Augmented Strategy live stream with Bruno Pešec and Dominik Dellermann
A better way to make decisions in the 21st century.
Today's business world is more uncertain, complex, and fast-changing than ever. The winners of tomorrow will be those that are able to take informed decisions at the right time.
Based on our research and experience, Dr. Dellermann and I came up with a specific process to leverage both data and human intuition to make better strategic decisions. We call that process augmented strategic decision making.
Watch the live stream recording below to learn more about:
- what's the state of the business world today,
- relationship between speed and quality of decision making and business success,
- how do traditional and modern approaches to strategy differ,
- how do different roles in your organisation benefit from data,
- what are the four critical data sources for augmented strategic decisions, and
- the fastest way to start today.
And last, but not least, we also announce our forthcoming book, Augmented Strategy. Click on the image below to learn more about it and where to get it:
You can watch the recording on LinkedIn or by clicking play below:
BRUNO: Hello, good morning. Good afternoon. Good evening, whatever the time is in your part of the world. Welcome. My name is Bruno Pesec, and I help business leaders innovate profitably. Today, I'll be joined by the amazing Dr. Dominik Dellermann, and we're going to be talking about augmented strategy. How to combine data with human intuition to make better strategic decisions, faster.
Now, before we dive into the topic, I just want to welcome you to LinkedIn Live, where you are watching this. So, on the bottom, or the side of your screen, depending on the layout, you should see a comment button or something similar. So please feel free to use that to leave any questions, reflections, feedback, whatever comes to your mind. I should be able to see that and share that with Dominik so that we can respond live. Besides that, this should be automatically recorded, you should not have to look it up anywhere on the internet since you signed up for this live. It should just remain in your history, and you can go back to the same link and re-watch it. So there, you won't need to hunt down for any separate recording. We're expected to go for around an hour, maybe a little bit less, but as I said, if at any moment you need to drop off, this is automatically recorded. So you will be able to re-watch it.
Now before I take Dr. Dominik onstage, I just want to share how I actually got introduced to him and what drew us together. That was, as he'll present a bit later, Dominik is and was doing some great research on actually using artificial intelligence and something that can be used to make better decisions in different contexts. And as some of you might know, I'm quite involved in the corporate innovation and strategy ecosystem. And I'm always interested in how we, as humans, can both innovate better, perform better, without sacrificing, necessarily, humanity, respect for people and etc. So it was very easy to have a chat together and we were discussing some very critical issues that both of us experienced and we found some commonalities. That was the starting point that led us to coin the term augmented strategy that we will be talking about in a moment. Dominik, how about you tell us a little bit more about yourself as well.
Welcome to the big stage!
DOMINIK: Okay, thanks for the intro. First of all Bruno, I'm happy to be here with you. I'm Dominik, I'm the CEO and founder of vencortex. What we do is we actually build software for augmented strategic decision-making to basically use AI and data to help executives make better decisions in today's uncertain world. And I mean, I couldn't tell the story better than you Bruno. So the only thing to add is really what I really enjoyed about the discussion is that, you know, we saw the same kind of problems, but we also saw the same kind of solutions and typically, when you are in the field, I mean, we need to be honest. The field is very dominated by classical strategy consulting.
Like, it is often in the mindset: No, no, that's all about intuition and gut feeling. It's not about data, it's not about insights. I think it's shifting right now, but many, many people have just the opposite opinion and I really like that you also have this mindset of, yeah, we need to, it's not about automating, it's not about replacing you or making you less important. It's about making your life as an executive easier by giving you insights. Because, I mean, we also know it in our own companies as executives. It feels much better when you make a decision and you have the insights that you need to make it with confidence then making a decision by just saying, “Ah, today I feel it's a good idea or maybe not.” That's basically I think one of the important things. Related to that, I would say also that we see that many organizations are heavily investing in AI or intelligent technologies for automating stuff and also making decisions on a very small scale. But actually, the most important decisions are very manual, very paper-based, let's say, and not supported by data that much and I think that's the interesting part that brought us together and we are excited to talk about the topic of augmented strategy today.
BRUNO: And I think you mentioned so many important things and of course we’ll touch upon them some when we start going deeper, but I think it's very important to stress the fact that we deliberately, both Dominik and myself, are focusing on this augmented strategy instead of, I don't know, AI strategy or something like that because it really is about using technology to augment humans. To allow us to be even better at the things we are doing instead of, what Dominik said, trying to replace people. Try to replace people in the decision chain or something similar. I mean, that I believe is a better future than trying to replace us or part of us with some technologies.
