ThoughtWorks Data Visualisation: Good for Business

ThoughtWorks Data Visualisation: Good for Business


>>Welcome to the HIVE. My name is Andrew Woods. I’m the manager of this new facility. The HIVE stands for the Hub for
Immersive Visualisation and eResearch and it’s a new facility in the university
intended to support and encourage visualisation and virtualization, simulation
aspects related to all things visual. The primary aim is to encourage research
outputs in this field and it’s seen as a field of great opportunity and great potential
for enabling us to do things a lot better. So as I mentioned it’s a new facility. We launched on the 27th of November
and we’re still in the process of commissioning so there’s still a lot to do. I might just provide you with a quick
rundown of the four displays we have here. These are sort of the most obvious part of
the HIVE the four visualisation systems. Each of them have their own
characteristics and allow visualisation to be done in a range of different ways. The first of the displays is what
is known as the Tiled Display. It’s a media wall. It’s another way of describing it. It’s a ten square metre area of LCD panels
bigger than some people’s swimming pools and over that area we have
24 million pixels. The wall is made up of 12 full-HD LCD panels,
media grey panels with a very small border around the sides so they can
be shown together very closely. There’s a whole manner of different types
of content that can be shown on here. We don’t have enough time to go through all of
those different options right at this moment. The next display on your
right is called the Cylinder. It’s a three metre high screen, eight metre
diameter curved screen when you’re standing in the central point it fills
180 degree of your field of view. This can run in stereoscopic 3D using
the provided 3D glasses over there. The screens are filled with three projectors
which are mounted from the ceiling there and this can be used for a range of different
topics and tasks including virtual environments which we’ve got here which we’ve
got here illustrating a project on the HMAS Sydney
which we’re working on. The screen behind you is called the Wedge. It’s two rear projected screens
mounted at 90 degrees to each other. Those screens can be angled outwards to form
a flat screen as well, 2.8 metre diagonal, full-HD resolution on each display and can also
be run in stereoscopic 3D so we’re anticipating that screen there would be used for
visualisation of volumetric data or business data for example or
viewing of stereoscopic content. There’s a demo loop there. There’s a set of a whole heap of glasses
sitting on the small podium there so please, once this session’s finished please come
through and have a look at that screen as well. Each of the screens the glasses only work on that particular screen so
these two are stereoscopic. These two aren’t. The screen on your left here is known as
the Dome or it’s actually a half Dome, 4 metre diameter and when you stand at the apex
of the screen it fills your full peripheral and primary vision so when you
come in here don’t stand too close. We don’t want you walking
off the screen for example. The purpose of today’s session is to talk about data visualisation primarily
looking at business data sets. We’ve invited ThoughtWorks on
the campus to present this topic. I want to be clear that we’re
not endorsing their work. They’re just providing an
illustration of what is possible. They have some very good illustrations of
visualisation and what it is capable of. They also have done some range of work
with some groups on campus as well. So I will now hand over to David
and also give you my microphone.>>Thank you Andrew and first
baton pass of the afternoon. So as Andrew said we’re here today to talk
about some organisations we’ve worked with and the benefits that they’ve realised through
data visualisation and we’ll also talk a bit about the approach that we’ve tended to
use there which is quite a lightweight and rapid approach to reaching a visualisation. So before we go any further I’ll just make
sure I introduce the two of us properly. I’m David Colls and my colleague–>>Ray Grasso.>>And ThoughtWorks is a software product
development consultancy so we’re based here in Perth but ThoughtWorks is a global group. So I guess the key thing is visualisation
is used for a purpose and what sort of problems do people solve with visualisation? Well one of the earliest examples takes
us back to Victorian London and the middle of the cholera outbreak and some
of you might be familiar with this. In the 1850s over 600 people died in the
cholera outbreak and John Snow was seeking to understand why and to do that
he drew a picture from the data. He collected data about where the
deaths had occurred and for each house or each location along the street where there
was a death he drew a black line to indicate where people had been dying and as he put
this picture together it quickly became clear that there was a problem near
the Broad Street water pump and using this visualisation he was able to
convince the authorities to remove the handle of that water pump and was able to convince them that that was the source
of the cholera infection. And this was before there was even
a mechanism that could be understood for transmitting cholera from a water pump. It would be another seven years before
Louis Pasteur introduced the theory of germs and then the prevailing wisdom of the
time was spread by miasma or bad air. So this was a pretty good outcome
for a drawing based on some numbers and he was subsequently able to convince
the authorities that dumping raw sewage into the public water supply was a bad
idea, a great legacy of public hygiene from that particular data visualisation. But if we come back to the present
