#123—Christina Alaimo: The Hidden History of Data and its Role in Modern Strategy

Cristina Alaimo, is the Assistant Professor of Digital Economy and Society at Guido Carli University in Rome, Italy and soon to be Associate Professor at ESSEC Business School in France.  

Her research focuses on innovation catalyzed by data-based services and the consequences it has on organizations and society. Cristina also studies the broader ecosystem of data exchanges in which digital platforms are embedded and how these new platform ecosystems emerge and evolve. 

In her recently released book, DATA RULES: Reinventing the Market Economy, Cristina and her co-author Jannis Kallinikos, dive deep into the unprecedented social and economic restructuring brought about by data. 

In this episode, we discuss: 

• The fascinating history and role of data in our society—well before even the tech boom, with its origins in writing itself 
• How many of the social constructions that we know of, even our own digital identities, are shaped and created by data 
• The four functions of data—and how business ecosystems are evolving the traditional functions to create new business models and value chains 
• What business leaders need to know to seize the new opportunities the evolution of data is creating, even breaking out of the traditional concepts of industries of business as we know it today 

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Episode Timeline:
00:00—Highlight from today's episode
1:01—Introducing Cristina + the topic of today’s episode
2:55—If you really know me, you know that...
3:48—What's your definition of strategy?
04:52—The historical function of data
08:40—The link between data and institutions 
11:55—The interrelation between data and writing
15:12—The four functions of data
18:08—The data making process
22:43—Data's impact on ecosystems and platforms 
26:38—TripAdvisor case study
30:25—Redefining industry concepts
33:02—Future of competitive advantage 
37:44—How can people follow you and continue learning from you?
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Additional Resources:
Link to book: DATA RULES: Reinventing the Market Economy
LinkedIn: https://www.linkedin.com/in/cristinaalaimo/
X: https://x.com/cristina_alaimo

Thank you to our guest. Thank you to our executive producer, Karina Reyes, our editor, Zach Ness, and the rest of the team. If you like what you heard, please follow, download, and subscribe. I'm your host, Kaihan Krippendorff. Thank you for listening.

Follow us at outthinkernetworks.com/podcast

Transcript

Kaihan Krippendorff: Great. Well, lovely, Cristina. Thank you so much for being here with us. It is a pleasure after reading your book and listening to other podcasts to finally get to ask you some questions directly.
 
 Cristina Alaimo: Thank you so much for having me.
 
 Kaihan Krippendorff: I'll be honest with you. It took me a week of sitting on the beach reading your book to really give it it's due. It there's so much we can cover, so many questions we can cover. I'm going to try to represent our audience who are lead strategy and innovation, a large enterprise and ask the question that I think that they want. No.
 
 We're not gonna be able to cover everything. I'm gonna start with the 2 questions I always start with this on this podcast. The first is just to get us to know a lit you a little bit more personally, could you complete this sentence for me? If you really know me, you know that.
 
 Cristina Alaimo: Well, that I've been working on data for over a decade now. So 10 years of data work, here represented in this podcast. No. And I've been working in 3 countries now. I was I'll be starting actually in September with the third.
 
 But, you know, like UK, Italy, France, and I'm a I'm a very avid traveler and love traveling and a very avid reader as well. So Yeah. I think that's pretty representative of me.
 
 Kaihan Krippendorff: Awesome. Awesome. And you're originally from Italy. It's How did they benefit me? Said it.
 
 Yes. Awesome. Your English is better than mine, however. So Oh, impressing. Second question, what's your definition of strategy?
 
 Cristina Alaimo: Yes. So here, you'd touch me on something that I really, really like because I do think the strategy provides future orientation. So it's a kind of a plan production. But I also think that future end tech orientation is not provided only as a plan. That you, you know, walk through or you respect or you set out and then you actually achieve.
 
 But also emerges during interaction with the environment. You know, traditionally environment for strategies or leaders, strategic leaders in markets, of course, competitors, collaborators, or other companies. But today, this environment is made of data, and that's what we think, and it's how we think data as we intend and we conceptualize and how we study is set to change the landscape or strategy and our own ideas of strategies as well.
 
 Kaihan Krippendorff: Great. And when people hear that answer, they're thinking of digital data, I think. They're thinking of the new data forms of flow of the IoT devices spitting out information shared with APIs and processed by AI. What I love about your book is and at the very beginning, you start talking about these clay pieces that represent data. So first, shift our perspective so that we're not thinking of just the novelty, the new role of data, but talks a little about the historical function of data.
 
