2022 – Year of the Chief Data Officer?

Ian Murrin, CEO, Digiterre 

0:00

Hello, everybody, thank you very much for joining the session, which is really asking the question Is this the year of the CTO, the chief data officer, and I'm joined by some wonderful panelists. So I'm going to ask them to introduce themselves in order, but I'll start with Enrico, and move on to Kevin, and Jose, and then say, eat and all that juice myself at the end. So Enrico, could you give us a minute or two on yourself and your background? And also your interest in data?

Enrico Camerinelli, Non-Executive Director, Supply@ME Capital PLC

0:26

Yeah, thank you. Yes, I am in recall committee, nearly done in from Italy. And the non executive board member of supply at me supply me is an inventory monetization platform, basically will turn inventory into digital assets that can be traded. And so this allows companies to offset the inventory value from their balance sheet. So the interest on data is quite significant. Because, you know, we have to turn physical assets into digital data, and therefore, the management of data and the constant correlation between physical assets and digital assets. It's extremely important for us.

Ian Murrin, CEO, Digiterre 

1:08

Thanks, Enrique. Very useful, Kevin.

Kevin Kindall, Senior Data Scientist, Hartree Partners

1:12

Hello, everyone. My name is Kevin Kindle. I'm joining from Houston, Texas. I'm a senior data scientist with heart three partners were a commodities trading shop based in New York, we trade, energy and soft and so on. And my interest in data is, because without data, you know, my I would not have, I couldn't do my job. I do work on a lot of valuation problems, I work on risk management problems. I do occasionally fundamentals, forecasting, and so on. And all that's very, very data intensive.

Ian Murrin, CEO, Digiterre

1:46

Thanks very much, Kevin. I definitely get to find out more about some of the stuff you do and opinions later. I'm looking forward to it. Jose.

Jose Lopez, Digital Strategy Director, TradeFlow Capital Management

1:54

Hi, everyone. My name is Jose Lopez. I'm the Digital strategy director preflop Capital Management. We're a fund management company that invest in a semi bulk commodity trading, and we do small trades up to 15 million US dollar, therefore we don't compete with banks are more likely we collaborate with them. And what makes us special is that we do not lend money, we enable the trade by owning the commodity and managing the logistics. Therefore, for us data is key. Our fine has a fantastic record of over three years and a half. We do praise globally. We're based in Singapore, I'm actually participating in this panel from Singapore, we're very techy. I like to say that we digitize as much as we can. And we use a lot of data, artificial intelligence Iot of things with the objective of becoming efficiency and scalable. The mission of tray flow capital is to help to reduce the gigantic gap of 1.5 trillion that exists in trade finance between the SMEs and the financiers. And for this purpose, we have partnered up with ICC with the International Chamber of Commerce to help to reduce this gap. It's an honor to be here in this panel.

Ian Murrin, CEO, Digiterre

3:11

Thank you very much. Jose, that's really useful. I look forward to hearing more about it. Saeed

Saeed Amen, Founder, Cuemacro

3:18

Thanks for the invite. Sam, the founder of Q macros is a quantitative consultancy firm, developing trading strategies and analytics primarily for macro markets. So it's particular currencies, but also we've done a lot of work on commodities as well. And as they say, I guess like Kevin, my interested in data, because it's it's a key part of what I do. So sometimes can be forecasting, currency moves. Other times, it just might be trying to create indicators as well. And I've also co written the book of alternative get data, a guide for investors, traders, and risk managers looking at all sorts of unusual data sets and how they can be monetized from a trading perspective, as well.

Ian Murrin, CEO, Digiterre

4:03

That is fantastic. I want to dig into that a bit later. So thanks very much say sticky on some of the subjects around, you know, monetization and stuff. So great panel, I feel honored to be kind of chairing this panel, and I'm the CEO and founder of Digitaria. We build both front office and middle office systems for organizations in the space, but also a lot of data architecture. So clients come to us to try and understand how to build an architecture that supports trading applications across the enterprise. So data to us is core to what we do and fascinating for me personally. So without further ado, let us roll on ice. My first question for the panel is what is the distinction between the role of a CDO which you know, on the face of it makes sense chief data officer and that of a CIO, which is a chief information officer when we're trying to make sure that the data gives us information rather than just as data versus a CTO versus a CEO. Perhaps that's easier. And finally Chief Digital Officer I'd love to hear from each member of the panel, like, what's your view on the distinctions between the two? And some of the sort of pros and cons of such a role? Enrico?

