Transcripts
1. Introduction to Data Storytelling: Hello friends. I welcome you to the
new training program on data analytics. I correct myself, storytelling
using data analytics. Yes. We have done tons of programs which teaches us how
to analyze data. We used data analytical tools as simple as Microsoft Excel. Or we might go into complicated
tools like Power BI, and others which are available. What is more important? Is it important to analyse
data or is important to communicate the message
that you want to communicate. After analyzing the data, we all learn the skills
of analyzing data. We know how to draw
complicated graphs. We know how to draw the
meaning from the data. But when it comes
to presenting it to our audience in a
meaningful way, we just go blank. Or anyone who feels that
I am good with analytics, but I cannot present my
data to my stakeholders. When I explained to
my stakeholders, they do not understand the message I'm trying
to convey because they feel I'm Technical and they want some action-based insight. In this training program. I'm going to take you with
a step-by-step approach on how to present your
data in such a way, in such a mesmerizing way
that your stakeholders want to listen to you and look
forward for your analysis. It's important that how
do you set the context, how do you set the climax, and how do you make
your audience? Are you stakeholders? Take the action
that is required. You will also have a project
work which you need to complete at the end of
this training program, which will give me
confidence that you have understood and applied the
simple six step approach. And still this is effective for you to get the action with your audience have to take because you are telling
them the story. It's not a Harry Potter
story of nonfiction. But we are going to make
our stakeholder decisions, take actions based on the data analytics
that we have done. It is important for us to present it in the
most effective way.
2. The Story of Six Blind Men: We all love stories. We love listening to stories when our grandmother
was cutting us. We love stories because it
has a suspense element. It has the hero, it has the context. Now, I'm not going to teach
your storytelling for just impressing are enjoying
few moments of your life. I'm going to teach you how
to tell stories using data. Because data has an
impact when you are working in any business or
you're working in a needle. So I welcome you to the
storytelling using data. Can I learn the skill? Yes, this scale is something that you
can definitely learn. It is not a talent which
you are born with, but it is a skill
which can be acquired. I will be giving you a six-step simple framework
of how to tell stories. After you have done
data analysis. The stories which can make your partner's pick the
action that you want, which can make stakeholders give you the support that is
required for your project, which can help your
venture capitalists or angel investors to
invest in your startup. How should I be telling a
story which is so captivating, which makes them
take the action. That is the framework which I'm going to cover in the
following lessons. Have you heard
about the story of six blind men and an elephant? So the story goes like this. Was, they were six blind men and elephant was put
in front of them. They're all touching the
different parts of the elephant. And they are trying to
analyze what is this. Somebody has touched
the the trunk of the elephant and was feeling, or it's a large pipe. Somebody touched the tail of the elephant and felt
that it was like a snake. Somebody touched the
body of the elephant and felt it was the
role of afford. Somebody touched the leg of the elephant and felt it
was a trunk of a tree. All of them with
feeling that it is something what they have
understood in their life. Right? They were trying to see that elephant from the objects which they have seen in life. Same thing happens with us when we are presented
with graphs. We try to interpret based on whatever analytical skills
I have as an audience. Now, it's your duty to
educate these blind men. These men are blind. They don't know how
to analyze the data. That is why you are
working in that group. You are definitely especially. But how are you going to explain them that how these
six men will be going around the elephant to identify elephant
as an elephant and not as different other objects like a tree or a
wall or a snake. The six framework which
I'm going to cover will help your audience to understand
that it's an elephant. It's a beautiful elephant which can make lots
of things for you. And I would want
you to invest in this elephant if you are thinking about
raising the funds. If you, I want you to
support in my project, if this elephant
is your project. So it depends upon
your contexts, but how you will
be using your data analytical skills in explaining
the story and making your audience see what
you want to see them and not what everybody wants
to understand on their own. So who is this course for? Anyone who needs to communicate something important using data? Analysts sharing the results of their work can also
join this course. Students who are doing visualization thesis should
also be joining this. Managers who need to communicate
in a data-driven way. This goes is
definitely for them. Philanthropist
providing their impact and leaders in
forming the board. If you are in any
of these roles, definitely this
course is for you. Bad graphs are everywhere. You have seen
Steinbach and we'll leave it to the audience
just like the way we left the elephant to the six blind men
to sit and analyze. What do you think what
happened with segment one, segment two, segment
three, and so on. And they analyze the width
they have understood. You have seen pie charts, what the survey results, again, leaving it to the audience to go and analyze
what they want. And even if you see
something is your story or ease your presentation
matching up with a graph which you're
presenting on this slide. When we survey results data, we are doing it as we put all the questions
on the left side and take a 100% cluster
graph and then say that, okay, this is how the
colors are moving and then the audience have very hard
time in analyzing this. We want to show some
performance over the time. We can't Leslie and easily go ahead and put multi-line
chart and tell the audience, oh, we did also things. We love to present. Something very complicated
because we believe in the concept that if
you cannot explain, go ahead and confuse
the audience. I think once you complete
this training program, you will be in a
position to produce graphs with a clear outcome
and clear communication. The more will come
in the next lesson. Thank you.
