#15 - Gary Nuttall - Developing Digital Trust

- BimTube website: http://bim.tube

Welcome to this episode of the BimTube podcast. Today, our guest is Gary Nuttall, MBCS CITP, an emerging technology consultant. Gary shares his background in analytics and his journey to becoming an independent consultant. The podcast host, Steven, and Gary discuss the concept of distributed ledgers and blockchain technology. Gary explains that a ledger is a record of transactions, and a distributed ledger is a single ledger shared among multiple participants. They touch upon the idea of public and private ledgers, where the access to the ledger can be restricted.

In addition,  Gary explains that parametric insurance is based on specific parameters or trigger events, such as flight delays or crop damage due to excessive rain. It utilizes smart contracts on blockchain or distributed ledger systems to automate the compensation process. They discuss the efficiency and speed of parametric insurance compared to traditional insurance, highlighting examples like flight delay insurance and crop protection insurance.

About Gary and Distlytics
Distlytics Ltd is a consulting firm founded in 2016 by Gary Nuttall, a recognized figure in the field of Blockchain and Distributed Ledger Technology. With expertise in commercial insurance and legal sectors, Gary has been listed as one of the "Top 100 Blockchain influencers" and has spoken at various conferences and events since 2015. He is known for his articles on emerging technologies, particularly focusing on blockchain.

Gary on LinkedIn: https://www.linkedin.com/in/garynuttall/
Distlytics Ltd: https://www.distlytics.com/aboutdistl...
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Links Mentioned by Gary
- What is New in the 2022 Gartner Hype Cycle for Emerging Technologies
https://www.gartner.co.uk/en/articles...
- GTC - The Conference for the Era of AI and the Metaverse

BimEnable Training

Visit the BimTube Podcast website at bim.tube to learn more!

Transcript

Transcript

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welcome to this episode of the Bim tube podcast with me today I've got Gary

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Nuttall so thank you for joining me and welcome Gary Hey Steven thanks for uh inviting me

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thank you and we were just chatting before we started recording about what I mean by Bim so in this context I do mean

0:24

better information management and I'm aware it can mean many things and maybe we'll talk about that

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um but so thank you Gary again for joining me we obviously we've had a little bit of a think about the kind of

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topics we'll cover but just to begin with what I ask all the guests if you could if you could just introduce

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yourself if you don't mind of course I know you and but also your backgrounds I think it's really important to

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understand how people got into the role that they're in so people start from a different place so over to Gary just a

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quick introduction and your background please sure thing so I I promote myself as an emerging technology consultant

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these days and I've been doing that for about seven years and it took me about five years to realize that's what I

1:04

actually do um so it's straight away it's one of those things about how do you label what you're doing that kind of thing so I

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help organizations Better understand emerging Technologies what the risks and issues are and so I get involved in

1:17

really interesting projects around things like blockchain artificial intelligence data analytics

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um crypto related things all all sorts of um Kitty in the Sweet Shop type stuff

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reality but I've been hands on with some of this stuff so I'll help companies now

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actually um better understand the reality from a mess and it's kind of interesting but

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we'll go into my background a little bit more later on but the company I set up is called Distlytics and originally

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that was meant to be because my background is analytics distributed Ledger which is blockchain type of stuff

1:53

so distributed Ledger analytics dyslexics it sounded like a good idea after a glass of wine and when I

1:59

realized that there was no website registered as that what makes it come to realize is that what I actually do is I

2:06

distill things through analytics so I'm kind of repositioning what Distlytics 

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means so I I help companies understand that the reality of some of these Technologies

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great thanks Gary and um I mean we I mean there's so many things

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we could pick up on because you mentioned blockchain and distributed Ledger and of course there'll be lots of people listening or watching this that

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have no idea what their things are so we'll get on to them but just briefly if you could also just give a

2:35

your background just instead of your just briefly your career progression we can always go back to some elements if

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you don't mind that'd be great thanks sure so I started in retail in the mid-1980s

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um as a computer operator which was a job there doesn't exist anymore literally changing computer tapes on on

2:54

computers and that guy that was in the retail industry where I got the first exposure to data analytics and did Big

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Data before it was called Big Data um moved on to Pharmaceuticals commodity

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trading Insurance the wine industry probably a few other Industries I forgot

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to mention as well um over about 30 to 35 years and then as I say about seven years ago uh became an

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independence great thank you that that that's the most concise and

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um Thrifty summary of someone's career I've ever had so I like it so it goes with the theme of distilling I like it

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you're a man of your word so thank you I so I will I mean clearly there's loads of things we can touch on but let's cut

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to the chase with people that think oh what what is that so maybe blockchain but could you just give an overview if

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you don't mind of what a distributed Ledger is or are and then the the typical common uses so

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I assume people know the most obvious ones but maybe some of the alternative uses would be great and then we could maybe draw on a few of them sure so we

3:56

start off with a really Basics what is a ledger a ledger is a record of transactions in some way quite often

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it's a financial ledger so you know I sense the 50 pounds on Friday type thing

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and you write that down and that's a ledger of the transaction now typically ledgers are computerized these days no

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we don't write them down in paper Ledges anymore but we'll keep that Concept in mind for a minute and we'll park and

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explain how a distributed ledger works but more so we've got this transaction you know

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I've sent Steve 50 pounds on Friday you've sent Colin 25 pounds on Thursday blah blah blah so each business each

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individual has their own Ledger of transactions the problem with that is

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that each of us maintains our own Ledger and each of us then has to reconcile that we've not lost any data between

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them and everything so it's actually quite inefficient so what we can do is through the Magics of technology and

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databases is that we can operate off a single ledger so imagine it is like a

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big spreadsheet in the sky or on a cloud for example which is just a single Ledger of

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everybody's transactions everywhere now with that being a single Ledger what it

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means is that we all have to agree on what contents are which gets rid of all the reconciliation and overhead straight

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away but we're no longer having to compare things um and we've got that single source of

5:20

the truth and it's fascinating in my in my career um in analytics we always used to talk

5:27

about the single version of the truth all I've actually realized it's better to have a single source of the truth at

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least we all know where to go to um to make sure the data is consistent so we've now got the single Ledger and

5:40

what we want to do is give a copy of it out to everybody so we distribute it so there we go we've got a distributed

5:46

Ledger it's just that everyone's got an identical copy of the information that's it probably the starting point on

