Expert Interviews

Webinar with James Siddle - Multinetwork Data Analysis

In this webinar James and I talk through the mulit-network data analysis that he has completed to identify the knowledge flows between experts on the Zapnito platform.

Published Jun 20, 2019

COO & Co-Founder, Zapnito

Liked by Charles Thiede and 3 others

James and I talk through some of the history of his data analysis work and discover some interesting insights into how expert communities when viewed from a multi-network perspective can provide deeper insights into how knowledge flows between, not just within communities.

Community centrality within the multi-network view becomes a key point discovery and something we expect to be further revealed as we grow.

If you're interested in this and some of the other enterprise level capabilities of the Zapnito platform please get in touch.

Video Transcript

Hello, my name's Jon Beer.

I'm the CTO and Co-founder of Zapnito

and with me today, I have Jim Siddle.

So Jim has been working with us

sort of on and off over the last few years.

He's done some work with us in the early days.

Data analysis, and today we're going to revisit

some work that we've done over the course

of the history of Zapnito.

So hopefully it's gonna be a very interesting

conversation around the sort of multi-network

view of Zapnito, so Jim, would you like to introduce

yourself, who you are and your background?

Yep, so I'm James Siddle,

I'm a software engineer by background,

although a few years ago I started

to get interested more in data analysis and visualization.

These days, I'm an IT consultant,

IT contractor based in London and I've,

yes, as you say been working with yourselves,

pulling together a few interesting visualizations,

looking at some of the characteristics

of the Zapnito network, looking at sort of the trends

and also producing some nice visualizations,

which hopefully tell quite an interesting story.

Okay, thanks Jim.

So just quick background on Zapnito

for those that aren't familiar.

So we provide essentially a software as a service

expert community platform,

and that's mainly used by media companies,

societies, any company that has online communities

of experts and they want to bring those experts

into a branded online community to share,

to share expertise.

So in this talk, or in this kind of,

what's the word?

Is it a video blog or is it a webinar?

Let's make up a word, it's a webinar, probably.

So James and I will go through some of the background

of James' work, which is essentially analyzing

the relationships between the experts on the communities

and the knowledge, the flows of knowledge between people.

And so it's quite an interesting topic area for us,

in terms of understanding how the communities get used,

not just in a single community perspective,

but in a multi-community perspective,

or what we would call multi-network perspective.

So seeing who those influences are,

not just based on their following,

but also who's influential in their multi-community view.

It's very interesting, and then particularly

in this used case, we've looked at Springer Nature

as a used case, 'cause they have many,

many communities with Zapnito.

So the background of their data is quite interesting

from a scientific research perspective as well, so.

So just, let's dive in, let's not waste any time.

Let's get to the exciting stuff.

So, Jim, do you want to give us a background

onto the sort of story so far?

So essentially, yeah I think we started working on this site

five years ago now, so as you can see,

this is a little sort of excerpt from

the first blog post that was looking

at the Zapnito network.

So this was actually showing the very early

sort of structure, I think just pretty much just after

you'd kicked off, you know, actually launched the company.

And so what you can see here is actually the,

everything, so this was all companies,

or all you know, sort of, all networks,

all kind of smushed together,

so we did a little bit of an interesting sort of story

telling very early on, looking at some of the early

trends, basically.

But things, as you say, moved on quite a lot since then.

So as you can see on the next slide.

Do you want to explain this diagram a little bit?

What are the blobs, and what are the lines,

just as sort of an intro?

Yeah, so essentially, in all of these diagrams in fact,

the sort of common pattern is that the,

each of the blobs represents registered profile experts.

And each of the connections is potentially the,

it represents a flow of knowledge between two people.

So the flow of knowledge being the consumption

of some article, possibly watching video,

essentially, you know, the passage of knowledge

in some form, and also, this one's worth noting,

the size of each of these blobs is representative

of the, well I think it actually changes based on

the different diagrams, but the general trend

in the Springer Nature diagrams which you'll

see on the next slides, is that size represents influence,

and the influence is measured by the reach

of the spread of knowledge of one of those profiles.

The idea was to really just represent the profiles,

the experts and the flows of knowledge.

Great, thanks Jim.

So this diagram represents sort of a very early

the end of 2014, isn't it?

October, roughly.

Where we had two or three customers,

with a couple of different networks,

and sort of looking at the overlaps

of where we were back then,

and to see, I remember us looking at this back in 2014,

and we were all kind of,

our eyes were sort of wide open at the time.