3. Project work: As you get into the
storytelling with data, It's important for you to
also submit a project. I will share the screen of
how your project should be. This is a six-step
approach that I will be covering in the
storytelling using data. It's important for you to understand the context
or the background. The most appropriate visuals
that you are going to use. Which type of graph is
the correct graph to represent how I
went to declutter. What is the learning
that you have got when you learn the
concept of de-cluttering, how have you improved
your graphical analysis? This concept? How you got the audience focus our attention to the action which you wanted them to take. You have to think how you're
going to build a story. Please write down the story
that you're trying to build. And then once you
tell the story, how did you present it? I invite you to fill this
excel sheet and if possible, if you want to do a small video recording and then upload it as
your project work. I'm looking forward to it. You are going to enjoy
this journey as much as I enjoyed creating it. So let's get started.
4. BAD graphs are everywhere !!!: Bad graphs are everywhere. Nobody sets out to
make bad graphs. But it happens again and again. And at every company throughout all the industries and
by all types of people. It happens, it happens
with the media. It happens to add newspaper,
the news department. Every way we find that
people are not good, showing us the right graphs. It happens exactly
with the people whom we expect
them to know more. These are some examples
which you see on the screen. Visual the survey
results where we struggled to understand what the pie chart is
trying to tell me. We have shortened the
clustered bar graph for our customers and
your stallion that oh, did it improve or
they did not improve? We tried to show the
customer satisfaction scores in again, a cluster graph, leaving it very difficult for the audience to
understand what did we improve our customer
satisfaction or which exactly parameters
has stopped it. We show the performance of different departments in
the non-profit support. And again, we could get lost. Are we getting lost? Or are we losing our audience? You might have heard
this question. When you ask someone, show me the data, what feeling does
that evoke in you? But perhaps you
feel uncomfortable. You feel uncomfortable
because being able to tell stories
with data is a skill that becomes even more
important in our world of increasing data and the desire for the data-driven
decision making. For an effective
data visualization, you can mean difference
between success and failure when it comes to communicating the
findings of your study, raising money for your
non-profit organization, presenting it to your board, or simply setting up your
audience for the success. Over many years of experience, I have found that one key
to success is being able to communicate effectively
visually with the data, because a picture is
worth a thousand words. The six key lessons, I'm going to cover it in
detail in the next session.