5:53

that great thank you I I've certainly um I mean of course we've talked about

5:59

it and you've kindly presented as well when through um a group that we were involved with

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that the British computer Society about blockchain and I remember there's lots of people turning up it's it's of the

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moment I mean for you you've probably been doing it for uh longer than most

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obviously so it won't be of the moment for you but it my point being most people will have heard of it now I think

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that's when things have broken through most people have heard of blockchain even if they don't know what a distributed Ledger just one thing that

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always comes to mind is security and I know that was something we're probably going to touch on that's all but

6:36

in in my days I have to do with information and information governance Information Management security how do

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how do you keep the information secure on a distributed Ledger if by definition it's public how how does that work yeah

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so there's two two main ways um the foot the first of which is like any other system it can either be a

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public or a private Ledger um a public Ledger but like Bitcoin we've probably all heard of Bitcoin

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that's that's got a public Ledger behind it which means everyone can access it there's no restrictions at all what we

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also have is what's called a private Ledger this is like a members Club so you think of the banks for example

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Barclays HSBC they could make their Ledger private so that only participants

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can view it right away so that that's a starting point so it may or may not be

7:26

open to both lose depending on how you want to use it then we take it a level further and we introduce

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um a piece of mathematics called cryptography and cryptography I describe it as the

7:38

mathematics of keeping secrets so if we go back to what I described before of a distributed Ledger of everyone having a

7:45

copy of that ledger one of the problems that straight away is you don't really want it that everyone can see everyone's

7:52

transactions and so what we can do we can put cryptography on the top of it which is a way of making information

7:59

secret so that only I can see information that's related to information to me you can only see

8:06

information related to you or Barclays can always see those and so on so cryptography fantastic I get involved

8:12

with a number of companies doing it keeping secrets mathematically yeah that I mean I mean the the reason I

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mentioned that it often comes up with the other bin which I will I'll just put

8:26

it out there the building information modeling and the what I mean there is more the international standards ISO

8:31

9650 for the benefit of people listening and watching and things like digital twins have you got any examples of where

8:39

distributed Ledger technology is being used either in digital Twins or sort of metaverse that kind of uh area so you

8:47

you left the head straight away by mentioning metaverse because that's a great example about digital twins are

8:53

being applied in that way so go back to this digital Ledger for a minute let's put some security onto it which is

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what the blockchain layer is that's the security protocol that ensures that it's all distributed and it's all secure and

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then start thinking about as you say building Information Management we've got all this information which extends

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into this realm of the digital twin where you've got digital representation of a physical object now it's secured by

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cryptography sits on a ledger so now we've got the same benefits that I was describing before the fact that

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everyone's got common access to what they are allowed to access so the idea of a digital twin on a

9:33

blockchain is again that you use the same kind of methodology think of it instead of it sitting on a simple

9:38

database it's taking it sitting on a distributed database called a blockchain so we kind of flip the words but it's

9:44

commonality extend that further and think about how this digital twin

9:50

you can now see it you know you can see a 3D representation of it but building

9:55

model and that within potentially a virtual reality environment and now

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you've got the metaverse were you know many people might be familiar with games like Roblox or Call of Duty or whatever

10:09

which it kind of metaverse like in that they are second or third worlds but

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imagine if they're on Roblox for example anyone who's your children will know that their kids play Roblox they play

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those games but you can produce things you can build things you can build

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buildings you know we're building blocks and so on but you need the ability to

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then transfer that to different environments at the moment if you do something in Roblox it's within the

10:39

Roblox ecosystem if you do something within a different platform it's not

10:44

transferable if it all sits on a distributed Ledger as the common infrastructure sitting

10:50

underneath there it makes it that much easier to transfer these digital artifacts and this is where things like

10:56

digital twins of buildings for example is coming into play so I'm seeing more

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and more examples of companies who are using traditional Bim in the original

11:07

context of building information management and to support the physical design and management of buildings but

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then taking that digital twin and putting it into the metaverse and then putting a VR layer on top of it which

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means you can do things like if you're doing a new store for example you can do

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um aisle layouts and presentations and work out where the staff are going to

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work and what the people flows are and everything so it's all very exciting lots and lots of company doing research

11:37

into this as is always the case um hyper myth beats practical reality so

11:43

there aren't that many real examples of what's going on yet and I tend to be more looking at the potential as opposed

11:50

to the actual stuff at the moment where is it I mean this is partly rhetorical but if you want to answer

11:55

it's fine where do you think digital Twins and are on the the Gartner hype cycle yeah

12:02

so so for those who are familiar with the hype cycle we start off with the initial um explosion of enthusiasm

12:10

um which they call the peak of inflated expectations this this is where um marketing PR announcements

12:18

um completely outweighed the reality of what's going on then you get um a leveling out it's kind of like the pla

12:25

um a kind of a plateau stage but just beyond just before you get to that Plateau you get what they call the

12:31

trough of disillusions and this is where reality hits and actually the the technology never measures up to the

12:37

marketing hype and then over time it levels out and it improves in there so with digital twins I think we are seeing

12:44

that that is kind of stabilizing that that is leveling out companies are beginning to use that were that aligned

12:51

with the metaverse and blockchain distributed ledgers I think we're about to fall off that clip Cliff of the hype

12:59

cycle and we're about to go into the trough of disillusion phase but out of it you know that things will stabilize

13:05

reality will kick in and the technology will kick up with uh catch up with what the marketers have been claiming

13:12

yeah and I I think it's it's interesting because we both have I mean obviously by

13:18

definition technology backgrounds and data management backgrounds I I think it is um

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a lot of these things are challenging to communicate the potential benefits to people that don't have that background

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do you mean so it might be immediately obvious some of the benefits in a distributed

13:36

Ledger I know you've explained them but to people with that background but it's quite hard to keep having a different

13:42

story or selling the benefits when it does hit the trough of disillusionment you know it's like um have I mean

13:49

I know you said there weren't many examples necessarily digital twin at this stage but have you got any tangible

13:55

examples of where blockchain is implemented or you either with you've been involved

14:00

what I'm thinking are things that are maybe not in the financial I don't want to literally put words in your mouth but

14:06

I will things like land ownership land transfer things like that have you been involved with that that's I'm interested

14:12

in that one I I'm certainly there were some projects that I've tried and have not succeeded so I do a lot of

