What on earth is happening to data?

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The full interview with Chris McEleavey, a founding partner at Bulien.

Here Chris talks about what he has seen in the changing landscape of data, and how now data needs to be seen as not just as driving new business, but increasingly as part of the DNA of any business… and the challenge for businesses now is the sheer volume of data and just what to do with it.

Some interesting insights, especially on how the tools and techniques are developing to make this data, and the associated found knowledge now available to all.

Find out more about Bulien mobile-> Here.

Interview Transcript

0:00
Hi, everyone, I’m here with Chris McEleavey, who’s a founding partner at boolean and Boolean array data, a data company or a data. And they do a lot of work in data analytics as well. So, So, Chris, very welcome. And thanks very much for joining me today.

0:14
Hi, Chris. Thanks for having me.

0:16
So we were chatting a bit earlier around, I suppose data on sort of your kind of sweet spot in terms of data and data analytics. And obviously, we’ve been through the pandemic, and we’ve seen a lot of sort of digital systems being put in as a result of that, I mean, sort of, have you sort of seen that evolve over the last sort of 12 months or so there’s been a sea change between where we were before and where we are now?

0:36
Absolutely, I would say 100%. Yes, for many different reasons. And some of these things have come across because they were, a lot of the larger companies especially were caught out by this, the smaller companies found it a little bit easier to adjust, I think, are the newer companies. Because they’re more agile, they’re easy to it’s easy to pivot. But I think a lot of companies, I do have first hand experience with a few who were caught out simply because for instance, they didn’t have the capability to allow their employees to work from home, they just didn’t have that setup, they didn’t have the infrastructure in place. And this was a wake up call for them in that regard. Obviously, with that being extended for a prolonged period of time, and then realise that, you know, the new normal might not be exact as what my mother taught me, you know, that there will be kind of a change back to what it was, but it’ll never be the same. So they’ve now got this infrastructure in place, they’ve realised people can work remotely. And yeah, so there is a bit of a change there. But also, the other side of it, where it was things like the analytics side of things where they realised they just didn’t have the information they needed to operate under the new circumstances as well. So we’ve seen companies realising that, wow, we need to be looking at our data more, we need to be making more data driven decisions. And they’ve taken this opportunity over the last year or so to actually, you know, move ahead with that as well. So we’ve seen a lot of change.

1:55
And how much do you think it’s going to be around, companies have got the data and they just didn’t use it, versus companies didn’t have the data, and now they’re putting in digital systems to actually use it?

2:06
I think there’s a bit of both, but I think overwhelmingly, companies have data. And, of course, one of the things I get I get asked quite a lot is, you know, the platform’s we offer what vertical? Is it for? Is it for the public sector? Is it for retail? The answer is anyone with data. And I don’t know a company that doesn’t have an abundance of data. And I’ve said this, a number of times data should be on your balance sheet. And people don’t know the information in their data. That’s, that’s what we do.

2:33
The digital systems, we seem like a massive implementation of digital systems stay sort of as we’ve gone through the pandemic, and there’s like everything putting digital experience in, etc, etc, as, as they sort of spit out data as well. So once they put in, they sort of spit out data, and you can then use that data to do things. I mean, what are some of the, the innovative use cases, you think that’s sort of like a sort of point now, where people are sort of looking to try and generate customer improvements in customer experience or better outcomes? I mean, what was what have been some of the things?

