomes ordered and simple. That's the journey of the idea right. And as that journey happens, if you take that journey well, and not falter in your journey, you will neither be simplistic or complicated. So when you start faltering on accepting complexity, you will either become simplistic in your thinking where you will come up with a solution that makes you look good, even though it doesn't have any meaning with the problem. Like everything is AI today, there's a buzzword around that you just use the word AI and you'll see some people think they can get away with it. Or it becomes too complicated, where the interactions are so obfuscated that it creates fear. So when you're simplistic, you are complicated that those are bad ways of dealing with the organisation via fear. So actual complexity, authentically accepting it. And then moving in the journey to keep make it simple is the right way to go about it.
Jonas Christensen 37:10
Hi, there, dear listener, I just want to quickly let you know that I have recently published a book with six other authors, called "Demystifying AI for the Enterprise: a playbook for digital transformation". If you'd like to learn more about the book, then head over to www.leadersofanalytics.com/ai. Now back to the show.
So Dhiraj, I'm interested if you could give us a concrete example of how you've done this to sort of show us the the art of the possible you can deidentify the client, by all means. But I'm interested in an example of where an organisation started out, perhaps a bit a little bit locked up in its decision making ability to actually having this transformation and then changing and actually work came out the other end.
Dhiraj Rajaram 38:02
Sure, sure. So let me tell you a story. Right. I mean, this is a large home improvement retailer, very well run organisation recognised the fact that they have to be doing better and working with the contractor or professional ecosystem in their market. And as they saw this, there was a top down perspective, a top down slogan, created called "own the Pro", we got a win among the pros, or the contractors. And as they were going about their journey, and they saw we were working with them, and they saw the problem space as a network. And when they manifested the problem space as a network as many many small problems and seeing which problems are working with which problems and how do we work to have more of a market share among those contractors or professionals. We quickly realised that these contractors or professionals should not be thought of as another customer segment, but actually a distribution channel for ending to end consumers. And these contractors, they have a lot to offer the organisation in learnings but the organisation has so much power and size relative to the small contractor that that interaction is not a peer to peer interaction. So because of which the pros we are not learning from the pros, but we are actually telling the pros what to do and that perspective was there. So once the problem space was seen as a network, what what emerged out of that was that, hey, maybe it's not just about communicating downwards to the pros, but also learning from the pros. And these small business owners or the pros have to be more professionalised to make them come to a way come to a level where it's peer to peer with the organisation so all the pro became pro the owner, which is professionalise the owner. And now we had to learn about the whole engagement model, we had to change the engagement model, how we would engage with them in the store. So we had to create new areas for the pros to interact with each other. We used things like video analytics to see how the pros were moving about relative to other customers, and so on and so forth. So combination of data that existed in the organisations, a new data had to be created. And and that combination of existing data and new data allowed for a certain level of insights, which were otherwise not there. And that helped them in their journey to be successful with the pro ecosystem. So this, if you notice some things here was that the initial problem that they came up with was not the final problem that they solved. And what came in between the initial problem and the final problem was reframing through complexity, science, through seeing the problem space as a network, through seeing the existing knowledge as a graph. The graph on existing knowledge allows for new learning, a graph transforms existing knowledge into a new learning. So when you have a two dimensional graph, it's all there, the data is all there. But just by putting it into graphs, out emerges a learning, that's the same thing that is happening here. dimensionalizing, the problem space, the knowledge ecosystem allows for new learnings emerges emergent characteristics come out of addressed complexity. And that emergent characteristic is something that you want, innovation is nothing but an emergent characteristic in the organisation. And that helped us so existing problem into new problem and the new problem is solved. Now a better problem is solved a more connected problem is solved. And therefore that allows for the organisation to have empathy with all stakeholders better.
Jonas Christensen 41:52
Yeah. And it takes a certain discipline, it takes a certain belief set, it takes a certain trust to actually go, let's spend the time defining the problem itself before we go and come up with solutions. Because you could have come up with beautiful solutions, I'm sure to the wrong problem. Very good story. And very relevant example, I can really see how how often you spend not enough time on defining the problem itself, there is often a very clear problem. But it's not the problem that connects all the other problems. And therefore you sort of solving lots of problems with with one stone.
Dhiraj Rajaram 42:32
I joke about it Jonas, saying that the the most important problems hide behind the most useless problems. They send the useless problems in front of the people, so that the important problems protect themselves from being solved. So it's a survival of the fittest for the problems, right? A problem wants to exist, and it doesn't want to die. And the solution is all about killing that problem. No more remains a problem when there is a good solution to it. So the problem what it does the problem space, what it does is it obfuscates the good problems, the important problems, and it covers itself with a lot of useless problems that other people see very easily. So the ease in which you see a problem is indicative of the fact that you're probably solving the wrong problem.