5. The Six Step Framework of StoryTelling with Data: The six key lessons about
storytelling with data. Number one, the context
or the background. Are you aware of it? The most appropriate visuals? Which type of graph
should I be using? De-clutter. Focus the attention of your audience to what
you want them to. Think, how you want to
present your story, and finally tell the story. Let's get started. Today, our graphical
skills are at this level. You want to show that the ticket volumes receive and the ticket volume processed. If I'm going to show this to
my manager or do my client, what am I asking
them to interpret? If you whatever you understand, I would request you to write down in the comment
section below. The context and the
background is very important. Let us understand how
we set the context. Please approve the higher of two FTEs to backfill
those who quit last year. The ticket volumes over time, it is the same data. I have changed the
way I've presented. It is now being shown
as the line graph. And we can see that earlier in the month of January to May, we were able to reveal receiving high volumes and we were
able to process all of it. There is no gap between the
gray and the blue line. They are perfectly matching, are overlapping each other. Once we the employee
squid in the May, we nearly kept on getting the incoming volumes for the two months and we
manage that volumes. But we started
seeing the gap from the month of August as we are not able to cope up with the volumes that
we are receiving. And hence, you can
see that there is gap between what we are receiving
and what we're processing. This will definitely result
in the customer complaint or the customer
dissatisfaction when I show information in this way
rather than the previous way, do you think you are able to communicate
the message which you want when you get the approval for hiring the two
people that you want, the title is very important. The key takeaway is important and selecting the right type
of Israel is also important. I have got that tension
to where do I want? I have used a different
technique of doing it. Let's take one more example. This is the soviet is free. How do you feel
about doing science? And post? How do you feel
about doing science? People said, we're
bored, great, and so on. You can see that
there is a pie chart. One of you might
easily identify, oh, the green portion
which was okay, was 40% earlier in now
is reduced to 14%. Are they excited? The blue portion was 1938, so I'm literally
struggling with my eyes to find out where did
the improvement happen. Did they get bored
or they get excited? The colors are also very
close to each other. I'm not able to relate whether it is the ball which is 19%, which became 38, or is it the excited which was 19 became 38? So I need to ensure that I
use the appropriate visuals. If now this time instead
of the pie chart, if I use a bar chart, how would you feel about
signs before the program? Majority of the children
felt just okay about science because my
O K bar is 40% and my Aqiba possible Graham has reduced to
voting booths where it had these
children modified to I have written it down
in the blue color. After the program, most
of the children were kind of interested and
excited about science. This very clearly shows that
the choice of the color should be exactly what I
want my attention to go to. The blue color over here is about the post
program results. I haven't used any legends, but the color speaks for itself. I have written down
the before command in the gray color because
the gray color is what represents
the data relating to the survey before
the class had started. This data is in response of
100 students who participated both in pre and post
a session response. Let's take one more example. Average retail product
price per year. These are the prices
for product a, B, C, D, E, over from 2008 to 2014. What do you understand? Yes, product a and product
B seems to be going down. Product C also went down and E and E is going
up and there is Lord, what do you want
me to understand? That could be your
next question. What do I want you to understand is to just giving a
title which says that, okay, this is the average
retail product price for you. Let's try to tell the audience what do I
want them to understand? Let's take it further. To be competitive, we recommend introducing
a new product below the 223 average price point is $1.150 to 200 ridge retail
price average byproduct. And we found that the recommended
range is this because whenever you are starting a product which is
below this range, you end up taking
up your product. Sales little bit
higher than that. If you have products which
are at a higher price, they eventually
tend to come down. If everything is
coming to the average. Why not? We introduce new products which are in
the range of 150 to $200. What are you communicating? Did it become easy for your
audience to take an action? Yes, the next product I introduced should
be in this range. For me to become successful, I will have profits and I
will have more market share.
6. BIG idea and 3 minutes story Lets learn with example: Important for us to understand why we should tell
our story in 3 min. The idea behind
telling stories in 3 min is to give a big idea. The idea behind this concept is that what will boil down to, so what ultimately to a
single concise statement. You have to really
know your stuff and know that the
most important piece, as well as the most essential
in important information, should be stripped down and be communicated
to your audience. While it sounds easy, being concise, it's often more challenging
than being bogus. Mathematicians and
philosophers find it really difficult to say
what has to be put. I would have written
a shorter letter, but I did not have the time. This sentiment is
what will help you. A three-minute story is
exactly what we want. If you have only 3 min to tell your audience what
you need to know, and what would you say? This will be a great
way to ensure that you can clearly articulate
the story you want to tell. Being able to do this, we'll remove you from
dependence on your slides, your visuals of presentation. This is useful in
situation where your boss asked you what
you have been working on. And if you can find yourself in an elevator with one
of your stakeholders, want to give her
a quick rundown. If you are half an hour, agenda gets shortened to 10
min or say five-minutes. Will you be able to