So it's very interesting that you bring that forward again and that whole discussion, why we say that augmented strategies about combining intuition with data is because human intuition is still important but it cannot be the only thing. But the same is for data. Data is important, but it cannot be the only thing. It's like these two together, like if we think about our brain in a simple matter, it's a very sophisticated decision-making machine, right? So it's our job to feed it with some good data, combine it with past experiences, use this intuition and then try to make the best of the situation. And I think actually Dominik that that's a good segue into kind of a bit deeper comment on the situation today, or what is driving this, kind of, how did the context of both decision-making about the company's changed, let's say in the last twenty to thirty years. And you did some amazing research on that. So actually, for everybody watching that's just tuned in a little bit late, Bruno Pesec is here, Dr. Dominik Dellermann is on the other side and we were talking about augmented strategy, how to combine data with human intuitions to make better strategic decisions faster. So we will be walking you through the research and some of the things that we did and put together and we are about to start it. So Dominik, give me just a second and let me know if you can see this?
BRUNO: That's perfect. And I will actually put a small invitation to those that are joining in. So you should see it on the bottom of the screen. So if you joined, please feel free to just type a short comment. So that we know you're here and from what country you are joining in. Because I'm always amazed by from what different places people tune in. Dominik, as we said, so you've been telling me more about how today's business became more complex, but maybe you can share even deeper.
DOMINIK: Yeah, I think, when we, when we look at today's business environment, we see basically three dimensions that make your life hard, kind of. But also that creates amazing opportunities for companies that we see. So, the first is uncertainty, the second is complexity and the third is the fast-changing. We’ll start with uncertainty. We see for example, that now with the COVID situation, but also with many other things, that there are things in the world that are kind of unpredictable at all. And there are many things in the world that would be predictable if you had enough data and reducing uncertainty is basically one of the most important things for companies because if you are able to leverage this uncertainty in a positive way - so let's say there is a new technology, or new customer needs, coming up and you are able to leverage this as a company, then you have an ultimate advantage because you will out-compete your competitor who may be much slower and doesn't have this information available.
The other dimension is complexity. Complexity is basically something that when you have this many interrelated elements and they all influence each other and they often influence each other in a way that is not linear. And we humans are very good at thinking in a linear way. So if I increase A or increase B by a linear amount. So let's say if I, and that's basically why most of the decisions in the past were driven to reduce cost because that's very simple, right? If you reduce your cost, you will increase the margin and yet. If you make the same revenue perfectly fine. Easy to understand but unfortunately, it's not that easy, even with this cost-cutting decision. Why is it not that easy? Because, in the end, your company is also consisting of many, many different elements which we call actually the business model. So that's the mechanics, how your business makes money and many times you will have effects then that are, first of all, not obvious and second of all, not linear. So let's say, for example, you decide to cut costs. And you think well, actually marketing is spending way too much money, right? So, why should they spend so much money? And on the other hand, you see a competitor coming up. Now you start reducing the customer acquisition cost by just saying, yeah, we spent less money on marketing, but it will have a nonlinear effect on your revenue and can really break your business in the end and it sounds kind of obvious but that's what's happening in many, many companies.
There's this very interesting example from nature, or from the environment. I think back in the 90’s when they reintroduced the wolf in Yellowstone, National Park, right? I thought it was an amazing decision because they want to protect different species and they want to bring the wolf back there. Initially it looked like, yeah, we will bring back the wolf and then we will have another animal in the Yellowstone National Park. What actually happens is that the wolf started hunting the deer. The deer didn't eat so much of the smaller plants of the grass and the population of beavers was rising dramatically and what happened in the end is like, the Beavers changed the flow of the rivers in Yellowstone National Park. So it's totally crazy when you think about it but that's the reality.
Also, in your business, when you tweak here, something totally different will happen. And if you don't understand this complexity, if you're not able to manage this complexity, you will never figure out in advance when you make a strategic decision on what impact it will have. The last time I mentioned- yes?
BRUNO: Yeah. I just wanted to connect, but you actually moved to the last dimension because it's so common, the unexpected and unintended consequences. And I want to stress again to everybody watching and listening that it isn't about having a crystal ball to kind of see the future and predict all these unexpected consequences, we have to think about them but it's exactly the fast changing that Dominik is about to talk about that actually helps us cope with it. If we have such strategic feedback systems that can actually tell us, quickly feed us back data, quickly back, then we can adjust the direction, not stick to it. So kind of, I mean, it's a great example from nature because we have so much to learn from nature, right? Like continuously, after all we are natural beings. So kind of, it makes sense to leverage what we are seeing around. That too is data and insight that, unfortunately, we often ignore. Sorry, I just wanted to share a reflection.