Enrico Camerinelli, Non-Executive Director, Supply@ME Capital PLC

5:09

Yes. Let's see, I've taken notes, here are all these different acronyms. So let's start with so the see saw the chief is not because you know, these people necessarily have to be heading among, you know, huge sort of groups of, of employees, but it's rather, you know, the responsibility you have towards the company. So in my for my understanding from my research, also, my daily job is doing market research market analysis, that the chief data officer is basically the one responsible to turning raw data into information. And so it's providing them the size of the hacker they say, you know, the raw material, which in this case is information to the Chief Information Officer, CIO, the Chief Information Officer basically takes the information that is extracted from the raw data from the CEO, and then basically helps the company understand what kind of information is useful, how should be managed, what should be updated, what should be removed. So the CIO is then the one who basically feeds the CTO, because in order to manage this information in a proper way, you need to have a sound technology infrastructure. And also the CTO CIO, the Chief Technology Officer is the one who has to make possible the data extraction, which is part of the CDOs responsibility, but also how to manage the data that is certainly intimate information across the company. So once the CDO CIO and CTO role align, and have, you know, sort of fulfill their tasks, the CEO, the chief operation officer can actually establish you know, how to run the operations how to do in the most efficient way, because he or she has all the necessary information, and has the system in place to extract information and manage it at the best way possible. And finally, the Chief Digital Officer, let's say, I would say is at the service of all these individuals. So, you know, at the end of the day, of course, you can do these things manually. But the best way to do it is to, you know, automate and have a digital access and digital sharing. And so I would say that the See, see the chief data officer, the Information Officer, the operations officer, and who else did I forget these, the technology officer, they are basic, all the internal clients or the digital officer.

Ian Murrin, CEO, Digiterre

7:49

That's really, really interesting, a very comprehensive So to summarize, you're saying back to the CEOs role is around raw data, the CIOs is turning that into usable information to run the organization that CTO is, in a sense, building some of the capabilities to get the data leverage the data surface a data is that and then the CEO, the Chief Technical sorry, the Chief Digital person, is a customer of that information. Is that a fair summary?

Enrico Camerinelli, Non-Executive Director, Supply@ME Capital PLC

8:14

I would say it's no, the the Digital Officer, I would say is the provider of the service of all the above the others? Because that would be my opinion. Brilliant.

Ian Murrin, CEO, Digiterre

8:27

Thank you very much. That's very comprehensive. Kevin, what are your thoughts as a gambling man,

Kevin Kindall, Senior Data Scientist, Hartree Partners

8:34

For keeping things simple, you know, when I think of a chief data officer, I think, I think in the context of physical trading, so the focus of the chief data officer ought to be on the data on utilizing the data as an asset, and coming up with various governance policies. So if you look at a typical trading desk, you know, you have price data, and so on, but you also have a tremendous amount of fundamental data that's extremely specific to that particular market. So there's a lot of specialness. So for those of you who do physical trading, you understand this. There is sometimes information sometimes it's quite obscure, but it's very, very useful for decision making, and gives you some sort of has some sort of forecasting value. And, you know, the the challenge for a CTO is when he looks at a large organization, he has to understand the business well enough to know that certain types of data is very valuable. There's a timeliness aspect to it. There's, you know, has been a useful form. For some desk, they may have programmers, you know, they're very comfortable with relational databases or data lakes. For other desks. They largely run off Excel. But whatever platform they're using, the chief data officer is focusing on the data, how its utilized, why it's important, and so on. When we get into these other roles you focus more on The IT platform, the delivery mechanisms and such.