 Cristina Alaimo: Yes. Absolutely. We learned so much ourselves by looking back at the history of data. And I think we learn it because we sort of jump over the misconception that with digital data editing a new, blah blah blah. Well, partly is, and perhaps we will say something about what is new.
 
 But there is also some continuity here. That we can trace if we look back at, for instance, what the function the data had before they were digital. Because strategy leaders or company leaders know very well, they have always been there. And even before modern corporation companies or business, they have been there in our societies, and they helped. Our societies developed.
 
 And you mentioned Clayton. We have this example from kind of almost 8000 years ago, so we really looked back in this case. So this PlayTalk, and they were little sort of artifact, little shapes, like cone shape, little clay tokens, or cylinders shape. And the each of this different shape represented something like 1 cheap or 1 working day or someone or a goat, I don't know, something. And they were used as token to signify transaction.
 
 So for instance, I was giving you a ship, and then you were giving me back 1 of these little cylinders play token, and this was sort of, you know, like, an evidence of transaction. All they were using, for instance, when you know, ships were transporting goods. So for instance, in a satcheel, there were different kleitokinen, and this was like a sort of you know, receipts.
 
 Kaihan Krippendorff: If you Like, a receipt, like, an accounting of what's in the yes.
 
 Cristina Alaimo: Absolutely. Yeah. And but we because of this, all of a sudden, it was possible to work on the idea of exchange, not just on a given exchange happening in an even moment. Because if I have my little bag with my little clay tokens, then I can go to someone else, and I and I can say, look. I have this stuff.
 
 Can you ensure my trip? Can you ensure my trip? Or I can go back to the temple. The temple was at that time, you know, the sort of administrative center. And I could say, look, this is what I got in the whole months.
 
 And so you know, calculate my taxes on all of these goods that I have been, you know, sort of exchanging. So Here, I think this is very helpful because it gives us the idea of the function of data for society. Yes. They are not just something that helps us help us, you know, with the administration. They do help us understanding the word in your ways.
 
 Right. And that's what we mean. What by women, like, cognitive artifact. Cognition, like, all of a sudden, an exchange is something we can work on, and at the same time, we can build our own institution because we open up our mind or a different type. Talk to
 
 Kaihan Krippendorff: yeah. Talk to me more about that because 4, I started looking at your work. I kind of thought of it as there are these institutions, and they couldn't run themselves well, but then as they learned how to create, manage, and analyze data, it became easier to manage. But what I'm getting from you, it the causalities not Unidirectional. What was the what's the linkage between the merges and advancement of data and institutions?
 
 Cristina Alaimo: That's a very good point because we tend to think that data tools that we can use to reach some goals. And this is very much linked to what we were discussing before, linked to strategy, the definition of strategy. But in reality, if we stand, our understanding of what data are, how can they be used. So they function for society and organization, we start discovering interesting things. For instance, new data require new way of looking at things which require new institutions.
 
 Example, medical records, patient records. They foster a different profession, which is the profession of doctors. Which is linked to different institution, which is hospital. Or all these other things, they wouldn't be there. We doubt this tight link between how we work and the way we know things.
 
 Which are linked to the data we can gather or we can produce around certain things such as patience. In fact, in the book, we go, as far as to say, that certain kind of data, a new kind of data, they produce novel objects. And biopic, we mean these ideas of the words that get crystallized Uh-huh.
 
 Kaihan Krippendorff: And like exchange being an object.
 
 Cristina Alaimo: That I Absolutely. Corporations as I just mentioned. Yes. For users, if you think about you, in our own days. Right?
 
 In in today's world, what is the users? A user is a collection of data. It's not me. I'm Christina, but, you know, I'm very different on what I look when I browse the Internet. You know?
 
 Yeah. My user profile is the representation, not of me as a person, but of my data behavior. So all the data collected around my behavior online that become a user profile name, Christina, in reality is a total new way of looking at Christina.
 
 Kaihan Krippendorff: So let me let me see if I can let me try to restate what you're saying in my words, and you tell me where I'm off earth. What we're saying is that many of the social constructions by which we understand are modern world or our ancient world were enabled by the creation of data. And it allows us to now operate at this higher at a conceptual level. Is that
 
 Cristina Alaimo: That you did it beautifully. Yeah. Absolutely. Yes.
 