DOMINIK: No, perfect. Yeah, I mean, you're totally right. Like I said, when you have this information feedback quickly you can basically act. And if you would have all the time in the world to collect all the information and then make a decision, that would be, of course, amazing. But the reality is different and something that we experience. So I'm from Germany, and what do we see for example here? Here we have a very conservative mindset I think and we have this mindset of, “Change is, first of all, something bad. So let's stick to what we were doing in the past.” Sorry if I am insulting anyone, but I think that's the overall German mentality on average. What happened in times like COVID is that you need to adapt super quickly and you don't have the time to say, okay now let's sit and wait for six months and then sit and wait for another six months. But you need to react quickly and you need to adapt quickly. And you're so basically, from the outcomes of the crisis, there were companies that were able to leverage this and say, “Amazing. Now we have a new use case, a new business model we can adopt.” Remember Zoom? No one basically, always when I was sending Zoom links, people were saying, what the hell is Zoom? When crisis arose, people suddenly started to use it and everyone was saying, “Yeah of course. Let's do a Zoom call.” And I think they heavily benefited from that because they took on this opportunity and they leveraged it. And that's the thing. You need to be, you don't have this kind of strategy that holds for the next ten years and it's set in stone but you need to constantly adapt to survive. Basically.
BRUNO: I completely agree. We have some Germans tuning in and watching and you're a German yourself, but you're not watching. You're talking and I don't see, I don't see any outrage happening yet. So I guess that there is agreement and I also want to reference kind of the data that we have here on this slide that ninety-two percent of the firm's will need to adjust their business model to survive in the 2020’s. And again, I want to draw attention to the fact that the natural happening of the things is decay. If you do not do anything for your strategy or business model, if you're keeping everything straight, the only thing that happens is decay, decline so we need to intervene all the time to either keep the status quo, which is what most of the organizations are doing, or to get to the next level and augmented strategy that you're talking about is actually leveraging all the data and the human intuition to get, continuously to this next level. To avoid this natural decay. As I say kind of, because this is an actual happening, we cannot completely prevent it. But that is why we always need to reinvent business models, reinvent our strategies. It cannot be set in stone for twenty years. It shouldn't be changing every day. Because if it's changing every day, then you're a startup, and that's fine. But if you're an incumbent and you've been in business for fifty years, okay, maybe it's time to change your business model, but not tomorrow. Then again.
DOMINIK: Yeah, I mean you see you see this also now with - I mean in the beginning there were always new business models coming up, Facebook, Uber or something. And you see that also they write they have been existing now for what like, ten? Fifteen? Twenty years? These companies constantly evolve their business model. They reinvent their business model all the time and that in the end makes them successful. And I think we, especially in Europe where we are dominated by this, let's say traditional industries or hardware industries, energy, manufacturing, we are used to business models holding for a hundred years. So that's something that we have many companies here, I mean Daimler, and companies like that, they are more than a hundred years old, they’re basically, on an abstract level, they're doing the same business model now for one hundred years and if this it's changing, it's a huge thing. But something that you need to get in your mindset.
BRUNO: Dominik, forgive me if I misrepresent or oversimplify your research, my takeaway was that you focus on, and are still focused a lot on kind of this relationship between speed and quality of decision making and business outcomes. Right? It's kind of this connection. And what I'm about to show is exactly this statement, right? So if you could tell us a little bit more of kind of what is the connection between fast and accurate decision making and business success?
DOMINIK: Yeah, totally. I mean if you think about it, it's super obvious, right? What is a company? What is performance? It's just a sum of decisions you make. And the sum of the decisions you basically execute in the end. And that's why decision making is so important. And surprisingly, it's a field or when you go to a company and say, “What's your decision making process? What kind of tools do you use for decision making? They will look at you and say, “What do you mean with decision-making? We do ABC, I don't know.” But the important thing is to think about it as the most important capability that firms have because, in the end, this decision making, like the decision you make today will define your success tomorrow. Research has shown that it's one of the most important predictors actually for success. There is a correlation of ninety-five percent. Bain and Company, for example, figured it out a few years ago, and it doesn't matter in which industry you are in, which geography, which company size, it's the same across all of them. And that's basically the reason why this gap between the big tech companies now and the traditional companies is getting bigger and bigger. Because they basically understand that using data to make informed decisions, to basically know how to navigate, some certainty helps them to achieve financial outcomes.
And when we talk about better decision-making, I like to talk about it as optimal decision making, because in many times when it's super uncertain, there is no one single best answer, and I think that's catching up with what you, what you said in the beginning. It's not like you have a crystal ball that says you, oh my God, you need to go left. And then you need to go, right? It's about helping you to take the best action in the situation. The best action is very different. If you are a company with five-thousand employees, fifty-thousand employees or fifty employees. It's totally different for different industries. And when we talk about this optimal decision, we see they're basically four main dimensions that are important. The first is, of course, the quality. I mean you want to figure out after you made a decision that it was right, it was a good choice, it could lead to the outcome that you wanted to achieve. The second is also efficiency, because you don't have all the money in the world to spend on making a decision and if you take too much effort, it's also not a good thing. So you need to kind of balance the effort and the outcome to be efficient. The third dimension is obviously speed. because again, if you take forever you will not be able, sometimes you will even not be able to make a decision at all. And that's I think something that you said, that are definitely on decline, because that's just the natural things that happen. And we see basically that in many organizations, this is one of the most common decisions, is not to decide. Just waiting to see what's happening. And then waiting too long means the opportunities are gone. The universe made the decision for you and you're out.