14:20

presenting at conferences and events in there where I I describe myself as a pragmatic evangelist in that I talk

14:27

about how this stuff's actually being used not about how it could be used so in the insurance sector That's my kind

14:34

of Niche if we do a Venn diagram of blockchain technology and insurance were

14:39

they overlap does it says Gary not all in the middle uh very much my strength so I do have examples in the insurance

14:46

sector with already doing policy Administration claims handling and so on using blockchain

14:52

so they're all real world things there looking in terms of land registry there was a great example

14:59

um in Central America which I'm not going to give the country because it actually went live and then it got

15:05

pulled back um because it hits the reality of politics in that region so they they

15:11

were trying to do a land registry which wasn't successful uh simply because of politics warmer has been successful is

15:18

in Estonia and Estonia has introduced its whole e-governance program where it's put a

15:26

digital asset register in place on a blockchain whereby they've registered all their property or their land but

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also all of their people and their health records and everything

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else and the reason being you know some of the benefits of blockchain technology is it's immutable which means it's

15:45

almost impossible to change so it's like etched in stone it can't be can't be changed and it's distributed which means

15:52

it's resilience against being attacked by hackers or governments or whatever

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um and it's consistent as well so the Estonian government's implemented that a few years ago and I I describe it

16:05

jokingly as being because there were that sometimes the neighbors might come to pay a visit for the barbecue at the

16:11

weekend and don't go away for a few years which unfortunately is exactly what we're seeing Elsewhere on the

16:18

borders of Russia at the moment so Estonia actually implements it a digital land registry and it was interesting

16:24

that a few years ago I was giving a conference speech about this and there

16:29

was a lady who was from Estonia and actually said about how successful it was and then someone else put their hand

16:34

up and they said oh yes my country has done the same it's like oh what which country are you and they were from

16:40

Georgia which is another former Soviet satellite state so it seems there's a

16:45

commonality of themes that in terms of land registry the idea being that um if

16:52

your com country is temporarily visited for a number of years um you want an ability that when the

16:59

Invaders leave that you still know who owns what and everything and blockchain kind of makes it natural for that

17:06

the raw um less controversial examples of land registering anything about in the UK

17:12

land registry was very much paper based it's an outgoing digital and there are

17:18

some projects and initiatives of trying to replicate that around the world using blockchain it is quite slow I I keep

17:26

describing we hear about the people process and Technology triangle which

17:31

isn't really a triangle it's a hierarchy and the most important thing is the people because they're the ones who block it the processes happen

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Technologies in incidental but the thing that makes all that uh flow is data and this is

17:45

where a lot of the time the problem is actually around getting the data because I'm on something with the UK land

17:50

registry is I think it's like at least 40 of land in the UK isn't on the land

17:56

registry because it only goes on the registry at the point at which you get to register to a title change so if it's

18:02

been in the same ownership for 200 years it won't be a land registry entry taking us forwards

18:09

um there are examples now and this is where it starts coming into the metaverse of companies trying to do real

18:15

estate via blockchain so the idea that you'll have fractional ownership of property

18:21

um is really interesting because different countries have different laws about how many people can own a property

18:27

or how many can own land I think in the UK it's about 16 or 20. and so that

18:33

they're introducing concepts of having land registration on a blockchain of more than 16 to 20 people on a secondary

18:40

market so it means that you can resell parts of it now that finally links in

18:45

with the metaverse where there are metaverse entities that represent the

18:51

physical representation so you buy a plot of land in a metaverse that actually represents a shopping mall in

18:57

Dubai so there are there are projects on that so quite a long-winded Scenic journey of where this space is going

19:04

I I think that was great Gary I was um

19:09

yeah well through some of the work I've been doing we've been looking at Estonia as well so that that certainly does come

19:16

up in a very positive way about their maturity with the interoperability and uh so yeah just just to Echo that

19:24

absolutely and you sort of you did touch on it one thing I was going to ask actually you've value that one of the

19:29

the last sort of sentences you were saying there was about the value represent digital representation of an

19:35

asset and therefore the value the the question I was trying to formulate earlier was like how do you

19:41

attribute how do you attribute value to the actual data now I know you weren't actually talking about that you're talking about a representation of value

19:48

which is manifested physically but in in my mind I'm not too sure if I'll be able

19:54

to structure the question now but how do we value the data itself is is what I'm thinking the actual data the actual

20:00

information do you think there is value in the actual data itself irrespective of what it links to in the

20:07

physical world what do you think about it yeah yeah so I I sometimes use the analogy and you've probably heard it

20:13

many times about how data is the new oil um and I actually for my analogy I

20:18

actually explain how it is that there are similarities and as much as sometimes you don't know where you're going to get it from you have to refine

20:25

it you have to clean it um you have to do things to it to make it fit for purpose there are different

20:31

types of oil for different purposes and the same data and ultimately the the

20:36

fundamental function of oil is that you either burn it to generate energy and or

20:42

you use it to all the wheels of your business in some way and it's the same with data you either have to consume it

20:48

in some way so this is where the like the social media platforms uh burn your

20:54

data in order to figure out you know what you're buying habits are and what your likes are and all this kind of

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thing and all use the data to actually oil the machinery your organization

21:06

and that's one of those things that it's a bit like when you stop putting the oil in an engine

21:12

you can't really easily value the value of the oil until the engine seizes up and it's the same with data that it's

21:19

not easy to put a value on it but try stop using it and try and make decisions without it so it's almost as though data

21:27

kind of has a that I think you'd describe as a hygiene cost it's not the

21:33

presence of it that delivers value it's the absence of it that delivers cost um so the way I've just run it in the

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past sometimes my role um I was an advisor to one company and someone said what's your job as an

21:45

advisor I said it's to keep the CEO out of jail and this is actually how data comes into

21:51

it at times that sometimes we use data we keep talking about making better informed decisions and sometimes it's

21:59

the decision of not what to do but what not to do and that's kind of hard to put price on

22:05

it for keeping the CEO out of jail is a good one but certainly in financial services things like fines levies

22:12

prosecutions again it's an unusual data but it can be very useful for that

22:18

yeah I guess it'd be based on what you just said how to give specific examples

22:23

based on the sensitivity around it but I mean I I'm just thinking about the decision-making side of it that's

22:29

a lot of the sort of things I do for the DW's getting like like you with the analytics or the or the data side of it