day then we see that all sorts of organisations are using visualisation for all
sorts of purposes, corporations, governments, NGOs, formal and informal alliances of all
of those then individuals participating in hackathons and similar activities trying
to combine data sets and produce meaning from them especially where they are
complex and difficult to understand. And why are people trying
to visualise this data? Well, it’s because it has a number
of advantages for us as human beings. Visualised data is easy to understand because
it engages our innate cognitive mechanisms. It’s also the case that once you’ve
understood a data visualisation you can use it to pursue further investigation, so once
you know what it’s showing you can use it to interrogate whether it’s
showing you what you expect. It’s a shared view. So with a visualisation that’s shared among a
group the entire group can focus its energies on making that picture better
rather than disagreeing on what the picture should look like. It’s a holistic summary you might say
that nothing gives you the big picture like the AV picture and it also
provides new insight so you might set out to create a visualisation
that will show you certain things but you can be pretty much guaranteed
that you’ll see new things in the process of producing that visualisation
and for those reasons, for the reasons of visualisation
it’s good for people. It’s good for business because
business runs on people. But in particular we’ve seen with our clients
that they’re looking at two major outcomes from data visualisation and one
of those is to increase engagement to produce compelling communications or
build brand awareness with an audience that might be external or it might be
internal or it might be a combination of both. And their also using visualisation to try
and gain insight into their operations to have a very lightweight approach to drilling
into complex data and seeing what leaps out as opportunities for improvement. But to talk about engagements
we’ll hand the baton over to Ray.>>So as Dave mentioned kind of one of the
themes I guess that we’ve found with some of our clients reasons they’re using data
visualisation is around increasing engagement. In the first case we’ll look at is increasing
engagement by telling a story over a complex set of data what is inherently a complex story
trying to tell that in a really simple fashion and so the client we’re speaking about are
the Independent Market Operator or the IMO and for those who don’t know who they are, the
IMO, one of their responsibilities is to operate and develop the wholesale electricity
market of Western Australia so big producers of energy and big purchases of energy. They operate in these various
markets trading sums of energy and the IMO job is to facilitate that. When we first spent time with the IMO sort
of discussing the data in their world, I mean they send in a lot of different
data across these different markets and they have a lot of different stakeholders
both in the government and in the public and within the industry itself and the
kind of approach they had taken up to that point presenting this data was
kind of this style of presentation. This is something we pulled straight from
their website when we first got there. It’s sort of a static graph showing a bunch of
figures and forecast lines and things like that for the people sort of within the market
there’s probably a certain level engagement with the raw numbers but for a lot of
the average people they sort of see this and eyes kind of glaze over
and they sort of move on. So I’d just like to cover the approach
that we went through with the IMO and one of the specific pieces of the
project that we that we did with them in taking this data and trying to get
the engagement up and increasing it with their audience and this is sort of the
broad sweeps of the process and it was sort of a broad question that started the whole
project really then there was a data discovery portion where you really dig into the numbers
to actually see if it backs up the proposition and then really rapidly refining a solution
to communicate a visualisation over this data. And the broad question that started the
overall project really was just a hunch and it was actually a conversation
with the CEO of the IMO and he basically effectively
said some of the big players in the market are doing some
weird things with their trades. It’s unusual. See if we can tell that story and show that
story to people so you kind of take a step back from that and go, well that’s pretty
broad like what does that mean? And so the first reaction and the first step
that we would generally recommend is to sort of dig into the data to try and see what
this story is if this is actually a story. So that’s what we did. We grabbed a lot of the information
straight from their core systems, threw it out into really
simple files like CSV files that we could look at in
Excel and stuff like that. This is sort of where we started so we kind
of graphed out all the different participants. Each of these coloured lines are
a different generator or retailer. The top there it shows their total volume
of trading in this particular market and this one here shows who sold energy
into the market and the bottom one shows who bought energy from the market. So you look at that and it’s still
really noisy and really doesn’t say much so the exploration kind of
continues and so these are sort of snapshots along the process that we took. So here you can sort of see now we’ve
broken it out for an individual participant so there’s two participants
there so that the green bit above is how much energy
they’re selling into the market. The red below the line is
how much they’re buying. These are quantities and this is sort of
