This workshop introduces advanced methodologies for problem-solving and innovation, focusing on the integration of AI, design thinking, and systems thinking, and how data visualization can drive actionable insights.
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Hello everyone.
See some of them are joining in the um
in the room.
So um hello everyone and welcome to the
workshop five. I'm your auntie Selena.
Just 24 hours is gone and now we meet
again. Um as usual, let me do a quick
audio check. If you can hear me clearly,
thank you all of you for your active
participation in yesterday's section on
the LA. So, many of you showed strong
interest and were very engaged during
the Q&A section. We really appreciate
that. As the proposal deadline draws
closer, um we know some of you may be
wondering how to effectively use data or
which data sets best fit your ideas.
Hello Sharon, thank you. Um to help you
move forward um we have invited um
experienced speakers who will share
concrete advice and also some practical
case studies. As you know um always seek
advice from the best. So if you have any
questions during the sharing, please let
us know in the chat box. The theme of
today's workshop is from design thinking
So for the first section we have three
speakers from Inoatch Consulting. They
are Mr. David Chong managing partner,
Mr. Paul Lee, senior partner as well as
Mr. Michael Tong um partner of the innov
consulting. So may we first invite uh
Mr. David Chone to share with us.
>> Okay, man. Thanks. I would like to share my
my
>> Okay. Can you see my slide?
>> Yes, we can see it. Thank you.
>> Okay. My section is start. Um today I
would like to share with you about
holistic problem solving with AI design
thinking and systems thinking that's me
in my part I will share about two method
help you can solve the problem of course
this help you to build a big and good projects
projects
in my section we have three speaker the
first speaker is myself and the second
and first speaker is my colleagues Paul
and Michael and I will let them to
introduce theirel in their part all of
us is come from Ino Edge. Now I will
little bit talk about Ino Edge. Ino Edge
is a market leader of design thinking
and innovation management in Hong Kong.
Uh we are 10 years we have 10 years uh
history but we still moving ahead.
In fact we represent the world largest
um design thinking and innovation
organization in the Chinese world uh
including design thinkers academy and murals.
murals.
Apart from consulting, we also provide
education services, social innovation
services and also in China we coverage h
all innovation and designing services.
In the last 10 years, we create so many
measurable deliverable of our projects.
There is some of them. Last but not
least, uh apart from business, we also
like to share our measurable results uh
for the public. That's why in the last
10 years we conduct uh volunteer uh talk
over 200 of course including today um we
want to share our best practice to the
public and let the people who want to
improve ourself improve your project can
has a concrete experience and best
practice. Today I have five agenda the
today agenda I have five topic. The
first topic is about overall of AI and
power design thinking. The second is I
will talk about real case study of Hong
Kong how Hong Kong community development
using design thinking way to enhance the
effectiveness and efficiency. Afterwards
I will share about design thinking and
systems thinking. In my part, I will
talking about the theory and my
colleagues Paul and Michael will use
case study to present how design
thinking with system thinking can create
different impact across different area
such as a healthcare areas and also a
car rental area especially uh Paul case
study is talking about the car rental
how car rental can impact ESG
aspect and also So the consumer behavior
also enhance also affect the ESG
practice. Um that's why today we have a
theory tools and also case study.
Of course I know that uh all of you is
more concerned about uh the projects uh
in fact based on my understanding the
project 135 is all talking about people.
how we help the elderly, how we let the
customer pick up efficiency or how we
help the stock team uh to let the stock
management more efficiency and also
maybe some of you is concerned about
treasuring to and four and I believe
that the system thinking can help you to
create a good ecosystem to motivate all
stakeholder work together to enhance the
effectiveness not just the company and
the systems.
Now I would like to share about the
foundation knowledge first. Um I will
share about little bit about classical
design thinking and also AI design thinking.
thinking.
I believe that you all heard about
design thinking. Design thinking is a
global recognized methodologies for
problem solving. Let's talk about 150
year times all the world low design
thinking is really good methodology to
solve problem especially the human
problems. That's mean if you want to
motivate your customer buy more products
is design thinking. If you want to
motivate your employee to working harder
design thinking also and there's three
important core value of design thinking.
Empathy how we empathy with the user and
motivate them. How we amplify our users
painoint and expectation or co-create.
In design thinking we are not just
talking about we we want to do we
co-create with the user to do something
to make something happen. And the last
thing is for design thinking the lamb is
design thinking methodology and the
outcome of design thinking method is not
thinking it's result oriented that mean
revenue growth
market share etc etc that's why so many
enterprise using design thinking is
product service innovation business
model design and process improvement
that is the outcome of design thinking
when you use design thinking your
outcome should be a product or services
that help your customer fulfill their
desire and also the supplier can do
this. That's feasible. Of course, these
products can vary. That's that's mean
the organization, the enterprise can
sell these products to maintain their operations.
operations.
Uh maybe you don't heard about designing
before, but I believe that you all
should know Harvard Business Review. In
fact in Harvard business review so many
articles is about design thinking and in
2011 uh 202 there's a special edition
it's called 10 must read on design
thinking and they select the well-known cases
cases
how Pepsi Cola win Coca-Cola in the
world how Samsung from OEM to a global
brand so many successful case all about
UECO also promote design thinking for
the world to do the projects to achieve
the sustainability goal. That mean
designing is not just about helping
business growth also help about ESG
apart from the global level in China
design thinking case is also a focus of
different city that's mean design
thinking is not only used for global
level also available also apply for ch
China level apart from the national
level Hong Kong government also promote
decide income since 2011. When you read
the recap of policy address of 217, Hong
Kong government motivate business,
social services and education use design
thinking and they publish four case book
to collect the successful case uh for
for the enterprise or organization in
Hong Kong successfully use design
thinking to create their growth and
business improvement. There's some cases
uh it's about my team
Maybe you think about oh design thinking
is very good uh in global level,
national level and Hong Kong level but
how to implement design thinking in fact
very simple there is a framework for
implement design thinking is called 4D
model 4D model the 4D model we call
double diamond model the double diamond
model we divide problem solving into two
important part the first part is problem
space that that's mean what's your
problem what's your root cause
Afterwards based on your root cause
develop a solutions which fulfill the
root cause. That's a very simple model
and in reality the double die model will also
also
define into four step. The first step is
discover that's mean discover the
information about your challenges and
then define the mission critical cause.
Afterwards based on the mission critical
cause develop different ideas. Last
transform your ideas into prototype and
makes thing happen. There's the
traditional model of design thinking. Um
apart from the process there's so many tools.
tools.
For example, if you want to develop, if
you want to discover the people want,
you can use design research. If you want
to define mission critical recourse, you
can use persona. If you want to develop
so many ideas, you can use different
things way. Of course, you want to
transform ideas into the real products.
You can use a rapid prototyping. Maybe
you are take some photo you you you take
some photo on this designing tools but
or it's gone or it's gone because
designing is very good. It's very good
in last 15 years. However, the
traditional design thinking we call this
DT 1.0 zero use so many times maybe you
need to conduct 100 research and you
will need one month and two month but
for today I believe that just one week
the market demand will change therefore
in 2023 many people is argue about
design is very good but time consuming
how can we enhance the execution
effectiveness of design thinking therefore
therefore
in 202 to four there is so many new
approach across the world they all
propose using AI for design thinking we
call AI and power power design thinking
that's mean we use AI inject into
different tools to enhance the
effectiveness and efficiency there's so
many paper talking about this and one of
the paper
they conduct a research and measure the
DT with AI I and traditional DT way they
found that if AI using in design
thinking execution process there will be
enhance the efficiency that's mean AI
cross DT is really powerful
and today we will talk about AI design
thinking uh we call it DT 3.0 zero. Why
there is so big improvement in AI design
thinking? Because in AI design thinking,
this is not just using human way. We use
AI agents to help us to complete the
task. What is AI agent? I would like to
share a one minute video to you and let
you know what is the updated most
trended most uh uh powerful design thinking.
thinking.
Six Inoedge AI agents supercharge the AI
empowered design thinking process
driving smarter and faster innovation.
In the determine phase, AI clarifies
strategy by analyzing complex data and
furating market trends. During discover
and define, AI reveals deep user
insights, uncovers emotional patterns
with 90% accuracy, and sharpens problem
definitions. In develop and deliver
phases, AI amplifies creativity through
rapid ideation, [music]
prototyping and testing to craft
impactful product and services. Finally,
in the drive phase, AI sustains growth
[music] by motivating the teams to take
action to change and driving
organizational transformation.
Ride on the [music] synergic power of
six AI agent turning 26 weeks of project
work into just one week with up to
[music] 95% efficiency.
Six in okay I believe that you have some
ideas about AI agents. Uh in conclusion
we use AI or robot to help you to
execute deciphering process and that is
the designing 3.0. I would like to
introduce two tools for you because I
know that um whatever you you uh you
want to solve challenge one two three
four five you need to conduct a market
research and there is some tools help
you can conduct a really fast and better
market research uh we call AI design
research not design research AI design
research we are using a technology
called social listening technology I
believe if you want to solve the problem
about uh change one three and five. Uh
if you talk about people lead, these
tools can help you.
Um we go back to designing 1.0 first. Uh
in designing 1.0, we need to amplify our
customer. We want to understand that
lead. We need to develop a questions.
Ask them what's your experience? How do
you think? Uh what is the good
experience, bad experience? We use
questioning skills. We have to develop
question first and then we need to
conduct the on-site research. On-site
research we talk about the real user. Uh
we observe the environment. We observe
their behavior. After you develop the
questions and also conduct the interview
and observation in onsite you can find
the pingpoint and expectation. What is
the pingpong in their journey? That's
the way lead time. If you need to
conduct 100 interview I believe that uh
from question design conduct and analy
data maybe one month but today one week
will change everything that's why in deciphinging
what social listening very simple we
conduct a research to connect all the
information from social media that's
mean when someone's buy something uh he
feel really good he share in their
social media wow the product is really
good I like it there's a really lal
sharing and data oh
and vice versa someone buy product the
product is reverse he complain and share
I have very bad bad experience in that
products he share is in social media
that's mean we can collect different
user opinion in social media in last 10
years Um, of course, you should select
the right social media. That mean if you
uh want to collect the information from
the middle age person, you have to
search on so uh Facebook. But if you
want to focus on the young maybe
Instagram is your focus. After you
select the right social media and also
the right keywords, you can collect so
many information. Show that any related
information, any related opinion about
your product or services in which day
and you can digest the information.
Finally, you can find so many related
wording. If there's so many words about
your product is happy, surprise that
mean your product is very good. However,
if there is so many wordings, bad
wordings, negative wording about your
products, then maybe you have so many uh
space for improvement. Total different
approach. But
the for social media uh social listening
technology this more precise and we can
collect large size. Uh in in the last 10
years, we use
on-site interview. We collect 100 people
opinion. But in social listening we can
collect 10,000 because we can collect
all the information from social media in
last one years
and after you collect the information I
believe that you will think about I know
the customer lead I want to develop the
products as soon as possible especially
uh you have to submit the proposal. Um
maybe in the last generation we may use
p pencil and pen to draw our ideas.
Maybe sometime you draw a 3D model.
Maybe sometime you you you fabricate a
small size small scale prototype but
it's not good because your judge or your
user cannot amplify cannot experience
what's your product. That's mean today
I've use another technology called AICG
technology. AICG technology is very
clever. when I draw something
he will know that I want to draw was and
then after conversation
uh such as I want to a green one I want
to be a a a a old style
a emperor style they can generate a 3D
model for me that's mean today AICG is
really powerful and useful way to do a prototyping
prototyping
I believe that you all know that AI
design thinking not just using AI and
power also provide so many advanced
tools for you to enhance your product
effectiveness. Now I would like to share
a cases how Hong Kong people using AI
design thinking to enhance the community development.
development.
uh before I share the homework case in
fact free Samsung using AI design
thinking to enhance their uh human to
enhance their human error reduce and
also the measuring cost and also earn
earning wage that is a measurable
results of AI design thinking also one
computer access computer also using
design thinking in their internal
competition and two winners help company
create you can see 5050
million US dollar there is a business
value for designing projects that's mean
design thinking 3.0 zero can create
measurable deliverables.