You think, oh this is incredible,

and it's really amazing, but sort of now looking at the data

you produce now, which we'll look at on the next few slides

it's just, it gives me tingle, gives me goosebumps

to sort of, to see how we've gone forward, so.

Yeah, yeah. All right,

let's have a look.

Yeah, so as you said there, the next step,

so this is from last year.

So we did another iteration, so this was focused

just on the Springer network expert communities.

And so yeah, this isn't showing anything else.

This is really just focused on those scientific communities.

But again, the principle here was to show

the, all of those key influences

and the key knowledge flows, so for example,

I think there's quite a nice example there,

you can see in blue.

There's the, I think that's the microbiology community

in the Springer Nature network.

And there are you know, there're a handful of key people

in that community who, they play an important role

in disseminating knowledge,

so they're writing articles, or they're writing papers,

that a lot of people are viewing.

And then you can also see there are some,

there's one example there, where there's a person who

is sort of bridging two networks from the brown one,

yeah exactly. The white dot there

in the middle is it?

And so that, so the white dots by the way,

in this particular diagram,

they represent individuals who are members

of multiple networks.

So these are people who join, they sort of join multiple

communities, so they've taken an interest

and registered their interest in different

scientific disciplines, and so there's quite a few of those.

But actually you'll see that the colors represent those

individuals who are about just part

of the single community.

And certainly there are some people who really focus,

there are some people who really diversify.

Yep, so what's this network up here?

It's got lots of white dots, isn't there, relatively

in comparison to its size,

compared to some of the other ones.

I don't know if you know which network that is.

Yeah, so from what I remember,

that's bio-films and microbiomes.

And there is a story, 'cause that comes up a little

later in the slides, so we can sort of talk

about that as well.

I don't think we ever looked into the reasons

for the, why that has more multiple sort of,

it may actually be for the reason that we'll--

Yeah, I think I know why, we'll keep it to ourself,

keep it as a surprise.

Don't wanna steal your thunder on the slides.

In a general sense, it's obviously you can see

the transition from where we were in 2014, to 2018,

in terms of scale and complexity of the multi-network view.

It's quite a step upwards.

Exactly, and I think the other thing that's also

interesting, is that sort of at this stage,

because all of these communities are from a single customer,

and they represent scientific domains that are,

either closely related or you know,

there are definitely relationships

between these communities.

I think the network tells a more,

more interesting story overall,

than the sort of earlier version that we did,

was just like, well here's everything,

let's see what it looks like.

We focused on Springer Nature, there's a lot of cross

pollination I think.

Yeah, I think the diagram from 2014 was very much

your kind of, it was like a taste of what was possible.

It was like, oh, you know, kind of like,

actually we've got something pretty interesting here,

and to see it actually plan out you know,

a few years later has been a real story I think.

Yeah, this slide just summarizes the,

really just a preview of what we've already talked about

a little bit, which is that in 2019,

we've done another iteration, so this is an update

to the data visualization, repeating the process,

Springer Nature focused again.

And again, we chose the same visual representation

and layout broadly speaking.

It's not identical, but it's, we've attempted to mirror

the representation from before,

so that it's possible to just sort of have some consistency,

for instance in the colors.

And yeah, the information, the rest of the information

on this slide is just describing the,

that it's you know, these registered profiles,

and the flows of knowledge in the network.

And then you kind of move on to, yeah, so 2019.

So, yeah, basically the latest iteration,

as you can see, yeah we have the same,

roughly the same layout, but the main difference

in my sort of, we'll see this in the additional

sort of slides, we'll see the next sort of,

yeah in the next slides, is essentially scale

and connectivity of the things that are really

sort of changing drastically over the course of the year.

And yeah, I think you can certainly see that just from

you know, the sort of just visually in the slide,

you can see that there are a lot more

of these sort of connections springing up between

the communities. Yeah.

And I think that's really sort of, tells a story.

And so, in terms of the, let's say the metrics of the graph,

we can see those on the next slide,

if you want to.

I was gonna ask you about the position of the communities

actually Jim, because I think when we talked previously,

we talked a little bit about centrality

as a measurement scale.

And you can see obviously,

I think it's Ecology and Evolution community here,

kind of in the center, and the microbiology community

above it, is centrality a metric that's tracked

on this diagram at this point or time,

but it's not is it?

It's something that's, we're gonna look at in the future?

Yeah, exactly, so as a distinct metric,

that's something that I think we would want to determine

how we would calculate that on the network,

and essentially try to come up with a number

which places the network just in terms of their connectivity

and their sort of their centrality.