over time since the inception of the market. So you start to get a bit more
of a sense of who’s doing what. It’s a bit easier to sort of dissect but the
story still wasn’t quite there so we sort of split that graph out even further. So now we’ve got it broken down by the hour
so you can sort of see each trading interval as sort of roughly an hour and the
volumes that they’re trading at that time and the top there is the largest generator so
particularly you would expect the largest seller into the market and here is the largest
retailer so effectively here we expect to buy the most energy out of the market. And there’s something interesting about this. Now it won’t be immediately interesting to you
guys generally but you can sort of see here from about midway through 2012 the largest
generator of power started buying lots and lots of power and the largest retailer of power
started selling lots and lots of power and they we’re doing it overnight. So basically from 10 o’clock in the evening
until 7 in the morning and if you look at these two they effectively
reflect each other so this sort of high level visualisation you can
get a sense of what was going on and so at this point we knew that okay there
is some interesting trading behaviour here that would be useful to actually illustrate. We kind of worked out that there is a
narrative behind this and so at that point is where we really switched gears into that
final sort of solution part of the process. Here it’s really about shifting into the
communication of realm rather than the data sort of digging around, so you have a story so how
do you effectively communicate that story? So you start with paper, lots of
brainstorming and sketching and ideas like how can we show these
different players and the volumes that they’re contributing
or taking from the market? Once we sort of had a general
direction we wanted to go in then we really went into
sort of solution realm. So the channel here that we’re working
with was the Web so via the public website so we were working in HTML, Java script, CSS,
typical web technologies and our part was just to really with the IMO was to build a solution
and integrate it as we go rather than kind of Photoshop it up and imagine it as an
idea and then come to implementation work out that it wouldn’t actually be implementable. In fact that’s the approach we took and this
is a sort of snapshot of different milestones or points along the evolution so we kind of
had this sense of the market in the centre and the different participants sort of around
the edges and the different volumes of energy that they were contributing or taking from
the market as sort of flowing out from there. And so as we started to evolve that
that kind of resonated with a lot of the folks we were talking to but
we realised that we needed to kind of anchor it in time a little bit more. Just having a single snapshot was
difficult for people to know where they were so we added this timeline type presentation
on the left which you sort of saw from before. So here you can sort of get a high level
snapshot view over time and you can sort of drag in and see the individual trading volumes
and so what we finally end up with come over to the right side over here was
this presentation piece in the middle, so here we have this kind of selector or
this little window, this monthly window over the duration of the market and on the
right you can see that the market in the middle and the different volumes from the different
participants around the edge, so for instance, in September, you can see
the largest generator there at the top buying a really large
amount of energy out of the market. And the largest retailer there, that was
almost magical, wasn’t it– selling it. Let’s see if I can scroll this up a bit. There we go. So there’s a lot going on. There’s a lot of the information within
this single visualisation and it’s a theme that you’ll see again when we get into
the portion that Dave was speaking about. Once you get sort of, once you can navigate
this, once you understand the layout of what you’re seeing you can actually assume
a lot of data really quickly and you can sort of interact with it and explore it
in a way that just a table of figures and a static graph just doesn’t allow you to do. And so if we come back that
was again an example where one of their clients were using data
visualisation to try to get this information out to their different stakeholders,
getting more engagement with people. Second case is really around
amplifying engagement so again, this whole idea of using data
visualisation to get people closer to the data and to the organisation. And this client is basically the folks
from the Desert Fireball Network, the fireballs in the sky if you haven’t
heard of them based here in Curtin. Phil is right there from the DFN. Hi Phil. [Inaudible] So for those of
you who don’t know what the DFN is about and Phil don’t throw stuff
at me if I butcher this. Meteors are flying through the sky and
they sometimes explode into fireballs and the Desert Fireball Network
is basically a network of cameras throughout the
Australian Outback poised on the sky taking long exposure
photographs continuously and then Phil and his team afterwards take those photographs
and use the fireballs in them and the positions of the cameras to basically get the
trajectory of these fireballs to work out if they maybe hit the ground and maybe
where they’ve come from and that can lead to expeditions where you’re actually trying
to recover the meteorites or calculating where they come from and the reason
geologists will do this is because this kind of information can help make us understand
more about the origins of the universe. It’s pretty grand stuff. It’s pretty inspirational. You can see– is that running with the live one? But there was different shots there so it’s