Now I will share a case uh we conduct uh
with uh Hong Kong government in Hong
Kong. Uh the case is talking about a
samurbo a fabric market. Uh I know that
uh I I I believe that some of you are
Hong Kong people. uh you will know that
some people is a uh uh is a area so many
uh hawkers they sell about the
secondhand fabric and there's so many
hawkers across different uh uh street in
central pole government think that it's
not good because when they facing a big
rain a big rain uh the staller maybe uh
many problem that's why the government
build a little center for them uh We
call it sanable fabric market. Uh in
fact the market is very good. They
provide a hygiene. They provide
electrical all the things. However,
there's lot of customers. There's a big
problem for them. That's that's why the
government Hong Kong design center
invite us to discover what happened. Why
do people not go to the Leo fabric
market? And the project and the project
was conducted in December of 2024. Of
course the first step we use social
listening to collect and conduct so many
information uh in social media about
what is their uh opinion about the
samurai fabric market. Afterwards, we
also conduct
a little bit a small scale on-site
research to carry away the AI information.
information.
And then we found that wow
there is so many things they found that
the new fabric market cannot fulfill
them. And luckily we found that the
result from social listening and also
from on-site research is aligned that
mean the research is successful. After
we con conclude the ping pong we develop
different ideas and also we develop so
many new services.
The last step is we propose the ideas to
the district counselor to think about to
think about uh uh how they implement it.
Now I would like to ask you one
question. Our projects our projects
from data collection to execution
what is the duration of our project
which I present in last five minute. Uh
please type your opinion uh in the
chatbot. If you think that my project is
talking about two months please type A.
If you think about our project is one
month type B, two week type C and one
week type D. If you think about our
project just two full day, type E. And
if you think our project is two half
day, please type. Okay. P join. Okay. A
B. Okay. Any others?
Think about how long is my project? A. Okay.
Okay,
if time allow I will ask all of you.
However, today time is really uh
constraint. Now I would like to share.
Okay, there is a government poster peace
peace focus on the right upper hand corner.
corner.
Our project was conduct just two half
day. You will think wow two half day.
Yes. Uh in fact the event is two half
day. uh we invite the public uh uh we
invite so many uh people from university
from uh uh uh social service uh
organization to conduct the research but
before that we conduct a social
listening report before uh if I add the
social listener report development is
talk about one week with we we kind
about one week compare with the same
projects I work for the gang human
ar in 2022 we use 28 week to conduct the
market survey and also control the
information but in 2024 we just one week
just because we use different approach
of design thinking in 222 all the all
the task conduct interview analyze the
information bank brainstorming always
using human only however in 2024 or we
use AI agents such as social listening AICG
AICG
etc etc that's mean AI design thinking
can enhance the time not just 45 8% maybe%
maybe%
uh maybe this can help you to conduct
your projects apart from AI design
thinking uh at upcoming five minute I
want to share about
one more tools for you Because design
think is is good for focus on people
demand. But
when we need to solve about the social challenges,
challenges,
we need to we are not just concerned the
just one people. We need to concern the
whole system. The whole system. When you
need to concern the whole system, you
may use design thinking and systems
thinking. And what system? As you can
see all the things all the things about
your subject is a systems
and in a business ecosystem is not only
about customer and design thinking is
focus on customer but in reality maybe
your business partner your competitor
your supplier or employee also affect
the results that mean if you want to con
concern or conclude or consolidate and
co-create all the stick together. System
thinking maybe can help to you
uh because a system thinking is
concerned about the system. Therefore,
sustainable is also calm in the outcome.
That's mean you lost
just focus about the good experience of
the people use the products. Also think
about how the product impact the
environment. That's mean in the design
thinking with system thinking approach
we have four criteria for outcome.
In design thinking we focus on one
people. In system thinking we focus on
all stakeholder in the systems
and there's so many tools for system
thinking to co-create with design
thinking. I would like to little bit
talk about uh the first thing yes system
map this is very important because in
design thinking we just focus about the
level one that's mean interpersonal but
in system thinking you need to concern
about different level and my pilots will
use the case to show about what is
different level
when you can see the different level you
will we will find the gap between
yourself and the systems and how to
synergize together.
Apart from this, we also use different
way to dig deep to find the root cause.
Uh one of the most powerful uh tools of
system thinking is the uh causal loop.
Cos means we found the relationship
between uh two important things. Uh we
have two loop. The first look is called
we reinforcement loop. That's mean
something up and also moving and other
thing up. Just for example, if you run a
restaurant, if your
customer satisfaction up and I believe
that the total number of customer will
go up, that's good. However, at the same
time, there's another is called
balancing rope. When there's more
customer, if your lot increase the
manpower, the service level will go down
and the service level go down and the
customer satisfaction rate will go down.
That's a balancing law and this two look
is affect whole systems and one of my
partner will lo the case we'll talk
about it in reality
there is not a individual analyze we
need to talk about the different interactions
interactions
in fact and other tools for system
thinking is we call 12 leverage point we
think that if if you want to solve of
the problem. We have 12 leverage point
or or we can say there's a 12 solution.
The one is we found out the the trigger
point in the loop and we can help to
change and also we can adjust the
parameter and also we can shift the M or
is the leverage pawn to change the
system. Last but not least and we have a
we have to plan a process to execute
your solution and tradition in
traditional way we may focus on the
financial and operation but in the
system thinking we also need to focus on
the high level and subs single level I
want to use one uh diagram to show in
traditional way we just focus on small
part of our planning but there's so many
space we may have a blind spot and
little bounce back uh of your products.
Now I would like to conclude my my my
sharing. Um I believe that today's uh
all projects all product development is
not talking about one customer. We need
to co-create or master different
stakeholder in the business ecosystem. I
believe that if you need to master every
stakeholder in the business ecosystem,
design thinking and system thinking can
help you. Okay. Now I would like to
invite my partners uh Michael to share
about the cases how system thinking can
use for different sector. Michael I pass
the floor to you.
>> Thank you David. Um can everyone hear my voice?
voice? >> Yes.
>> Yes.
>> Okay great. Uh let me share my screen first.
Okay, great.
Okay, I would like to share a case about
um hospital uh the patient experience.
Um the PowerPoint actually here is a
little bit wordy is is the purpose is is
trying to share with you in cases if I
can't go through within 15 minutes for
the details. Okay. Um he's talking about
the transforming patient experience. Um
John Hawkins Hospital's systematic
redesign through integrated system and
and design thinking. Actually David has
shared some theoretical background. So
here's the cases to demonstrate how the
design and system thinking can integrate
together. Okay, let me talk about a
little bit about the background of the
hospital. Not sure not sure is everyone
notice about this hospital. Uh it has a
long history since the the 17th century.
Um it's a large nonprofit uh hospital in
US. um but it is really famous and for
consecutive years they've ranked number
one in US and they have many firsts in
the industry and apart from that
actually we we seen that they are also
uh famous in the design thinking
industry and they are the pioneer in
applying design thinking
okay why I'm saying that um uh the John
Hawkins uh um hospital is a pioneer in
the healthcare design thinking. Um since
day one the founder actually focused the
hospital not just only for the for the
uh medical treatment. They are aiming
for the patient wellness focus and
introduced novel ideas about the hygiene
and sanitation.
And in 2012,
they opened up a new compress. And the
upcoming story that I'm going to share
is about this new compress. Um,
from the screen you're seeing that is
pretty different from the from the old
one. Uh, it's really modern. But
actually, apart from the architecture,
uh, it focus a lot about the experience,
the patients experience as well.
We seen that design is not just about uh
uh what it looks and feels like just is
what um Steve Job has quote designs is
how it works and this is the case really
demonstrate that apart from the from the
outlook of the hospital and the the more
modern details uh it captured a lot
human oriented uh elements in the hospital.
hospital.
A little background of this uh case
study. Um the case study started in
2009. Uh the hospital is going to move
on to a new place and the re relocation
will be happen in 2011
and the senior management is want to
undergo a systematic redesign of the
hospital uh rather than just a
geographic move.
Uh in the following in the following
page I will I will discuss what's really
mean by systematic redesign.
um actually is applying design thinking
plus system thinking. Uh you will see
that um the design team is not just only
the designer. It involve a lot stakeholders
stakeholders
uh in the in the group for the for the
uh design practice. Um including the
administration and management uh rather
than they just only signing off for
approval, they are part of the team. uh
medical staff of course um doctors,
nurse, technicians, supporting staff is
uh as well which is pretty rare in many
design thinking process uh which
includes different kind of supporting
staff like the customer service uh
workers uh the custodian uh staff and
definitely end users as well.
Here we want to see how it's going to
upgrade its system and how to define a
system. Many people will think that
system is talking about the hardware,
the facility or how the how the
department, the logistic is going to
work together. But under the umbrella of
design thinking plus uh system thinking
we are trying to define the system in a
and we're going to see why uh the John
Hawkins hospital they deployed the
design thinking plus system thinking as
I mentioned that uh the hospital is
really pioneer in design thinking
The reason why they want to include
system thinking is they want a holistic
holistic problem solving view. And from
the result of the findings they find
that some some some arrangement in the
patient transportation and environmental
service they are really critical and
this finding has been always being
neglected or overlooked.
Sustainability is mentioned by David. Uh
the beauty of combining design thinking
and system thinking. Sustainability is
the crew and we are seeing that some new
arrangement about the hiring and
training will will uh was uh imposed uh
after the the the the projects finished.
Stakeholder inclusion and ownership. It
mentioned about that um all different
level of um uh staff and also the the
patient uh is included rather than just
only saying that the designer uh make
the call.
And the beauty of this is we seeing that
the buying from uh in the very early
beginning rather than hey just just what
you have been been given. And the last
point here is the enhance the problem f
uh problem uh framing. So instead of
just only looking at the patient care,
we care more about the patient
experience. Just like in the business
environment, we care about our customer
experience rather than just only at the
moment that they they they buy the product.
product.
And even though some kind sometimes that
we are seeing that innovation uh may not
be really necessary may not be really
necessary in a disruptive way. It can be
some small minor changes that may really
help a lot in the whole e ecosystem perspective
achieving the balanced outcome while the
integrated thinking. Here's the key
elements uh from the from the high level
perspective the desiraability the
visibility the viability and the sustainability
sustainability
here are the elements that we seen that
the integrated model by combining the
the design thinking and and uh and the
system thinking what we want to see to
achieve the human center uh with real
data support and also is a holist istic
and and viable view and the most
important one actually is built to last.
That means that you are not just only uh
solving a pinpoint things and it may
arouse some other problems to come. It's
trying to look at the problem from a
So mastering every stakeholder within
the ecosystem we seeing that uh we want
to identify the all the stakeholder uh
within the system. Um starting from the
patient we are putting in the center and
since the topic uh we're going to to
look at is the patient experience then
from there we seen that is is then over
to employees to partners and the
employees including all different
parties uh from the from the hospital
rather than just only the the doctor or
nurses uh just like the the cleaners and
the transporter. They are really
critical in the whole experience journey
and the partners uh we referring to
different uh departments. The people
from different departments just like the
diagnostics and the nursing team whether
they are really working closely together
to offer a pleasing experience for the
patient or the connection is disconnected.
uh rule makers actually is very obvious.
He's talking about the the the boss or
the leadership or the administration who
set the rules. Apart from apart from uh
the the daily job that they are working
on for the for the rules uh uh setting
are they really notice about the
details? Um suppliers here we we we
mention not just only the external
supplier. Uh we mentioned about the
internal supplier as well. Uh just like
the uh the inter relationship uh for
different for different departments. uh
they've got the the upstream uh uh
supplier and they are they may be also
the the the
middleman for the downstream uh uh receiver
receiver
comparators here we referring to others
uh hospital what they are really
offering in the market uh the key
insight we seeing that to improve the
patient experience the hospital has to
map and reshape the entire atward rather
than just only pinpoint to a particular
um uh scenario and that's the beauty of
for the ecosystem is just what um David
has been shared uh we seeing that uh the
interpersonal starting from the patient
putting in the center uh the
interpersonal relationship with the with
the family, the friends and also how it
deal with the the doctors, nurse we
putting in the center first. Then the
next layer is David has mentioned there
six layer there and the second layer is
the micro system the local community
talk talking about the office hospital
room uh that you are staying in uh the
nursing station and they can be viewed
as the micro system uh within the whole ecosystem
for the missile system. This is the
terms that really are not really often
used. Uh it's talking about the
connection is the is the middle in the
middle of the of the whole circle. Uh
that's the connection between places
like workplace. Um just like for
different department
we seeing that that's a that's a
scenario for patient transportation
uh to work along with other department.
Uh sometimes the patients uh provide a
feedback saying that uh they are not
really respected. um they just like uh
they that's been treated like a good
moving from here to there and the ESO
system actually is talking about the
institution itself that means the the hospital
hospital
to make change happen um the hospital
leadership had to change their rules and
also um the the the mode of working just
like the the the community just like the
the the teamwork they have to form
rather than just only the senior
The next level uh going up is the macro
system is talking about the government,
the nation, the culture and the top
level or actually is the outer level of
the whole circle actually is the bio.