3:01
I mean, it really runs the gamut. I mean, obviously, we’ve we’ve been doing a lot of work with the public sector, the NHS, and they have a very different drive to another area that we do a lot of which is, which is online, retail, they have different drivers. But one thing that they’re starting to realise is that, for instance, an online retailer is losing customers. And the we asked why and up to now education will go and talk to the salesman or the account manager for that particular customer. And we’ll find out why. And of course, that’s just a hunch. So the fact is that we have so much data, you can’t be doing those hunch driven decision making things anymore, because it’s not accurate. So what we find is things like churn analytics, why are we losing customers? Why are we gaining customers, all of a sudden, all these kind of things, if you’re not set up to trade online, then 2021, you’re not really going to survive very long. If you’re in retail, we are seeing a big shift in the public sector is generally speaking are quite a way behind the public sector, the private sector, and that’s still true, but they’re really starting to push forward understand that because they have masses of data. And they need to be making better decisions based on that. Now, traditionally, what we’re seeing is large companies with large data using Excel or SQL. And of course, this just isn’t fit for purpose. It’s not 1996. We’ve moved on a long way from that. We can’t be saying we need Can you give me the answer next month? I mean, that’s just, that’s just that’s gone. I’m sorry. You know, we needed the next 10 minutes. That’s what today is. But the use cases. I mean, I’ve been in this game for a long time. And the use cases now coming up, that are just popping up like bubbles everywhere, all the different things and I’m just going with the flow. But yeah, it absolutely runs the gamut. We still have things for sports analytics, where they’re looking at injuries in professional sports, men and women, you know, all the way through to how long have you likely to wait in a in a hospital,

4:49
there’s a sea of data that’s out there. I mean, there’s huge amounts of data that’s out there, and you need to try and find the answers to something in there. And it’s not just a matter of like let’s just bring the ocean in and then will result? How do you approach that mean? Is it the use cases that sort of drive where the analytics are? And what are you sort of seeing? It seems like what’s driving the the analytics and what’s driving the methodologies and almost like the problems you’re trying to solve for

5:14
that? Well, if hit the nail on the head, the simple fact is that traditionally, if you go back to the 90s, the amount of data that was floating about is generated hourly now, that exists in the entire world in 1995, generated by the hour. Now it’s ludicrous. So primates are not equipped to go and look at this kind of, you know, this level of information. So you’re seeing the rise of AI machine learning these kinds of things, and the platform’s we use can actually bring in masses of data and filter out and looking for signal within the noise. But what we’re also seeing is, companies that five years ago, were saying, well, that’s just science fiction, we’re nowhere nowhere near that. We don’t have the staff who could do that. All of those things are untrue. First of all, absolutely entry. Kids that are coming out of university now we’re equipped far better than than we were. Absolutely. I mean, they’re way ahead of where I am I just lost in the sea of sorcery. It’s great alone. But these are they’re all coming through. And they’re wanting to say, Well, why don’t we do this? Why don’t we do this. But we find that we go into a company that has traditionally been using Excel, and you have a bit of resistance, people who are still stuck in Excel and that kind of thing. You show them the new platforms. And it’s like, wow, okay, so that thing that I do once a month, because of the time restraints in Excel, I can automate that and it runs in 12 seconds, what else can I do, and those are the people that start taking it off. Now, when it comes to the use cases, they know their data better than I ever will. But once I get them up and running with this, then they start off and running. And that’s what we want to see. And they’re answering questions that they never bothered even asking before, because it just wasn’t worth asking the question. And now they’re getting it all out. This is why I’m saying this, the it’s all about pulling the information from the data. And you need to use things like machine learning simply because of the data sizes, we’re talking about what’s being generated daily,

7:00
but it’s quite interesting. You talk about machine learning with AI, we talk about all these statistical techniques, and you know, data, lakes and those kind of things. But at the end of the day, there’s also that human element involved in terms of like, well, where do you point the huge processing power? You’ve got to basically define it. So you sort of the the humans that are involved to then work out? Well, how do I interpret it? Right? 100%? I