communicate your message? If you know exactly what it is and what you want
to communicate, you will make it fit in the time slot that
is given to you. Even if it's not one that
you are prepared for. The big idea boils
down to the concept. So what, most of the time
when we tend long stories, the answer from the audiences. So what, so what we can do is, can we think about some big idea which can make my audience not
ask those towards. This concept is based on Nancy Drew book
called as resonate. It was published in 2010. In this book, she says that the big idea has
three components. Number one, we must articulate
your unique point of view. It must be conveyed. What is at stake. It must be a complete sentence. Let's illustrate
some examples of the three-minute story
and the big idea, liberating some
summary learnings from the science example. The three-minute
story, a group of us in the science department
where brainstorming about how to resolve
an ongoing issue we have with our
incoming fourth graders. It seems that when kids get
to their first science class, they come in with this attitude that it's going to be difficult. They are not going to like it. It takes a good amount of
time at the beginning of the school year to
get beyond that. So we thought, what if we can try to give kids
exposure to sign sooner? Can be influenced
this perception. We piloted a learning program last summer aimed
at doing just that. We invited elementary
school students and ended up with a large group of second and third graders. Our goal was to give them
earlier exposure to science. In whole, performing some
positive perceptions to test whether we
were successful. Be surveyed the students
before and after the program, be found that going
into the program, the biggest segment
of students that is 40 per cent friend just
okay about science. Whereas after the program, most of them were shifted
to a positive perception. That is, nearly 70%
of our students express their level of
interest towards science. We feed this demonstrated
the success of the program and that we will not only
continue to offer it, but we will expand our
reach going forward. So I can read the
story in 3 min. Let's understand the big idea. The pilot summer
learning Cam was successful at improving student's perception
about science. And because this was success, we recommend continue
to offer going forward. Please approve our
budget for this program. When you are articulating
your stories in this clear and concise, creative content in your communication
becomes much easier. Let's shift gears and discuss the specific strategies when it comes to planning content. I'm going to show you a
video from the author. Who is going to tell you
how to articulate stories? Real story told you the data doesn't need
fancy charts and graphs. In fact, he might
deliver it with nothing more than a whiteboard
and a marker. Now let me give you an example. In June of 2000, Andrew Morefield
started an online bank to help make loans
to small businesses. He said it was exhilarating and terrifying at the same time. But as with a lot of
companies that startup, there were times when there wasn't enough cash
to pay the bills. In fact, he told me first-time I couldn't make
payroll was the worst. Having to choose who
got paid and who didn't was emotionally draining. But the way he handled it was a masterpiece of
storytelling with data. And he did it with only five
numbers on a whiteboard. Here's what he did. He pulled all 25 employees into
a conference room. And then he wrote a number at the top of the white
board and he said, that was our bank account balance at the
beginning of the month. Now, below that, he wrote to
other numbers unexplained. Those are the revenues we
expect to get this month and the expenses that we have to pay to keep running
the business. And then he drew a line
and added them all up. He wrote the answer
underneath and he said, that's what we'll have
left at the end of the month to pay your salaries. And he circled that number. Then just to the right of it. He wrote another
number, any circled it. And then he said, that's how much your monthly
salaries add up to. And then he paused and let the audience assess the stark
dilemma in front of them. You see the number
on the right was three times the size of
the number on the left. And then he did something
else, rather unusual. He asked the employees, all 25 of them what they
thought he should do about it. Now, he assumes, of course, that the fairest thing
to do would be to pay everyone a third
of their salary. But the team surprised him
with a different suggestion. They thought a better method
would be to pay a third of the employees all of their salary and the
other two-thirds, none. Cool. Andrew was horrified. I mean, how could he possibly choose who to pay
and who not to pay? But they surprised
him a second time when they offered to
help there as well. They told them that
they would decide among themselves and their
criteria would be based solely on who
needed the money most urgently and who could wait
a month or two to catch up. So Andrew left the room so
they could talk in private. When they call them back in, Andrew got his third
surprise of the day. The people on the list to get paid were not the
ones he expected. He thought that the
younger employees with a smaller salaries would be in the most desperate position. But among themselves, they
decided that the older ones, the ones with families to
feed and mortgages to pay, had the most immediate
commitments. Know, several of
the younger ones still lived at home
with their parents or in an inexpensive apartment
and had no family to support. They were the ones who
volunteered to go without. So Andrew learned a lesson from that experience that
he's used to this day. When faced with a difficult
decision that will result in people being
disappointed, do two things. First, be real, open and honest with them
about the situation. Lay all the facts
out in plain view. And second, ask the people affected how they would
decide if it was up to them. Nine times out of ten, they'll come to the same
conclusion that you did. And at that point, it's far
easier for them to accept your decision because
they recommended it. And occasionally, as
in Andrew's case, they might even suggest
a better solution that you wouldn't
have even thought of. The story. Very helpful. It gives us an idea of
what needs to be done. In the next chapter, I'm going to cover how
to pick that. I jumped. Thank you.