Ian Murrin, CEO, Digiterre

10:03

Okay, good. Thank you very succinct, nice, clear dividing line. Thanks very much, Kevin. Jose, what are your thoughts on this subject? Yeah, well, it's

Jose Lopez, Digital Strategy Director, TradeFlow Capital Management

10:11

On both panels. And I would say I will just remark, in Rico's definition would be 99%, when it becomes a gigantic organization, I have the luxury of having every single C suite possible available, right, which is no longer the case. And all this always happens to be an overlapping between movies, the digital officer, who is Information Officer, ways, and so on. So reality of things that they do overlap. And depending on the size of the organization, I don't see particular in my organization that we could have four different specialties, and moreover, is the same person with different hats. So there's, there's a clear, physically or overlapping for them. And to the very good point, from Kevin, on on the data and the raw data, and so on. I do think that, from a macro perspective, what is critical is that data is wrong. It's absolutely valueless, without without bringing it to before transforming into information. And I would like to remind anyone that information is only valid at a specific time. If they have it's not ready, if it's not available, or is too soon, or raw, or it doesn't fall into the hands of the right person. That information is meaningless, right. So I support my both fair paneles definitions, I think they're great.

Ian Murrin, CEO, Digiterre

11:29

Brilliant, thank you very much, as I say, What are your thoughts? Perhaps bring you also your experience with alternative data sources

Saeed Amen, Founder, Cuemacro

11:36

Here? Yeah, definitely, I get a lot of work to say it's probably going to sound familiar given given some of the previous remarks. But the way that I would look definitely as a CEO gain would say, definitely somebody who's responsible for moving the raw data in some sort of structured form, which is usable by the rest of the organization. But I would say the CD obviously needs to have a lot of people reporting to them. Maybe data strategist to find the data data scientists to crunch the data as well. And at the same point, bringing to mind Kevin's point that the other roles are, I would say very much kind of to support the process. So you need to have a good tech stack and the CTO, for example will be will be responsible for that as well. So it's very much a team and team oriented player. But I would also agree that maybe all these subs are always going to be more for big organizations and the smaller firm, you know, you can't really justify having so many of these roles, but it's key, I would say to have a chief data officer to kind of drive change within an organization.

Ian Murrin, CEO, Digiterre

12:36

Great, thank you. So we've got reasonable consistency across the piece. Fantastic. So moving to the next question around, okay, maturity. So I've heard this a lot around our industry is people thinking that our industry is not as mature, as some industries might be maybe financial markets, or even the tech industries around the use of data, the gathering of data. So what can we learn from other industries with respect to and has spent them being further at the maturity curve? In Rico? What are your thoughts on that?

Enrico Camerinelli, Non-Executive Director, Supply@ME Capital PLC

13:08

Well, part of my most of my activity is working with financial institutions, but I do have a supply chain management background as well. So I'd say keep going to say back into my supply chain days, I would say data in that in that aspect. So supply chain management is not to be confused only with logistics. So it's not only moving your goods from one place to the other results are planning is understanding how to do it, when to do it, how much to hold, especially these days, we've seen with a with a actually these years now with the pandemic, how the full planning and logistics are are important. And so, in the in the supply chain world data is very much used, especially in the negotiation phase with suppliers as well as establishing for instance price schemes or establishing you know, networking logistics networking policies, because the more data you have, the more you can appreciate, where to position your manufacturing establishments, how to serve his clients and so on Also how to decide production where do you want to have Make to Stock so basically, producing based on forecast or producing on demand moving into the financial world, the use of data is even more robust and more important and coming apart of supply of finance, which is more related to commercial banking. But the most advanced that I see is on the retail side consumer. So we see now all the sprouting of these technologies called API's, or application programming interfaces that basically allow you know to capture, you know, the information or the preferences of the customer, and then providing services or advising you about services and opportunities based on on the on the data is collected based on permission, of course of the customer. So, I would say my best advice if anybody asked me one is not necessarily to look what's happening in your specific sector, but look at what's happening out in other sectors, and then try to, you know, turn those experiences and best practices into something that can be applicable to your own.