22:36

have you I mean how how do you think things have improved say in the last decade so for example the fact that we

22:43

can have an open conversation about analytics and business intelligence and talk about it relatively openly assuming

22:49

people know what we mean is have you seen a like a an improvement in that

22:55

conversation and say what dashboards do you want or is that still is that still an effort to get people there well well

23:01

actually I I find that what we found is that the general

23:07

population become more familiar with the idea of data-driven decision making now because we all do it even if it's like

23:14

we just go onto Amazon and when we're doing a comparison between two products we look at what's got five stars instead

23:20

of what's got four stars so so we'll we do that in some way but businesses are waking up to the fact that true dates

23:27

driven decision making really helps them I I've got an example or many examples from my career were

23:35

um implemented a new business intelligence solution and this mashed load of data together of

23:42

what we were selling what Revenue we were generating all that and it's fascinating because the day it went long

23:47

later than this was over 10 years ago now um the day it went live within 15 minutes I had a phone call from the

23:54

marketing director's secretary and this this kind of goes back to the old world for a moment she said oh the director's

24:01

just been on he's looked at your system and switch it off because it's wrong and come down to his office now

24:08

so quite scary you know new system just online and everything and I go down to

24:13

the director's office and he's surrounded by the typists and secretaries so very very much old world

24:18

still and I got through this um Armory of people

24:23

and I got summonsed into his office and he actually had a computer which was a start for him

24:29

um and he said oh your system's wrong like what do you mean my system's wrong he said well I've just been looking at the numbers that your new Final systems

24:36

just come up with and if they were right we'd be going bust in six months so switch it off and Source it out

24:42

and he was actually absolutely right um that we were going to go Boston six

24:47

months because the data was correct and what they'd never realized up to all this point is that um there were certain

24:55

business transactions we were doing that we were increasing the number of them without realizing they were losing his

25:01

money and so this is one of the great things that I actually was able to use the

25:06

system to show to him what we were doing is that we were giving customers rebates for more than they were paying for the

25:12

goods effectively handing money over and somebody was right that we were kind of

25:17

get lost that that was a great project because it actually meant within 15 minutes

25:22

um he went back to the supplier Who's involved in this um we worked out they were committing a

25:28

thing called um fraud in what they were doing and we'd have never known about it if it

25:34

wasn't for this thing so straight away I had my greatest Advocates on the board about using analytics and our payback

25:40

period on a project of about 30 minutes in in total I think I'm seeing more and more of that

25:47

now where company are companies are by default using the information to make those those kind of informed decisions

25:54

yeah that's so really good example because the immediacy of it I think that's um

26:01

that can be a challenge can't it sometimes where there isn't necessarily the immediacy because there's a

26:06

strategic approval by definition what people are doing is a strategic approach and then they don't say see the

26:11

immediate sort of payback or um I was going to offer

26:18

um the slow-moving rail crash example as well

26:23

um which was where something was happening over a period of several months and it was a it was a different company

26:30

and this time they had a product that they thought was doing really well when we looked at our sales the sales were

26:37

going off you know fantastically it looked really really good we looked beyond that however and we looked at the

26:43

overall Marketplace and we realized that our market share was growing as well so double whammy you know sales people are

26:49

happy sales were going up marketing what happened because of our growth in my shows going up but when you actually

26:56

tracked it over several months what you noticed was that the market itself was declining

27:01

and the competitors had realized that this was a market that the demand for it was dropping away so they were dropping

27:07

out of the market which is one way we were growing our Market sure that's why our sales were increasing so we were

27:13

investing heavily in a product for which there was about to be no demand

27:18

so what it meant was when you graph that out over several months it was quite a slow Trend at first but it became

27:24

obvious that we were investing in something as strategically was a bad idea and that that was a slow-mo moving

27:30

rail crash that happening in real time you Gary reminded me of that Margin Call

27:37

have you seen that film yeah I absolutely love that I know I can't get

27:43

enough of it I'm fairly obsessed with it but even though I don't understand all the details but I think that's the point

27:48

um I don't think nobody did at the time but it but yeah that's an amazing example of um Loosely based or I guess quite quite

27:56

linked to reality of data um data decisions but I I think I'm just

28:03

thinking about the things we're talking about and again it's by definition people need skills in these areas you

28:08

know analytics for example what you know if someone was starting out in their career you know we've covered quite a

28:14

few things analytics um distributed Ledger what do you what do you think people should like look for

28:20

what should they study I mean there's so much going on I know putting on the spot here but what are the

28:27

things they should search for the roles you know what where is there a skills Gap I guess is what I'm saying if you

28:32

were to employ people today what skill sets would you be looking for okay so I

28:38

I think it's generally accepted that within about 10 years most

28:44

current jobs won't exist but but don't panic because that means that the new

28:49

jobs don't exist yet that they will come you know some of the stuff I did at the beginning of my career there's doesn't

28:55

they don't exist anymore it's just the nature of it so I always say to people your job in yourself is to keep yourself

29:04

appraised of you know what's going on that kind of thing and don't actually look too far ahead look at what's needed

29:11

in the near term because invariably what happens in the long term is never what you expect

29:17

um so I say to people you know look at what's happening at the moment so there's a load of hype around artificial intelligence uh chat GPT which is one of

29:26

the open AI Foundation is causing waves so go and read up about it learn a little bit about it think about how to

29:33

affect your current role your future role your direction that kind of thing YouTube is your friend YouTube I find is

29:42

a phenomenal resource you know for learning new things and what what I always say to people as well

29:49

is go to where your passion takes you and do more of it so if you like being

29:55

analytical do more analytical things if you like operational things do more operational things get better at what

30:01

you're good at as opposed to trying to get good stuff that you're not good at um and if you're in an organization

30:07

already look at how you can use that to

30:13

help you learn about things whilst helping that organization because they'll be supportive of it now um if

30:19

you can come up with new ways of doing things that don't cost them too much money and don't expose them to risk will give them a benefit what why would they

30:26

not choose to work with you on that yeah thanks great I just I often think

30:32

about the thing the data you know Digital Data and Technology covering and then I always

30:38

talk about Information Management I argue and digital transformation are they the same thing no of course they're

30:44

not but I think it covers so many things doesn't it nowadays that it's uh I feel

30:49

well I'm not sorry I don't mean that I feel it must be quite in sometimes impenetrable for people that was one of