And the last dimension that is important is learning. It's an ongoing learning process for organizations, for individuals. It doesn't matter if it's on a strategy level like what we are looking at, or if it's on a personal level, When you make a decision, you need to be able to learn. Because if you make a mistake, you don't want to make it again. And that's something that is also super important for strategic decision-making. Where typically, no one even kind of tracks what exact decision was made. What effect did it have? And then learn from that.
And that's what we see on a global level, we have this feeling like, okay, why do they make the same mistakes over and over again? And that's basically the dimension that is super important. If we are not able to learn, we are not able to adapt to these new requirements. And it’ll make it super hard for you as a company to perform in those times. And like I said, this gap between all performances and the average companies is getting bigger and bigger. Like really, what, out of the S&P 500, half of the S&P 500 were replaced in the last twenty years and the lifetime of a S&P 500 or Fortune 1000 company is drastically reducing. On the other hand, we see that the top companies, like, basically there's a big concentration of market cap in these top companies. You see this for example again for our German friends in the automotive industry. Where you will suddenly have these outcomes like Tesla is now worth more than all German OEM’s together and that's something that has a drastic impact on your decision making. Because when your competitor now has the capability to acquire new companies, to invest in new R&D and to really scale, you're basically limited obviously because you have much less market cap and then it again influences your company in a bad way and that's why you have this circle of going up.
BRUNO: I mean, you shared so much so I'm happy that this is being recorded. Obviously I'm biased, I agree with everything. I want to underline a few things, or again to those listening and watching, offer a few practices that actually work. So what Dominik said, let's start from the beginning. Learning is very important, but people forget that large companies - so, be it winning or losing companies. It doesn't matter. Companies are dead entities. They're just abstract, Companies do not have knowledge. It’s the people in those companies who have skills, capabilities and knowledge. So if Dominik leaves your company, you cannot say that you're the new expert on AI or something Dominik was the expert in. He's not with your company anymore. So if there is no one else who has the same expertise, you cannot claim it. That’s easy to understand. What's more difficult to understand is if Dominik is in your company and you are a hundred-thousand person company. Again, you don't have that expertise unless every other employee can reach Dominik and get his input or feedback and people often forget that they do have subject matter experts. But just because they have them, if they don't have a platform to share their expertise, it's as if they don't exist and companies must remember that.
And another great thing Dominik just said is like, it's fascinating, we keep making the decisions but it seems like we're not able to learn back from them. This is again what we are talking about, combining data with human intuition. So you need data on what has happened? What are the inputs? What are the outputs? And then you need to reflect on that. Then you need to interpret that data to get the insight. When you capture that, it becomes a new type of data that can be reused again and again and again. That's not that difficult to do once you realize you should be doing it, but it requires discipline. It requires systems and unfortunately we are not seeing that many companies putting in the work to do it.
Of course let me take a look at your picture…I don't know any of those, if they are exactly doing it. But for example, Amazon's decision-making system, they are, no matter what you think, they are efficient at decision making. So they are investing continuously learning how to make decisions faster, better and more accurately. There are different types, Ray Daleo, and his system. I think I mean it's a very interesting system, it might be way too much for most of the people. But, you know, good inspiration is out there. And when we are talking about the learning - so, the easiest way to think about it, you have a strategy that's not set in stone and you have business, but your business isn't the world. You are part of the world and your strategy is kind of, hopefully, a way for you to win in a specific marketplace. So that is the interface between your company and the world around you. And your strategy is tested in the marketplace. So, kind of, what happens in the marketplace - you need to have ways to learn from that so that you can adjust your strategy so you can adjust your business. So it's like a double feedback loop rolling back and forth, back and forth, back and forth and that is the best and easiest way to test a decision.
Decisions kind of get tricky in a way when the decision is good because sometimes you make all the right steps or all the good steps for example, but the outcome isn't what you wished for. Was that a good decision? I would say yes, if it was based on the information you had at hand. Maybe it didn't turn out as you expected, but you cannot have a guaranteed outcome every time. For example, innovation is a great area where that applies. No matter how well you work, there is no guarantee that your innovative product process, whatever, will be a great hit. Even if you test everything, it happens. But you still have to make the best decisions because then you increase your likelihood of actually succeeding.
What Dominik and I did is take all of these discussions and try to paint the picture of, “Hey, this is let's say a traditional approach to the strategy and this is a bit more modern approach to the strategy.” And I invited Dominik and we did our best to avoid the pitfalls you know of painting it black and white. “Oh, the old is just bad. And the new is just good.” I really dislike those LinkedIn balls or images, you know, when it's very simple. You know, project thinking bad, product thinking good. Things like that because reality isn't often like that.
We have done things in a specific way because they're working, it's not just cargo cult. So that is the approach we have taken to trying to, kind of, paint the old and let's say, a bit newer way of doing strategy. So one of the big differences is, if you look at traditional approaches to strategy, Dominik already said that they're driven by management consultants and similar, and it's very very heavily planning-focused. So we try to kind of foresee everything that might happen and then we create plans and then we stick to the plans.