Ian Murrin, CEO, Digiterre

15:26

Good, I get that point. And so you're effectively saying, there are different use cases and levels of data maturity, but that's partly driven by need logistics and stuff like that. So it's not the winner, sorry, less mature, but there are things we can learn from other industries, which makes sense. So thank you very much, Enrico. Kevin, what are your thoughts on this, particularly with respect to the gambling industry, which fascinates me,

Kevin Kindall, Senior Data Scientist, Hartree Partners

15:48

But there's a lot of things that that come to mind when we talk about this. And, you know, in my view, you know, I've been in this business for a while. And it always seems that the energy business, particularly the physical trading seems to be a perpetual emerging market, it seems that, you know, we never seem to, you know, adopt the latest technologies, it seems like, we're always seem to use the same old, same old tools. You know, when I think of the data, I think of several different types of data, you have trade data, you've got price data, and even today, price discovery can be can be a bit of a challenge. And then, as I mentioned, before, you have this, this large amount of fundamental data, where you have supply and demand and logistics. And, you know, even like, when you talk about price data, you know, for the physical markets, a lot of this is still supplied by brokers. So, you know, there's not, you know, from the physical sign, most of that's not listed market, you don't have a settlement committee, or a settlement or an algorithm at an exchange that's providing a daily settlement price. And so a lot of this will come from brokers, and, you know, many times they will send this out either as a, an email or a PDF document attached to that email, or an Excel spreadsheet. And, you know, you look at this and you say, Well, you know, surely there's a better better way to disseminate price data, it's something that's more secure and more auditable? And yes, indeed, there is. But there's no incentive on the part of, of the broker community to do so. is almost as if someone has to come up with a solution. And essentially, give it away for free.

Ian Murrin, CEO, Digiterre

17:32

Got it? Okay. Yeah.

Kevin Kindall, Senior Data Scientist, Hartree Partners

17:34

Yep. But, you know, other industries,

Ian Murrin, CEO, Digiterre

17:36

He can draw upon, that you think do this better?

Kevin Kindall, Senior Data Scientist, Hartree Partners

17:40

Well, you know, you you can look at, at the data intensive industries, like Amazon, they do a lot of customer profiling, you know, when I go shop on Amazon, and click on something, but don't buy it, it's not uncommon, you know, to see advertisements for that going forward. And, of course, you have the casino business does an excellent job, customer profiling, you know, they monitor loss rates, and so on real time. So there's a timeless aspect to that. And, you know, in their business, it's, it's very interesting, because they all have the same games. And, you know, how do you attract people to your, to your casino, you know, in the old days, it was big, they would build a bigger building, and, you know, build a casino in the shape of a pyramid or something like that. But that became cost prohibitive, and your internal rates of return start to start to go down. And so rather than doing something bigger and better, you have to run a run a much smarter operation.

Ian Murrin, CEO, Digiterre

18:40

But really, we touched on this earlier, he's a brilliant, so you're really talking about tying the supplier to customer data much tighter in a tighter way than we do perhaps in our industry right now. Amazon does it brilliantly. I didn't realize gambling did it quite so well, between they do? Yeah, that's brilliant. Thank you, Kevin. Jose, what are your thoughts?

Jose Lopez, Digital Strategy Director, TradeFlow Capital Management

19:00

Yeah, I think the the example of the casinos and client profiling is probably the the best of the best examples. But I will look to the Guru's in my opinion, if you're talking about data, you need to look at Google, you need to look at Facebook, you need to look at visa, you need to look at PayPal. All these guys know, even Apple. They know I think they know you better than we know ourselves. They know exactly how much you're going to spend, what are you going to buy? When are you going to spend it? When can you go over the limit in and that might be a little bit more related to payment. But at the very end what they are doing is collecting raw data, absolutely raw data, how many websites so you clean how many times you do payments with your card, how many times you check your computer, and so on and define a pattern. So if I have to choose where which industry would I follow? I would say the tech companies are the ones that are actually leading The past of data analyses and, and creation of valuable information, which is the ultimate objective.