30:55

the motivations for doing this podcast right is some of the jargons and that jargon for people to get into it you

31:01

know talking about digit do you talk about digital transformation is that some is that the language that you would

31:07

use with your clients uh just throwing one one of the terms out there not not not really because the digital

31:14

transformation uh for a lot of people might create a website for my company initially it meant go online you know a

31:22

number of years ago and then digital transformation meant stop using paper processes let's actually capture it in a

31:29

spreadsheet or a form or something so digital transformation people almost think of it as a a binary thing of well

31:37

you're not digitally trying transform now you are it's actually a spectrum and it's constantly evolving one

31:43

uh with a possible exception of the insurance industry which until two years

31:48

ago which was still using paper-based systems and everyone's pretty much digital now in some way it's how they

31:56

exploit that and how they utilize that um and how do they figure out how to improve customer service

32:03

um by reducing the amount of data that you ask people to fill in that don't know about you but if you're full of

32:08

foreman and you think why do they need to know this you know I've been an account holder with you for 20 years why

32:15

do you need my address you know why do I have to type it in there so digital transformation I think is an ongoing

32:21

Journey it's a constant Improvement type thing yeah and I I think that's an iterative

32:27

approach is something that we'd be familiar more in the I.T side of it but

32:33

again certainly with a sort of a Bim and building information modeling we used to talk about level level one level two

32:39

level like maturity and again they've quite rightly said no no it's a continual Improvement or it should be as

32:46

as you've said as every sector should be have you gotten a thing um to say and

32:51

this is just this is just for me if you indulge me about parametric insurance that's very very specific and uh just

32:59

thrown out there but you're the man to ask so first of all what is it for people for me throwing that in there and

33:04

have yeah tell us about it yeah so parametric insurance is Insurance based

33:10

on a parameter so that parameter might be something really really simple thing think if you're on a flight for example

33:16

and people have flight delay Insurance well if your flight times are delayed or it isn't it's binary okay and so what

33:24

parametric insurance does is it looks at these events as what called trigger

33:29

events so your flight was either delayed or it wasn't so really simple example if

33:35

your flight's delayed pay compensation not nice and easy you can do that in an actual spreadsheet can't you have a nice

33:41

simple Excel formula if flight delays pay compensation so parametric Insurance Builds on exactly that and what you have

33:49

and this is where we're using technology jargon now on a blockchain or distributed Ledger system you have what

33:55

are called smart contracts which are the worst name things ever small contracts

34:00

are not as smart for contracts they are computer code they're a small computer program and their purpose might be for

34:07

example to monitor flights um information and so if you take out a

34:12

policy kind of work with a couple of companies who are doing exactly this um to ensure against your flight being

34:19

delayed it monitors it's ease of your flight was delayed and if it was you get paid compensation lovely and simple I

34:27

did a presentation with a group of insurers a year or so ago and I said to

34:32

them right any of you who ever had a flight where it's delayed blah loads of them had how many of you claim on

34:38

insurance very few of them which is very south of the insurance industry and I said okay of those of you who claim how

34:45

long did your insurance claim take and they said oh it was really really fast it was really good it only took about two weeks it's like okay with parametric

34:53

Insurance you can do that in two seconds and in fact even better you don't even have to make a claim it just happens

34:58

automatically so that's an example apply that now as a company in Germany called

35:04

Lisa risk who I did some work with did of let's have parametric Insurance on

35:09

crop protection insurance so imagine you're a farmer in Sri Lanka and it's generally accepted if it rains

35:16

more than say 80 centimeters in a month you know you've had massive floods your crop is wiped out what you don't want to

35:24

do is fill in a claim form have an insurance assessor coming out they haggle over the damage to crop it takes

35:30

months and months blah blah blah what you want is if it rains more than a certain amount get paid compensation so

35:36

that's exactly what they've done with aeon who an insurance broker and they have crop protection insurance which is

35:42

parametric so you can then extend that to other things like um solar arrays you

35:47

know if your solar panel doesn't generate a certain amount of electricity over a certain time but you're contract

35:54

it's deliberate then take an insurance policy on it nice and simple again so if you can apply simple math simple logic

36:00

that's an example where you can use parametric Insurance great thanks thanks Gary certainly the smart contract is um

36:07

an immediate parallel with the the digital twin and the bimworld as well they're obviously that's where things

36:14

are going in in a similar and parallel and linked and converging though

36:19

um vain as well so certainly um there's efficiencies there as as far as all the things that we've talked

36:25

about what it what what's like the next thing coming or what's I know you've maybe we've mentioned it already things

36:31

like AI in there but you know what really are the next sort of emergent technologies that maybe we haven't

36:37

explored yet maybe you've already mentioned them or maybe there's terminologies that you haven't mentioned yet but what what should we look for

36:43

there are so many um on the distant Horizon that it's almost not worth looking at them you

36:50

know people talk about um Bob bonio engineering nanobot

36:55

technology um Elon Musk will talk about integrating

37:01

compute capability he he talks of the merger of silicon and carbon in other

37:06

words having brain implants this kind of thing that's kind of so far ahead of the scale that I don't want to go there

37:14

um because it's at least two years away and that's a very long time in technology terms these days the more immediate stuff is going to be things

37:22

like Quantum Computing which is beginning to happen now Quantum Computing exponentially increases the compute

37:29

capability of what's going on but again it's a kind of a hype phase at the

37:34

moment you can access Quantum Computing via Google and IBM but it's relatively

37:39

Limited at the moment more current is things like artificial intelligence so everyone has heard of AI

37:47

now it's actually been around for over 50 years you know artificial intelligence you can trace its Origins

37:52

back to mid-1950s but it's taken that long before it comes

37:58

normal so things like chat Bots um conversational

38:03

um type things people think of chat Bots where you go onto a website and says hi how can I help you today well actually

38:10

think of those of our true intelligence and then think of them not just in chat as in text but um audio so they actually

38:18

speak to you and Microsoft have got some good examples and Google as well of

38:24

actually having spoken chat Bots where you don't actually realize you're speaking to a robot so I I think all of

38:31

that's coming on quite quickly because that removes the need for call centers to do call handling that kind of thing

38:38

running queries um longer term hopefully they'll actually use the AI to improve the

38:44

products and services so they don't need the call sensors in the first place so AI definitely blockchain that kind of

38:50

thing blockchains and infrastructure technology which means ultimately you shouldn't