The bio level actually is talking about
the environment, the natural
environment, uh the city, the local community
community
from the research that the hospital that
they has been carried out. Although the
although the case is dealing with
patient experience, it found that uh the
really uh root causes has to be going
into the middle to to to look into it
which is the uh mass system that that
means it's connecting the issues uh
between two departments and it can be
solved from uh even outer circle uh the
the the the the level is the ESO system
that means he's changing the rules and
changing the logistic arrangement.
Okay. Um we we end we going to discuss
about the the uh 4D model or actually we
putting here is a 6D for the uh double
diamond design thinking model. The first
D actually here instead of going into
the discover we talk about the determine
first setting the priority for value creation.
creation.
We always have a challenge statement
when we want to kick off a project just
like the project that you are going to
to enter into the hackathon. Um the
challenge statement from the design
thinking lenses which we are trying to
put it uh in is how do we enhance the
patients experience during and after
relocation and he here he here is the
big picture that the senior management
want to see how to how to solve it.
But from from the from the view from a
systematic way by including uh uh system
thinking to design thinking we're trying
to set setting the systematic systemic
uh priorities for value creation
and it considering John Hawkins is a
system what does the hospital do to to to
to
uh support the the patient experience?
versus the simply considering patient
For the discover we we are we are seeing that
that
uh data collection is very important and
from the from the double diamond model
we are seeing that the first step we
have to diverging our thinking first
that means is exploring all the
information that we may collect. uh we
seen the the the case study uh start
from the training first. It's talking
about have a common ground for the
people to to understand what's about the
design thinking and what's about the
system thinking uh for all the
stakeholder in the in the design team.
Triangulation in data collection means
uh it include the desk research that
means all the third party datas the
internet news uh and field research
includes the the observation the the
interviews and design research
and from all the findings the team
concluded and finding that the
discovered they discovered that the
patients value more than just only
medical care from a hospital definite
medical care is very important important
but from the patients experience sense
we seeing that uh if it's just the very
basic that the patients they are expected
expected
and also from the trends from other
hospital we seeing that uh successful
med medical procedures may lead to uh
negative patients feedback due to poor nonclinical
nonclinical
uh experience
a share understanding of the hospital as
a system and highlight gaps in
Then the second stage that we are
entering is the define. We want to
narrow down. We want to converge back to
the to the to the uh particular scenario
and we we are trying to recall a
framing. Uh we're trying to reframe the
question to focus on supporting the
patient experience
versus considering patient care leading
to the realization that there's two key
finding which has been prioritized. The
first one is the patient transportation
and the second one is the the
environmental service and these are not
really tied with the medical care. It's
not really tied with the the doctors
whether they are performing good uh
For example, uh the tools that from the
uh design thinking uh we we use the
problem statement to describe the case
or or describe the the the scenario.
Start from the very early beginning.
We've got the challenge statement uh
which I put it in the in the uh top rep
bosses. you will see that it is a very
generic or actually it's a very very um
uh open um challenge statement from the
problem statement we seen that we want
the details uh just like starting from
the very early beginning we know we want
to describe the background the situation
for example as I mentioned about the
patient transportation we want to see
why is it that the the the situation
happened and how it impact the patient.
For example, uh here we we put into the
persona to describe uh how the patient
what kind of patient why they are
anxious and why are they they have to
wait for for the next step they they are
going to to go on. We care about their
emotion and we want to know what's their
emotional goal. We want to knows about
their needs and what's their expectation
and what's the barrier that trying to
trying to uh stopping them from from
having a a better experience.
For the for the second problem statement
is in a similar way. We are seeing that
patients room is also very important uh
contest and and many patients really
reflect that uh they can't have a really
speedy and quality uh uh arrangement for
their room or for the for the area they
have they can take a rest after the
admission. So in the same approach we
have to know the emotional details and
what's the expectation and what's the
development actually is the
brainstorming session. It goes back to a
to a to a ideation a converging approach
to to having all the design teams to
brainstorm together to see how to come
up with the solution. So um the details
did uh in the in the paper they didn't
explicitly uh say in the what's the the
the solution that it comes up but from
the papers we seeing that the result is
trying to reduce the weight time and
also enhance the efficiency for the
patient transportation. That means from
from after they they finish the the
operation if they have to move back to
their to their uh uh hospital room uh is
it being treated in the prompt way for
environmental service whether is the the
room the the hospital room is clean
enough uh they can get back to their
room on time after the the admission and
whether the the quality of the the
hygiene is is good enough or not.
Seems like all these details are very uh
I would say uh simple but actually this
affect the the the whole experience in a
really uh intensive way and that's the
reason why the hospital prioritize these
two problem uh to develop the solution
in the develop in the developed stage.
We seen that uh the performance uh with
all the datas has been improved and the
feedback from the patient uh uh is uh is
good and positive and the management
team really trying to to improve the
situation by providing the the trainings
in and also uh in the in the recruitment
uh procedures
and trying to make the outcome a
sustainable system redesign
for the last D uh which is the drive for
the for the for the for this D actually
it's driving the team to go uh onward
after the project uh to keep going
because design thinking is not just only
a u uh uh one run uh process actually
it's a iteration uh from the
documentation that we we we found uh
after 10 years they they make some
announcement uh to talk about the the
patient experience. We seeing that um
they are they are focusing in in writing
on the um design and system thinking
logic to build the infrastructure to
create the alignment. uh they
standardize and enhance the patient
experience by using the integrated uh uh
thinking model. Uh the measurement has
been put in details for the whole
journey and also to come up with the uh
improvement plan using the the lean uh canvas.
canvas.
Okay. From the whole case that I have
been shared uh we we seen that system
systemic uh redesign improve the
performance and also the experience as
well. Inclusive stakeholder engagement
is really important. That means that uh
all different level of stakeholder need
to be involved if the if the environment
is really is really a complicated or
complex situation.
share foundation with design and system
thinking training. Uh making sure that
the their language are the same and the
ideas can be bought in in the earlier
stage rather than the final proposal
being submit for approval. Reframing the
problem and identification of high
leverage pawn. Uh actually Paul will
discuss more about the leverage point
here. Um and the leverage point in in
the case that I've been just shared is
the patients transportation and
environmental service is the most
critical and important one uh that has
been brought up from the from the um uh research.
research.
Sustainable and systemic solution really
help the hospital to keep their their
position in the industry and and
improving improving the the patients uh experience.
experience.
Level power dynamic actually is trying
to break down the traditional hierarchy
in the in the hospital or in the company
uh to make a faster uh fixing solution
uh because sometimes the hierarchy uh
produces um uh politics or making the
system uh broke down just very silos. So
So
from the case that I've just been shared
and refer back to the projects that you
are going to to go after for the
hackathon is there any is there any
implication that you may you may see uh
because we we are finding that all the
five projects that that has been come up
for the hackathon uh is all related with
the system but one of the key words that
within the system is the is the human
oriented whether we seriously consider
all the all the stakeholders within the
system to make the journey or to make
the experience in a in a good way.
So the final page here for the
conclusion actually is design thinking
ensuring the ensuring the process to be
human center uh empathy and trying to
achieve the the viable uh solution and s
system thinking adding on the system
thinking we're trying to have the
holistic view and the final I put it
from the plus to across because after
the AI enabled model we have seen that
uh it can achieve the sustainable
innovation in a more faster and easier
way uh uh nowadays in the market. Okay,
here's my sharing. So I would like to
pass on to Paul um to discuss about the
case for car sharing. Okay, thank you Paul.
Paul.
>> Thank you Michael. Thank you for your
sharing on the John Hawkins Hospital
case is fantastic one. So uh okay hi
everybody this is Paul uh I'm the senior
partner of Ino Edge consulting group and
my specialties will be on business
consulting on uh business uh model as
well as strategy and um another field of
uh expertise will be the uh social innovation.
innovation.
uh these two are all the complicated
uh situation that we have to consider a
lot of external stakeholder or the whole
system and today I'm going to share with
you sorry I'm sharing slide my slide
today I'm going to share with you on the
case that uh about ESG
Okay. So today my topic is uh
exploratory study on leverage pawn and
rebang effect go more car rental. So the
the object will be host go more car
rental company and but the two key
points will be the leverage pawn and
rebang effects. Don't worry about it you
may have not heard about this before but
then uh this these two are key elements
in system thinking that how it can help to enhance the design thinking process
to enhance the design thinking process and make it more effective for this
and make it more effective for this case. Okay.
case. Okay. So I'm going to go through with you who
So I'm going to go through with you who is go more and what's about the rebind
is go more and what's about the rebind event leverage points before going into
event leverage points before going into the case and then I'll dissect the case
the case and then I'll dissect the case step by step. Okay. Go more uh is a car
step by step. Okay. Go more uh is a car rental company from Denmark which is
rental company from Denmark which is founded in uh 2005
founded in uh 2005 but in 2016 they shift their focus not
but in 2016 they shift their focus not only for car rental but also for car
only for car rental but also for car sharing P2P rental. So and very
sharing P2P rental. So and very important point one of the mission will
important point one of the mission will be taking care of the planet better. So
be taking care of the planet better. So they add ESG element into their
they add ESG element into their corporate mission. So that would be a a
corporate mission. So that would be a a strategic level decision. So but and
strategic level decision. So but and what does it mean? It means that via the
what does it mean? It means that via the service they want to reduce the cost of
service they want to reduce the cost of ownership uh for driving increase
ownership uh for driving increase convenience optimizing the uh
convenience optimizing the uh utilization and all in all they want to
utilization and all in all they want to reduce overall carbon emission by
reduce overall carbon emission by effective car sharing service that's
effective car sharing service that's their mission so with this understanding
their mission so with this understanding in mind so we know what go more is
in mind so we know what go more is trying to do and a little bit recap from
trying to do and a little bit recap from what David has share on the left hand
what David has share on the left hand side is a reinforcing loop and balancing
side is a reinforcing loop and balancing loop adjust In real world there's no
loop adjust In real world there's no separate loop not just loop by loop it
separate loop not just loop by loop it is we uh it is usually loops with
is we uh it is usually loops with another loops. So like the abdoption
another loops. So like the abdoption rate of some new product by work of
rate of some new product by work of moper
moper and with more abdoper there will be more
and with more abdoper there will be more abduction rate and increase in a
abduction rate and increase in a reinforcing loop but in meanwhile to
reinforcing loop but in meanwhile to certain level the abdoption rate will uh
certain level the abdoption rate will uh reduce the number of potential adopter
reduce the number of potential adopter and we call market situation and it will
and we call market situation and it will decrease the abdoption rates. So this is
decrease the abdoption rates. So this is usually more complicated is not just a
usually more complicated is not just a single circle or cycle. So it will be
single circle or cycle. So it will be loop by loop and I'm going to show you
loop by loop and I'm going to show you something shocking but just don't worry
something shocking but just don't worry don't panic but this is typical as a
don't panic but this is typical as a system dynamic of a whole caution loop
system dynamic of a whole caution loop you you don't need to read any detail
you you don't need to read any detail but there's a factors affecting each
but there's a factors affecting each factor so you can can see a lot of
factor so you can can see a lot of reinforcing l balancing l of loop a lot
reinforcing l balancing l of loop a lot of interconnecting
of interconnecting uh situation that is the real life that
uh situation that is the real life that is the complic uhation of system and if
is the complic uhation of system and if you overlook certain point there could
you overlook certain point there could be backfire and this case is showing you
be backfire and this case is showing you what will be the backfire and how to
what will be the backfire and how to prevent it.
prevent it. So in traditional uh logical thinking
So in traditional uh logical thinking with root cause analysis we want to uh
with root cause analysis we want to uh deduce the root cause and then try to
deduce the root cause and then try to solve problem one by one. So in this
solve problem one by one. So in this case like I want to have a efficient
case like I want to have a efficient caching surface which will enhance the
caching surface which will enhance the fuel efficiency. So improve the fuel
fuel efficiency. So improve the fuel efficiency that's why they are positive
efficiency that's why they are positive with each other. they are the in similar
with each other. they are the in similar direction but then with fuel efficiency
direction but then with fuel efficiency improve that means the emission will be
improve that means the emission will be lower. So that is the conclusion of the
lower. So that is the conclusion of the logic. A good car sharing program will
logic. A good car sharing program will reduce carbon emission and that's the
reduce carbon emission and that's the purpose and that's the mission for go uh
purpose and that's the mission for go uh more how they can reduce uh carbon
more how they can reduce uh carbon emission and save the world.