While with, let's say, more modern approaches, we start by defining the outcomes. “This is where we want to arrive and then we keep monitoring, we collect data from the market, etc. Are we going in that direction? If we are not, we should be adjusting.” Which is again, taking us to the next level in a, let's say, traditional approach, it's more about quarterly and annually, or biannually, whatever checkups of the strategy. Because, for whatever reasons, because it was a planning approach and it took six months, you don't want to repeat that process. So you want to have it spaced out. While in a modern approach for today, strategy, it is about real time decisions. Real time data gathering, real-time updates from the feedback loop which we had just been talking about. And again, when we start looking at traditional, it is more external expertise, getting in people to help you complete the whole process, getting in external analytics, getting in external insight and then trying to parse that and feed it back into your strategy.
While, ff you look at all the modern digital companies, one of the things that they have the benefit of is much easier access to data. So they have these continuous measurement systems that they can feed directly back into their strategies and into their business. So how our customers are reacting, was the click-through rate was the customer acquisition cost was, you know, customer lifetime value. It becomes so much easier when you have all the data at hand. And as I already said, traditional strategy is often considered like a sacred object. “We made our five-year strategy plan, we stick to it, we execute, execute, execute.” While a modern approach is recognizing that, “Hey, if we had the data, we had made the best decision we could at that time, we made the best strategy we could have. But that can change at any moment. Therefore, we have to recognize and accept that our strategy is dynamic.
Dominik, I love the last one. Maybe you can kind of expand a little bit on it. Because, we say that in tradition it's kind of like data is a reporting tool but modern systems use it as a strategic asset and I just love that statement.
DOMINIK: Yeah, I think that's something that you see like with many companies and when you when you tell them like, that's basically going back to something with these reports versus measurement kind of thing. It's very similar. You go to a company and they tell you, “Yeah of course we have data. Of course we use data.” You tell them, “Yeah. But, so how long does it take you to figure it out, to answer a question like why did we make this decision last time and how did it influence our business?” Then people will look at you and say, “Wait, I need to look it up in five-hundred Excels, two-hundred PowerPoints and maybe someone wrote something on a paper and we threw it away.” That's basically this mindset of using data as a reporting tool. So you put something in an Excel sheet and a statement that one of our clients said that, “I really like this, maybe Microsoft should reprint Excel and PowerPoint as the strategic management software because that's I think the most popular IT tool that is used in this context, because in the end it's like, “I put something on an Excel sheet, after that I put it in a PowerPoint slide, I go to an executive, I show them the numbers and said ‘this is what will happen. This is basically how we did.” Thumbs up and thumbs down and basically the decision is made on that. But what you actually want to do is like you want to kind of take this data and to store it in a way so you can reuse it. So basically, you have a way to say, “Well, we don't need to make, for example, competitor analysis again, because another department was doing it two weeks ago and we assume nothing changed in these two weeks so we can reuse it there.” And then someone else is saying, “Yeah in this data asset, we also know that we have data from another market here. Now, we can put this together and basically reuse those kinds of things.” And then, it's also easy to go back and say, “What was the state of information we had when we made this decision in the past?”
This kind of mindset of starting to think in this way is super crucial because typically, when you are in organizations, they all think about, “Yeah, we have so much data and we have, it's a value and we need to leverage it.” But the most important data that they actually could have, but stored basically in the formats they cannot reuse it, and it's in this field of strategy. And no one there kind of thinks of it as a data asset, they all think about their machinery data or the data of the ERP system. And it's super important to shift this mindset of. It's not, it's not just to show what you did. It's really about using it as a strategic asset
BRUNO: I completely agree with you. Excel is a management system. Unfortunately, it happens way too often so it's kind of a spreadsheet as a way of managing. But it's completely accurate. Yeah. As I said I just love thinking about data as a strategic asset, and we are going to be talking a bit more about four critical data sets in some minutes. So, before we move to that, we have a few more differences or differentiations between traditional and new. And again, in traditional, there is a very big emphasis on financial outcomes. That's not bad. So as I said, left and right isn't good or bad, it's kind of just the traditional standard approach to the strategy while right is a more modern approach. So in a more modern approach, we do take financial focus but we add cause-and-effect relationships. And Dominik was already saying earlier, during the stream, how it's difficult to actually understand this causal relationship because we often think in linear relationships. But unfortunately, it isn't that easy, especially in the complex environment Dominik has described earlier. So the modern strategy accepts that and then tries to figure out these relationships and accepts that some of these relationships will have to be surfaced through actually doing the thing, not being able to necessarily foresee in the future.