38:56

need to know you're using it it doesn't matter you know you don't think about oh am I using Microsoft SQL server or

39:03

Oracle DB or whatever you're just using it and blockchain with being an infrastructure protocol layer you won't

39:09

even notice it so I I think people will see AI more and more and simply you know

39:15

go and use your favorite search engine um and it is getting integrated with

39:20

well with Google they're beginning to use Bard at the moment which is now on technology Microsoft are introducing

39:26

chat GPT they're introducing things called Microsoft copilot which is where your office documents will be integrated

39:33

with AI so you might remember many years ago the windows little um paper clip helper of highlights I see

39:41

the alternative a Word document can I annoy you please well this time around it'll be of genuine help

39:48

but it but it might not look as cute and jump around on the uh yeah because the first thing was how did you disable how

39:55

could you disable it right when you opened words so but I I so thank you for listening all of them I'll certainly put

40:00

some links into because again there might be I'm sure there will be people not familiar with things like chat GPT and um so I'll link link to them I mean

40:08

I've been looking at these things myself and they're very good uh there's other maybe not so amazing ones that I've used

40:15

that you paid for and um but they're still good they're still good there is some even though it's artificial there

40:21

is some form arguably of creativity or it appears to be creative let's put it that way in writing copies

40:28

so it's quite scary but I I think all these things are coming together I was when you were talking there I was just

40:34

thinking about the we talked about block blockchain and security I I think what what do you see as one of the biggest

40:41

hurdles to overcome as far as adoption of sort of the current technology so I'll leave it quite vague and open on

40:49

purpose because you mentioned the people process technology after as you already have articulated it's often the people

40:55

what's one of the big bigger or biggest challenges at the moment that we need to overcome with people to either adopt or

41:02

upskill the current Technologies and solutions is there anything yeah it's

41:08

painfully simple it's usability um but people I I describe as like

41:13

flowing water they'll take path of least resistance if you want people to adopt a new system

41:20

um make it easier for them to use that new system and the current method so what we tend to do is a kind of a carrot

41:27

and stick approach with a lot of Technologies and it tends to be more stick and no carrots whereas in actual

41:33

fact if you offer people a new system they say why do why should I use this new system well it's quicker faster

41:40

cheaper and easier for you to use it'll take you less time less effort and it'll give you better results but they'll do

41:46

it you don't need to promote it and this kind of thing that's what I say to people if you need to do explain videos

41:52

to explain how something works then it's not intuitive you know the number of

41:58

companies I've worked with they said oh well we've developed this intuitive solution and we've got like a 30 page

42:04

user manual on how to use it no I I didn't need training in how to use

42:09

Google original as a search engine anyone who tries out chat GPT will discover very quickly you don't need

42:16

training and how to use that he is totally intuitive look at how children Embrace technology and how

42:22

they'll use certain things they'll just go and use YouTube to find something as far as I know no one's been on a how do

42:29

I learn how to use YouTube YouTube video because it's intrusive you do it you search it okay so you usability is

42:36

absolutely the critical thing make it easier for people and they'll use it anyway yeah it's it's a really good

42:43

point I think to for some maybe not for us but for some people that's forgotten about do you

42:51

know it's like almost the last thing absolutely I mean bizarre because that's clearly

42:57

one of the first things that should be thought thought about I I what I was going to mention I I I couldn't recall

43:02

when I when I asked the last question was about people often ask me does it are people using Ai and I I mean well

43:10

that's like when you on Netflix when it's recommending or on Amazon in the background somewhere correct me if I'm

43:16

wrong Gary you'll be the manager well I mentioned chat Bots and when you

43:23

talk about those you think as them as you know separate things like call center things but if you're using uh

43:29

voice recognition Siri Alexa any any of these things they're all AI based if

43:35

you're doing something like you're using a mapping tool in the public domain not

43:40

not the specialist ones Google Maps or Bing Maps they're using AI as well you

43:46

look at a recommendation engine you know if you go on to Amazon you know people who bought this also bought this that

43:53

that's using AI on that so we're already using a lot of AI and companies are

44:00

embedding the capabilities so that you're using it without realizing it so my mobile phone uh when I took a photo

44:08

it has got AI capabilities built into it to enhance the picture to stabilize it

44:15

to change the color balance all without me even realizing it yeah and I think they do um quite

44:22

scarily they'll do facial recognition to map to group the photos as well I think that's in the phone as well apparently I

44:30

mean that's absolutely interesting that it's actually in that capabilities within the the phone itself which is uh

44:37

remarkable if you think about it my phone's got an ability it has as you call a face tracker so like if I move

44:43

around it will automatically track me so it knows what a face looks like

44:49

no and I I didn't tell it I didn't you know describe exactly me and all this kind of thing it uses AI for that and

44:56

then then you extend that the trials going on uh in some parts of the country moments around facial Payments

45:02

Technology so rather than paying cash or using a debit card or using touch to pay

45:09

you just look at the screen and it automatically does an identity check works out who you are goes to your bank

45:15

bonds make sure you've got the money makes normal automatic payment and this is where I I have a bit of a

45:22

problem at times that I'm torn because on a technology basis that's phenomenal it's absolutely incredible on a societal

45:30

basis that's quite scary Black Mirror material or you know hang on that you

45:35

know um a computer is now deciding that I can buy a ten of beans and supermarkets

45:40

um from an automation point of view Brilliance from a societal point of view it's got some challenges

45:46

yeah Biometrics I mean we just maybe move because I'm quite interested in

45:51

that one but I maybe we'll park that for another day the biometric thing but uh there was one terminology that I thought

45:57

again I try not to go too Tech but we we're going we're going all in Tech today why not is is Big Data right

46:04

because you know is that term still used what is it do you talk about Big Data

46:09

again apologies for like lobbing these in here but it's just these terminologies that come up and people think so big big data do we still talk

46:17

about that and what is it absolutely so the original definition of big data

46:22

which was about 15 or so years ago now was any data that was too large to

46:29

process and analyze using conventional Computing technology so basically lots of lots of it okay so

46:37

the original computers I worked on in the 1980s literally filled a building where we use teradata systems and

46:45

Honeywell things and these these were big mainframes that literally filled up an anti-building and on one of the

46:51

computers we had the entire UK electoral role which at the time I think was 46

46:57

million people I can fit that on my mobile phone now so

47:02

the concept of Big Data meaning an awful lot of data has kind of gone away so we went on to initially what we call the