7:19
mean, AI does what the human tells it to do. And this is where people have this, this the here AI and machine learning the thing that Terminator our cash machines are going to take over the world? Of course not. This is completely guided by what we decide, we ask it a question, and it learns from the information we give it. And it says, based on all of the things you’ve told me, this is what we predict will happen. It’s not it’s not sorcery, and it’s not machines taking over the world. It’s completely controlled and governed and we make decisions based on all it is a statistical analysis, just statistical facts. So then then the human makes a decision. But also the human is building that AI. I mean, I don’t need to understand machine learning algorithms. I’ve got smarter people who understand that I just need to understand what it’s doing, and the answers that I’m getting. And then I can go and make a business decision based upon that output. But you can’t do that in Excel it first of all, it’s manual, you’ve got to do manual if if statements and things and you have to impose upon it what you think is linked, what am I what ml algorithms are doing in businesses to say, If this occurs, this is likely to occur just on a massively complex and grand scale. But without the human, it’s utterly meaningless. And a lot of the platforms we do, they go out and you show it to a data scientist. And you can see that first of all the blood drains from their face, and they think, Well, I’m gonna lose my job. No one has ever lost. No data scientist has lost their job for what we do more than been hired. Simple fact is that you these are tools for data scientists and analysts. And there is a massive shortage of competent data analysts and data scientists and always will be, you’re never going to complete data science, what we need to do is make those people more efficient. So we can give them the tools. So they’re not spending time cleansing data and doing you know, all of the sort of stuff that can be done automated in a machine. We need them to be interpreting and analysing data. That’s what they’re for. How much

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9:09
specialist knowledge do you think there isn’t that in terms of like business specific knowledge? So if you’re going in and you’re doing analytics, how much do you rely on people to really understand the businesses and understand the dynamics versus you can just pull out almost like mathematically, in terms of

9:23
relationships, it’s 90% Human simply, again, simply because I can come in and just take out 90% of their work in terms of and then we’re sorry, of what they’re currently doing. And then they go in? Well, there’s all the other stuff that they need to do now, which they can do in the same amount of time. But the simple fact is, like I said, when I go in, it’s letters and numbers. That’s what your data is letters, numbers, including images, doing sound, it all gets converted to letters and numbers. They need to interpret it and they need to understand what it is, isn’t how you enter your business. And I see a column that says 243 million. That’s a number to me, but to you, that might be a sales revenue per quarter. might be whatever I don’t know. So it’s meaningless without the human, it’s just letters and numbers. But also I need to know what is important and impactful to the business. I don’t particularly need to know the person does. And this is why it’s always gonna be driven by the human,

10:12
the maths and the tools and the techniques means you can then take on more complexity, right? So you can start to think about the extra complexity, right in terms of like, other variables, how they interact with each other, those kind of things.

10:23
That’s exactly it. And that’s what it’s doing. It’s just empowering the human, it’s just giving it we’re having the same conversations now about people losing their jobs, when they invented the desktop calculator. Did that did that result in people losing a Cosmo did Excel, same conversations with Excel did that result in massive job losses, of course, not. The other another cotton gin, the same thing. I mean, you know, this is this is just always happened throughout history. This is just a new revolution. This is a digital revolution that we’re going through at the moment, and we’re just starting out. But one thing that this is doing the tools that we’re using, it traditionally say traditionally, in the last 10 years, it’s already traditional, the data scientist would be a bottleneck, because in this in this organisation of 30,000 people, we’ve got two people who are capable of doing this, the tools now say, anyone who has a business knowledge and a bit of knowledge of data, can now do what those people are doing. So now instead of a bottleneck of Elizabeth and Kevin, in a 20,000 workforce, we now have 5000 people who can do this job that is the democratisation of data across the organisation, and therefore the efficiencies are just skyrocketing. You know what, I’m still going out and looking at places and that you’ve got a team of 10 people who are sitting doing things in Excel. And those people really need to start looking at the new dynamic and these new things because I can automate everything they’re doing. And it would take me a week to automate everything they’re doing, and then they’re gone. If they’re not going to move on. However, the knowledge these people have and the skills these people have, because they take on the new platforms, they’re going to be experts in these platforms, and they’re going to be doing 100 times what they were doing the week before. And that’s what’s exciting. And that’s what we’re finding 99% of companies are doing that that’s that’s what happened. That’s where the

12:08
less this is a sidebar on Excel. I do think Excel is like the cotton gin. That’s just like you said, Yes, I think it has been revolutionary. I mean, spreadsheets. That journey have been revolutionary. And the fact that you can do you can build models on Excel is a testament to just like how wonderful they been. Now whether the perfect tool to do everything is something else. But it’s or they aren’t. They have been revolutionary over the last 1020 years. I do think but you know, maybe it’s time to move on. And maybe it’s time you just you don’t use the bicycles. Wonderful. We don’t use it to travel to Durham or to Durham to Edinburgh, right.