Jonas Christensen 43:21
Yes. And necessarily, if you jump at the most obvious problems, first, you're really just getting marginal improvement, not sort of stepwise improvement, because you're not going deep enough into the set of problems.
Dhiraj Rajaram 43:33
I would actually say, I would say, when you do that, you know, it's not just about marginal improvement, you're getting marginal destruction, I would say in the organisation, because every second that the important problem is not solved. It is doing more disservice to the organisation. Right? So the time that you take away from solving the important problem, and you're putting it in the superficial problems that exist, is actually you're hurting the organisation even more, and making it harder to solve the important problem.
Jonas Christensen 44:06
Yes. I feel like I live that every day with lots of requests coming at my team and all seemingly relevant and all fair in their own right, but they're just not the biggest rocks for for the organisation. And the constant trade-off between keeping people I suppose happy that you give them something but also having time to spend on those those bigger problems. So that's a constant challenge, I'm sure for our listeners. Now Dhiraj we're almost at the end. I have one kind of big question for you to finish off because you strike me as someone who's very, you reflect a lot on things and you're a deep thinker. What's something that you used to believe to be true, maybe 5-10 years ago that you have really changed your mind on recently?
Dhiraj Rajaram 44:51
Many things, many, many things. So that's a very long list. But I have at a higher level. I would say that And I personally, when, in my journey, what I have understood is that life is a data set. And there are no mistakes or good things or anything like that it's just a dataset. And the more Sigma there is, there is more learning in that. And, and therefore, one of my biggest learnings has been to not be so hard on my regrets, and use them as channels for learning. And I look at that as a price of admission for learning. If I felt the pain of doing something that hurt me, that's the price I paid, and then the lesson is mine. And I feel like people who say you have to learn from others don't pay that price. And then it's not theirs. The lesson is not theirs. Your lesson is yours only if you learned by doing and felt the pain and pain, or suffering is very, very important. Now that pain and suffering should not become misery. And that attitude to not make pain and suffering misery is something that I have been constantly working on. I'm not still good at it, I would say. And one of my favourite books has been this book called The Tao of Physics, which is an interaction between Eastern mysticism and physics, is written by Fritjof Capra. And that's a book I read at least once every three, four months. And I watch Interstellar probably every month, every month, once. So those things have helped me to constantly be okay with certain beliefs and thoughts that I had before that I don't have. And many times I've seen that you have to be authentic about things that don't put you in good light, probably, but still being authentic about that becomes very, very important. And I could name multiple things on that front. I mean, I could, there used to be a time where I was far more motivated by extrinsic motivation than I am today, where I was motivated a lot more by validation from the external world, validation from investors, validation from social media. And those times led me to a place where I had a potential for learnings through suffering and pain that I created for myself. And that's something that I keep, I keep reminding myself. So of the fact that I should not be oriented as much towards extrinsic motivation, even this conversation that I'm having, and eventually it will go on social media and all of those, you know, scare me a little bit that maybe, "Am I slipping into extrinsic motivation again, and feeding my ego more than I should"? So I'm trying to be as authentic as possible to answer a very deep question that you asked,
Jonas Christensen 48:09
I think you've succeeded on that. And I really appreciate your answer on it and your vulnerability at the end there. And I think a lot of people are stuck in that paradigm, because we are getting bombarded with ways to measure ourselves against the external world constantly. And it is very, very hard not to do that. Even if you're not wired like that. It's just the way that society is structured at the moment. Now, Dhiraj we're at the end, my last question today is where can people find out more about you and connect with you?
Dhiraj Rajaram 48:41
I'm actually quite easy to reach. I'm on LinkedIn at the Dhiraj Rajaram. And you can also visit our website, mu-sigma.com. You might find all the quirks associated with me in multiple videos there. And if you can deal with that, and still feel like you want to talk to me, I'm always available to have a conversation. But again, Jonas, thank you so much. This was a very nice conversation. appreciate you inviting me.
Jonas Christensen 49:10
Absolutely. And listeners do go and check out Mu Sigma's website, and especially those videos. I watched them myself and I found them entertaining, but also very enlightening. And it did shift my thinking in how I might approach problem solving going forward, just as this very conversation has, Dhiraj. So I really appreciate you taking the time to join us today and to share your knowledge, experience and your life journey with us today. And it's been very inspirational to listen to, and I wish you and Mu Sigma all the best for the future.
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