The algorithm that lays out the diagram

is a force layout, force um...

Yeah, basically, a force layout algorithm,

so it uses sort of a bit of a physical simulation

of springs and connections and that sort of thing.

So it sort of approximates a physical layout.

And in this layout, what you end up with is these,

they're classed as, sort of closely connected,

will naturally tend towards the center,

and I think that's exactly what's happened in this case.

And the two that we see here actually right

in the sort of center, are,

there's the Ecology and Evolution

and then Research Data was the the other one which

is particularly central.

Now Research Data is, it makes complete sense why,

exactly, it's, it makes perfect sense why that

would be in the center, because it's something

which is totally cross disciplinary.

And I think Ecology and Evolution is a factor

of the fact, it's because it's something which is also

all of the other disciplines make reference to,

is in my view, so you know, all of the sort

of microbiology, chemistry disciplines,

they're all fundamentally about life sciences,

and Ecology and Evolution are so central to that

in many different ways, so that's my,

why that's so central. Yeah, no, it makes sense,

it's you know, chemistry community over here

on the right hand side is obviously very much

about chemistry, biology and microbiology up the top,

and then you have ecology and evolution

which is, as you say, much more cross-disciplinary

type community and that's why it's gravitating

towards the center there.

So it's interesting to see those types of communities

which are more centralized.

I think it's something we'll be working with

Nature with, to try and understand a little bit more about.

Yeah, exactly, exactly.

Yeah so in terms of the sort of, just the metrics

that we've got at the moment, so main things are,

yeah, as I was saying, the network size is changing

pretty drastically, so I think 50% more registered

profiles since 2018, so pretty substantial growth.

And so yeah, the sort of tagline or the sort of way

I'd refer to it is the community of registered experts

continues to grow rapidly.

In terms of the connections themselves,

yeah, there are double the number of connections,

and so really that just shows that the overall scope

of knowledge flowing within the network

is really increasing fairly, you know, drastically.

I s'pose there is an emerging network effect there,

in a sense, because you're doubling, you've got 50%

more registered profiles,

but then they're global connections,

there's obviously double, so,

from an edge to node perspective,

it's going to exponentially increase.

Yeah, I think so, so as you have you know,

one profile is added, it's gonna be making multiple

connections. Yeah, yeah.

So, that actually relates to their sort of overall level

of connectivity, which I think I talk about on the next

slide as well, if you want to move forwards.

You know your slides well, Jim.

Yeah, basically, another measure of you know,

sort of network measure is that of connectivity.

And I think there's a 15% increase in connectivity

since last year, and the average number of connections

has gone from 3.94 per node to 4.43.

So yeah, it's essentially, there is this sort of thickening

out of the network itself as this sort of,

the number of connections that have been made

between experts and the sort of volume of knowledge flow

is, is kind of, is increasing for each of those individuals,

as people kind of come and join.

So are you aware, in terms of this type of analysis,

are you aware of any kind of benchmarks or thresholds

in terms of connectivity measurements,

that lead you know, I guess, wondering whether if you get

to a connectivity level of five,

or a connectivity level of six,

do you tend to see things happen?

Does it tend to be like a trigger,

a trigger for something incredible (laughs).

I don't know, is a benchmark you get on these types

of communities, Jim?

It's a great question.

So from what I remember, there are models that we could

apply from, I'm sort of thinking from sort of the social

science experiments, that, I have seen, sort of seen this

previously where there were some models that look at what

those tipping points are in terms of their connectivity.

But I think what would be a really interesting experiment,

would be to possibly model what that would be

for the Zapnito network, so to say,

well, at what point would you expect

some sort of exponential growth in terms of the level

of you know, under what circumstances would you expect

that to happen?

And I mean, I guess, the sort of,

I guess relevant in terms of Springer Nature,

but there are quite a lot of models in terms

of spread of diseases.

So you'll see, you know, spread of disease within

a population, there will be like a threshold level

of, I think it is connections between people,

and it's, once you reach a certain number of connections,

and number of contacts, then basically,

you then have this sort of...

You can't contain it any longer, I suppose.

It sort of becomes, yeah.

Okay, yeah, interesting, I think it might be worth

us analyzing some other data sets through time

and seeing if we can establish a tipping point.

That would be really good. That would be worth doing.

I've got some ideas for that.

Okay, great, so.

Yeah, so then in terms of yeah,

the other sort of growth metrics, so these are a few

of the numbers that I pulled out.