12:41
Great analogy. Great analogy. Also don’t use the pennyfarthing. So yeah, I mean, the gramophone was revolutionary, I have Spotify, same thing, it just things that things that have been better. Excel was one of the best things ever written. It’s just being horrendously used. And I don’t blame people who do it actually, it’s it’s some of them I do, because they’re just stuck. And they don’t see the future. But the the, the tools that came out earlier, were the trailblazers, we wouldn’t be doing what we’re doing now, without without Excel. It was that revolutionary. It was incredible. But you mentioned the cotton J. We don’t use that anymore. I assume I’m not. I’m not an expert in textile. But this is, you know, it’s a natural revolution and progression. But yeah, I mean, we’re now on the verge of losing, and hopefully losing the internal combustion engine. So you know, we’re getting rid of diesel and petrol burning motors, and that I hopefully will live to see the end of that. And it’s the same thing. It’s just a natural progression. But yes, I totally agree with you excel has been in it today was absolutely amazing and mind blowing piece of kit.

13:45
And when you talk about, like some of the new things that are out there, and I know we’re chatting about some of the modelling stuff and the data manipulation stuff that you guys have been looking at, what’s the state of the art? Where are we going to because used to turn the handle used to write the agency mystical programme sitting in the back, but I mean, there’s there’s new things coming out with like this new technology, this new extra processing power, you can sort of like there’s new algorithms that you can now use me and what Where’s where does it go next? Well, we’re using

14:10
sort of platforms, we have our cutting edge, and there is that for that reason. So again, the traditional data scientist would be sitting there, coding an algorithm writing it and writing the whole thing, the ETL process, the, you know, the whole data blending and connectivity to be writing in code, we use code free platforms that do exactly those things. So everyone can do it. You don’t need the ability to code and in any language at all really drag and drop and everything can be done, then. So there are three stages of analytics really, the first stage is where 99% of all companies globally currently are, which is what happened. So that is just reporting business intelligence tools, excel in these things were reporting and what happened in visualising the data to say this, this occurred in the past, this is what happened. This is where we are, then you have descriptive analytics. The next step is predictive. So a lot of them are at the cutting edge companies moving into the predictive section. And that is where we’re looking at the kind of machine learning tools, a lot of them are doing that in a traditional manner. The tools we’re now using, you know, particularly data robot and Alteryx, they give us the ability to put data in and say, predict that and it will then choose using machine learning what algorithms are best suited to your data, your data types, what you have in your data, and it will then start churning, testing, testing against itself, retesting, changing the parameters, and it will do this automatically, things that I’m incapable of doing. So I’m not a data scientist, I can now do so all of this automatically choose the best algorithm train it for me automatically. And I can just think and predict patterns. And the results we get with that. Obviously, we’re more than happy to go up against what we do. We do hackathons, we do a kind of thing. And we’re consistently right at the top of these things, then, of course, the next step from that is prescriptive analytics. Now we know what we think is gone. Well have a good idea of what’s going to happen in the future. What do we do about it? And this is kind of validation analytics. And very few people are at that point, yet, we have the tools to do this. But to everyone show, it’s just witchcraft. And why

16:05
would you say that? You know, the existing businesses, I suppose, where businesses today fits across those four characteristics. I mean, because you’ve got some sort of, you know, much more traditional kind of businesses, often they’ve been around a long time, you know, and they might set one and then you’ve got but then you’ve got these new sort of like, either Neo banks, or you’ve got new fintechs, or you’ve got news, or like social media or some of the tech company, where does it sort of sit on that continuum?