But the assumption is, if you have access to data, if you have a good decision-making system, then we will be able to identify this relationship. Identify these critical levers to drive the business to make the changes that have impact. And then that ties into the next differentiation, of course, when you plan something for six months and then you create a beautiful sacred document, it becomes kind of dogmatic in its approach. This is what we made, this is what we're going to do. While the modern approach to strategy cannot be anything but pragmatic. We are facing real data. We are facing innovation in the decisions. This has been our product services, whatever we decided. Buy new technology, buy new systems. This is what has happened. This is the result. Now we have to adjust based on the data, based on the insight that we have.
And again, what Dominik mentioned earlier, moving from business unit thinking, silo thinking, to more of business model thinking. So business models, the definition I like to use, basically defined is - how you create, deliver and capture value. So inherently, that is much more holistic and systems-driven thinking than thinking purely in business units. Pure business units work if you're one business unit, because then it's a total business so you kind of go outside of it, but the moment you have three, five or more business units. Eleven support units, you're getting in a total mess and if you then just allow the strategy to be purely just business unit driven, you might get some local optimizations. But you might actually hit local maximum instead of a global maximum. And again this is something that most people have recognized, but when they go back to their work, to their situation, they will still focus on their domain for a multitude of reasons. But we have to break out of that thinking.
And then again, the difference. So both approaches want to make the best decision. But as I said, in the modern approach to the strategy, we recognize that best decision in-the-moment as fast as possible. So what Dominik said earlier, it is okay not to make a decision. It's not okay not to make a decision because you didn't know what to do. So kind of, if you decide, “We are going to wait this one out.” That is a decision that we have to respect if it's based on data and the insight. But if the time passes because you were indecisive, because you didn't know what to do, or how to approach this, that's not the same. So again, a modern approach takes this. “Okay, we have what we have, let's move as fast as possible to make the best possible decision at the moment.”
And finally, at the end, what we, we've been talking all the time about it. Modern approach to strategy really tries hard to combine human intuition with data. It tries to make us super decision-makers. It does not try to replace us with an external expert or an AI or some algorithm to make the best mathematical decision. It tries to combine the best of all the worlds while usually traditional approaches, you know, forget about the Johnny, forget about this and this data, get the market expert, get the industry expert, get the panel of experts, you know, pay professionals to make this for us to make the best thing for us, and then we will take it and run with it. Unfortunately, that's not how human psychology works. So it doesn't always turn out the best.
And as I said, I will now turn off the invitation.
So these are kind of just highlights. And as I said earlier, it's not bad/good. It's like, “Hey, this is what we believe is the best in the traditional approach to strategy and the best in the modern approach to strategy.” And it's often the case as you start to transition from one way to the other way, it's a combination. It's not A or B. It's kind of, you know, some things we maybe still prefer that approach because that's still relevant for our business. For the way we have always done things, it gives a result. It makes sense, therefore, we keep to that, but we start taking some practices from a little bit more modern approach to strategy. Hey, we recognize if you want to combine data and insight. We want to make decisions faster. We want to focus on outcomes and relationships, not necessarily just on objectives and etc. So it's more like a transitory period. And I know Dominik, feel free to share, I mean your Decision OS helps people actually, in organizations, achieve effects like this to achieve a more modern approach to decision making and strategic planning, right?
DOMINIK: Yeah, totally. And like you said, it's not that you kind of need to switch in one moment from A to B. It's really about helping your organization to start with shifting from a traditional way to a more modern way and to make it easy for you to get all the support that you need for that and then constantly kind of move there. And I think it's worth it already when you start with small things and you might start with trying this approach for very small parts. Let's say, you're now going for a strategic decision and you just want to try out this new approach of decision making. It's definitely worth trying it and then seeing the benefits. And then constantly shifting because it's also a shift of mindset, in many areas and many roads. I think this is something that comes over time, but that you can start and hit quick wins super easy.
BRUNO: It's always about those quick wins, right? No matter what you talk about change, it's always quick wins, quick wins. Now, I'm looking at the time and I just want to shortly touch upon people that can benefit from this approach and then actually move and spend maybe around ten minutes on different data types that are critical because we are swimming in so much data. So I think it's very important to realize what type of data is actually most useful.
So back to this. We are focused very much on strategic decision-making, but decisions don't happen just with one person. At least not in bigger organizations. There are different players and we just talked about this, I don't want to call them personas, but roles. Of course, your place might not have these exact roles but they will be doing these tasks. If you look at it, you have executives that have to ultimately make the decision but they get the data from people who are at the front line, they do some analysis, be they business analysts or not, but you know, they can collect the data, they feed it usually to their managers, managers try to interpret collective and more data, add to it and then present some scenarios. Some options, strategies, possible decisions, they present that to executives. And then they together are trying to make the decision. So, all of them benefit from different types and different processing of data. To business analysts, it is about completeness and having up-to-date data because they're trying to collect what is the situation right now. While to the manager, it is about efficiency and speed of collecting data, interpreting data and creating options. And then to executives, it is about being able to respond to what is happening in their business context and they want to be consistent in their response. Not to decide one day to engage, decide not to engage or thirdly to ignore or something.