7. Choosing effective visuals: Let us now proceed to understand the importance of choosing
some effective visuals. On the screen, you can
see that I have just displayed one number,
90% of something. It's not always important
that I pick up a graph, which is complicated to
explain my point of view. Sometimes beautiful communications
can become effective if used with simple text or
just one or two numbers. Let us understand
this in detail. If you look at this example, this is how a traditional
graph will look like. This is a data based on the source which
is mentioned below. Children's with the traditional
stay at home mothers. The percentage of children with a married stay at home mothers with
their working husband. And you can see that the
percentage has dropped. It's such accomplishments. Yes, the title is clear. The graph is clear, and we're comparing
27th, 1970 with 2012. Can I make it a
little different? Yes. Let's try this. What I did now is just used
a simple number. 20% of the children had
a traditional stay at home mom in 2012
compared to 41% at 1970. A simple text make over. What happens with this. That we're making
sure that we're communicating the information
that is more important. What do you see on the screen? The tables? Tables are
a very effective way of communication when you want the people to use
their index finger. It is for comparing numbers and tables
are very effective. But be cautious
because it requires an index finger to move around when they're
comparing the numbers. Please avoid using this
during a live presentation. It is a very great
tool if you're using, when you give them handouts or you are sending an
email or a report. In case I need to use my tables. One thing that I keep in
mind is that designed to fade into the background and let the data take
the center stage. We put so much of
effort in making sure that the table looks
beautiful and we'd lose the concentration
on the data. So let's take the example. This is a simple table, but this is not a
characteristic because here the borders are heavy
and the text is green. Can I make it little better? I made a lighter border so
that the data takes important. Yes, this is little better
than the previous table. Can I increase it further? The data should stand out and lead the data.
Take the center stage. I have made all the
grid lines disappear. Minimal border or it might
be very light green. Because the reason I'm putting up the table is
because I want to communicate the
data and I do not want to communicate
our shoulder borders. I hope you got an
important point. Let's take it further. We want to now understand
what is a heatmap. You guys would have used heatmap or seen heatmap in
multiple places. Whenever I want to compare a relative magnitude
of a number, heatmap is a good tool because it leverages
the colors in the cells and people can use it. So if you have something that
you want to concentrate, that the red is a pain and
the green is the good one. We can use heat map like this. Remember, there are two
views of the same data. I can show the data
in a tabular format, like the one on the left side. Or I can show the data in this format where I'm
actually coloring the data. Now you can see very
clearly that category five for a and C is
beyond 50 per cent, which was not very easy to be
spotted in a normal table. We can also see that
the categories, the location as a
has lesser problems with category a and C
because it's almost white. So I can use heatmap along with my tables to make emphasis on the data
that I want to show. I hope you're getting
ideas of how to beautify the communication
you want to make and not get lost in just
preparing these slides. As we take the journey of
effective communications, you would have
seen scatterplots. We should make sure that
we use scatterplot. Whenever my x and y-axis
are continuous data, it means I cannot use if I have categorical data or
data like branch a, branch B, brand C, or stream a stream the stream seat location
like not east-west. So these are examples
of categorical data, which I have covered
in a separate lesson. So I can use a scatter
plot whenever I want to establish a relationship
between my x-axis and y-axis. One important point
to be kept in mind is please ensure your
x-axis is a factor that influences your y-axis represents the fact that
that is getting influenced. In that way, you will
make sure that you are representing the
information on the chart. People Rho correlation
using scatter plot. You would have also
seen time series plot. As you see the sales in
thousands with eight months. The beauty with this
time series plot is that it will tell you, is there a trend? Is there a seasonal factor? Is there an oscillation? And so on which we will cover, I'm not going to
get lost over here, but what you have to make a
note is that you cannot use a line chart if your x-axis
does not have time as axis. So you can use line chart or time series plot
only when your x-axis represents the time and
you cannot sort this data. I hope I've made my point clear. So let's understand
the next one. This is called as a slope graph. Instead of getting
lost in details, Can I have a slope graph which helps me compare my
total graph with others? Right? We would have
seen vertical bars. Horizontal bars. You will have also seen vertical stacked bars and
horizontal stack bars. These are some visuals like
waterfall, n-squared areas. You need to be very careful that how will you make
to tell your story. Because as you know, when you do storytelling, you're actually solving a
puzzle for your audience. If you only send numbers, you will only be creating puzzle in the mind
of your audience, like the figure on the left. With this. What did you learn? Please write it down in the
discussion section below. And I will see you
in the next class. Yes, you can do on the scale.