16:29
That’s interesting. So, like I said, I think as I said to you earlier was the, the older, more traditional leviathans, that we have these huge companies that have 100,000 people working for them, and they’ve had the rigid regimes and structures in place, they find it very difficult. So we have major financial organisations globally, who are just operating an Excel that backing things upon magnetic magnetic tape. These are institutions that are just so slow moving, you mentioned neobanks, neobanks, I think they’re incredibly important. And the other day we spoke about these, the they have kind of dipped their toe into the future, these organisations are setting out with a base level set upon the new regime, the new way of looking at things, those I think, are going to be driving it, it’s just as, as we’ve identified the problem as the funding. So they get to a point where, yeah, they’re pushing the boundaries here. And then a big bank just buys them out to shut them up, or they just can’t compete for funding. And the bigger banks just continue doing what they’re doing. I’m not naming any names there. But we are starting to see some of the bigger organisations taking on this machine learning. And, you know, getting rid of the predictive models that are written in the 70s. That’s still being used, we’re starting to see clue. But what’s really exciting for me is the small, agile, sub 100 People kind of companies who are starting out from day one, building on, on on on machine learning analytics. And it’s it’s very exciting to see. But I think if you don’t move on, as if you sit and rest on your laurels, because you’re big, and these guys are small, that’s not going to be the case all the time.

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18:03
Yeah. As far as I think it’s gonna be interesting to see how things, how things sort of expand. I mean, there’s so much data out there questions, just really whether we’re using, are we using enough of it? Do you think there’s been a change in even AI? Right, so AI has been a it’s been, it’s been bandied around a lot, I’d suppose over the last over the last, like 234 or five years. And to certain extent that these are statistical tools that was, you know, a lot of them developed in the 70s. Right? In the 80s. Right? Do you think we’re sort of almost like, reaching PKI. And but now there’s a realisation around what it actually is. And now we’re actually getting to the execution stage,

18:40
I think we’re just dipping our toe into the execution stage. This is this is the start of the the AI revolution, we are right, sort of grassroots level here. Ai itself hasn’t changed, it’s still the same thing. It’s just a collection of algorithms that are calculating certain patterns. But we have an increase in, as you mentioned, the processing power, the increase in data size, which allows us to, to crunch more data. So it’s just becoming more sophisticated. It’s a more sophisticated version of the same thing, in the same way that it’s that what we’re doing now is a more sophisticated version of Excel, because we just have this power to be able to do this, and we’re moving forward with it. But the amount of data that’s being generated now, it’s just exponential the amount of data. And the fact is that 99.9% Of it is just noise problem is that we didn’t have the processing power to be able to find the signal lost in that noise. And now we do and when developing that, so it’s becoming more sophisticated. It’s the same thing just going up. So we’re just we’re just at the infant stage,

19:37
when you’re sort of on the ground, sort of talking with clients. Do you think there’s a greater maturity around understanding around data and what it is versus maybe even what there was even five years ago? Is that is that kind of evolving? Yes, yes,

19:49
five years ago, AI machine learning was just witchcraft and sorcery. And of course it was just a fad. Of course it was never going to catch on. And you get a lot of them now are saying so tell me More about this AI thing, it’s maybe not an increase, it’s an improvement in awareness of it, but maybe not the knowledge of it. So there is still a lot of fear and hesitation around it. Because they do think this magical black box thing. It’s just mathematics. Obviously, it’s mathematics that I chimp like me can’t understand. But I don’t really need to, because I’ve got the tools that much smarter people and I have written. And that’s the thing and I can, I can now go out and talk AI ml, and I can understand what it’s doing. And I can understand the impact it’s going to have, and I can talk to leaders of businesses who were doing this, and they can understand the way I’m approaching it. Because if I put a data scientist in front of them, they’re not gonna understand where they say, another lie. So there’s a lot more awareness. But I think it’s instead the I can’t become a data scientist thing is now going away when they’re seeing that the tools that are on the market can help them become that data

20:52
scientists. Yes, I suppose some of it needs to be, I need to have a solution. And this is how I can get to this, I can use this tools to get to a solution as much as I understand the math to do it.