47:09

three vs of big data which were volume variety and volatility I think it was so

47:16

volume how much of a um volatility how fast it's coming through and variety how different it is

47:22

we've now taken that further and I talk about the eight these of big

47:28

data which include veracity variability velocity that there's all sorts of other

47:33

things in the marketing speak so yes we do still talk about big data but if you

47:39

think about Excel as an example Excel I can't even remember how many rows and

47:44

columns there are in the latest version of XL which means you can handle up to 16 billion

47:49

um data points ever remember correctly so that's pretty big so as I say that

47:54

the old definition of Big Data just mean lots of data has kind of gone away but we look at things like

48:02

um there are companies who are doing analytics around shipping movements so where every container is going around

48:07

the world and this kind of thing billions of transactions a day and it means they can look at that to work out

48:13

what the world economy is doing and this kind of thing that that's a huge volume of data where you're tracking ships

48:19

containers products vessels ports and all this kind of like and actually do that to see what were the business

48:26

activities going on that's an example of Big Data so we do still use the term big data but is become very broad now I

48:33

actually talk about the importance of small data because the small data that I can mess you up at times and I use

48:40

examples of um where companies have actually gone bust because of very small

48:45

data so don't just think of the Big Data what I guess you have to be careful what

48:51

how much you say but what what is an example of what what do you mean by that like a missing spreadsheet or emails or

48:57

what even smaller I know of a company who went bankrupt because of a single

49:03

letter and that letter was letter s and what it was and it's in public domain as well so I can talk about it

49:10

okay and it was a mistake that was made um I think it was about 10 or maybe more

49:15

years ago now by company's house so companies house maintain the the registerable companies in the UK and

49:23

there was a company the exact name which can't remember now but they did actually

49:28

um file for closure that they went bankrupt and it's something like you know Johnson and Sons okay so but plural

49:38

and this company went bus and so companies House made the Amendments on the database record to say that Johnson

49:45

and Company had gone but so they missed out the letter s there were two companies almost identical names

49:52

that company that had actually been in business for over 100 years was perfectly liquid perfectly solvent uh

50:00

suddenly wasn't because it suppliers did credit checks and noticed that they'd gone bust even though they haven't and

50:08

so when supplies stop supplying you you do go bus so this company that's had a course action against companies house

50:15

where they they went into solvency simply because of a single character

50:20

means that the wrong company had been set up to the the

50:26

um bankrupt and actually sent them out of business and I think it was significant core um significant

50:32

compensation they got for that that's a really simple example a a

50:37

non-terrestrial example would be the Mars Explorer with NASA were they got a

50:43

really simple calculation wrong I think they used metric instead of Imperial so at the point at which the Mars Explorer

50:49

this satellite type thing you'll probably send onto Mars was about to blast the Rockets to slow to descent

50:55

down and they use the wrong unit of measure and it means that they got the

51:00

um the parameters propulsion wrong and so it disappeared with that trace and that was millions and millions of

51:06

dollars worth of uh stuff that were just went so yeah several examples of that

51:11

kind of thing of units of measure being wrong single letters being wrong it's about checks and balances isn't it or

51:18

lack of it and it comes back to that people Pro and we know we know it's we've talked about that for a long time

51:24

in the I.T sector but people process technology but it does come back to that people and process you know that that

51:32

could have been caught obviously if people were better trained or whatever in the scenario but also if there was a

51:38

robust process to pick up you know some kind of tolerance for the

51:43

or maybe with the second example but certainly with the I mean I've maybe I wouldn't be amazed now but early in my

51:50

career I was amazed at different units of measure in different sectors and the

51:55

different ways of writing the date the the dates I I just love there is

52:02

actually an ISO date format which is great because it makes a lot of problems go away but but you're right things like

52:08

dates um measurements this is why I'm always cautious when I work on multinational

52:14

projects where you always have to upfront agree what units of measure

52:20

you're going to use are you going to use Imperial or you're going to use metric and so on yeah and um another example of

52:26

that is in calendar a few years ago there was an airplane that nearly crashed um and Eric Canada Boeing 767 I think it

52:34

was simply because when they put fuel on board they'd request so many tons of

52:39

fuel problem with tons is that there's an Imperial ton and there's a metric ton and the pilot I think thought he'd

52:46

ordered in metric and fuel that put in an imperial and the first they knew about it was when they were at 36 000

52:52

feet or whatever and both engines stopped and the pilot and co-pilot looked at the dull nails and like we're

52:58

on human say a time thing and they literally run out of fuel and turned

53:04

into what was called the Gimli glider where this Boeing 767 was probably the

53:09

biggest glider in the world for a while um so that could have resulted in literally hundreds of deaths from a

53:15

simple mistake the the one thing that saved them was that the part one of the pilots was a glider pilot he said oh

53:22

okay I'm just in a big glider now and he'd landed it safely at the shoes that I feel I mean it is interesting that a

53:28

specific example of thumb the fact that the word that someone thought let's add it let's add another n and an e and

53:34

we'll just uh no no one will worry about that one or

53:40

um I did look up the stand because coincidentally I was doing some training yesterday and we did actually mention the international standard for date it's

53:46

ISO 8601 of course most people wouldn't be able to quote it but myself included but

53:52

there's a standard or a schemer should I say that just just out of interest that that we that the central UK government

53:59

is promoting for the built environment sector called Kobe anyway it's a schema and within that

54:05

you know eight eight uh eight six zero one is the preferred data structure

54:11

yeah no no actually I hope they go for 8601 hyphen two which is a slight

54:17

variation which includes the Nordic region of our own correctly right this shows just how sad you can be when you

54:24

can talk ISO stand up at that level well well I'm throwing it in there on purpose just to you know not show off that we

54:31

know them but just say they are out there you know because people talk again if people haven't if you'd like I say

54:36

get get their hands dirty with data where our lives have witnessed where different organizations I've worked with

54:42

have forgotten to specify you know and then we we just get dates in various formats or certainly early in my career

54:49

I've had to cleanse data have you got anything I know we're talking about it now and but again maybe just an open

54:55

question have you got anything to say this about data quality I know we've probably touched on it multiple multiple times just about what some of the common

55:02

big challenges are and what you know what's been done to improve data quality I know that's fairly nebulous but yeah