So there're essentially three communities

which are pretty much just new, since 2018.

There's chemistry, device and material engineering,

behavioral and social science.

So they've all kind of been introduced more or less,

you know, during 2018 at some point.

And the growth they're showing is sort of from

a standing start, chemistry is now 8% of all profiles

are in the chemistry community,

so it's shown huge growth.

We noticed that when we launched it.

You know, it sort of, we launched it with Springer Nature

and it just took off.

It absolutely makes sense, you can just totally,

completely see it, it sort of appears.

And you sort of see similar, you know,

profile with device and material engineering

and behavioral and social science,

which are not quite as dominant, but still showing,

you know, quite major growth, you know,

from a standing start.

So yeah, wouldn't surprise me if they you know,

sort of, particularly behavioral and social science,

it's just a, it's a whole field in of itself,

and so if there's a sort of a push from Springer Nature

for instance, to sort of encourage, you know,

kind of, it will be able to create profiles

and participate in the community then, yeah,

you can expect that to grow substantially.

And then yeah, I sort of pulled out a couple of other

interesting metrics, so there are certain specialist

communities which are becoming established,

so they were in the network last year,

and they are in the network this year,

and they're showing a lot of growth relative

to their small size.

So digital medicine is kind of one of the,

one of the most interesting ones,

which, 'cause it's showing 400% growth over the last year.

And interestingly, that doesn't surprise me in the least,

because I'm currently working for a med-tech company,

you know, doing pieces of work for them,

and just sort of seeing, you know,

reading about what's going on in the field.

It's huge, huge area and so,

I would you know, really expect that to grow massively

I think that's the story isn't it?

We see that with quite a few customers,

if they, in terms of the scope and purpose

of their communities and if they manage to hit a trend,

you know, they can really ride that trend.

We've seen that happen several times

with different customers.

Yeah, yeah, makes sense.

And I think precision oncology has doubled in growth

over the past year, and again, that's somewhat more

you know, sort of particularly specialized you know,

field, but it's still showing you know,

there's a lot of growth in these sort of specialist

communities, so as experts sort of learn

that there is a community for you know,

my particular field, then they're gonna want to participate.

Yeah so that's everything on the growth

and I think I had a couple of other slides,

so, yeah, so, really just sort of looking at the overall

trends in terms of the maturer communities,

so just one thing that I pulled out that I thought

was quite interesting is that the top three communities

do remain the same.

So they are microbiology, ecology and evolution,

and the science of learning,

so they're still.

So in a sense, they're still growing, right,

to maintain their top spot?

Yes, exactly, certainly must be.

In the sort of size of the,

the growth this year.

Yeah, so those are still growing communities,

but they're still you know, sort of,

they're at the very top.

There's a lot of interest in those,

had a lot of sort of I guess, vibrancy in those communities

for them to continue being relevant.

And yeah, there are some, these other shifts

that are evident, such as chemistry is the 4th biggest,

from a standing start.

And then as we talked about before,

micro-films and microbiomes was incorporated

into microbiology, so this is kind of an interesting

point in that you know, there are some communities

which become established and are sort of,

well, you know, think that this is a community

in and of itself, but actually as you know,

the Springer Nature sort of observed,

it's like, well actually it makes more sense

for these communities to be one thing.

And so hence they were incorporated.

And I can imagine that over time,

that is gonna be a trend, you know,

there'll be sort of some you know,

some communities will join together,

some will split apart.

And one thing that I would, that I think will be interesting

will be to look for and try to predict those trends

in the future, and I think you can kind of see that

in you know, in the previous, you know, 2018,

that the two were so close together,

microbiology, and micro-films and microbiomes,

it's like, okay, these two galaxies are in the process

of colliding and becoming one.

Yeah, I think, well the astrophysical, astrophysics

analogy is quite in a way, it's quite accurate in a way,

because you've got, there is an element of push and pull,

and gravity in there and I think if you look at that 2018

diagram, you could see in there that they are so closely

related and you would wonder,

is there a reason why they're,

is it better for it to be separated,

or is it more beneficial to the members of that community

for it to be merged?

So rather than having two places for them to go,

so, it's interesting to sort of be able to look

at the diagrams going forwards and say actually,

you know, you might not think about it,

but actually those diagrams would be better served

if they were combined.

You might even see natural splits maybe,

I guess if the connectivity within one community

is fully evidently not so, not so connected around central

you know, people in the middle,

then maybe you could say, look,

there's a split happening.