21:03
Whatever I said, That’s ultimately what it’s all about. And on the flip side, when we talk to data scientists and I say I employ some they, what why do we do data science? And you get an answer back to write the best algorithm to make the best predictions? And the answer is no, no to that no to that, oh, to make this model more accurate, no, to build a model, get it into production. No. The reason we do data science is to improve revenue, to improve profit to decrease costs decrease time. That’s why we do it. Otherwise, it’s just utterly pointless. It’s just a hobby. So it’s understanding. And that’s the new wave of data science scientists, or business users, rather than data scientists who sit in a darkened room and do maths that no one can understand. We now have data is citizen data scientists who are business minded and business driven, and are using these tools to make business decisions. He talked

21:52
a bit about data and the volume of data we have. So I was watching a documentary. It was a science documentary. It’s like, just think about how we’ve gone from, remember, like, kilobytes kilobytes used to have, you know, like, it talks about how many K was when we had megabytes, then we’d like gigabytes. Now you might get a terabyte, and they’re talking in terms of petabytes, and so like, in zettabytes is gonna be, isn’t it? And the amount of processing powers and data there is, is just huge, and they were trying to find signals in the noise. Exactly, exactly the same. So it’s almost like the conversations moving on. And we’re sort of getting up the scale almost like every, every couple of years, we seem like we’re going up the scale. I mean, do you think do you think that kind of level of data is going to come into into business?

22:32
Yes, I think it already is. Certainly with some of the larger businesses, we actually work with a few who are dealing, you know, petabytes of data, and then need to be able to get the noise, get the signal from the noise in that. But yeah, that exponential growth, I’m gonna get this completely wrong. But there was a there was something like the the data that existed in the world in 2010, is now generated every day. That was something that so that this is an exponential increase. And I remember, Bill Gates said something along the lines of you’ll never need more than one megabyte of RAM in the PC, something ludicrous to that effect. So yeah, it’s that there’s exponential growth. year on year, if you were to look at that growth of data, it is absolutely vast. And that’s why AI is moving along, because the processing power is moving on. So the AI is k is able to do with that what’s changed what’s actually doing it, it’s cool. It’s sitting on more powerful platforms that are available to more and more people, which means that you can crunch more and more of this data, but the size of the data that’s existing in some of these companies, that they previously have just thought, well, most of that is useless, but they don’t actually know that they just haven’t had the power to look into it. And we’re now starting to unlock that with these tools, then we’re going to get some interesting decisions made,

23:48
particularly the relationship of this plus this plus this, and then this and this, and this, and this, and this can be really sort of predictive writing. And once you start joining things together, yeah, which is, I suppose what we do as humans quite a lot, but it can just be able to do it in on a vast scale.

24:01
We do it without knowing it, the calculations that go on the human brain, but are vast and incredibly complex. But yeah, we don’t realise we’re doing it, even in my brain. So that’s, that’s something we do every day, how to pick up a cup of coffee is just incredibly complex. But yeah, when it comes to business decisions when it comes to business calculations, exactly what you said that and let’s put in, most of the viewers here will be familiar with Excel, so I’ll put it into that term. Let’s say you had an Excel spreadsheet with a couple of petabytes of data, and 200,000 columns, which columns are related to the outcome of column A, like we can’t do that we can’t do that calculation, but machine learning

24:39
cap, the fact we’ve got so much extra processing power, and again, going back to to the days of the ZX Spectrum, and Vic 27 conversation about that the other day. I mean, do you think coding has to be really super efficient, and we used to spend an awful lot of time trying to get coding as efficiently as possible. In fact, we’ve got access to all this extra data actually text Processing pair, do you think the coding has become less efficient? Because we don’t have to be?

25:05
So yes, it very much matters. We use code free platforms, which actually provide, like batches of code that interlink to each other. Yes, it might not simply because as we’ve discussed, yes, the processing power has gone up. But so the data, and it really does matter. Ultimately, if you have a dataset that runs in a decent amount of time, and you get your answer, then maybe not. But I would always say yes, because we have to future proof this against exponential data growth, has it gotten any better and more efficient? Sometimes?