8. Turning Bad Charts into Compelling Data Stories Dominic Bohan TEDxYouth@Singapore: Which slice of pie is largest? Let's do a quick show of hands. Who thinks red is
the largest slice? Any takers? Who thinks yellow? Couple
of votes for yellow, you think it's blue,
it's the largest slice. Few votes. Any takers for grain? Grain is a popular choice. And what about purple? No votes for purple. And it's not purple.
And one more choice. Who thinks they're all the same? Very popular. The room is divided
between yellow, green, and all the same. Let's visualize the
exact same data as a simple bar chart. And now the answer is
instantly obvious. Even if you had a hunch
that it was green, how confident would
you have been insane? Which color comes second? Third. And so on. The pie chart has
failed miserably. And this is far from the
worst chart that's out there. I'm a data storytelling trainer. I see bad shots everywhere. The world is full of charts
that look like this. Maybe you see them
in your workplace or infographics meant to look
fun and cute, like this. Or worse, still, the crammed
della cram of bad shots. I give you this. My vision for the future of us is a
world in which no human being shall ever again have
to suffer the indignity of trying to piece together a confusing abomination
of a chart like this, decorated like a Christmas tree. Why am I so passionate
about data visualization? And why should you care? Well, humanity is creating
more data faster than ever. You've probably heard a
bunch of hype about it. Slogans like daughters
the new oil, or little factoids, like, we create more data in a year now than in millennia of
human history combined. And the thing about this hype is, it's actually justified. Daughter really is
transforming our world. But data is useless unless
human beings can interpret, analyze, and understand it, and use it to drive action. To make sense of data,
we need visualization. And for our visualizations
to land and make an impact, they need to have a message that our audience cares about. In other words, we
need to tell a story. I believe a data storytelling
can change the world. The most impactful data
stories can even save lives. Okay? So I know that's a big call. So I want to prove it to you. And today, I want
to share with you three simple techniques
that you can all use to tell compelling
stories with data. You don't need any
specialist expertise. You don't need to be
a statistician or a Data Science Diet scientist
to apply these principles. So three simple principles. The first is, choose a
human-friendly chart type. What do I mean by that? Well, let's take a
look at an example. So we've got some data
here from a personality tests that I took
and my friend took. And we want to compare
our results across each of these five major
personality dimensions. What do we think the
differences are? Quite difficult to do
with this pie chart? We saw earlier with our
little experiment that pie charts have some
pretty severe limitations. And this is because
with a pie chart, we're forced to
decode angle an area. And human beings
are much better at perceiving numbers that are encoded using simple bars,
using simple length. So this experiment that I performed on you earlier
is very similar to a series of experiments
that was performed back in 1984 by two researchers
named Cleveland and McGill. And Cleveland and
McGill were fascinated by this question of which charts are human beings good at interpreting and which
charts do we struggle with? So they showed that
participants series of lines and bars and shapes
that encoded numbers. And we call these options up on the screen elementary
perceptual tasks. And they measured how
good the participants were at deciphering
each of these tasks. And they ranked them from the
tasks that we're worst at, the tasks that we're best at. And we have some clear winners. Human beings are
best at perceiving numbers encoded by
length and position. There are go-to choices for
human-friendly chart types. So let's visualize our personality test
data using position. Not much of an
improvement, right? So this is called a radar chart, and it uses position, but it uses it randomly. There's no reason that these personality
dimensions should appear in the particular
order they do, or why they should
form a pentagon. It pains me to say it. But if you actually take
a personality test, you're very likely to see a
massive a chart like this. This chart is becoming popular consultant
Siri than using it. So why does this train wreck
of a chart proliferate? Well, it could be that the consultants
wanted to distract you from how much
they're charging. But it could also be that this chart does look
kind of interesting. I have to admit some
people would even use the most dangerous word in the English language
when it comes to data visualization
to describe this chart. And that word is cool. Whenever I hear this word, whenever someone runs
up and tells me dumb, I've got this cool new
chart I want you to see. I shudder in fear because I'm about to see a disgrace
of a chart like this. Once again, this
chart uses position randomly and it uses area, which we've seen from
our pie chart example is not a good way to
encode numeric values. And we can make it even worse. We can make the bubbles
dance around and light up. Modern software
packages allow us to do more new and exciting things with data visualization
than ever before. But just because we can
doesn't mean we should. So I want to convince you
that simpler is better. And I want to come back to a simple human-friendly chart
type that uses position. And now we're going to
use positioned properly. We're going to align
these positions that show our values on
a common scale. We're going to change
our data to a dotplot. Suddenly the insights
are immediately obvious. I'm much more extroverted
than my friend, and she's much more agreeable. I'm not the most
agreeable person. And on the other
personality dimensions, well, almost the same, which is maybe why
we're friends. I want to move on to the next key to effective
data storytelling, which is to be a
ruthless minimalist. And to explain this concept, I want to use a
personal example. So I've been in a relationship