So there is this thread between how everybody's organization uses data in the strategic decision making context. Because decision-making, as I said, isn't just for the elite or for someone somewhere. Everybody makes decisions all the time. We as humans are very sophisticated decision-making machines. We are making them all the time. So everybody benefits from using data and intuition to augment their strategy. Now as I said, I don't want to spend too much time on this but actually the next thing which I think is very cool and very important.
So Dominik please tell us more about this. What are we looking at?
DOMINIK: So we basically try to structure the most important data sources that you have in your organization, or that you should use for augmented strategy. And, first of all, when you look at this you should kind of see that most of the times when organizations talk about data, they call on the on the lower left, the operational data that they have. So they say, “Yeah, we have so much data in our CRM, we have so much data in our ERP system, on our machinery data and so on.” And this is a very, very internal focus. And its, second of all, a very, let’s say a traditional focus on what we actually think data is. We always try to think of data as something, some numbers, right? So when it's not a number, it's not data. So I cannot use it. But there's basically two other dimensions that we have here. First of all, we need to go outside. So like, like you told us before, it's basically your business that is operating in the world. It's not the world. So you need to extend to what's outside.
And basically, this comes to what we call ecosystem data. So ecosystem data, we kind of called all the data that is around you that can be about competition, technology trends, that can be about macroeconomic conditions, could be something like how the COVID situation is influencing inflation in your country. All this kind of information that is around you and that kind of gives context to the operational data that you have, let's say, to your revenue numbers. And then you have, apart from this internal and external, you also have data that kind of tells you what has happened, and you have data that tells you kind of what will happen, data that is more leading. And many times when we show this to people they kind of say, “But what do you mean? Like, what's customer experience in terms of data, what's expert knowledge? It's not data right?” And they think about it in this way because you haven't formalized many times in ways that are not easily accessible.
For example, text data. Maybe you can use data about customers for their feedback. There is so much information about the emotions they have about your product, how much they like it. This is basically the, the data that kind of the in the end defines if they will buy which defines if you will see revenue and your operational data, maybe in the next quarter. And with technology, we have this great ability to use this kind of data that is soft. That is unstructured. That is not easily accessible for most existing systems. Or when humans want to analyze it because if I give you twenty-thousand customer reviews, you will need some time to go through all of them and say, this was Customer A and he was happy and he basically said we should improve that. That's not a usable approach. But with technology, we are able to analyze this kind of data that was traditionally not seen as data and this basically extends the scope.
And of course also the expert knowledge that we have, like Bruno said, when you have some expert in the domain and the expert leaves the company you don't want to get all the knowledge lost. So you need a way to, first of all, formalize this kind of knowledge so to store it and then to use it as a data and not to say, “Yeah actually we made a Internal Wiki or Confluence page some years ago but again, no one is able to check it because so much information that it takes me too long. So I decided not to even look at it.”
So when you start thinking about it as a data source, and you kind of store it already in a way that you can use it again, it makes your life much easier by changing your mindset about it. It's not, it's not just something sitting there for humans to look up. It's also something that can be a real data asset that you can combine with all the other data and then basically use this for decision making. And that's how we came up with this for super important sources of data. The classical, operational data inside your company. The ecosystem data about what's happening around you, like customer experience. And the expert knowledge that you hold inside your organization.
BRUNO: And I think that that was an amazing summary and we could be talking much more, but there is a very good question coming in. So I will actually switch to that for a moment.
Let's see if - yeah. So Pascal is asking us, “In the simplest way, in the simplest phrasing, what's the difference between classical data-driven decision making, and the type of that we are talking about, augmented strategic decision-making.” So maybe Dominik will have a sentence on that. Then I'll have a sentence on that because we are asked in a simple sentence,
DOMINIK: Okay. And I think the main difference is that when you look at classical data-driven decision making, it's already something that is quite recent, it's emerging. But then, many times you look at problems that are super small, right? So for example, you are in marketing and you want to make a good sales campaign. So what you do is you make a decision based on data, maybe what's the best LinkedIn campaign, what's the best audience? This kind of thing. So it's a very, very isolated problem but that you want to solve. When we talk about augmented strategic decision-making it's a much more global problem and it includes much more different facets of the world that we need to integrate which comes back also to this complexity. They have some relation between each other that we kind of need to figure out and this basically makes the problem much harder but also creates much more value for the organization if made properly.
BRUNO: That was right. Wasn't the sentence, but it was great.
DOMINIK: I tried.
BRUNO: I'll try a sentence. So augmented strategic decision making is about high stakes, high systems, broad systems decision-making. So it's kind of systemic, it's across the organization and it will lead to something important at the end. Of course it can be smaller decisions. Now I went out of one sentence too, that was four sentences.