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25:33
Maybe Maybe we get all that when you get those petabytes of data, petabytes of data, that’s when it sort of drives efficiency, because as the need isn’t.

25:42
Exactly. And I think, certainly the platform’s we use it’s geared towards doing that. So absolutely, yes. But of course, there’s always a human involved. And some do it more efficiently than others. It’s an interesting question, how does it become more efficient? Because the need has decreased the need his degree decreased on a more day to day basis? Well, back in the day, you mentioned that expectrum, I remember basic back in like things like vehicle and electron, but you didn’t have any margin for error, you’re dealing in eight kilobytes. You had no margin for error. So it just simply had to be or didn’t run. It wasn’t the slowly thing it was it didn’t work. So yeah, I probably I think you’ve got, you’ve got a little bit, you can go outside of the lines a bit more. I think that it’s

26:25
a little bit maybe maybe more forgiving. Yes. So you still have to get it right, I suppose. But

26:30
yeah, and the when you’re looking at things I often say with the platforms we use when when someone’s sizing a server. But how much RAM do we need? Smile says yes. Yeah. Just just because it’s so cheap, it’s not really having the conversation about, you know, so this is the minimum spec, this is what not just look, it’s 100 quid for that for another, whatever. 64 gig of ram and let’s just put it, who cares? Just throw RAM at it. So

26:57
we’re on this terrific arc and terms of like, data, data capture processing power? I mean, where do you see going from here? I mean, what’s the what’s the next step? For us?

27:05
An obvious one for me. Sounds like an answer from five years ago, the cloud. The reason I’m saying that it’s still in the future is that most people are still not doing this, we’re certainly seeing a lot of public sector organisations, certainly, they still have the five year old, 10 year old mentality, we can’t put it up there because of security. Okay, they’re not a security specialist. I have security specialists that I’ve spoken to who have told me that’s utterly bizarre point of view, because it’s much easier to crack into their data where data warehouses or data centres, and ultimately, it’s just someone else’s computer. So I think moving to the cloud is where it’s going to go. And I think you’re going to move towards, instead of the 8020 in favour of local data storage, I think that’s going to shift in terms of 8020 to cloud, that’s where it’s going, the cloud offerings are becoming more more efficient, which we’re seeing embedded analytics within Cloud Storage. It’s no longer just a big hard drive sitting somewhere, we’re seeing more offerings, we’re seeing things like snowflake, breaking free, which is a really impressive platform. But also seeing improved network speeds, which gives me which gives us faster connectivity, to remote data sources, large data to be transferred over networks. So that if we see this, the increase in network speeds at the way it’s going, then we’re going to be seeing everything shifting up to the cloud. And that’s a boring answer. But we’re still not there, we’re still sitting with people having a server room full of fans going and that to me, is just very 90s, everything’s going to shift a bit cloud embedded analytics, one, one, stop shops, with neat things that are going to be pulling multiple data sources together in an automated way. That’s kind of where we’re pushing things, I can also see a lot of business decisions being made automatically using it up. But the minute we need to do efficient ml and make those those algorithms useful. We need huge data sources and huge data coming in from multiple sources. constantly having one ERP system and then going and do analysis on that. It’s very isolated, it’s very standalone, it’s just what happens to be in your nominal ledger, it’s not really going to give you much interesting insights. So I think that’s where we’re going cloud based embedded analytics, with all of your data sources talking to each other automatically.

29:24
It’s almost like the computers becoming, it’s becoming networked into it. So your computers becoming decentralised. Right, so you get all the computer sit and like just like the data sitting everywhere, and it becomes one big version of what you’ve got sitting on your desk. And that was one one thought that I was thinking about when you talk about that is almost like the environmental impact of that as well. So there’s been a lot played around Bitcoin and the cost of generating Bitcoin at the same will be true and I mean, they’re created in server farms as well. Do you think that will come come back as well just in terms of like the cost of cloud storage and cloud processing because it is so Sitting on the other side of the world. Yeah, I don’t have to think about it because it’s sort of just something I access remotely, it doesn’t affect my, my day to day. So I’m not as aware of things do you think? Do you think that will become a bit of a theme?