55:08

the the starting point really goes above data and it comes up to more in terms of

55:14

business level type stuff so if you're going to come up with a data quality definition of a piece of data first of

55:21

all Define what the data actually is you know what you mean by it so we're talking about you know tons for example

55:27

you know metric and Imperial this is one of your starting point in any data quality initiative needs to be a

55:33

business glossary where you have a clear definition of what the business terms are you know there's a another industry

55:40

I know that the healthcare industry which uses the word section now section in the context of Gynecology

55:49

and you know childbirth and that means that you know the performing a section the C-section I kind of thing

55:56

um in the mental health side things it means being sectioned where you get locked up and everything I know an

56:03

example of a mental health health nurse who actually had that word use upon her

56:09

at one point and she thought that when she was going to be sectioned when she was pregnant and nearly run out of the building so that's an example of you

56:16

know one word having two very different meanings within a building because they

56:22

both worked in the same hospital within the same industry so think now about the risk of us using

56:28

the same words across different Industries you know and um I I did something with someone a while ago it's

56:34

really funny that project management space where he's talking about how we was uh running this pilot project I

56:42

thought it was really really interesting what sort of Pilots are like he said no no I mean a pilot project as in you know

56:48

a Beginnings thing oh I thought you meant to Aviation so it shows how we use these words so data quality it comes

56:55

down to first of all making sure we're using the right words in the right way for the right meanings and then

57:00

understanding as well that data quality isn't a fixed point in time but I use

57:07

what's called a cypok model which is the supplier input process output customer

57:13

model at each of those levels there may be different data definitions of quality

57:19

requirements so on a building side you know something people may be aware of on

57:24

Boom side of things and the quality of what gets put into a brick or a

57:30

foundation might depend upon the nature of that brick and how it's going to be used and so you don't want to use a low

57:37

quality brick which is fit for purpose when it's produced but isn't fit for the

57:42

purpose it's being used for so this is ties in quite nicely with data quality then of making sure that the data

57:48

through its journey is fit for purpose I have seen some improvements on that what

57:54

I tend to find is that a lot of people think of data quality as being a project that you do and it's happened and it's

57:59

all there whereas again it's an evolving constantly monitoring constantly improving situation

58:06

yeah thanks Gary I um I think we're almost up to time but I I think of um

58:13

I think I think of these Concepts quite a lot um in my data because the the challenge we've got I think a lot with

58:20

others is they will always talk about semantics and taxonomies and and I think we know what they are but they don't

58:27

they don't mean anything we managed to other than me saying them just now ontologies for example we've managed to

58:34

get away with explaining it without using them so I just thought I'd throw them in there where where people come in with that jargon and then the irony is

58:40

people don't know yeah this is this is where it gets meta because people don't know what they're talking about the irony is they're trying to make things

58:47

meaningful and you just did it by saying matter as well so the thing about jargon

58:54

achieved two things it actually makes simpler conversation within a profession

59:01

so if you're in the medical profession they talk jargon because it's shorthand notation for them so actually jogging

59:08

can be good within an industry it's when you step outside of it um that it has the second useful

59:14

function which is to be protective of that industry so I I sometimes say that jargon is used within the industry to

59:21

help each other communicate and to prevent others from getting into that industry yeah I I can well I completely Echo that

59:29

certainly with the original Civil Service background it maybe wasn't intentional but working in the

59:34

government and in it I've seen lots of jargon are now in engineering and I mean I'm sure you see it as well and I think

59:40

like you gave the example of a dictionary or a catalog I think that's

59:46

conceptually people can understand that but it's flabbergasting how we don't see

59:51

it often like in the example you gave I mean that's a real quick win if you like

59:57

let's just sit down and describe that what we do as businesses and and try to work out the terminologies I've I've

1:00:04

literally had most of my time with you but you you thankfully have already mentioned where Concepts maybe people

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can look up but just to ask you directly are there any specific websites and you

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may have mentioned them already so just for the summary for me to add the links that you recommend people to go to

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particularly about the things you were talking about with insurance um and well whatever you want to say

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maybe just a handful I know there's probably hundreds but just a few that you'd recommend it's interesting because it's the one question I always struggle

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with even though I often get asked it about words you go to for all this kind of thing because it's such a wide range

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you were mentioning earlier you know the wide range of things that are happening and it's the same with learning about

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things happening it's a it like a data fog type thing so I don't really recommend anything beyond just going

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onto a search engine and randomly typing stuff in at times which sounds incredibly unstructured and occasionally

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you will end up going without a complete Rabbit Hole of Discovery um but just do something like you know

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we mentioned got the hype cycle earlier on there is public information around Gartner who are an analyst and an

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analysis company who do research into what big and emerging topics are go and find out what they're writing they write

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some great papers about emerging Technologies and that go and take a look at something there thanks Gary and and

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the last question again you you might say look again which was absolutely fine but are there any specific like physical

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events I know most events are online now but if people want to get out are there any physical events you recommend or you

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that you've been to recently maybe they'll have to wait for next year I've completely transitioned away from the

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physical world you know I I now live in the metaverse which which is kind of fascinating so I actually find that

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there's a massive amount of online um events going on which are a blend of

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real world and you know physical type thing so if you take a look at what the

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likes of Nvidia for example Nvidia or age set manufacturer and do a lot of the

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video Imaging type stuff that they just held a four-day conference

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um which was partly physical partly virtual uh talking about the metaverse and Ai and all the stuff they're doing

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absolutely mind-blowing what they're getting into um I attended virtually last week an

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event that was in Dubai which was about artificial intelligence and I kind of thing so to be honest I've kind of moved

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away from The Real World and I'm living I'm living in the metaverse that's good well maybe maybe that's where it is for

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some people I I um yeah it's I I have to sort of make good reasons to go to things

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nowadays let's put it that way so yeah I'm completely with you but I've had my time with you I could literally talk all

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day but thank you so much for your time and your insights and um anything we have mentioned that I can refer to I

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will and put links in the in the bottom but I'd like to thank you uh Gary for your time a pleasure and if anyone wants

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to reach out just Google or use whatever search engine just Google Gary Nuttall emerging technology I think I appear as

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the first 20 results in a particular space thank you very much Gary Nuttall from distalistics and

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um who knows we might do this again one day but I found it very insightful so thank you Gary a pleasure great thank

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you

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[Music]

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[Music] thank you