Well, Pascal, we definitely need to think about a one sentence summary, so thank you for that invitation. And what maybe helps is actually the last thing that we want to share for today before we break off and that is actually the roadmap towards augmented strategy because that's also in the approach as we said. Augmented strategy is about combining human intuition with data. It doesn't posit any of those as superior, but as equal in our attempt to make the best decision. And there is from our practice, with our clients, a specific way that's fastest and produces the best results. So Dominik, maybe you can quickly walk us through this roadmap and what did we mean by this?
DOMINIK: And so, what we try to say here is that first of all, the first step to get started as you need to identify the decisions that are relevant for you. Because like I said, you want to start small and you want to see results quickly. So it will not be my strategy. It will be a small kind of decision and typically you use the ones that are the most relevant for you, which means basically the ones that have the most impact on your business. The second step is to start measuring things like being active and thinking also, it's not about this kind of data we don't have. So we forget about it. it doesn't matter. We need this kind of information, so we need an approach to measure it. And the third thing is starting connecting this data across the business. And this is something that we see also many times that you have companies that have acquired sophisticated ways of analyzing data. But it's super siloed in different departments and it never goes up to the executive to make this decision. Connecting this gives you a way to basically use what you already have.
And the fourth thing is to start then also with monitoring your decisions. So what did we decide, what was the information we had, and then how did it basically influence your business? So if we make a decision that has to go in and grow and to increase our revenue by twenty percent then we need to monitor it. Does it actually improve our revenue by twenty percent? Maybe it's just fifteen percent? Maybe it's fifty percent? Or maybe it's not increasing at all. And then basically going back to the way of thinking and starting reasoning about why it was the case? What was the reason, was the decision wrong or maybe just a few things.
And the last thing is all about culture in the organization. So to make this scale across your organization, it's extremely critical to make this part of your organizational culture to start thinking in a way of augmenting versus automation and to give executives a way to better understand data. Because, obviously the way how data is represented influences you and all this kind of thing. So if you have this kind of data literacy inside every executive and also inside everyone else in the organization, it will help you to basically use data in a valuable way. And to also spread it across the organization.
Like I said, it's an approach that you can start with small steps. Basically with a single decision, go from there and then expand it decision by decision, department by department, and basically at the end really do it for the whole organization.
BRUNO: So I'll just remove the image and I just want to share. Again, I'm looking at the time I see that we're almost out of time. So I just want to share, you know, we shared so much today and again everybody that was watching, you know this is being recorded so you can go back, take notes, rewatch different parts. Basically what we covered today is our approach to improving strategy by using the data, specific type of data, and human intuition to make better decisions.
We covered, you know, why is that important in the world today? How and why are different traditional and modern approaches to strategy. What are the four critical data sets. And a proposal for augmented strategy in your company. And there was a great question around what is actually augmented strategic decision-making. So as we try to say in one sentence, it is for high stakes, systemic decisions that span across the organization. They can be small, but they still span across the organization and they will make an impact and it's important to cover all this.
Now, we also tried to put that in a book. I don't know Dominik, if you have it at hand?
DOMINIK: I have it.
BRUNO: Oh you have it? Yeah, it's print, copy print proof, and yes what Dominik and I said, like, this is very important. Let's try to put it. And to those that are interested, let me just share. So, the title of the book is the same as the title of this livestream. (Augmented Strategy: How to combine human intuition with insights from data to make better strategic decisions, faster.) So we tried our best to put everything in a very short and usable format. So when Dominik and I were discussing, like there are so many books out there on data, data analytics, data strategy, normal strategy. I mean, there's a bunch of them to my right side and they're all thick tomes. They are great for learning, but if you just want to take it, dive into it and start getting better decisions. Start improving your strategy. It usually, unfortunately, doesn't happen. So we actually challenged ourselves and said, “Ten thousand words is the limit.” And we managed, we managed to keep within that. So the book is actually going out on Amazon today and we have some cool bonuses running until January tenth, something like that. So if you actually managed to order before January tenth, you can get all the different bonuses from digital copy to some other elements as well.
I think that it, Dominik, for today? I think we really shared everything about augmented strategy and we're looking forward to also expanding on that, creating more content for everybody so that everybody can benefit from such an approach. And as I said, Dominik is a very humble guy. He's actually Dr. Dominik. So, this isn't just a fun site thing, but it's based on a lot of research and practice. Using that in the work with our clients, helping them improve their strategy, be more innovative, get better results. So in my opinion, it’s the best of both worlds. Pragmatic application with - what would I call it? What type of research, practical research! Pragmatic application with practical research.
Any closing words Dominik?
DOMINIK: Yeah, first of all, thanks for listening. And if you want to learn more, feel free to basically get a copy of our book or reach out to us directly and we are happy to help your organization and see how we can basically create value for your organization together. So thanks everyone for listening. It was a pleasure and I hope to hear from you soon.
BRUNO: Thank you Dominik for tuning in and sharing generously. And as Dominik said, thanks everybody for listening. Take care.
© 2021, Bruno Pešec and Dominik Dellermann. All rights reserved.
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