 Kaihan Krippendorff: Love it. I had this amazing. Amazing. I wanna go back to the clay just for a second. I thought it was fascinating.
 
 You have these clay pieces. But then you'd also talked about now you also have these, like, clay balls that would wrap around a bunch of pieces describe those and and what social construction or what social artifacts does did that represent?
 
 Cristina Alaimo: Well, it's very similar to what we just said about a user or, you know, a patient. Once you have all of this little clay token in a search, her back or ball. Then all of a sudden, they tend to sort of be less evident, less relevant, and what comes out as significant in that moment is this bowl. For instance, in the case of plateau pen, that was 1 that was then prescribed with something, you know, and you will signify, for instance, another thing, another major, a major, or, for instance, attacks able year. I don't know.
 
 Like, well you know? And all of a sudden, you know, a taxable year is not something that they exist in reality. You know? You don't damage it. You don't manipulate it.
 
 You don't see it on the road like a user. You don't see you don't encounter like, you know, on the road or a patient. If you think about all of these entities, what do they have in common? They are socially constructed through data. And they operate at a different slightly different level of reality that allow us to work a society.
 
 With our institutions, with our own idea about each other, about other organizations, with our own rules, understanding of the world.
 
 Kaihan Krippendorff: Fascinating. So yeah. I understand. So I instead of me showing you a sheep, and pointing. I now have a claim.
 
 We have a common understanding, and that represents the sheep, but it is not the sheep. But we can then trade. I can say what's on the boat. Now this is before writing, and what I understand is writing is introduced in in society. Not to write about gods and write about religions, but it's actually to keep track of trade.
 
 But what's interesting what I learned from you with writing is that it's less malleable. It's like if I have to if I say, I'm gonna sell you 5 sheeps instead of 6 sheeps, I have to rewrite the tablets. Just talk us about where writing came from. Yep. Yeah.
 
 If you don't mind, just Yeah. Absolutely.
 
 Cristina Alaimo: So there are a few very imminent color that have done this new job, and we refer to them. And there is this kind of widely accepted notion that writing does not come, as you said, because, you know, or poetry or whatever, but really comes because of administrative needs. So because I need to write down stuff
 
 Kaihan Krippendorff: Like accounting?
 
 Cristina Alaimo: Record accounting. Yeah. I need to record stuff than is where we can trace back the notional writing. Now that's what we know so far, and I'm couch cautious on this because, you know, curiosity and a little bit, this kind of knowledge of very past history is rewritten as we speak with new technologies. And so Mhmm.
 
 I would be surprised to see the thing may change in the in the future. But what is fascinating, I think, is what you ramping is and it has been sort of started as a technology of memory. As a technology. Uh-huh. Because it records.
 
 Right? And by recording, you may think objectifying Like, it objectifies things. It's very similar to what we are talking about right now in terms of functional data records. Right? So once they are there, they are there.
 
 So there is, like, you work with them, not with your original thing any longer. And writing is a little bit similar. So it sort of crystallize, objectify something so much so that when we read the something that has been written. We are not aware of the context where it has been written, but we are aware of the writing. So we know stuff as they have been written down.
 
 We don't know if in reality, they were as they have been written or not. And this is something that, you know, it's really similar to what data do. So we talk and we draw parallels with writing because they have this in common. They are technologies, and, you know, we think a technology just as digital technology, but no. Data have, you know, sort of along history, and some of this history come not only from machines or, you know, from touring for from computation.
 
 Much of the history of data comes from the technology of writing before. And that's what we write forcefully state in the book. Let's look at all, you know, the ancestors of digital data, not just 1 side. Yeah. Because then we have we learn from that a lot.
 
 Kaihan Krippendorff: I love it. So, yeah, we're kind of tracing this history of data from clay pieces to writing. We are going to get to digital and algorithms. That's where, you know, but in order to help people, I think, follow along why this is important. Just talk to us about these the functions of data.
 
 I mean, for us, the immediate thought is, oh, it helps us communicate things, but you say that it does more than that. What are the functions of data?
 
 Cristina Alaimo: Yeah. We said 1 already, which is this cognitive function. So data help us understand stuff in your way. So even creates new stuff that help us understand the word in your way. I mentioned the example of, you know, for instance, the Clay token, and then and the example of Exchange is a great example because you didn't even have the idea of Exchange before.
 