But for causal loop we try to complete the circle. What if the emission is
the circle. What if the emission is really reducing? So in short term there
really reducing? So in short term there won't be effect but in long term we can
won't be effect but in long term we can see that with the emission is getting
see that with the emission is getting lower there will be less pressure from
lower there will be less pressure from the society to to subress the emission
the society to to subress the emission from driving and with less pressure that
from driving and with less pressure that means there will be less motivation or
means there will be less motivation or less uh leverage for the car sharing or
less uh leverage for the car sharing or so the efficiency won't be improving. So
so the efficiency won't be improving. So then we can see from starting with a
then we can see from starting with a positive on the efficiency of car
positive on the efficiency of car sharing looking back it will result in a
sharing looking back it will result in a negative impact. So we call it a
negative impact. So we call it a balancing loop because it will be in a
balancing loop because it will be in a circle. Uh so this is the real circle
circle. Uh so this is the real circle and then factor is affecting each other
and then factor is affecting each other and eventually coming back to the origin
and eventually coming back to the origin but still this is good thing because
but still this is good thing because when fuel efficiency improve further
when fuel efficiency improve further improve emission is always going down.
improve emission is always going down. This is important. When fuel efficiency
This is important. When fuel efficiency is improving, emissions always go down.
is improving, emissions always go down. However, when we try to identify other
However, when we try to identify other factors, we can see that there could be
factors, we can see that there could be rebound effect. What is rebound effect?
rebound effect. What is rebound effect? So when fuel efficiency is going down on
So when fuel efficiency is going down on the right right hand side, you can see
the right right hand side, you can see the cost of driving is also going down.
the cost of driving is also going down. If fuel efficiency is going up, the cost
If fuel efficiency is going up, the cost of driving is going down. Sorry.
of driving is going down. Sorry. [snorts] But when cost of driving is
[snorts] But when cost of driving is going down that means you have more
going down that means you have more disposable income. You have more income
disposable income. You have more income you have more intention to drive. And
you have more intention to drive. And when you have more intention to drive
when you have more intention to drive your emission will go up. You can see
your emission will go up. You can see from the same factor fuel efficiency
from the same factor fuel efficiency improve. Last page we we saw that the
improve. Last page we we saw that the emission will go down. In this page with
emission will go down. In this page with this chain of variable we can see the
this chain of variable we can see the emission go up and eventually it will
emission go up and eventually it will helps to close the loop in reinforcing
helps to close the loop in reinforcing loop but then it it is a difference from
loop but then it it is a difference from what our original intention. The fuel
what our original intention. The fuel efficiency should lower the emission not
efficiency should lower the emission not increasing. So that it will be a problem
increasing. So that it will be a problem that is what we call rebound effect and
that is what we call rebound effect and that's the root cause of the uh
that's the root cause of the uh backfire.
backfire. So with a system thinking point of view
So with a system thinking point of view we have to consider all the path and the
we have to consider all the path and the node how they interrelated and connected
node how they interrelated and connected and there could be multiple effect on
and there could be multiple effect on the same note. So and then we have to
the same note. So and then we have to consider how to trick trigger the system
consider how to trick trigger the system effectively and to make uh the system
effectively and to make uh the system work for our purpose.
So the second concept the second concept we need to understand what's the
we need to understand what's the leverage pawn there's 12 leverage pawn
leverage pawn there's 12 leverage pawn you you will see every details of the
you you will see every details of the the pawn but the point is when we
the pawn but the point is when we analyze the system u middle uh which is
analyze the system u middle uh which is uh scholar who analyze that there will
uh scholar who analyze that there will be 12 uh generic leverage points for all
be 12 uh generic leverage points for all the system that you can make use of so I
the system that you can make use of so I try to categorize this so uh into four
try to categorize this so uh into four category first from 12 to 10 you see
category first from 12 to 10 you see inverse number. These are all in
inverse number. These are all in parameters. Parameters means we are
parameters. Parameters means we are leverage the system or try to trigger
leverage the system or try to trigger the system by some of the numbers in the
the system by some of the numbers in the system which is the like the tax rate
system which is the like the tax rate price the quotas or the f uh the
price the quotas or the f uh the referral and all these these are
referral and all these these are parameters but then the second category
parameters but then the second category is the feedback loop. Feedback loop is
is the feedback loop. Feedback loop is how we feed back the information to the
how we feed back the information to the system. So because sometime the feedback
system. So because sometime the feedback may not be immediately. So you know you
may not be immediately. So you know you drive more there will be uh more fuel
drive more there will be uh more fuel consumption but you only aware of this
consumption but you only aware of this when you see the credit card bill on the
when you see the credit card bill on the fuel consumption but not immediately
fuel consumption but not immediately when you are driving. So is the feedback
when you are driving. So is the feedback loop will be also be important. The
loop will be also be important. The third category from uh four to six
third category from uh four to six requests system structure. How we
requests system structure. How we construct the system and to adjust the
construct the system and to adjust the behavior and trigger the the all the
behavior and trigger the the all the causal effect more into our uh ideal
causal effect more into our uh ideal situation. And last but not the least is
situation. And last but not the least is the mental model. Mental model is to
the mental model. Mental model is to shift the mindsets of people human
shift the mindsets of people human beings. So you can see there's four
beings. So you can see there's four major category of a leverage point and
major category of a leverage point and they further break down into uh uh 12
they further break down into uh uh 12 leverage points in the in the books that
leverage points in the in the books that you can refer to
you can refer to but all in all you can see like a
but all in all you can see like a leverage some of the leverage point you
leverage some of the leverage point you can have bigger leverage effect than
can have bigger leverage effect than others. So now with this understanding
others. So now with this understanding we go into our case. Our case is when go
we go into our case. Our case is when go more try to build a more efficient
more try to build a more efficient casing system is ship is convenience.
casing system is ship is convenience. There would be unintended outcome.
There would be unintended outcome. People drive more emission goes up. So
People drive more emission goes up. So uh that is the backfire. What's the
uh that is the backfire. What's the missing piece would be the feedback
missing piece would be the feedback loop. They don't know the uh
loop. They don't know the uh relationship between all those variable.
relationship between all those variable. What do they do? Um so we try to dissect
What do they do? Um so we try to dissect the case using the double diamond model
the case using the double diamond model use uh we may not have determined and
use uh we may not have determined and drive this term. So we have to uh
drive this term. So we have to uh discover define develop and deliver in
discover define develop and deliver in the four step and see how they try to
the four step and see how they try to analyze the case and find the right
analyze the case and find the right solution. In the first stage is
solution. In the first stage is discover. Discover is that we identify.
discover. Discover is that we identify. So we have to build a causal loop. We
So we have to build a causal loop. We have to build a causal loop. Do it step
have to build a causal loop. Do it step by step. You try to map out all the
by step. You try to map out all the notes. All the notes are the variable.
notes. All the notes are the variable. They are none. There are variables that
They are none. There are variables that may change if there's system change. If
may change if there's system change. If there's any issues ongoing, there will
there's any issues ongoing, there will be variables. Once you have all the
be variables. Once you have all the notes, try to connect them with the
notes, try to connect them with the causal relationship. With this one going
causal relationship. With this one going up, the next one is going up or down.
up, the next one is going up or down. You have to identify and identify the
You have to identify and identify the color. So on this diagram, they're using
color. So on this diagram, they're using the green color as a same direction
the green color as a same direction while red color is opposite direction.
while red color is opposite direction. And then finally, you have to look back
And then finally, you have to look back and make sure that is in a circle. Then
and make sure that is in a circle. Then you can identify which part will be a
you can identify which part will be a reinforcing loop and which part will be
reinforcing loop and which part will be a balancing loop. So this is the
a balancing loop. So this is the fundamental understanding on the system
fundamental understanding on the system and once you have this diagram or you
and once you have this diagram or you call a map when you map out you can try
call a map when you map out you can try to define define where will be the
to define define where will be the potential mechanism to trigger this map.
potential mechanism to trigger this map. So in this case in particular they
So in this case in particular they identify five different trigger
identify five different trigger mechanics uh mechanism that can help to
mechanics uh mechanism that can help to change the behavior. The first is the
change the behavior. The first is the income mechanism which is uh your cost
income mechanism which is uh your cost reduce and then you will spend more that
reduce and then you will spend more that will be opposites the income mechanics.
will be opposites the income mechanics. So that will be one of the trigger. The
So that will be one of the trigger. The other one is respending. Respending
other one is respending. Respending means when you spend less money on the
means when you spend less money on the transportation you may spend money on
transportation you may spend money on other activities which will also incur
other activities which will also incur carbon emission. This is the respending.
carbon emission. This is the respending. The third one submission uh
The third one submission uh substitution. This is changing to
substitution. This is changing to others. That means some people who used
others. That means some people who used to walk or use bike or public
to walk or use bike or public transportation now with a effective uh
transportation now with a effective uh car sharing uh platform they may try to
car sharing uh platform they may try to drive. So it is not reducing but
drive. So it is not reducing but increasing emission and the other two is
increasing emission and the other two is the motivation mechanics which is the
the motivation mechanics which is the sense of guilty is getting less because
sense of guilty is getting less because it's greener or the time consumption
it's greener or the time consumption mechanics is which is when there are is
mechanics is which is when there are is more convenience is leads me to drive
more convenience is leads me to drive more. So these are all the factors that
more. So these are all the factors that may affect the system. So how do how do
may affect the system. So how do how do we do based on this different mechanism
we do based on this different mechanism and also the leverage points the team
and also the leverage points the team try to think about the different ideas.
try to think about the different ideas. So the ide in ideation stage develop
So the ide in ideation stage develop stage they figure out based on these two
stage they figure out based on these two framework they figure out 68 different
framework they figure out 68 different design strategy how to help to reduce
design strategy how to help to reduce the rebind effect.
the rebind effect. I give you some example like uh I give
I give you some example like uh I give you four example like on parameters
you four example like on parameters level on triggering the rebound
level on triggering the rebound mechanism on respending we try to
mechanism on respending we try to improve the product efficiency. So is
improve the product efficiency. So is further improve the car sharing
further improve the car sharing efficiency and all this in order to
efficiency and all this in order to offset the spending. So that could be
offset the spending. So that could be the parameters level. The second is on
the parameters level. The second is on the feedback level uh which is income
the feedback level uh which is income mechanism. So we use price feedback that
mechanism. So we use price feedback that means if you drive too much or we set a
means if you drive too much or we set a limit if you drive longer than certain
limit if you drive longer than certain distance will increase price using price
distance will increase price using price to feedback a signal to you that you are
to feedback a signal to you that you are driving too much. So this is a feedback
driving too much. So this is a feedback mechanism. On the third level is a
mechanism. On the third level is a system structure
system structure because people are using go more intense
because people are using go more intense uh there's a tendency to be trying to
uh there's a tendency to be trying to drive greener. So might as well we try
drive greener. So might as well we try to configure a personal sustainability
to configure a personal sustainability growth. So when you rent a car try to
growth. So when you rent a car try to set not only the mileage or the rental
set not only the mileage or the rental but also a half carbon emission rate
but also a half carbon emission rate that you want to aim for this uh rental
that you want to aim for this uh rental period. How much emission that I want to
period. How much emission that I want to control myself to be in. Then when you
control myself to be in. Then when you have this structure with your personal
have this structure with your personal sustainability go into the rental system
sustainability go into the rental system that may cause difference. This is the
that may cause difference. This is the motivational consumption mechanics. The
motivational consumption mechanics. The last one is the substitution effect. But
last one is the substitution effect. But then this is on the intention level on
then this is on the intention level on the heart on the mental level. We don't
the heart on the mental level. We don't want to adjust the mobility system. We
want to adjust the mobility system. We want to adjust this human uh human
want to adjust this human uh human settlement and urban center decide that
settlement and urban center decide that would be very uh high level rethink of
would be very uh high level rethink of the whole system. So may I invite you if
the whole system. So may I invite you if you are the decision maker 1 2 3 four
you are the decision maker 1 2 3 four there are four different uh policy or
there are four different uh policy or potential strategy which one would you
potential strategy which one would you try to deploy which one would you try to
try to deploy which one would you try to deploy uh to the system you can type in
deploy uh to the system you can type in the chatbot while I'm still going on
the chatbot while I'm still going on because you can still see all those uh
because you can still see all those uh numbers so while you you are typing your
numbers so while you you are typing your answer I'm telling you as a designer we
answer I'm telling you as a designer we have to analyze all the option with fn
have to analyze all the option with fn element ments with the physibility,
element ments with the physibility, effectiveness and desiraability on the
effectiveness and desiraability on the solution. Okay, let me look into so a
solution. Okay, let me look into so a lot of people using one option one which
lot of people using one option one which is to improve our uh productivities uh
is to improve our uh productivities uh productivity efficiency further. Okay,
productivity efficiency further. Okay, let's have a look what will be the
let's have a look what will be the outcome. So when we evate give a score
outcome. So when we evate give a score to every single uh aspect. So we find
to every single uh aspect. So we find that in general the uh high lower levels
that in general the uh high lower levels mean the number is higher from 10 to 12
mean the number is higher from 10 to 12 when the effectiveness is usually lower.