Ian Murrin, CEO, Digiterre

20:08

Yeah, cool. And I think that this interesting point, the creation of valuable information that they then act on is critical, I would suggest a lot of our participants to this event and others are in companies that have seas of data, it's just turning that sea of data into valuable information that you can action, perhaps in a more automated way might be the challenge. But thank you very much

Jose Lopez, Digital Strategy Director, TradeFlow Capital Management

20:28

not to take us to the point where Kevin was mentioning at the very end, the suppliers of informations on platforms, where traders can actually go and collect the information, either Limited is the same guys for the last 20 years. Yeah. So it is complicated to get the information. That's why I think we need to learn from from the masters of data analysis.

Ian Murrin, CEO, Digiterre

20:49

Right. Thank you, Jose, say, What are your thoughts on this particular? Again, I'd love your alternative data viewpoints all this?

Saeed Amen, Founder, Cuemacro

20:56

Sure. I guess my experience is mostly from financial markets. So I've worked in banks also done projects with quant funds, on the one hand, obviously quite mature in the space in terms of utilization data. And then also for commodity trading houses where, where typically the use of data is not quite at the same level as a as a large quant fund. I think the key point is trying to do what you can. So it's not a case of jumping towards the most complicated machine learning model. From from the start, just started, going on to Kevin's point about price data being in kind of unstructured forms, like spreadsheets, etc. So that's a good place to start all the data in terms of price data, try and capture that as best you can and put it into a structured form like a database. And once you have the data in a structured form, then you can start playing around with it and utilizing it and just take small steps to begin with. Sometimes people want the most complicated model, but usually the simplest thing is actually a good start and potentially could be quite useful. And that's the same I'd say for alternative data, don't necessarily spend months and months on a project and try and maybe start with a structured alternative data set to begin to kick your process off, I'd say.

Ian Murrin, CEO, Digiterre

22:12

So iteratively, effectively mine mine the seam iteratively and see what value you can get surface.

Saeed Amen, Founder, Cuemacro

22:18

So yeah, definitely. I would agree with that. Because ultimately, and that's the way you get buy in as well.

Ian Murrin, CEO, Digiterre

22:25

Yeah, that will make sense as you write that there are organizations, which are groups of people with different stakeholders all having different views and opinions and needs to try and satisfy with the data you're using. So a question for you, then I'm going to change the order a bit. This one I'll see directly is one of the steps then from taking us from raw data, which if I summarize the views of all of you, the CDO was more to do with the raw data. And the CIO is more to do with the interpretation and value add to that data. So what are the steps to going from raw data to decision making, ie information

Saeed Amen, Founder, Cuemacro

23:01

That I would add, flip it to the other way around? Actually, so I would say what are your use cases. So if my use cases, I want to forecast a certain commodity price, I want to I want to try and get more insight in specific market, go from there, and then slowly narrow down into the sorts of datasets are going to be useful. Hopefully those datasets are things that you collect yourself, or if they're not there, they're something that you can obtain externally. So once you go from your use cases, then you can narrow down and identify what sorts of data and then once you have the datasets, then you structure the datasets, create models around them and go back towards your and your use case, essentially.

Ian Murrin, CEO, Digiterre

23:41

Brilliant. So make it very problem problem focused and solve problems and surface the data you'll find the data to solve the problems find. Yeah, I

Saeed Amen, Founder, Cuemacro

23:49

Would definitely, because at least whenever I looked at data is when you try and get a massive data and you try and find something, just invariably, you don't find a stack. Yeah, exactly. So at least if you know if you can shine a spotlight on the right side sorts of data and potentially the right variables to look at, I think that could be quite helpful for you. And and the thing is a lot of people in the industry experience they might not be tech folks, but they kind of have a feeling of what drives markets. So they're the first people I would say to ask it's not to begin with, it's more of a markets problem, as opposed to data science becomes a data science problem later on, once the question has been asked. And so

Ian Murrin, CEO, Digiterre

24:30

could you expand on that bit? For example, what what do you mean by that? It becomes data science later on.