like a time lapse of some of the camera images in this spherical screen over there,
so it’s really interesting stuff. So when we first sat down with the Fireballs
team it was really to talk about a brief for a Smartphone app so really grand ideas
and now we’re talking about a Smartphone app. The Smartphone app the main idea of it was
really about bringing the general public sort of closer to the science and sort of
an outreach engagement awareness piece and there’s a really interesting
piece to this where it was about actually engaging citizens
in the science itself. So one of the core features of the
application you can see as described here and it really was this case where
someone’s out say, somewhere in Perth, and in the night sky they see a fireball. They pull out their phone with the app. They tap where it started. They tap where it ended. They record a bit of information about it and
that information goes off to the DFN folks and you get enough of these folks seeing the
same fireball it could help actually contribute data to basically complement
what was already in the DFN. So this core sort of flow is sort
of pretty simple and you can sort of see there fireballs are a pretty
spectacular thing but the information that describes it is actually fairly mundane. I mean you’ve got elevation. You have azimuth. You have with the duration in seconds,
really a lot of numbers effectively and so as we were sort of exploring
this when we were working it out if someone actually sees this
how are they going to describe it? Are they going to be saying oh yeah, it’s
like a minus four magnitude of brightness? It was to 12 degree elevation here
and it was about 5.2 seconds long. Like that just wasn’t realistic. What you’re more likely to get is someone
that will say something like that. Like it broke up and it was kind of green and
it went in this direction and so that core idea of can we put something together where
people are interacting with this information? This is really about authoring information. Authoring data kind of coalesced
in this first version where you would basically
build your own fireball. So this would be part of the capturing so once
you’ve tracked where it was you would come in there and you would set the colour of it. You would set the shape and how many
pieces and all this kind of good stuff and this animation would update, does update. I’ll go to my phone, I’ll show you
afterwards, in place and you can see that and it was a much more engaging
way to get people to actually give you the information rather
than going through a long boring form. So after that first milestone,
that first release we kind of asked the question can we go better
in other part of the application? In two other parts of the application
that we looked at was really that capture piece and the
sightings or viewings. So the capture piece as you can see
is really just a big button so it was like it started there and it ended there. It was quite static and the sighting piece like showing a sighting was really
just showing the textural information. It was just like a static
map of where they saw it. So we did do better with the great help of
the folks at the DFN and fireballs in the sky and so the capture process became
this sort of live, heads up display and then an actual star map behind it, so you
can see there these dots here are a star map and based on where the person is sitting and
the orientation of the device it would overlay in this sort of augmented reality way the
stars that they should be seeing behind there. It became a much more immersive experience. It’s something that was actually
just a stand-alone part of the app. I’ll sort of wave it in front of you right
now so folks if you can see that you can sort of see it’s sort of live updating. You can sort of see the horizon. There’s all the stars there and you’ve got
the heads up display and as you sort of go to capture it draws out the path like that and
then you get into your building of the fireball. So it’s a much more immersive experience
than just tapping those two dots. And then on the sighting
screen itself you know rather than just showing the aesthetic attributes on
the right there’s also this animated fireball over the star map as it would have been
captured, so again, here you’ve sort of both on the consumption side and almost the
production side you’re really trying to up the engagement and get
people to engage with the app in a way that they otherwise might not. So that’s the two examples sort of
within the whole sort of business goal of organisational goal of increasing engagement. I’ll hand it back over to Dave to
talk about operational insight.>>Thanks Ray. So I guess the next main case that we’ll be
looking at is gaining operational insight and I guess here you might summarise that
as trying to pick out something that sticks out like a sore thumb as a starting point. And something that stuck out like a sore thumb
to John Snow was the fact that nobody died in the brewery which was just down the
street from the water pump but that was because all the monks in there
spent all day drinking beer. How times have changed. This case is about when you need a big picture
view to kind of spot what doesn’t look right. And for this case we go to a
call centre and a call centre where there’s a big improvement programme and
the performance of the call centre is measured in terms of customer satisfaction which
is assessed by NPS or net provider score and by how much it costs to run the
call centre or operational expense. So those are the two key measures of
the performance of the call centre from the organisation’s perspective. The improvement programme is looking to achieve