when the effectiveness is usually lower. When it is in those kind of level
When it is in those kind of level effectiveness will be lower but on the
effectiveness will be lower but on the other hand we can also see that the
other hand we can also see that the physibility and disability will usually
physibility and disability will usually be higher. The other way around we can
be higher. The other way around we can see uh if we go to the left deeper to
see uh if we go to the left deeper to the leftish pawn that means from one to
the leftish pawn that means from one to three or the the top level leverage pawn
three or the the top level leverage pawn the effectiveness will be very high but
the effectiveness will be very high but usually the physibility and
usually the physibility and desiraability would be low. Yes, thank
desiraability would be low. Yes, thank you. Someone tried to do the most
you. Someone tried to do the most challenging one but softing and cost try
challenging one but softing and cost try to take the option four. Yes, these are
to take the option four. Yes, these are all possible solution but in real world
all possible solution but in real world how do we select? we have to wait the
how do we select? we have to wait the overall effectiveness against the
overall effectiveness against the physibility and disability. So in this
physibility and disability. So in this case what they recommend in the physics
case what they recommend in the physics is in this two because they have
is in this two because they have moderate outcome but they still have a
moderate outcome but they still have a high level effectiveness or high
high level effectiveness or high desiraability so it's more controllable.
desiraability so it's more controllable. So this is a particular case in this
So this is a particular case in this case I'm not saying that in the middle
case I'm not saying that in the middle level will be the best but you have to
level will be the best but you have to wait overall but the key takeaway is
wait overall but the key takeaway is with shallow leverage pawn so the back
with shallow leverage pawn so the back half 7 to 12 is easier to implement but
half 7 to 12 is easier to implement but less effective deeper leverage pawn will
less effective deeper leverage pawn will be high uh highly effective but it will
be high uh highly effective but it will be harder to implement. So at the end of
be harder to implement. So at the end of the day is a trinity. So we have to
the day is a trinity. So we have to balance optimize among cost, time and
balance optimize among cost, time and effectiveness to and then we have to map
effectiveness to and then we have to map the our uh design strategy back to the
the our uh design strategy back to the uh causal loop and see what will the
uh causal loop and see what will the impact to to this case.
impact to to this case. So at the end of things by just design
So at the end of things by just design thinking alone you may miss the causal
thinking alone you may miss the causal loop you can you cannot predict the
loop you can you cannot predict the unintended consequences and not seeing
unintended consequences and not seeing the whole system health. So with system
the whole system health. So with system thinking adding into design thinking. So
thinking adding into design thinking. So it will help to uh to add this
it will help to uh to add this visibility or the hidden feedback and
visibility or the hidden feedback and also identify the intervention points.
also identify the intervention points. Uh we always have to balance the
Uh we always have to balance the visibility and the effectiveness and it
visibility and the effectiveness and it helps to predict the unintended
helps to predict the unintended consequences.
consequences. Uh and we can design our solution not by
Uh and we can design our solution not by single point but in the system like a
single point but in the system like a level.
level. Okay, the takeaway of this uh case is
Okay, the takeaway of this uh case is there is rebound effect. This is real
there is rebound effect. This is real for real and with the causal look
for real and with the causal look diagram it can help to make the uh
diagram it can help to make the uh complexity being seen. So when the whole
complexity being seen. So when the whole team everyone can see is much easier for
team everyone can see is much easier for the further discussion. Leverage pawn.
the further discussion. Leverage pawn. We are not recommending just middle core
We are not recommending just middle core or the one end or the other end. You
or the one end or the other end. You have to be smart with a mix of some
have to be smart with a mix of some quick wins together with some lasting
quick wins together with some lasting changes that will be a combination
changes that will be a combination effect. And last will be design thinking
effect. And last will be design thinking with system thinking can provide you a
with system thinking can provide you a sustainable solution not only by
sustainable solution not only by sustainability ESG level but also that
sustainability ESG level but also that sustainable in terms of the whole model
sustainable in terms of the whole model the robust that won't backfire you. So
the robust that won't backfire you. So you can bring this to the cases on
you can bring this to the cases on warehouse transformation or supply chain
warehouse transformation or supply chain with traceability or end to end delivery
with traceability or end to end delivery or or uh other general tech. So these
or or uh other general tech. So these are all the case that you may consider
are all the case that you may consider much more than just one single pawn of
much more than just one single pawn of solution. So very last one the tips for
solution. So very last one the tips for your project as a designer innovator try
your project as a designer innovator try to understand the solution trigger where
to understand the solution trigger where will be the trigger try to analyze map
will be the trigger try to analyze map at least three level of uh leverage
at least three level of uh leverage points to see different depth and uh but
points to see different depth and uh but then you always have to have a balance
then you always have to have a balance between quick wins and deep change as a
between quick wins and deep change as a leader and decision maker you have to
leader and decision maker you have to understand that there would be
understand that there would be unintended consequence budget for it
unintended consequence budget for it budget for refinement and involve
budget for refinement and involve frontline workers like Michael sharing
frontline workers like Michael sharing in the case frontline workers is the
in the case frontline workers is the first one to see the feedback. So be
first one to see the feedback. So be engaged with them. So then you have a
engaged with them. So then you have a holistic point of view. Thank you very
holistic point of view. Thank you very much. Uh this is my today's sharing and
much. Uh this is my today's sharing and uh hopefully you can have some takeaway
uh hopefully you can have some takeaway and which help your hackathon going on.
and which help your hackathon going on. So I pass the stage back to the
So I pass the stage back to the organizing.
Thank you Paul for the summary and also the takeaway. And thank you our the
the takeaway. And thank you our the other two speakers um David and also um
other two speakers um David and also um Michael for giving us so such a
Michael for giving us so such a comprehensive sharing. So yeah I believe
comprehensive sharing. So yeah I believe most of you would have some takeaway
most of you would have some takeaway from it. So in case you have any
from it. So in case you have any questions you may leave it um in the
questions you may leave it um in the chat box and then we will ask our
chat box and then we will ask our speakers to answer it later on. So thank
speakers to answer it later on. So thank you three speakers again. Um right now
you three speakers again. Um right now um we will move to the second section
um we will move to the second section which is about the three eye of data
which is about the three eye of data visualization from ideiation to
visualization from ideiation to executive insight and strategic
executive insight and strategic implication. So let's welcome Mr. Edward
implication. So let's welcome Mr. Edward Lao partner AI and data from Hong Kong
Lao partner AI and data from Hong Kong center of data asset trading and
center of data asset trading and applications. I see Edward is here.
applications. I see Edward is here. Could you um say hi to us and sh start
Could you um say hi to us and sh start your sharing?
your sharing? >> Hello everyone.
>> Hello everyone. Hello Edward.
Hello Edward. >> Hi. Thanks. Um, give me one second.
>> Hi. Thanks. Um, give me one second. >> Sure.
>> Sure. >> Can see my screen.
>> Not yet. Right. Um, we can see it now. Thank you.
Thank you. >> Okay. How are everyone?
>> Okay. Um, well, good afternoon. Maybe some of you maybe see me um last week
some of you maybe see me um last week talking about AI and AoT, right? And
talking about AI and AoT, right? And today um I'll share with you about um
today um I'll share with you about um data visualization. So I'm coming um I'm
data visualization. So I'm coming um I'm coming from Hong Kong data of
coming from Hong Kong data of data asset trading and applications. Um
data asset trading and applications. Um well actually uh most of the time I'm
well actually uh most of the time I'm sharing about data as an asset but today
sharing about data as an asset but today I may um from another point of view
I may um from another point of view talking about data visualization that
talking about data visualization that can help um all of you to um solve the
can help um all of you to um solve the challenge um from the data hacker. So um
challenge um from the data hacker. So um triple I so what is that is idea insight
triple I so what is that is idea insight and actions.
All right. So for I mean from um from business challenge to strategic action.
business challenge to strategic action. Well this is this is start from problem
Well this is this is start from problem statement. we have to we have to clear a
statement. we have to we have to clear a uh make a clear problem statement. So um
uh make a clear problem statement. So um first of all we transform your the wake
first of all we transform your the wake problem statement into a specific one
problem statement into a specific one because when you when you um get your
because when you when you um get your challenge from the data hackathon um you
challenge from the data hackathon um you may have to uh be more specific to your
may have to uh be more specific to your solutions right so for example why the
solutions right so for example why the sales drop is is really big so make it
sales drop is is really big so make it more specific um such as which segment
more specific um such as which segment drops most in Q4
and then the first I it is idea So to identify the business problem behind um
identify the business problem behind um the questions for example um you have to
the questions for example um you have to know your data source analysis type as
know your data source analysis type as well as the time period
and then you go further that will be at the second eye is insight. You have to
the second eye is insight. You have to select the right data and answer find
select the right data and answer find the right patterns explain what happens
the right patterns explain what happens why it matters and quantify the business
why it matters and quantify the business impact. So there's two word
impact. So there's two word for for insight is so what so we got the
for for insight is so what so we got the data and then so what right so so and it
data and then so what right so so and it is it need to be related to the um
is it need to be related to the um business
business and here's come to the third one the I
and here's come to the third one the I is uh implications actions
is uh implications actions um usually we offer three different
um usually we offer three different options with a tradeoff and
options with a tradeoff and recommendations and sometimes maybe we
recommendations and sometimes maybe we can also
can also also offer a um trial because there's a
also offer a um trial because there's a few options and maybe we have recommend
few options and maybe we have recommend one of them and then do a trial right
one of them and then do a trial right before we we go make further. So make a
before we we go make further. So make a quick win. This is the importance.
All right. So um talking about data, right? So there's a lot of data for
right? So there's a lot of data for example there's a customer demographics.
example there's a customer demographics. Um here's the business question. So who
Um here's the business question. So who who are our most loyalty customers? What
who are our most loyalty customers? What do you think?
do you think? Well, you see in the in the chart uh
Well, you see in the in the chart uh reported. So there we have a um frequent
reported. So there we have a um frequent purchaser and with a customer age,
purchaser and with a customer age, right? So who make a a we
right? So who make a a we um what that mean is is 20 times. So it
um what that mean is is 20 times. So it is repeat purchase.
is repeat purchase. Okay. And it is only purchased for a few
Okay. And it is only purchased for a few times.
times. So the first the first one it is data
So the first the first one it is data visual. So which means it will show the
visual. So which means it will show the young the younger customers age 25 to 40
young the younger customers age 25 to 40 which is right here and it has more
which is right here and it has more repeat purchase.
repeat purchase. Well, of course, for the age between 30
Well, of course, for the age between 30 to 50, they also have uh rep purchase,
to 50, they also have uh rep purchase, but more of them they have um the the
but more of them they have um the the the purchase fees maybe is less 10 to 60
then. So what what is the insight which means is the older salmon show a decline
means is the older salmon show a decline engagement for the people from 40 to 60
engagement for the people from 40 to 60 and for the younger people 20 to 30s it
and for the younger people 20 to 30s it maybe have more loyal and engaged
then what is the action okay target to the young customers let's have a more
the young customers let's have a more product design and a marketing strategy
product design and a marketing strategy that target to the younger
that target to the younger customers
customers and for the for the age between 40 to
and for the for the age between 40 to 60. Let's create some retention program
60. Let's create some retention program to old customer to try to increase the
to old customer to try to increase the purchase to higher. Okay, repeat
purchase to higher. Okay, repeat purchases.