Jose Lopez, Digital Strategy Director, TradeFlow Capital Management

24:35

Because I would say

Saeed Amen, Founder, Cuemacro

24:36

The perspective of somebody who likes data, including myself is to try and find something in a data. And typically, you might end up data mining, some sort of solution was if you have some sort of guidance from a domain experts, then they can kind of guide you in such a way that you'll hopefully avoid kind of more spurious solutions within the data. So it's not okay. Just trying to Data Miner solution is trying to find something which they think can work and then verify it and go back.

Ian Murrin, CEO, Digiterre

25:07

So almost for my hypothesis and then validated or not through the day, yeah, it's

Saeed Amen, Founder, Cuemacro

25:11

Kind of more than traditional statistics. And I think particularly in these types of areas that markets, the signal to noise ratio is very low. Yeah, so potentially is more amenable to that. I guess in other industries, you can do a bit more data miners, you have so much more data, but at least within financial markets and kind of energy trading, that the signal, you just need a signal that works 51% of the time. And that's, that's good enough.

Ian Murrin, CEO, Digiterre

25:35

Brilliant, very insightful. Thank you. So you'd, Josie, Jose, any any thoughts from you on that question of your raw data?

Jose Lopez, Digital Strategy Director, TradeFlow Capital Management

25:44

Now, I was actually thinking, Oh, well, your side was speaking on. I would say this a human factor, we're not taking in account. When do you want to develop? What are the steps not from the raw, from the from the raw data and on to valuable information? There's definitely a human factor, the person who has experience I was thinking in the case of free flow, free flow is yeah, you can say, We're digital, we are used artificial intelligence as one. First you locate what is the problem? What is what you're trying to solve in, as I was explaining beautifully. And but there's a big component of the experts, the experts using tangible information that that the experts actually bring on onto the table. And then you start building? What do you need? Which platform? Which systems are you going to use in order to get to that information? Or to convert it? No. And so my, my two cents to the discussion would be let's not forget the the experts in that they bring some tangible value that actually is valuable.

Ian Murrin, CEO, Digiterre

26:54

Yeah, so I guess picking up on that same point inside the domain knowledge around that data is probably invaluable. Otherwise, just to see if data points without any surface value and information. Thank you very much. Yeah. That's cool. Jose, thank you, Kevin, you must have a view on this one.

Kevin Kindall, Senior Data Scientist, Hartree Partners

27:11

I do. Yeah, I very much agree with the other panelists. So what I'm going to say is, is basically the same thing in different words. So a lot of the requests for data come directly from the front office. So these are traders or desk heads that understand their business very, very well. And they're looking for data that if they had it would allow them to make better decisions. And, you know, many times it doesn't have to be numerically correct, it can just be directionally correct. And so they're looking for things that give them that competitive edge. And because the data itself gives them a competitive, competitive edge, it's very much proprietary, and has to be closely held.

Ian Murrin, CEO, Digiterre

27:56

Okay. Interesting. All right. So effectively building on the same points, but your point is, anything that's valid, truly valuable from a market perspective, has to be closely held, which kind of makes sense. Keep the secret sauce close,

Kevin Kindall, Senior Data Scientist, Hartree Partners

28:10

Right? I mean, we all generally speaking, have the same tools and so on. And so if you're looking for something that, that allows you to make better trades and your competitors, many times it does come down to the data.

Ian Murrin, CEO, Digiterre

28:22

Are there any insights from your side as to how that comes about? What what trade creates more secret sauce than others? I suppose in that question around moving from raw data to decision making, are there any obvious steps you see people making who are very good at that verse, he says, are less good at it.

Kevin Kindall, Senior Data Scientist, Hartree Partners

28:38

I mean, a lot of it comes down to the specialness of these physical markets, you know, you know, if you if you have a trader that's been doing this for 15 or 20 years, he or she can, can remember all sorts of events that have happened and how the markets respond. And you know, just being on on a desk, and you slowly pick up and accumulate that commercial knowledge. As your commercial knowledge starts to expand, you start to realize that, hey, well, maybe there's something, you know, if we knew this, or we could model this aspect a little bit better. It would, it would give us that competitive edge. And you know, if you do have your data, and you go back and your your back tested and so on, you can see that yeah, you can actually start to quantify that benefit. Yeah,

Ian Murrin, CEO, Digiterre

29:21

Right. I think there's a Pascal quote about Fortune favors the prepared mind. So I think that's probably what you're talking about here is understanding the context. Great, Kevin, thank you very much, and Rico, anything to add to those thoughts?