for about two years now. And it's often around this
time that your partner starts to ask about some of
your previous relationships. So I did what any good
data storyteller would do. And I put together a chart. So here it is. So thank you. I'm going to need your
help to fix up this chart. And so I wouldn't wouldn't blame her if she left me on the spot. Just for the bad
chart design here. So we're going to fix this up. This chart shows on the
x-axis my age in years. And I've estimated the intensity of some of these past
relationships over time. But before my audience, which is my current girlfriend, can understand
what's going on with this chart and
understand my message. We need to remove all these
distracting components, which we called chartjunk. Let's start with the
worst component first. This background color,
which serves no purpose. Now, this chart is just an
estimate I've put together. We don't need a huge
degree of precision. So let's get rid
of the grid lines. And we can also get rid of this chart title and
simplify our y-axis labels. Now there's one more piece of chart junk that needs to
be eliminated from this. Can you spot what it is? I'm gonna go against what we might have learned in school and university and say that every chart does
not need a legend. This legend is forcing
our audience to do what? Our eyes have to
track up and down between the legend and
the series labels. And we have to hold
a series labels in our short-term memory. It's diverting our attention from the message of the chart. Fortunately, there's
a better way. Human beings naturally
perceive objects that are close together as
belonging together. And we can take advantage
of this and just label our series directly by putting the labels close to the series. And we can further
reinforce and strengthen this connection by using similarity of color.
So let's do that. Now this chart still a
little busy for my liking. We've got a lot of color here. And color is a
devastatingly effective way to focus attention in
data storytelling. And because it's so effective, we want to use it sparingly. So I'm going to push
everything to gray for now to create a blank
canvas for storytelling. And that leads us into our third and final key
principle of data storytelling. Which is the everything we
put in front of our audience, needs to contribute to a clear key takeaway that
our audience cares about. So with our old
grade blank canvas, Let's take our audience, a story and highlight them. The pieces of this
story one at a time. So we start with my lackluster
first attempt at dating, consistently, pretty
low-intensity here. Let's call this a practice. And in my mid-twenties, I thought I could handle
two relationships at once. The data shows this was
not an effective strategy, with both of them pretty quickly plummeting
down towards zero. Now we've got the most dangerous and volatile territory of all the ups and downs of
this almost marriage. And my current girlfriend might notice that the
peak intensity of this relationship is higher than the highest peak of
my relationship with her. So I need to add a little
bit of reassurance here and just let her know
that I no longer speak with. And of course, the names have been changed in this example. So after that ordeal, we have the recovery period. I estimated the intensity of these relationships on
quarterly intervals. So sadly, none of these
dots made it into a line. And finally, we've got my
current relationship with a forecast of strong growth expected to continue
into the future. Now, I promised you at
the start of this talk, but I was going to convince you that data storytelling can change the world and
can even save lives. Now, this chart
might not even be good enough to save
my relationship. So I want to show you one of the most profound examples of effective data
storytelling in history. And to do that, we
have to go back to 18, 54 in the crowded
and squalid streets of Soho in central London. Back then, there'd been a drop dramatic and rapid outbreak of deadly cholera in a
straight cold Broad Street. And the prevailing view amongst physicians at the
time was that cholera was caused by a
mysterious foul stench in the Air Force
they called Miasma. So they were wrong. But those one physician
that dissented, a man called John Snow. John Snow believed that color was being transmitted
via drinking water. And he shared his theory
with his colleagues. And they replied, You
know, nothing John Snow. And so John Snow
collected some data. He collected data
on the locations, the exact addresses of every
color a case in London. And he marked down the locations
where they had occurred. And the more deaths there
were at each location. The larger these black bars
that I'll show you grew. And we can see when we zoom
in on the Broad Street pump, that we have the highest
concentration of cases around this pump. Back in those days,
people would walk to their closest pumped
to gather water. So what we see when we look at some pumps a little bit further from the
Broad Street pump, is the cases start to drop off. When we go very far from
the Broad Street pump, when no one could possibly
be walking there, the cholera cases
disappear entirely. So John Snow took his findings to the local parish Commission. And they finally said, You know, something John Snow, and they agreed to remove the handle
from the Broad Street pump. And within the day the
deaths had stopped. Now, the epidemic had already
picked by this point. So we don't know how
many lives were saved. But John Snow's contribution would go on to
shape the field of epidemiology and make
a massive contribution to the germ theory of disease. John Snow was able to
change the world with data storytelling because
he had a simple, human-friendly chart that use length to encode his values. He was a ruthless minimalist, made me because that
back then he didn't have the choice to add chart
junk, twins visualizations. And he had a clear
and powerful takeaway that his audience cared about. And I want to convince
you that you too can change the world until
impactful stories with data. And all you need at
your disposal is the simple tools that are
available back in 18, 54.