This is about the logistic cost. So you'll see that um well the shipping
you'll see that um well the shipping cost which is the the green one
cost which is the the green one has a is has a faster growing rate than
has a is has a faster growing rate than the revenue growth. So the brewing is
the revenue growth. So the brewing is the revenue. So it's a quite st um
the revenue. So it's a quite st um steady growth of the revenue but however
steady growth of the revenue but however shipping cost at the beginning is not
shipping cost at the beginning is not really high but however yearby years it
really high but however yearby years it is really and I mean uh at the point of
is really and I mean uh at the point of 2023 and if you we look at the trend it
2023 and if you we look at the trend it will maybe going even higher right so it
will maybe going even higher right so it really eat our profits
really eat our profits and what is the data we show
and what is the data we show the bar chart is showing year over years
the bar chart is showing year over years high increase in the shipping cost
high increase in the shipping cost and then so what for the insight the
and then so what for the insight the logistic cost will be going faster than
logistic cost will be going faster than the revenue then it reduce our profit
the revenue then it reduce our profit margin
margin and it is the real impact to the company
and it is the real impact to the company and because and the trend is emerging
and because and the trend is emerging when it's 203 you may you may imagine
when it's 203 you may you may imagine then um um the the profit will be much
then um um the the profit will be much more worse
and then possible Um strategic action is maybe the
Um strategic action is maybe the executive should export supplier
executive should export supplier diversify we are negotiate contracts
diversify we are negotiate contracts with the supplier or increase in
with the supplier or increase in automation to reduce long-term cost.
automation to reduce long-term cost. What that means is the ultimate goal it
What that means is the ultimate goal it is to protect the profit margin
and um doing data visualization AI should be one of the powerful tools to
should be one of the powerful tools to doing that. Um for example um it can
doing that. Um for example um it can help to generate different kind of
help to generate different kind of visualization from raw database to a um
visualization from raw database to a um to help the user to brainstorm faster or
to help the user to brainstorm faster or it can create a lot of different charts
it can create a lot of different charts right and highlight the correlation
right and highlight the correlation trends and maybe to find something we
trends and maybe to find something we overlook
overlook and for the impact AI definitely will
and for the impact AI definitely will give us a lot of um suggestion and it um
give us a lot of um suggestion and it um when you're doing the palm it use a rope
when you're doing the palm it use a rope um a rule based prompting for example um
um a rule based prompting for example um for example one of the problem is you
for example one of the problem is you are the CFO of the of you're the CFO of
are the CFO of the of you're the CFO of the the trading company right or um
the the trading company right or um you're the CEO of of the retail right so
you're the CEO of of the retail right so but um no matter what um make sure that
but um no matter what um make sure that just a kind reminder to align to your
just a kind reminder to align to your hyperon stream so because this is about
hyperon stream so because this is about IoT data
All right. So, um because when doing uh data visualization, I believe many of
data visualization, I believe many of you many of you will using Excel, right?
you many of you will using Excel, right? And talking about AI um and and and
And talking about AI um and and and actually the the Excel that will be uh
actually the the Excel that will be uh incorporated with a u um AI copilot
incorporated with a u um AI copilot which can help you to um handle this.
which can help you to um handle this. Let's take a look.
And you can hear the voice um from the video, right? Let me know if you cannot
video, right? Let me know if you cannot hear.
>> Hello, Edward. Um >> yes.
>> yes. Yes.
Yes. >> Um are you displaying the video already
>> Um are you displaying the video already cuz um we cannot hear it.
cuz um we cannot hear it. >> Okay. You you can you see it but you
>> Okay. You you can you see it but you cannot hear or you cannot see?
cannot hear or you cannot see? >> Yeah, we can see it. Um, we can see the
>> Yeah, we can see it. Um, we can see the bar is going on, but then we
bar is going on, but then we >> There's no no voice over.
>> There's no no voice over. >> Yeah.
>> Yeah. >> Yeah.
>> Yeah. >> Okay. Uh, let me check it. Let me check
>> Okay. Uh, let me check it. Let me check it.
>> Okay. Monthly revenue by product.
Monthly revenue by product. >> Can try again. How about this?
>> Can try again. How about this? >> Yeah, we can hear data to reference
>> Yeah, we can hear data to reference across tabs.
across tabs. Copilot creates a plan for how it will
Copilot creates a plan for how it will run those numbers, executes that plan,
run those numbers, executes that plan, showing its work as it goes, and prompts
showing its work as it goes, and prompts you to ask questions or iterate on the
you to ask questions or iterate on the solution it reached.
solution it reached. It looks great. So, you're ready to add
It looks great. So, you're ready to add that column to your spreadsheet. Now, a
that column to your spreadsheet. Now, a new ask has come in and you need to
new ask has come in and you need to understand how each product category is
understand how each product category is performing and ensure that you are
performing and ensure that you are selling at least 100K in product in each
selling at least 100K in product in each category per month. Let's start by
category per month. Let's start by comparing sales by category. You ask
comparing sales by category. You ask Copilot to create a simple bar chart so
Copilot to create a simple bar chart so you can quickly see which product
you can quickly see which product categories are selling the best.
categories are selling the best. And with conditional formatting, it
And with conditional formatting, it highlights the product lines that aren't
highlights the product lines that aren't meeting the $100,000 minimum threshold
meeting the $100,000 minimum threshold so you know where to focus.
so you know where to focus. Now that we know which products aren't
Now that we know which products aren't selling well, let's dig a little deeper
selling well, let's dig a little deeper and see what we can learn from the
and see what we can learn from the customer feedback.
customer feedback. Copilot in Excel can now reason over
Copilot in Excel can now reason over text like this raw customer data.
text like this raw customer data. Copilot analyzes all the customer
Copilot analyzes all the customer feedback from the past quarter and
feedback from the past quarter and surfaces the top three concerns.
surfaces the top three concerns. It looks like charging speed might be an
It looks like charging speed might be an emerging issue.
emerging issue. Let's ask Copilot to highlight customer
Let's ask Copilot to highlight customer reviews that mention charging speed.
reviews that mention charging speed. Now with the help of copilot you've
Now with the help of copilot you've taken a complex disparate data set and
taken a complex disparate data set and quickly analyzed it giving you a full
quickly analyzed it giving you a full picture of your revenue trends that you
picture of your revenue trends that you can bring to the upcoming business
can bring to the upcoming business review.
>> All right. So uh because you have a lot of data right so um data set it is
of data right so um data set it is really is a critical element for the um
really is a critical element for the um AI right? So um using may maybe you can
AI right? So um using may maybe you can using um the copilot function um
using um the copilot function um incorporate into Excel to help you um no
incorporate into Excel to help you um no matter through doing data cleansing
matter through doing data cleansing um to highlight um um the data you're
um to highlight um um the data you're looking for or uh well they can help you
looking for or uh well they can help you to create a lot of complicated um
to create a lot of complicated um formula right and then
formula right and then okay and of course um you need a a poe
okay and of course um you need a a poe right prompting for the for the data
right prompting for the for the data analytics right um so for example this I
analytics right um so for example this I I um share some of the example of the
I um share some of the example of the prompting when you're doing your data
prompting when you're doing your data analytics um for example um first one
analytics um for example um first one give the insight product a sales uh
give the insight product a sales uh decline then um give give some of the
decline then um give give some of the because well for for the prompting the
because well for for the prompting the the um the better you prom the better
the um the better you prom the better you input the better result you have
you input the better result you have right so so the old old traditional is
right so so the old old traditional is like garbage in garbage out right so
like garbage in garbage out right so everyone know it so um but same as
everyone know it so um but same as prompting right so make sure that we
prompting right so make sure that we have a uh very um use role based
have a uh very um use role based prompting so who are you who's the
prompting so who are you who's the recipient uh what is the context what's
recipient uh what is the context what's the objective and the task what is the
the objective and the task what is the constraint constraint is a powerful um
constraint constraint is a powerful um uh technique for the for the prompting
uh technique for the for the prompting right so um for example um give the
right so um for example um give the impact um the effect and six month
impact um the effect and six month forecast, right? And another one another
forecast, right? And another one another thing it is um based on three different
thing it is um based on three different insights about our retail businesses
insights about our retail businesses generate five strategic recommendations
generate five strategic recommendations with estimate effort and impact and then
with estimate effort and impact and then maybe you can also add your constraint
maybe you can also add your constraint right to limit it to uh um for example
right to limit it to uh um for example budget time frame or resources
budget time frame or resources and after after the the first the first
and after after the the first the first palm and then you may um doing the
palm and then you may um doing the second palm. So, so is the second sort
second palm. So, so is the second sort it is um giving this insight and then
it is um giving this insight and then generally static options with pros and
generally static options with pros and con for each and then um ask the AI to
con for each and then um ask the AI to the scoring of all those um options and
the scoring of all those um options and then to pick the first one right so um
then to pick the first one right so um um def define the the right format uh
um def define the the right format uh for the decision matrix for estimate
for the decision matrix for estimate impact and that one so um make sure that
impact and that one so um make sure that um we apply the very prompting when we
um we apply the very prompting when we doing um um the data analyst using AI.
All right. So um to be more sophisticated and then um we can still
sophisticated and then um we can still using um the Python in the Excel, right?
using um the Python in the Excel, right? So before that we just type the the
So before that we just type the the simple prompt, right? But if you you
simple prompt, right? But if you you know programming and actually you can
know programming and actually you can just um apply the PR python in Excel to
just um apply the PR python in Excel to make a good dashboard.
make a good dashboard. Let's take a look.
Let's take a look. We are combining copilot in Excel with
We are combining copilot in Excel with the power of Python
the power of Python most popular programming languages for
most popular programming languages for working with data. You're working on
working with data. You're working on your company's annual revenue forecast
your company's annual revenue forecast planning for the next fiscal year.
planning for the next fiscal year. Normally, you could spend hours running
Normally, you could spend hours running a manual analysis of the numbers.
a manual analysis of the numbers. >> Copilot can run Python in Excel, making
>> Copilot can run Python in Excel, making your job a whole lot easier.
your job a whole lot easier. >> We can hear it.
>> We can hear it. >> Copilot does an advanced analysis,
>> Copilot does an advanced analysis, reasoning over 3 years of historical
reasoning over 3 years of historical sales data. Like an analyst would, CPI
sales data. Like an analyst would, CPI opens up a new workspace where you can
opens up a new workspace where you can experiment to get the insights you're
experiment to get the insights you're looking for, all without altering your
looking for, all without altering your original data. It shows a preview of
original data. It shows a preview of what it's reasoning over,
what it's reasoning over, creates a plan to analyze it in a way
creates a plan to analyze it in a way that's immediately useful, and executes
that's immediately useful, and executes it with Python,
it with Python, giving you a quick picture of your
giving you a quick picture of your numbers.
numbers. Now you're ready to iterate on Copilot's
Now you're ready to iterate on Copilot's work. Let's ask it to forecast your
work. Let's ask it to forecast your annual revenue for the next 2 years.
annual revenue for the next 2 years. Copilot repeats the pattern, creating a
Copilot repeats the pattern, creating a plan for how it will tackle the task,
plan for how it will tackle the task, running Python in Excel to execute its
running Python in Excel to execute its analysis,
analysis, giving you a fast and detailed analysis,
giving you a fast and detailed analysis, and prompting you to iterate and ask
and prompting you to iterate and ask questions about its work. If you want to
questions about its work. If you want to make any of the edits to the Python code
make any of the edits to the Python code itself, you can do so directly in the
itself, you can do so directly in the workbook. This is the power of bringing
workbook. This is the power of bringing Python directly into the Excel grid.
Python directly into the Excel grid. Let's take it a step further. You want
Let's take it a step further. You want to understand how best to meet your
to understand how best to meet your revenue goals, which customers have the
revenue goals, which customers have the highest potential upside. So, you go
highest potential upside. So, you go back to your original customer data and
back to your original customer data and ask Copilot to use advanced analysis to
ask Copilot to use advanced analysis to rank customers based on upsell
rank customers based on upsell opportunity. As Python and Excel gets to
opportunity. As Python and Excel gets to work, it jumps back to your analysis
work, it jumps back to your analysis workspace. It prioritizes the most
workspace. It prioritizes the most helpful metrics and provides a preview
helpful metrics and provides a preview of the data it will use.
of the data it will use. It ranks them and within moments you
It ranks them and within moments you have a prioritized table.
have a prioritized table. You want to see a visualization of the
You want to see a visualization of the customer ranking and understand the
customer ranking and understand the waiting methodology copilot used.
waiting methodology copilot used. Copilot quickly creates a chart and
Copilot quickly creates a chart and gives you a detailed breakdown of how it
gives you a detailed breakdown of how it arrived at the answer. Within minutes,
arrived at the answer. Within minutes, you've worked with Copilot to create a
you've worked with Copilot to create a full analysis of your data, all without
full analysis of your data, all without writing a single line of code. And now
writing a single line of code. And now you're ready to send the analysis to
you're ready to send the analysis to your sales team and collaborate on it
your sales team and collaborate on it together.
>> All right. Okay. So this is another example to doing data visualization with
example to doing data visualization with your data set, right? Um it's much more
your data set, right? Um it's much more powerful. But of course um um if so even
powerful. But of course um um if so even if you know Python actually you can just
if you know Python actually you can just go in into um Excel and add the Python.
go in into um Excel and add the Python. The good thing is because of LLM, right?