Enrico Camerinelli, Non-Executive Director, Supply@ME Capital PLC

29:34

And well, maybe a little bit more prescriptive, but I think that you know, to move from raw data to decision making, you have to follow us a number of steps that are both operational and strategic. So for instance, the first thing is extracting you must extract data from all sources or data sources. So the the first strategic point is you know, from which sources sources you want to extract data The second thing is you want to do data cleansing and normalization. Because if you want to use this data and match it, or correlated with other data, you want to make sure that you can compare apples with apples. And so make sure that you can read the same data, although coming from not sorry, the same data, but data can be comparable. And do the, for instance, you know, pattern analysis, but you want to make sure that you do it based on actually what is your business objective? So at this point, doing data cleansing normalization, and enrichments, you have turned data into information, but then you have to distribute the information that to whom? Who is authorized, read that information, who's authorized to manage that data? And then how do you store it? So this comes back to the CTO know the rules for how you store for how long? And then how do you maintain it? How do you update it? And last, when or how should you remove it for how long should it be there? Yeah, all these allow not only to transform data, raw data into information, but also to make it useful to to the company itself, and to all the different stakeholders within

Ian Murrin, CEO, Digiterre

31:08

The company. Yeah, and I think that's a really good point very close to our heart, we tend to make this distinction between data science and data engineering, where the engineering is around, you could argue the plumbing and the architecture necessary to achieve what you are talking about. And the science might be about achieving the insights from that data. And we see organizations that have perhaps very good insights, but not necessarily good infrastructure to support the robust sharing of that information to maximize the value. I think it's a really good point there. Thank you very much. I'm going to hop over to another question, which is a bit of a hybrid. But basically, I think we've agreed there's there's a lot of data, some organizations have more, some have less and more mature abroad is a lot of data around inside or outside, and it's getting bigger and bigger. So the first part of this is, how can the CTO avoid the analysis paralysis? And say, you came out with a very simple and elegant way of let's focus on the problem and build out from that iteratively, which I think does make sense. But we see a lot of organizations where there's just data everywhere, and everyone's coming at them, like, you know, playing from the sky. So how do you avoid the analysis paralysis, given those often conflicting request requirements, priorities that change? Not just on the front, the trading desk in the front office, but middle office and risk and other areas? And secondarily, can the CTO or the business that was run by the CTO be expected to monetize any of this slide? Why don't you kick off on that one?

Saeed Amen, Founder, Cuemacro

32:32

Yeah, sure. So I guess Yeah, going back to my point to try and solve some sort of simple use case to start with, just to show that there is value in kind of spending time and money on data. Because ideally, in any organization, you'd want to have a perfect data lake with every single bit of data and in the firm their access to external data to begin with, that's the ideal use case. But if that's going to take a year, two years to create, then you're not going to get anywhere and by then, there's probably not going to be any budget, basically, for any data for it. So to start with a use case, which hopefully you can, you can strap relatively quickly. I would say in terms of monetization, there's several different way I was gonna say, Read, read my book, but

33:21

But I think there's two aspects in terms of monetization. So one is to directly to sell the data externally, this can be a bit of a hard sell for many organizations, because potentially, you're giving up some sort of secrets of how your data is being run. And also, it could be the case that it just doesn't move the needle for a large corporate anyway. And the second way to monetize data is obviously internally to if you're, if you're, for example, you're trading, or you're trying to streamline your operations, that's another way to analyze it. And then another way, is basically in terms of as a marketing exercise. So do you have some sort of interesting data which could be released maybe on a monthly basis to the market, where we'd have your name associated with it every single month, and just trying to produce your profile that way? So one example of that is ADP, a company which does payrolls in the US, they release a monthly number based on employment based on their internal data. And they just get financial press every three months for that. So there are there are several different ways to monetize it. It doesn't necessarily have to be you have to sell your, your data to funds, etc. It can also be through the marketing words as well.