30:11
Yes. And I think it is now. And unfortunately, it’s really out of my sphere of expertise. But yeah, you’re right. I mean, the simple fact of the amount of energy that has been burned to keep these networks alive, is going to be environmentally impactful. But there’s a necessity there. And necessity drives invention. So I think that that needs to improve. Because it’s going to come to a point where we’re not we don’t have an option, we need to do something better, or we switch

30:43
off Google. Yeah, I’m sure there’s the argument around doing the extra analytics, healthcare providers find better solutions, or healthcare products for customers versus watching the latest series on streaming as an example. So think streaming takes a huge amount of bandwidth and processing power just in itself, right. And we’re all guilty of that. Yeah,

31:03
it does. But what we’re also seeing a complete shift in the dynamic of working. And this is one thing we have spoken, spoken about the past is the ability to work remotely, and fully embrace globally. mass transit systems, the whole sort of commute, all these things that people flying to London and back in a day, you know, these kinds of things shouldn’t be a thing of the past. Because there’s no real need for that we set up when when Berlin became a thing we’ve committed to working from home. Now, that would not have been possible 10 years ago, it would have been possible for some, but not on a mass scale, like we’re now seeing. And I can see, we were working from home right from the beginning, because we were like, waiting, we’re a tech company. And we don’t see the need this in I’ve seen all the arguments, most of the arguments seem to be from people who are either affiliated with or owned large properties for commercial purposes. So yeah, everyone needs to go back to work, the office is still alive, the office is not dead, get back to work, because otherwise I lose my rent. I don’t think that is a valid argument, I think you’ve just maybe invested in something that has been a victim of it’s fallen by the wayside during, you know, a shift in paradigm. So that happens all the time. You know, some people invested in Blockbuster Video, unfortunately, that just happens. I think the whole you know that there is the flip side of this, reducing the amount of time people are commuting, reducing the, you know, the global mass transit systems for work and all that which don’t really need to exist. That is the flip side that could be balancing out that kind of thing. I don’t know, it’s not my area of expertise. But you know, there are massive benefits to the internet, as we know, despite the bad press,

32:43
it gets in terms of like working from home. Have you found that? And then that’s been pretty seamless, then from from? Yeah,

32:49
absolutely. We’re constantly like this, were talking now from different areas of the country. We’re constantly on, on teams on whatever platform or the platforms are available. So we’re constantly talking on these things all the time, we dip in and out exactly the same as if I was walking up and saying hi to my other colleagues on the other side of the building, and then we meet regularly. So we’ll meet at the pub with me for a hike or go for a meal, whatever it happens to be, because I’ve had a lot of people saying, Oh, well, it’s a social thing. So yeah, I believe it is, but you know, so that we have a really good relationship. I’m saying we mean, our team that seems to be literally seamless working from from home, or we’re also seeing people just working on their own time I, I’ve said this before, as well, I hire adults, I’ve seen so many people saying oh, well, they’re more productive. If I’m watching them in the I get them. I need them logged on, I need a camera on them. I don’t I’ve never understood that mentality, I think, hire adults who who are responsible and do their own work, and judge them on their output. If they want to do that, from sitting in a nice field and enjoying the weather. I don’t care. Why should I care? Yeah, if they have X to do and they do X that did a fantastic job if they did X pub.

34:01
Great. It’s definitely been a interesting, interesting journey we’ve been on definitely new world that we’re getting into, I think, interesting to see how it sort of sort of pans out reading.

34:10
Yes. This is very exciting. This is the start of a new new revolution.

34:16
Well, Chris, thanks very much for your time. I really appreciate it some, some really, really interesting concepts there and to think about sort of where we’re going in the future as well. And also just the reality around some things you can be done today as well, which I think is is also quite interesting, because I think the whole definitely a paradigm shift and a change of thinking that’s been going on. I think it’s just becoming more and more visible. I think. It’s been it’s been fascinating. So yes, thank you very much.

34:40
Thank you very much for having me who’s really enjoyed it.


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