The good thing is because of LLM, right? So um if you don't know Pyon you don't
So um if you don't know Pyon you don't know coding just using um the natural
know coding just using um the natural language and then it still do a lot of
language and then it still do a lot of things right so um either we can do it
things right so um either we can do it in Excel spreadsheet
in Excel spreadsheet or still using leverage the python to do
or still using leverage the python to do a much more better stronger data
a much more better stronger data visualization.
visualization. All right. So, well before there's a lot
All right. So, well before there's a lot of tools, right? And and actually today
of tools, right? And and actually today we are um a have a lot of datas. So, so
we are um a have a lot of datas. So, so the the the challenge is uh not find the
the the the challenge is uh not find the data. The challenge is how we get um the
data. The challenge is how we get um the meaningful data. So, what matters now
meaningful data. So, what matters now and what matters next. So, we have to
and what matters next. So, we have to the key the key work here is
the key the key work here is prioritization. So we have to prior so
prioritization. So we have to prior so because there's a lot of data right we
because there's a lot of data right we have to build and then prioritize what
have to build and then prioritize what is the urgent let's take care the urgent
is the urgent let's take care the urgent first some is some is good but it's good
first some is some is good but it's good but however we don't need to take care
but however we don't need to take care at this moment
at this moment so um we have to find out from the data
so um we have to find out from the data we have to find out some is signal some
we have to find out some is signal some is noise right so we have to find out
is noise right so we have to find out signal and urgent this is it require our
signal and urgent this is it require our immediate attention
immediate attention and if it is a signal plus importance.
and if it is a signal plus importance. Yes, it is also very critical but we
Yes, it is also very critical but we don't need to take it at this moment. Uh
don't need to take it at this moment. Uh we can do it as a long-term strategies.
we can do it as a long-term strategies. Okay. So we have to filter the signal
Okay. So we have to filter the signal from the noise. This is very important
from the noise. This is very important um especially when you have a lot of
um especially when you have a lot of data for example IoT data right that um
data for example IoT data right that um uh I have mentioned before and
uh I have mentioned before and especially for the executive u ready
especially for the executive u ready insights for example for your
insights for example for your presentation um to the to the to the
presentation um to the to the to the hackathon we have you have to do the
hackathon we have you have to do the exact ready insight right to find out um
exact ready insight right to find out um you have to find the the the signal data
you have to find the the the signal data from the law right and balance the
from the law right and balance the urgent response with a strategic
urgent response with a strategic forecasts
forecasts All right, here's example. So you have
All right, here's example. So you have from the data you have signal and you
from the data you have signal and you have noise
have noise and we have importance data and we have
and we have importance data and we have urgency
urgency right. So for the noise and importance
right. So for the noise and importance that is interest well uh um this is good
that is interest well uh um this is good to know but however um there's no action
to know but however um there's no action at this moment right for example um the
at this moment right for example um the website traffic
website traffic okay and something is like we have noise
okay and something is like we have noise and it looks like very urgent however
and it looks like very urgent however this may be a misleading or low value
this may be a misleading or low value right um and and and we don't need to
right um and and and we don't need to take here uh at all because as I
take here uh at all because as I mentioned we have to prioritize right so
mentioned we have to prioritize right so signal and importance this is this is is
signal and importance this is this is is we have to take a look and but however
we have to take a look and but however it is not urgent compared to here so for
it is not urgent compared to here so for the signal and important it is a statue
the signal and important it is a statue priority so not immediate action but it
priority so not immediate action but it is critical for the long-term success
is critical for the long-term success right for example the data show the
right for example the data show the younger customer prefer subscription
younger customer prefer subscription model then we have to um adjust our
model then we have to um adjust our production road map, right? And then
production road map, right? And then here we go. Signal as urgent. We must
here we go. Signal as urgent. We must act now immediately.
act now immediately. Okay. For example, a customer turnover
Okay. For example, a customer turnover dropped 10% this quarter is a key in a
dropped 10% this quarter is a key in a key market because they drop it, right?
key market because they drop it, right? Um then there should be have a urgent
Um then there should be have a urgent retention program. we have to take care
retention program. we have to take care because [clears throat] we don't want to
because [clears throat] we don't want to end have another um significant drop in
end have another um significant drop in the next quarter. So we have to find out
the next quarter. So we have to find out the signal and the urgency.
the signal and the urgency. All right. So it is it is the action.
All right. So it is it is the action. Okay. For the noise and importance we
Okay. For the noise and importance we dedicate. Okay. So we we just monitoring
dedicate. Okay. So we we just monitoring the dashboard. So if everything's okay
the dashboard. So if everything's okay and it's okay, right? No news no news is
and it's okay, right? No news no news is good news. Okay. for noise plus urgent
good news. Okay. for noise plus urgent drop it. Okay, we have to focus because
drop it. Okay, we have to focus because too we're too busy, too many datas, too
too we're too busy, too many datas, too many things and signal important.
many things and signal important. It is important but in the long run um
It is important but in the long run um so we schedule we schedule it. Okay. And
so we schedule we schedule it. Okay. And signal urgent then do it now.
All right. So um filtering so first thing is we have to filter the signal
thing is we have to filter the signal from the noise right um so we have to
from the noise right um so we have to look at the the patterns okay um no
look at the the patterns okay um no matter you're using AI or u traditional
matter you're using AI or u traditional your traditional way like special
your traditional way like special or using AI can help you to to define
or using AI can help you to to define the the the patterns such as it is a
the the the patterns such as it is a consistent downward trend so is if if it
consistent downward trend so is if if it is um um the revenue is going drop um
is um um the revenue is going drop um month by month then we have to take
month by month then we have to take immediate actions right um or trend
immediate actions right um or trend and uh and then we have highlight
and uh and then we have highlight urgency from importance one thing is the
urgency from importance one thing is the time sensitivities right urgent is is
time sensitivities right urgent is is the time right so for example there's a
the time right so for example there's a sen I'm sorry in the the fail
sen I'm sorry in the the fail transactions and then we have the alert
transactions and then we have the alert so using color coding thresholds
so using color coding thresholds right to uh to fret the the urgency
right to uh to fret the the urgency from the importance. All right. So two
from the importance. All right. So two key things here. Um find out filter the
key things here. Um find out filter the signal from the noise. Highlight the
signal from the noise. Highlight the urgency from the importance
urgency from the importance because you have a lot a lot of data
because you have a lot a lot of data set.
set. Right? So um some key way here is to
Right? So um some key way here is to bridge an analytics with the strategies.
bridge an analytics with the strategies. Go back to square one. What is the
Go back to square one. What is the objective? Right? What's the object?
objective? Right? What's the object? objective. We have to align our analysis
objective. We have to align our analysis to the business objective.
to the business objective. Okay. Second one, prioritization.
Okay. Second one, prioritization. Okay. Find out the or find out the
Okay. Find out the or find out the urgent
urgent instead of importance, right? Find out
instead of importance, right? Find out um um the signal and then map to your
um um the signal and then map to your hackathon stream.
hackathon stream. All right. there's a maybe there's still
All right. there's a maybe there's still a lot of urgent matters a lot of signals
a lot of urgent matters a lot of signals but uh you have to solve the challenge
but uh you have to solve the challenge right so so make to to narrow down to
right so so make to to narrow down to make sure that um all the urgency u the
make sure that um all the urgency u the priority it is aligned to the hacker's
priority it is aligned to the hacker's theme
theme all right so um another tool um um from
all right so um another tool um um from um from um also from um from Microsoft
um from um also from um from Microsoft >> hi I'm Ashoke and I'm here today to show
>> hi I'm Ashoke and I'm here today to show you our new analyst agent in co-pilot.
you our new analyst agent in co-pilot. We built Analyst to think like a skilled
We built Analyst to think like a skilled data scientist so you can go from raw
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data to insights in minutes. Analyst leverages a state-of-the-art reasoning
leverages a state-of-the-art reasoning model that we've optimized for advanced
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rows and a few tabs across customers and their monthly revenue. And none of it
their monthly revenue. And none of it has been cleaned or contextualized.
has been cleaned or contextualized. Usually, to make sense of this data, I'd
Usually, to make sense of this data, I'd need to ask my colleague who knows
need to ask my colleague who knows Python. But let's try the analyst agent.
Python. But let's try the analyst agent. I don't have to spend time writing the
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and it uses chain of thought reasoning so you can check its work.
Okay, it mentioned about um uh train of broad right coot because I I assume many
broad right coot because I I assume many of you um is quite familiar with the
of you um is quite familiar with the problem engineering because there's two
problem engineering because there's two um critical skill for palm engineering.
um critical skill for palm engineering. One is tot
One is tot um um and the other one it is is tree of
um um and the other one it is is tree of thought so it's a different branch
thought so it's a different branch different option and another one is coot
different option and another one is coot that is mentioned it is a train of
that is mentioned it is a train of thought so to find the reasoning step by
thought so to find the reasoning step by step the reasoning behind the data all
step the reasoning behind the data all right so um
right so um um IoT because this is IoT data hack
um IoT because this is IoT data hack right so um gives you another uh I mean
right so um gives you another uh I mean I I I uh u take one step further is A
I I I uh u take one step further is A IoT, right? Um well, some of you maybe
IoT, right? Um well, some of you maybe um um take a look uh the video I I
um um take a look uh the video I I record uh last week, it is about um uh
record uh last week, it is about um uh automation AI agent AIT. So um there I
automation AI agent AIT. So um there I just has up to recall it. So for the AIT
just has up to recall it. So for the AIT device, it is I and the years the device
device, it is I and the years the device to capture the real world data. For
to capture the real world data. For example, the motion, capture motion,
example, the motion, capture motion, amateurs, speed, um people movement,
amateurs, speed, um people movement, right? And AI is the brain, right? To to
right? And AI is the brain, right? To to analysis. Um if when you when you um
analysis. Um if when you when you um design your AoT framework, uh make sure
design your AoT framework, uh make sure that it mapped to the hackon
that it mapped to the hackon requirements using the IoT datas,
right? Um I I don't uh repeat this again and if you interest take a look um of
and if you interest take a look um of the of the uh my recording on uh last
the of the uh my recording on uh last week it is um from intelligent
week it is um from intelligent automations to AI agents and AOT. So I
automations to AI agents and AOT. So I um explain what is difference between
um explain what is difference between IoT and AOT and I give some u example
IoT and AOT and I give some u example right. So um including like traffic
right. So um including like traffic um doing the speeding um in the airport
um doing the speeding um in the airport talking about um um or or the the
talking about um um or or the the airport the the logistic thing or the
airport the the logistic thing or the crowd management the people people
crowd management the people people movement right
movement right um if you're interested um go back and
um if you're interested um go back and take a look the video later so uh
take a look the video later so uh because today's about data visualization
because today's about data visualization so um
so um so the IoT the device will capture the
so the IoT the device will capture the data or using um computer vision, right?
data or using um computer vision, right? And we're using edge computer. The
And we're using edge computer. The reason why we're using edge is because
reason why we're using edge is because of um um there very low latency and also
of um um there very low latency and also is um security and privacy because it
is um security and privacy because it didn't go to the cloud uh uh when we
didn't go to the cloud uh uh when we when they doing the processing. But of
when they doing the processing. But of course there's instant and they they go
course there's instant and they they go back to the to the headquarters for
back to the to the headquarters for further actions, right? So um it can
further actions, right? So um it can help for example it can help to find the
help for example it can help to find the pit times and um demographic insight um
pit times and um demographic insight um the trend was the highest density um and
the trend was the highest density um and using the tools that I mentioned um
using the tools that I mentioned um using Excel to create the dashboard
using Excel to create the dashboard um or to keep track the in store um
um or to keep track the in store um what's the customers what's the
what's the customers what's the customer's behavior in the store right
customer's behavior in the store right um on every day on the time and and uh
um on every day on the time and and uh on different area
and also um for example um about the customer
for example um about the customer preference on the product for example we
preference on the product for example we have different types of product ABCD
have different types of product ABCD right so um um for the age and the
right so um um for the age and the duration and you'll see the demographic
duration and you'll see the demographic right so for example The product D it is
right so for example The product D it is uh it is um they they prefer I mean is
uh it is um they they prefer I mean is is is more popular I would say uh for
is is more popular I would say uh for the age of um 40
the age of um 40 and and however um for the product D
and and however um for the product D however for product A they um they may
however for product A they um they may be is not that um attractive to the
be is not that um attractive to the customers
customers um for the age of from 20 to 40s right
um for the age of from 20 to 40s right so give it a
so give it a So, so there's a lot of uh AI algorithm
So, so there's a lot of uh AI algorithm that can help you to uh doing the data
that can help you to uh doing the data analysis, but of course you need a uh of
analysis, but of course you need a uh of course um you need you need a device IoT
course um you need you need a device IoT device to capture the data right
device to capture the data right and then so um as I mentioned IoT it is
and then so um as I mentioned IoT it is like the eye and the ear right to hear
like the eye and the ear right to hear the voice um um to capture the the
the voice um um to capture the the movement and then using AI to doing the
movement and then using AI to doing the um algor microphone analysis. So it is
um algor microphone analysis. So it is the slide that I I share last week but
the slide that I I share last week but um let me highlight again because you
um let me highlight again because you have a IoT data like people the people
have a IoT data like people the people movement the product that I just show
movement the product that I just show you um how they move the location and
you um how they move the location and then using the open data or alternative
then using the open data or alternative data. This is one of the requests on the
data. This is one of the requests on the hackathon, right? for example, weather,
hackathon, right? for example, weather, the traffic, demographic or ESG data and
the traffic, demographic or ESG data and then putting all data together using the
then putting all data together using the AI algorithm
AI algorithm to identify the patterns,
to identify the patterns, generate predictions, insight,
generate predictions, insight, give a suggestion
give a suggestion and all this need should be aligned with
and all this need should be aligned with your hyperphone frames
your hyperphone frames to solve the to solve your um challenge.