 Yep. But there are so many things even today that, you know, popped up here and there just because we have data to understand stand and, you know, wrap our mind around it. I'm thinking about even climate change, you know. This is something that wouldn't be there as we know it today without data and technology linked to the production of certain data. The second function of data is the knowledge function, which is slightly different.
 
 It's not the same. Because 1 thing is to understand and grasp reality in new ways. Another thing is to build knowledge about reality. And here, the example around patients, doctors, and hospital, I think, is telling because But let's go to the strategy people. You know?
 
 Like, the example of how many different sort of knowledge tool strategies to use. This is linked, and these are data objects, like the searcher with Play token we mentioned. Because they are stuff that are made and constructed out of data. And they are linked to a profession, which is strategy, into a view of the world, which is, you know, let's draw a plan, and then and let's reach our goal, and let's know our environment you know, and let's operate within the environment we know because of this. But the environment we know is as such only because we are able to get gather data around this environment.
 
 And with this data, we navigate. You know, like, and we can track ideas. So that's very relevant because this is the function of data links to professions. To institutions, to organizations, to society. So that's why it's lightly separated from cognition.
 
 And the third is media and communication.
 
 Kaihan Krippendorff: Yes.
 
 Cristina Alaimo: What we are doing today.
 
 Kaihan Krippendorff: Yes. Yes.
 
 Cristina Alaimo: We are doing via data. We communicate via data. Organization, organize via data.
 
 Kaihan Krippendorff: Yes. So we have sense making knowledge making and the communicative function. Great. So now let's go from writing to now the technology that we use to capture and share and manipulate data goes to digital. Talk to us about this.
 
 You talk about maybe this is too far of a jump yet. The data the data making, like, that we're seeing acceleration in data making. Maybe just lightly describe how the digital technologies for whatever we do with data, how that changes things.
 
 Cristina Alaimo: Yeah. 1 misconception which is very diffused is this is the data given. Right? I mean, there are out there, you gathered them. For instance, I make the example of strategy, the environment.
 
 How do I know a market again? I got the data around it. But what we tend to overlook is how these data are produced and the fact that the data is produced.
 
 Kaihan Krippendorff: So just describe it creates. Absolutely.
 
 Cristina Alaimo: Absolutely. So we spend a little bit of time describing and starting analyzing really what comes what leads to data rather than what happens when you have data only. So where do data come from? So and who makes decision around certain data rather than others? So who decides which data from a something that we think about as a market needs to be gathered and which data representative of a market just to make an example.
 
 We have so many other example we can draw upon. But this data making process so how data are produced is very relevant because usually it's what hides all of this decision making that goes into the product action of data, which is there. You know? You cannot avoid that because every app of selecting something from reality, an event, you know, a machine, a person, a behavior is elective selection. A suggestion is that to take something and you leave something out.
 
 Kaihan Krippendorff: Like, history is written by the by the conqueror. Absolutely. As an example.
 
 Cristina Alaimo: Or, you know, a customer, you know, a customer, it's behind a customer. There is a person, but you will never recording everything about that person or everything about that customer. So you need to decide what you will record about that customer. Right. What do you need?
 
 Like, per at heights or ZIP code or what do you need? Or, you know, gender I mean, these are decision.
 
 Kaihan Krippendorff: Yes. And but that 1 1 should linked that to something that I wrote about or studied a little studied a little bit. So there was a time when companies were growing but they ran out of cash. And we couldn't figure out why. And then there was this concept or this social artifact that as you might call it, that was introduced, which is called inventory turns.
 
 So you'd measure the amount of inventory you had and the amount of inventory you sold, and you divided 1 by the other, and it told you how quickly you were turning your inventory. Costco is a company here which does an amazing job because it doesn't pay for its inventory till after it sells it. So it's got a negative inventory turn. But this data concept changed how companies were designed and built and solve this problem. How would you explain how would you explain that dynamic?
 
 Cristina Alaimo: I like this example very much because I think it's a perfect example of this cognitive function of data. Yeah. So you need to solve a problem. Which means you need to understand the word in a slightly different way because you need to change your composition about it. Eventually, it needs to be sort of treated differently if we want to optimize cash flow.
 