a balanced improvement in both of those. But it’s a big call centre. It’s really big. It’s 200 thousand calls a day. They’re dealt with by 10,000 agents who are
across multiple countries and time zones and there are 500 or more products. No one is really sure how many products
are actually supported by the call centre. It’s a bit like that. It operates 24-hours a day and seven days
a week and this presents the challenge. We’re trying to improve this
enormous call centre but we have trouble picturing what it looks
like now because it’s so big and so diverse that we can’t really get a good handle
on how to start the improvement process or even what looks particularly wrong
about it that needs to be improved. We have some metrics around Q sizes and
around wait times and we have some levers that we can pull and push but
it’s not really going to– that sort of small scale fiddling isn’t going to
really achieve the objectives of this programme. So in this case we set out to draw a picture
of the call centre and much like Ray showed with the evolution of the short-term
energy market this was an evolving process and it’s quite fascinating looking into that. But we’re going to skip right to the end product
in this case and this is the end product. This is the picture we drew of a call centre. It might not immediately strike you as
a call centre but maybe when I talk you through it you might see
the reasoning behind it. This is actually a point in time in the
call centre, so this is about ten o’clock in the morning and it shows all of the calls
that are active in the call centre at this time. So there are about 3,000 calls active in the
call centre and each one of those is represented by a character on the screen,
one of the catatonic characters. The calls can be in basically one of two states. They can be in a queue, so
you’ve just called up. You’ve entered a few things with
your voice or with the phone keypad and now you’re listening to hold music. In that case then you’re in
queue and that would mean you’re in the orange section at the top of the screen. The longer you stay in the queue the further
down that orange section your call progresses. When the call is answered and it’s a
customer talking to an agent and it’s in the green section lower down the screen and again the longer the call has progressed
the further down the screen the call moves. Different types of calls or inquiries
by customers showing from left to right across the screen so that the horizontal
position determines the nature of the call. So on top of this there are some things that we
can also show that we know both upset customers and lead to increased operational expense. The thing that upsets customers
is and they hang up, the one thing that upsets customers is hanging
up in the queue so we’re showing calls that hang up in the queue with an exploding bubble. So those are where customers
have hung up and another thing that upsets customers is being transferred from
one agent to another because typically you have to go back into the queue again and that’s shown
with diagonal lines that go from a conversation with an agent back up to a
queue for a different call type. So that’s a picture of just a slice of time in
the middle of the morning at the call centre. But that’s not even the biggest picture
I guess, that’s still a small picture. What we can do is actually come
over here to the cylindrical display and we can watch the whole
day in the call centre unfold. So we go back in time a little bit to
start at about 7 o’clock in the morning now in the call centre and we’re
running a lot faster than realtime so we’re running 128 times real speed and
we could see all of the things that we saw in the static image over there but I’ll just
talk you through them again where it’s live. So when we set out to draw this picture of the call centre these are
the things we expected to see. We expected to see calls arriving. In this case these are bill inquiries
arriving in the queue for bill enquiry calls. We expected to see them progressing through
that queue and as you can see they’re moving down the screen there as they progress
and we can also see the abandons where customers are hanging
up in the queue up there. When an agent is available then the call
is transferred through to that agent and we can see calls being answered by
agents in the lower part of the screen. Again these are all billing enquiry calls
as they progress through their lifecycle and again we’re moving through the lifecycle
of that call progressing down the screen. So we can see billing inquiries there and we
can see a different type of call over here. We can see fault reports coming in over here
and right over on the side over here which some of you might be able to see we can
inquiries about fixed line moves so we can also see transfers in
and out of a particular call type. So here’s sales inquiries are presumably going
to other areas that relate to the product rather than a general sales enquiry and other calls are
resulting in a sales enquiry of some sort coming into this queue, so those are the transfers. Those are the things we expected to see but
then there were a bunch of things we didn’t set out expecting to see but we saw
once we looked at the visualisation. And the first of those was that some types
of demand that we would have expected in the call centre just were not existent. We would have expected people were calling
about iPhones but there is zero demand in that 200,000 set of calls, the iPhones which
is quite an unusual finding and it’s not one that we can necessarily answer
with this visualisation but at least we’ve found an
interesting question to ask, to understand how we can improve operations. Then to actually answer that question we
need to go somewhere else and we need to dig into the data and we need to use
different tools and a different approach so this is very powerful in