Yeah, and there maybe natural communities in there,

which have just not been detected.

And maybe then sort of value in helping sort of emphasizing

that and sort of encouraging you know,

that sort of, those communities and saying,

well you know, or possibly introducing.

Okay, all right.

So what's the next slide?

Ah yeah, so this was as we talked about before,

I think you'd hinted at, we talked a little about this

But yeah, I guess just to reiterate the point

that there are some communities that are more,

more widely connected than others,

so to me the way I was thinking about this,

is they kind of, yeah, they represent these

cross-cutting topics that are relevant

to all of the networks, and they're likely to play

a role in bridging, in you know,

sort of scientific disciplines.

So that could be an interesting you know,

sort of future trend to look at is,

you know, are there you know, sort of,

in some communities which are able to bring together

other disparate communities and sort of provide

some mechanism for cross-disciplinarity

which I think is, you know, is important in the sciences

to encourage you know, sort of dissemination of ideas,

in ways that you wouldn't find normally.

So I think that could be a very interesting

thing to observe.

I guess you could also, you could also look at this

and say, okay we know that there's you know,

the ecology and evolution community is fairly central

compared to the other two communities,

so you could quite easily justify promoting

the ecology and evolution community to other communities,

'cause you know, you know there's already a general interest

there, so, I think you could also use it as a kind

of justification to say look, let's promote this

other community to the members of this community,

'cause we know that there's already

a reasonable level of interest.

Yeah, exactly, so you can use it for essentially

for recommendations and for yeah, promotions,

that's a really, that's a good idea.

And then you can always have

the "I'm feeling lucky button," which is

the community that you've probably got no interest in,

but you know, just showing something of interest.

I guess when they log in,

you can send them to a random community

and sort of, I'm feeling lucky.

All right, so where does this, where does this,

where do we head to going forwards?

So, this is a sort of non,

this is an incomplete list of future

sort of areas to look at.

I just sort of put down some of the ones that

I've been thinking about,

but I think we've talked about a few others as well.

So yeah, so the one that I think would be particularly

interesting would be to visualize the expert connections

around specific artifacts and to sort of really

hone in on let's say, the detail in certain areas

in order to really understand more about

the way that knowledge flows,

and about the way that particular individuals

may be you know, sort of,

yeah, may be sharing knowledge.

So an example I'm thinking about is there was another

company I'd been working at, who were doing,

essentially applying a similar principle,

where they had a mixed network visualization

that was showing all of the people,

and it was showing all of the artifacts.

And then you could see the relationships between

the people and the artifacts, so you could,

it sort of helped to kind of explore

the role that certain artifacts had,

and who specifically was talking to one another,

so I think that will be an interesting area,

just to sort of say, well,

if you visualize that, what would it show?

You know, what could you learn from that?

Yeah, I think we should, we do know,

we do know that obviously the metrics are popular content

so it would be interesting to look at how that content

is being, you know, who's been looking at it,

essentially what influence has it had?

Yeah, exactly and I think it would also allow you

to place some popular content in context

of one another, so you could sort of see,

okay, well, let's take the top 10, you know,

articles by views, and then show the relationships

of you know, just show all the experts,

all the people who've interacted with that.

And then just see what falls out.

And you might see some interesting clusters

of experts who are like,

you know, here are two different pieces of content

from two different disciplines,

but you can see the way that those experts

are perhaps related to each other.

And they may not have realized, you know.

I kind of want them just sort of getting

into the detail of that.

And then I think another thing that I think,

you know, you and I have spoken about a few times,

is the, there are a lot of non-registered knowledge flows,

so there's a lot of views of content,

of sort of the Zapnito you know hostage,

you know, the articles in these communities,

where people are contributing the knowledge

but we don't have a good bead on who they are,

because they haven't registered as an expert

in the network, and I think that, you know,

there's a high volume of those.

So I think one thing that would be very interesting

would be to find a way to represent those knowledge flows.

For example, if we were just to go back

to the Springer Nature you know visualization,

would be to say, well can we just you know show

all of those in some form, that allows you know?

Maybe as a sort of like a second level,

you might have all of the experts

and then you have everyone else.

And to sort of show that in some kind of way.

I s'pose in that diagram it wouldn't have any dots,

would it, 'cause you haven't got a person to represent

the dots. I think that's

a challenge. Yes (laughs).

There's maybe some different way of representing

those flows, because we haven't got a number,

we have a number of views.