 So how do you do that? You start gathering data or you produce data, and you try to match this data with other data to create something new, a major, in that case. You know, that gives you an idea of what you have to do next. Right? So or how do you to behave, you know, vis a vis certain things.
 
 And there are plenty of examples of this very interesting knowledge object if you want because, you know, all of a sudden, inventory comes to be something different or to be managed differently because of what we know now about inventory or how we can optimize it. You know, monthly or yearly or whatever.
 
 Kaihan Krippendorff: And so now we have more data. There's this idea, 1 of the guests in this on this podcast Mohand Subravanya, he has the he uses this idea of the digital customer. So it used to be that your customer coming to store and buy 1 thing. You had 1 piece of data on them, which was what they bought and how much they paid. Right?
 
 But now if they're all going online, they are clicking and you're getting lots. So their customers come data, and then I'm also thinking about IoT devices. My smartwatch is measuring how much I walk. And so we have this explosion it seems in data making. We've already seen how just the adoption of Clay and Writing allowed for the creation of and changed organizations and entities.
 
 But it seems like now we're going into another era, and it's not only influencing entities, but You also talk about it influencing ecosystems and platforms. Could you just unpack that for us a little what what's changing and what are the likely implications or the possibilities that are opened up by this this changing towards Yeah. Digital?
 
 Cristina Alaimo: Absolutely. I think that there are many things to that we can say here, but I will limit myself to a couple of them. The first is the fact that we now have heterogeneous data forming, you know, this idea of customer, for instance, that you mentioned, to stay on the same example. But it is a genius data, which means, like, when we mix and match data that comes from different domains of life, it's fantastic because we discover new things. We open up you know, new environment, which means not only markets, but it means new ideas of word, as we mentioned, we can invent new services, new goods.
 
 Because all of us are then, you know, we can match, mix, and match. Different data together. A little bit like combinatorial innovation here just to, you know, be seem
 
 Kaihan Krippendorff: Explain you explain that word competitorial innovation?
 
 Cristina Alaimo: Yeah. We combine different stuff together, and we come out with a new thing. So Sure. 1000, we have a new idea just because we reshuffle and match mix and match stuff. In this case, the very interesting thing is that we mix we mix stuff data, they weren't meant to be together in the first place.
 
 Kaihan Krippendorff: So Mhmm.
 
 Cristina Alaimo: Right? So and that's totally new. That's something that happens today. It wasn't happening before because before, we had c load and vise. We had industries.
 
 So a car was made by certain knowledge, which means gathering data about the market of cars. You know, and, you know, supply chains of car production process and something that was strictly connected, but not much, you know, above it. Today, a car is made by all of these and by mixing and matching data on, you know, I don't know, digital consumption or an infotainment, what gets in the cars in terms of a software sense answer. Mhmm. Artificial intelligence and mix and match, you know, data and how they respond and how certain system responds to the stimulus.
 
 So as you see, there is doesn't exist in a way an a car industry as we knew it
 
 Kaihan Krippendorff: Yes.
 
 Cristina Alaimo: Or because It needs to consider so many other things and so many different data. And so data are heterogeneous. They aren't linked to knowledge, acceptance professionals, acceptance industries, acceptance way of doing things any longer. Wow. And that's connected to, okay, but do the institutions, the classic firm.
 
 The business organization we have, are they enough to deal with this complexity?
 
 Kaihan Krippendorff: So I'm gonna pause you for 1 second. Here's what I'm hearing. I'm hearing. Because data was siloed, and companies are defined by their data, they define themselves by their silo, which is to say by their industry or their sector. And now as data crosses silos, the concept of a company existing in an industry may not be relevant anymore or it may shift.
 
 Cristina Alaimo: Exactly. Yeah. Absolutely. That's what we say in the book. That that's what we say in the book.
 
 Kaihan Krippendorff: Yeah. You talk about TripAdvisor, for example, in the book. You make can you describe that for us? Just to help our listeners.
 
 Cristina Alaimo: Yeah. Let me say not to scare people that work in classic firm that this will not be, you know, like, a deal or not deal stuff. Right? Because it may seem like, okay. From tomorrow or everything will be different.
 
 No. We know very well that life is much more complex than that, but we point out to a gradual shift that can be seen a little bit more in certain environment, in certain domain, and a little less in other. So for instance, Tripadvisor in 1 extreme example. In the sense that it's 1 of the example in which this is activated a little bit more. What is it?
 