Ian Murrin, CEO, Digiterre

34:33

Thanks. There's a really good set of distinctions. I guess, in summary, we all see that the FT says X or, you know, coalition, or someone else says why. And that's a great way to monetize it, but indirectly as opposed to in his subscription model.

Saeed Amen, Founder, Cuemacro

34:47

Yeah, exactly. And I think, because in some instances, they'll say, look, it's just not worth our while to try and sell data because we're a massive company and subscriptions are going to be maybe a couple of million dollars a year so It's

Ian Murrin, CEO, Digiterre

35:00

Not worth it. Okay, cool. Thank you. Jose, do you have any thoughts on this, particularly around the monetization part? But also maybe the analysis paralysis problem? Yeah.

Jose Lopez, Digital Strategy Director, TradeFlow Capital Management

35:08

On both on both on the analysis paralysis, what I would say is, well, you need to be responsible, and there's going to be a CTO or CEO, on your on your back asking you for the information, or why wasn't ready. And that take us to the point that we were speaking at the beginning that information given you the ground time, or too late or too raw, this is not information, it says raw data, right. So on the analysis paralysis definitely had there has to be in companies, there has to be a perfect alignment with between the management and who's getting the information in, I see that. Even here at trade flow, when you're talking between the developers, the CEO, and everyone that is in the middle, there has to be a perfect understanding, what do we want? What information do we want? And when do you need it? Right. So that that helps to avoid the analysis paralysis? And then the question would be, rather than money Tyson, the could a CEO or its department, be responsible, have a budget? Are you a profit center? Are you a cost center? We're looking at it as a cost center. And I think there's a certain point where you need to identify the revenue that that valuable information brings to the company? Or what do you lose if you don't have that information? So at some point, you have to really locate what is the value of that information? And that turns it into, into a profit center.

Ian Murrin, CEO, Digiterre

36:35

Right. Brilliant. Thank you very much. I'm going to ask one last question, because when a couple of minutes, I'm going to ask Enrico, this question, which is, where do we go from here? What does the CEO function look like for the next five years,

Enrico Camerinelli, Non-Executive Director, Supply@ME Capital PLC

36:48

Or maybe five years is too long shot, but I would say the role of the CEO would be to make him or herself useless. Let me say this. The role of CTO is basically to inject into all the other sea levels. Yeah, we return that understanding of how much data is important, how to use it, and how to leverage it. So it's, of course, so the role of the CEOs role today is to do that kind of job to extract data and all the things we've said, his or her job is to instill this into the other so that he, the CEO can really move to the next step, then we'll move to the next level of data monetization and making a strategic,

Ian Murrin, CEO, Digiterre

37:30

So sort of almost, you're talking about a sort of mentoring guidance, kind of quasi strategy.

Enrico Camerinelli, Non-Executive Director, Supply@ME Capital PLC

37:36

I mean, of course, I've been harsh, it's not that we don't need any more CEOs in the future. The point is that what the CTO is doing today, I think, should be part of the skill set of the of those who are the C level have to make to make decisions, in terms for instance, of helping the company understand what kind of digital automation is needed for people to make decisions, so that you know, the level of attention of data can move to the next level. Right? Operational.

Ian Murrin, CEO, Digiterre

38:04

Thank you very much. Unfortunately, I can't ask this question of the rest of the panel because we're running out of time. So just to recap, it seems to me that we've come to a fair conclusion around the roles of a CEO, a CTO, the CIO, and there are I spent agreed upon distinctions which is great. We think it is a vital role. It's got a kind of a mentoring aspect and a strategic aspect. There is plenty to learn from third parties, I think we all can see that and it's it's never It's a never ending journey. There is no particular destination apart from in and Rico's view, you make yourself redundant, but I suggest the function will probably continue to evolve, maybe the title will change. But it's been really interesting, really fascinating. And I'm never going to gamble ever again based on what you told me Kevin. Never was going to but frankly, never to get to now it's a it's a losing streak. But thank you very much for your thoughts and input. I've really enjoyed it. I hope as an audience, you've all enjoyed it.