identifying what that should be, the question that needs further investigation. Another question that might
occur that probably doesn’t need so much investigation is why no
one’s calling about Blackberries. That one is probably a bit easier to explain. We also found again over here on this side
of the screen that there were calls coming in for business inquiries but there
were no agents there to handle them. So that’s not really a good
outcome from a customer experience or even from a business point of view that
all of those customers are just hanging up in frustration before
they get to speak to anyone. So another thing that we saw was kind of subtle but once you’ve seen it then you
can’t help noticing it and that is that these queues are supposed
to be fairly orderly. The call that has been in the queue the longest
is supposed to be the one that’s answered first but what we can see here is that there are
calls that are spending a long time in the queue but despite that there are new calls being
answered by agents before the calls are taken out of the queue so you can see calls rapidly
falling down in green past the orange calls that are moving very slowly in the queue
and that was an interesting finding because that’s not actually
how it works in reality. It turned out that there was an issue
with the way the data was being processed, so there was a large amount of processing
going on between the source systems and actually a single view of
this data of the call centre and in that processing there were
errors that were making it look like calls were jumping the
queue when in fact they were not. So but the rub here is that this same data
was being used to make operational decisions in a magnitude of millions of dollars
a year so it’s very important to ensure that it’s quality data and a visualisation like
this helps us uncover where there are issues that we might not otherwise find. And the final one was an interesting finding
around transfers so we might expect that and here customers are calling
with a help request. We might expect that calls are transferred
if customers call with two different types of inquiries in which case it’s a legitimate
transfer once the first enquiry is completed. It might be that the automated system that
classifies calls gets it wrong sometimes which it does and in that case an agent should
quickly realise and there should be a transfer from high up the talking area into a different
queue type which is the actual call enquiry but what we didn’t expect to see was calls
being transferred within the same type. We didn’t expect to see calls being sent to
agents who later said actually I can’t deal with this but it has been classified
correctly and so this is great opportunity to improve the performance of the call
centre to eliminate unnecessary transfers and to eliminate unnecessary cost by
ensuring the calls only get to agents that can deal with them properly. And I could talk about this all day and
we will leave it up once we’re finished but I’ll hand it back over to
Ray for now to bring us on home. And that means another baton change.>>Okay, so we’ll just wrap up
with a few take aways I guess. There’s learnings that we’ve had in the
course of the work we’ve been able to do with our clients and a few of those pieces
we’d like to just leave you with now. So we’ve covered the benefits. That’s probably worth just
quickly talking about them again, so there is data visualisation can be really
useful in presenting complex information simply, sort of efficiency of understanding is
another way of thinking about that I guess. It can be used to make interacting with
data something that’s almost exciting, a bit more visceral, so whether it’s like
the fireballs instance where the capturing of the data or the actual reading of data, the
consuming can be a much more engaging experience and then for the call centre example
using these high level sort of holistic, fuzzy visualisations of really complex
sets of data can help sort of lead you to ask more questions where you
can do more pointed investigations and take more pointed actions. So what are the kinds of triggers you
might find in your day-to-day work I guess within whatever organisation that you’re
a part of that might be a twig for you to think okay may be this is an instance
where data visualisation could be used? We kind of covered this a little bit
but when there is a complex story to tell there’s complex data
behind it but you have the data. The data is there. If there is not a shared picture certainly within an operational sense
but you have the data. The data is there again. You have the data but it’s boring to you. It’s boring to look at or it’s boring to create. Maybe there’s an opportunity there
for using these kind of techniques. And finally when it comes to execution,
so when it comes to building these kinds of things the approaches that we generally
recommend are starting small and both that’s within the scale of which you’re
trying to achieve and within the teams that you have and stay really lightweight. Use real data throughout so there will be a lot
of truisms that people will hold and even people with a lot of experience can be proved wrong
when you actually get into the information, so definitely use real data as early
as possible and use it throughout. And refine and adapt, so
none of these visualisations that we’ve seen the endpoint
wasn’t clear from day one. It was really an evolution and an exploration. I mean all of the solutions we’ve seen
they’re all custom software, custom created and they all took on the
order of weeks to complete. So with that I guess I’ll
stand back up and say thank you and if you have any questions
we’d be happy to here them. [Applause] [ Silence ]

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