 Exhibit it here. What kind of company if you think a little bit Tripadvisor here? Where does it operate in which industry? Certainly, we can classify fight in a hospitality industry. Right?
 
 Because it deals with tourism and portals and stuff, but this is a late development. At the beginning, it was a search engine, you know, sort of crunching destinations, suggestions, search result about destination for user, more or less like a Google for holiday. Then with the advent of social media technology became a social media. I mean, someone that is as old as me may remember that we were using app you know, on Facebook about cities I visited, which was, like, a sort of plug in from Tripadvisor that you could use on Facebook and stuff like that. So what I wanted to say is that it was a social media.
 
 So user were producing content about the trips, the travel they had, They were posting it online. They were giving data because of this. They were producing data. They were leaving reviews and rating, and Come on. Even today, Tripadvisor is Tripadvisor because of the user generated content.
 
 He was able to yeah. Right? To invite, let me say. But then it changed and it started to become all in 1, the destination, like, if you wanted to book a hotel, if you wanted to book a restaurant, if you wanted to talk about tours, in a certain destination, you could go there, and you can do everything. You can browse.
 
 You can check. You can say, but you can see review, but you can also book call deals. And you have all of these services, data driven services like best value auto or real time destination updated with prices, availability, matching and stuff like that. And all of this is done because Tripadvisor creates cross sectors, which are marketing, you know, advertising, tourism, hospitality, social media, and content product, and so on and so forth. And if you study to understand that that's the power of this data, availability, heterogeneity, you know, matching directionality.
 
 It gives something which is a company, Contours, which are not any longer of a class Yes.
 
 Kaihan Krippendorff: Yes.
 
 Cristina Alaimo: Or anything of a class in the city.
 
 Kaihan Krippendorff: Yes. Yeah. And it seems in your read in the reading it, there's also this other conceptual shift of the industry itself. I feel like, let's say, we sort of blur the lines between industries, but also the value chain of mining and then adding value and then packaging and then making the product so that that kind of a linear value chain is also changing because of the fluidity of data. What do you think the new concept is for industry if it's not a linear value chain.
 
 Cristina Alaimo: Yeah. When an industry is defined because of data, like, there are data at the center, not like a classic product. Let me say a car. Right? Let's go back to the car.
 
 Mhmm. But what the industry offers or what in how the industry creates value is very much data driven. And by the way, many hard industry are shifting to our data driven services as well. So we see also this happening in traditional industry. There, we can see that the classic idea of supply chain with the linear model is not any longer valid.
 
 It's not the only way in which different organizations come together to produce value which is reinforcing for each of the actual results. Right? Which is the idea behind it. So that's why we started talking about ecosystems. So people instant paying back on that ecosystem today, digital ecosystem, innovation ecosystem, there are various school here and ways of looking at ecosystem.
 
 We look at digital ecosystem, but we really see that ecosystem take, let's say, really they kind of substitute both supply chain and markets, if you want. Yes. Reacting creating a hybrid, and they do so because have they have this power of managing in a way which is much more agile is data driven product or services
 
 Kaihan Krippendorff: Yes.
 
 Cristina Alaimo: Putting together, you know, different actors creating data complementarity. So instead of classic product or services complementarity, data complementarity. Right?
 
 Kaihan Krippendorff: Got it.
 
 Cristina Alaimo: And these observed rules, if you want, which are not the classic product to market rules, I mentioned who the car or any other, you know, classic example, but more they are more tuned into the data roads.
 
 Kaihan Krippendorff: Got it. So then where it says, I've got so many more questions, probably time for, like, 2 more questions. Kind of where I wanna go is it seems to me that in this new concept of industry, where people initially or really went to was to an old concept of owning the diamond mine. So it became data is the new oil. It became about controlling the data or at least controlling the access to data.
 
 But I also am starting to hear things like, oh, no. Data is the new water. And there's a sort of debate. So my question is whether you have an opinion about whether it's gonna be oil or water, but kind of beyond that, where do you sense competitive advantages come from? Because these 4 profit institutions exist to create value and then capture value.
 
 How where what are the what are the strong points? Or what should companies be focused on if it's not gonna be the traditional sources of owning customers and owning inputs and having economies of scale.
 