Then create a business value and impact such as
such as predictive maintenance and safety,
predictive maintenance and safety, right? Or it is a supply chain
right? Or it is a supply chain optimization or demand forecast for the
optimization or demand forecast for the retail industry
retail industry or provide better customer ser um
or provide better customer ser um experience
experience and at the same time improve the
and at the same time improve the resources efficiency as well as the
resources efficiency as well as the sustainability because sustainability it
sustainability because sustainability it is one of the also requirement in the
is one of the also requirement in the hackon and I have shared that about the
hackon and I have shared that about the SDG because um United Nation have 17 SDG
SDG because um United Nation have 17 SDG that um you may want to take a Look
that um you may want to take a Look which one you can adopt. For example, uh
which one you can adopt. For example, uh SDG11
SDG11 it is about smart city, smart
it is about smart city, smart urbanization. SDG9
urbanization. SDG9 it is about infrastructure for example
it is about infrastructure for example traffic.
All right. So a lot a lot of data right. So um we can using special sheet Excel
So um we can using special sheet Excel um um um maybe leverage the Python to
um um um maybe leverage the Python to doing the visualization or even this
doing the visualization or even this agent that I just show um um agent for
agent that I just show um um agent for uh analysis and also maybe uh if you
uh analysis and also maybe uh if you want to build a dashboard uh using
want to build a dashboard uh using another tools uh PowerBI from Microsoft
another tools uh PowerBI from Microsoft and it and it integrate to the data
and it and it integrate to the data factory from the Microsoft fabrics
factory from the Microsoft fabrics because there's a data lake they call
because there's a data lake they call one leg so with the AI the data leg uh
one leg so with the AI the data leg uh and the as a foundation so on top of it
and the as a foundation so on top of it um you have a powerbi to doing connect
um you have a powerbi to doing connect to the data factory to create a
to the data factory to create a dashboard
>> reports she turns to power >> so let me show you um um so it's an
>> so let me show you um um so it's an example of using powerbi uh connect to
example of using powerbi uh connect to the uh uh uh data set and then it's
the uh uh uh data set and then it's similar to the spreadsheet and you can
similar to the spreadsheet and you can use also using lateral language
use also using lateral language um to doing the the dashboard
um to doing the the dashboard >> BI copilot to simplify her work.
>> BI copilot to simplify her work. Firstly, she wants to understand the
Firstly, she wants to understand the data set and identify possible reports
data set and identify possible reports within seconds. PowerBI Copilot suggests
within seconds. PowerBI Copilot suggests a list of recommended insights allowing
a list of recommended insights allowing Sarah to quickly identify relevant
Sarah to quickly identify relevant metrics. Sarah decides to explore the
metrics. Sarah decides to explore the sales value over time. PowerBI Copilot
sales value over time. PowerBI Copilot instantly generates a visualization of
instantly generates a visualization of sales peaks and valleys throughout the
sales peaks and valleys throughout the year, allowing her to quickly understand
year, allowing her to quickly understand seasonal demand patterns. Next, she asks
seasonal demand patterns. Next, she asks for the top selling products by revenue,
for the top selling products by revenue, and PowerBI Copiot returns a ranked
and PowerBI Copiot returns a ranked list, which she easily adds to her
list, which she easily adds to her dashboard.
To make the report more accessible, Sarah includes a narrative summary by
Sarah includes a narrative summary by prompting PowerBI Copilot to describe
prompting PowerBI Copilot to describe key findings. Within moments, she has a
key findings. Within moments, she has a clear, concise explanation that enhances
clear, concise explanation that enhances her reports readability.
her reports readability. In just a few minutes, Sarah has used
In just a few minutes, Sarah has used Enterprise Insights and PowerBI Copilot
Enterprise Insights and PowerBI Copilot to create her own custom reports using
to create her own custom reports using data originating from SEP.
data originating from SEP. >> All right. So um create a desktop will
>> All right. So um create a desktop will be uh much easier and you see that um um
be uh much easier and you see that um um um the using lecture language uh first
um the using lecture language uh first of all connect to the data on the
of all connect to the data on the Microsoft fabric and then uh create a
Microsoft fabric and then uh create a dashboard.
>> All right. So um finally this is about uh data visualization right but u of
uh data visualization right but u of course um the the ultimate goal for data
course um the the ultimate goal for data visualization is we have a story right
visualization is we have a story right so data stories telling is important so
so data stories telling is important so I just capture there's a news from Wall
I just capture there's a news from Wall Street Journal it's interesting so um
Street Journal it's interesting so um it's a company uh they they they need a
it's a company uh they they they need a lot of story terror of course maybe um
lot of story terror of course maybe um no matter the stock market right it's a
no matter the stock market right it's a lot AI company. So um um they have two
lot AI company. So um um they have two or for the retail a lot of retail
or for the retail a lot of retail company. For example, this morning I I
company. For example, this morning I I um this morning um I just have a uh uh a
um this morning um I just have a uh uh a global um fashion companies from from
global um fashion companies from from from UK and then they using um I I
from UK and then they using um I I service services help them to using AI
service services help them to using AI um and we also talking about the brand
um and we also talking about the brand um how to for example using a um to boo
um how to for example using a um to boo the brand on social media. So and the
the brand on social media. So and the reason why they're using um avatar is is
reason why they're using um avatar is is same case brand promotion uh for example
same case brand promotion uh for example coochie helping kind um they're using
coochie helping kind um they're using arbiter to help them to um increase um
arbiter to help them to um increase um the the sales um the conversion I mean
the the sales um the conversion I mean conversion u like from online
conversion u like from online social media to the e-commerce and uh
social media to the e-commerce and uh make a even make the production cost is
make a even make the production cost is much cheaper uh compared compared to the
much cheaper uh compared compared to the traditional video shooting, right? A
traditional video shooting, right? A physical video shooting is really
physical video shooting is really expensive, right? Take times. But for
expensive, right? Take times. But for the arbiter, it's just a few hours and
the arbiter, it's just a few hours and then you make you make a pretty good uh
then you make you make a pretty good uh u video and and in 10 different
u video and and in 10 different language, right, from Chinese to
language, right, from Chinese to Japanese
Japanese um or you can u spread all over the
um or you can u spread all over the world, right? So um for storytelling
world, right? So um for storytelling using AI to analysis data and then human
using AI to analysis data and then human we then we can craft the story that
we then we can craft the story that create customer value of course with the
create customer value of course with the um assistant from AI. So data be to
um assistant from AI. So data be to generate the insight from AI and the AI
generate the insight from AI and the AI um have the insight can help us to uh
um have the insight can help us to uh prepare the storytelling and then make
prepare the storytelling and then make the call to action to the customers to
the call to action to the customers to increase the conversion rate.
increase the conversion rate. Because uh for retail uh or for a lot of
Because uh for retail uh or for a lot of uh um consumer goods customers actually
uh um consumer goods customers actually buy from the story instead of from the
buy from the story instead of from the product because they trust the brand
product because they trust the brand this the the the brand behind the
this the the the brand behind the product right so it make the
product right so it make the differentiation.
differentiation. Um
Um all right. So
all right. So um I think um I so um I am trying to um
um I think um I so um I am trying to um uh w up um um on time. So um share with
uh w up um um on time. So um share with you about triple eye of data
you about triple eye of data visualization from idea to insight and
visualization from idea to insight and implication. All right. So um um and
implication. All right. So um um and actually um data actually is is really
actually um data actually is is really an asset right. So, so um um have
an asset right. So, so um um have business ready. So, think about that and
business ready. So, think about that and maybe incorporate this idea into your um
maybe incorporate this idea into your um data story. Okay. So, um thank you much
data story. Okay. So, um thank you much and thanks for listening and good luck
and thanks for listening and good luck for your hackathon. All right.
>> Thank you Edward. Yeah, thanks a lot for your sharing.
Yeah, thanks a lot for your sharing. >> Thank you. So um just now um at work
>> Thank you. So um just now um at work share a few guidelines for example like
share a few guidelines for example like noises and signal importance PS um
noises and signal importance PS um urgency. So these are the elements that
urgency. So these are the elements that you consider or think about when working
you consider or think about when working on your initial proposal.
on your initial proposal. So um let's move to the Q&A section.
So um let's move to the Q&A section. Right now we will share our house live
Right now we will share our house live with all of you.
So, Edward, would you please help uh release the stop sharing?
release the stop sharing? >> Sure.
>> Sure. >> Thank you.
>> So, right now it would be the Q&A section. So, if you have any questions,
section. So, if you have any questions, please feel free to type in the chat
please feel free to type in the chat box. Um while waiting for the questions
box. Um while waiting for the questions let me also take this moment to thank
let me also take this moment to thank you our all of our speakers again and
you our all of our speakers again and also thank you to our co-organizers
also thank you to our co-organizers Cyberport as well as uh HKSTP and our
Cyberport as well as uh HKSTP and our government funding organization the
government funding organization the digital policy office for their
digital policy office for their invaluable help and support along the
invaluable help and support along the way. We would also like to thank all of
way. We would also like to thank all of our sponsors, tech partners, media
our sponsors, tech partners, media partners and supporting organizations
partners and supporting organizations for making this hack a success.
Right. Um earlier um yesterday we sent an email with all the important details
an email with all the important details about about the proposal submission. So
about about the proposal submission. So please let us know if you did not
please let us know if you did not receive it. And um please share the
receive it. And um please share the challenge statement your team selected
challenge statement your team selected on or before 30 of January for our
on or before 30 of January for our record. Also um as a recap um please
record. Also um as a recap um please submit your propo proposal through the
submit your propo proposal through the link uh mentioned in the email on or
link uh mentioned in the email on or before 15 of February
before 15 of February and do remember to send us an email
and do remember to send us an email after your submission to iotgs1hong.org.
All right, seems like we don't have any questions at this moment. If you do have
questions at this moment. If you do have any um please contact us. Um
any um please contact us. Um so um I think
so um I think after our Q&A section we appreciate um
after our Q&A section we appreciate um if you could help us to fill in the
if you could help us to fill in the event survey on the screen because this
event survey on the screen because this will help us to improve our future
will help us to improve our future workshops. And if you have any topics
workshops. And if you have any topics that you are probably interested in, you
that you are probably interested in, you may also fill in the forum to let us
may also fill in the forum to let us know in the future um when you have
know in the future um when you have time.
time. So our next workshop will be held on um
So our next workshop will be held on um next week will be held next week. That
next week will be held next week. That would be the last workshop before the
would be the last workshop before the initial proposal deadline. If your
initial proposal deadline. If your proposal involves some location based
proposal involves some location based data that would be a must attend section
data that would be a must attend section for you. We are very grateful to have S3
for you. We are very grateful to have S3 China Hong Kong Limited to share their
China Hong Kong Limited to share their platform and also practical guidelines
platform and also practical guidelines on how to work with spatial data
on how to work with spatial data effectively and that will be completely
effectively and that will be completely for free. So don't miss this chance to
for free. So don't miss this chance to learn the usage of such essential tool
learn the usage of such essential tool during your IoT data hackform journey.
during your IoT data hackform journey. We hope all of you can make the most out
We hope all of you can make the most out of it. Again um thank you so much for
of it. Again um thank you so much for your participation. Our workshop will
your participation. Our workshop will end here and we look forward to seeing
end here and we look forward to seeing you next week. Stay tuned and have a
you next week. Stay tuned and have a nice weekend ahead.
nice weekend ahead. Thank you and bye.
Thank you and bye. >> Thank you. Bye-bye everyone. Good luck.
>> Thank you. Bye-bye everyone. Good luck. flag.
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