Yep. And we have

you know, we know the sort of individual who has

produced a piece of content that's been read,

so we kind of have almost a sort of,

a number for an expert to say,

okay you have a score, which is the number of you know,

non-registered knowledge flows out from you,

and so there was some way of representing that.

So it could influence the size of the dot

on the other diagram, in a way, couldn't it?

Yeah, it would be one way of doing it.

Showing here's a completely different take

You've almost got an expert-centric view,

which is-- Yeah, you might get,

you might get a completely different view on that,

because you might get lots of views on unregistered content

which is currently being ignored,

and that person's influence on the existing diagram

might be small.

But actually in reality, it should be large

and so it's, there's a missing important piece

of data that we're missing.

A bit like dark matter I s'pose,

the non-registered flows are our dark matter

that we need to uncover.

I'll stop with the astrology analogy, all right.

So what else, astronomy yeah.

Astronomy yeah.

Yeah, so the next one was sort of revisiting

the role and the impact of non-contributing influences.

And actually this kind of relates to what you mentioned

there, which is there may be other contributions

that either they're registered profile,

yeah, in fact I think it would be the registered profiles.

There'd be other ways in which they are influencing.

And so, that will be something we sort of need to dig

into the data that's available about other activities

that you know, the registered individuals partake in,

that have an influence.

And I think the example I had was,

in one of the very early, in the very first blog post,

we're sort of looking for the,

I think I called them the lurkers.

Was the idea of individuals who were,

due to their position in the network

and due to the connections that they held,

they were actually particularly well placed

to be highly influential, because they were,

they were acting, they may not have realized it,

but they were acting as something of a bridge.

I think the interesting thing there would be if you can

identify those individuals, let them just say,

well okay, these three or four people, you know,

just due to their location, their centrality,

you know, they haven't maybe contributed much

in terms of articles.

You know what, if you were to encourage them to,

they would probably have a higher degree influence.

They're sort of, yeah okay.

So they would have a double impact factor for example.

Impact factor is kind of a phrase that gets used

obviously in science research.

But you can have an impact factor attached

to an expert and based on various

information points about them.

Exactly, exactly.

And yeah, the-- All right.

Sorry, go ahead Jon.

I was gonna say, that sounds good.

And then yeah, so the final point there was yeah,

measuring the impact of individual knowledge flows

in sort of downstream sharing, so that was a little bit

more sort of speculative, but I think what I was thinking

there was the idea that you know, if you have someone

you know, take, absorbs a piece of knowledge

or reads a piece of knowledge,

how has that action affected

what else they've done in the network?

So could you start to trace the flows of knowledge

through the network?

And I think this might be something that would need

a bit more sort of bit more R and D in terms

of possibly collecting additional data

from you know, from the way users are interacting

with the knowledge. Yeah.

Interesting to sort of start to map that out,

and-- And do it in a privacy

and compliant way.

Consent to it and all that sort of thing, so.

Yeah, as you said, I think the one that we talked about

but isn't listed here, is the idea of actually measuring

the centrality of the networks themselves.

And sort of just get a bead on that,

say, well can you measure that based on the experts

that are in there?

So, and there's yeah, I mean there's lots of other

possibilities but I think that's a nice summary.

Right, so is that the last slide?

That is the last slide.

Perfect, all right, well.

Thanks Jim for providing an overview as to the historic

work and the recent work.

It's been great to obviously see it come together

and it's always, whenever we see a new diagram emerge,

I have to say, it's always very motivating

and inspirational to see it come together

and really enjoy talking about the new ideas,

and things that we can come up with

to analyze the data.

It's always the way, you look at the data on,

you know, you think, oh there's things in there,

but you wouldn't necessarily have anticipated, so.

It's always fun when we get the time to do it, so.

Great, yeah. Thanks very much.

Very nice to talk to you, and yeah,

look forward to the next diagram whatever it may look like.

All right, great.

Well, if anybody's got any questions

about what we've talked about today,

send them through to us at Zapnito

and we'll, probably myself or one of the team

will come back to you.

I expect Jim is a bit hard to get hold of these days,

sometimes, so you know, if you get hold of Jim,

then you're doing well.

There's always lots to do.

Yep, great, thanks Jim.

Great, thanks Jon. Cheers bye.

COO & Co-Founder, Zapnito

A technical and operational leader and hands-on COO with 21 years' experience implementing web technologies. I lead all operational activities at Zapnito. Get in touch with me to learn more about how we operate or to share your feedback at any time.

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