 Cristina Alaimo: Yeah. So a little bit of this can be answered through, you know, the kind of classic platform plus from strategy and ecosystem strategy's idea. But what do we do in the book and what do we really sort of try to drive relentlessly is that that's certainly not oil or not no resources that can be harvested. But, you know, we have we have gotten the vocabulary wrong, I think. Here is this is a this is an example of stuff that where the vocabulary sort of constrained the thinking mean, data are not like oil that is found out there or water that is found out there, data produced.
 
 Right. If we if we overlooked this, I'm not saying they are not resources. They are resources, but they are not that kind of resources.
 
 Kaihan Krippendorff: Yes.
 
 Cristina Alaimo: Yet our mind wrapped up into these But simplify the idea, the data can be harvested, collected. Think about all the vocabulary we usually use. Yes.
 
 Kaihan Krippendorff: Right.
 
 Cristina Alaimo: Like, we skim this passage, which is the real value. Production You know? Yeah. A real competitive advantage for company. How would you produce data that are new and they open up?
 
 Remember, like, the cognitive function of data? New ways of looking at things. Wow.
 
 Kaihan Krippendorff: That's great.
 
 Cristina Alaimo: Come on. And this is not something that you go out and you harvest, and if someone or some company has done it, you sort of block it because other companies would not be able to access that data because that company has had access before. Now I don't think that we can move forward as a society with that. We need to understand data in a more a slightly more complex fashion because they are complex artifact. Let's not discount that.
 
 Kaihan Krippendorff: Wow.
 
 Cristina Alaimo: Let's not start, you know, oil water potatoes think. Yeah. Let's go. You know? Yeah.
 
 Let's make our life a little bit more excited.
 
 Kaihan Krippendorff: You know? Fascinating. Thing. That's awesome. Yeah.
 
 And there may not be a physical analogy that offers a good metaphor for the for data?
 
 Cristina Alaimo: No. We just constrained our thinking by doing that and saying, you know, it got that first. Let's stop it. Or no. He has produce that.
 
 And if you acknowledge that, it when you regulate, for instance, things are much more complex But, yes, they should be much more complex because we are all wrapped up into a data society. Companies, customers, you know, stakeholders, governments, public, you know, the public and so on and so forth. So we need to be very mindful when we talk about data and respect the complexity of data as a cognitive knowledge, cultural, and communicative artifact that produce value for businesses, but also for society.
 
 Kaihan Krippendorff: Yes. Wow. Okay. I have got you the more I read of your work and the more I I it opens up more questions. We don't have time for more questions.
 
 I my questions are things like, you know, if the customer now is the employee instead, if we don't have industries, what do we have? We don't have value change. If we're not selling individual things, but experiences, how do you do that, and what is the underlying language that we should use that would more accurately not accurately describe data that opens up the possibilities of how we could use it and not be limited to the metaphorical language. So much so much that we could but how can people continue deepening their understanding, connecting with you, learning from you. Certainly, we highly recommend people buy get data rules, reinventing the market economy.
 
 How else can people connect from you, learn from you?
 
 Cristina Alaimo: We really wish to open a conversation which go even beyond us in the sense that, for instance, we do it. It's been an extraordinary conversation, and I really hope like that's This kind of different way of looking at data may we try to implants that seed. You know? Like, okay. Let's talk about data differently.
 
 And let's all talk about data in the sense that companies, you know, and people, customers, as a society. Let's talk about that. Let's reason around what kind of society do we want? What kind of For instance, how do we best define markets today when markets operate differently and what are the implications for strategy? I haven't seen that much of a conversation sparkled yet.
 
 Kaihan Krippendorff: Yes.
 
 Cristina Alaimo: So, you know, connect to us, but, you know, you are also doing an amazing job around this kind of stuff. Let's continue the conversation. The book, the website
 
 Kaihan Krippendorff: What's the website?
 
 Cristina Alaimo: Data rules dot org, and Yeah. And let's talk about data in a different way. We call it a social science of data rather than just data science. Yes. Break a little bit, you know, the idea.
 
 Okay. Let's open up and let's dig a little bit and see what's going on. So these are some of the ways which all participate into this important discourse.
 
 Kaihan Krippendorff: Beautiful. Well, Christina, thank you. For digging so deep and looking around so many corners for us, pulling it together in a book. I really think this could be 5 books. And I'm gonna read it again.
 
 And thank you for opening up this conversation with us. Thank you.
 
 Cristina Alaimo: Thank you so much for having me.