9. Creating Clear Contrast: As we proceed to understand the importance of de-cluttering. I said, creating a clear
contrast is also very important. You said doctors get
the Mexican started. Can you spot the fire truck from this list of cards that I just picked up
from my nephews toy box. And give you a few seconds. Yes. Because you're doing
it on your system, you would be using
your finger and trying to find out where can
I find the fire truck. But they do feel that
it was trainees for you to identify the
fire truck because there are multiple other
things are in which are in the shades of red. So if I would have
made a clear contrast, see, I have grid out everything else and have just
bought it a file. I hope some of you were
able to identify and even if you were not able
to identify, Do not worry. The purpose was not to find the fire truck where it
was about to teach you. That. Sometimes creating clear
contrast will make it easy for the audience to
read through your message. This is why is
contrast important? I just showed you one example. Let's understand this photo. Can you see the weighted
performance index, the main source distribution
of new customer segment, new mandatory by education. There's a lot of information that we can see in all
these four examples. But what didn't,
what do we find? We find that because we're
using multiple colors. I'm not able to get
the attention of the audience to the point
that I want to say. I will have to then tell a long of stories
to make it clear. Let's take it further. A quick visual perception
is very important. So if you would see this, you can very clearly see that the blue ball is
smaller than the, bigger than the pink one. But how is it that the
blue is at the bottom? Maybe it has a higher weight. So it's important. What is the audience thinking that makes a very
important point? So fastest finger first
round, let's play this game. I would be giving
you a few seconds, and I will give you a task. You will get thirty-seconds
to find the best eager. You already. Let's get started. Yes, I will wait for you. I'll give you 30 s. You might see that all
of them look, see. How can I identify
the best Eagle? Yes, exactly in the same way. If you want to show too much of information in your graph, the audience might get confused. All of them, Lucy,
what should I do? Now? Again, let's play
this game. This time. I will just give you 3 s to find the best IQ. Are you ready? Here you go. How easy and evidence it was. All of them are the same one. But because I made it
very prominent, very big, it was easy for you to identify it in a fraction of a second. I'm going to use the
same same mental ability for you to build your stories. It is easier to spot an eagle
in a sky full of pigeons. But it is difficult to find out which eagle's wing looks special if you're
showing all of it. So if I have to show that this is the eagle that
I want you to focus on. I might want to
use a contrast of the things that I don't need and focus their attention to the
place I want you to see. So to achieve a visual contrast, we keep in mind three
important things. Position, color,
and added marks. I will tell you in more
detail as we go ahead. Fastest finger around again. You will get 30 s to count
the number of fire trucks. Are you ready? Let's get started. Yes. As you can see, you
have to put a lot of effort in going line
by line to identify. Can you see a fire truck or not? If you have things
which are in red, you pause by to see whether it's the shape of a file or not. So if I would have made
it a little different, Let's play the game again. This time, you will get 3 s to count the
number of five trucks. Yes. Isn't it easy because it isn't a contrast color and
restaurant isn't great. Let's play the game again. This time. Again, I will give you only 3 s to count the number
of firetrucks. Integral of a gray contrast. I have kept the original color, but I've favorited in the
backgrounds so much that the attention of my audience goes only to the fire trucks
that I want them to see. The contrast can
be in any format. Let's play the game again. You will get 3 s to count
the number of firetrucks. Yes. This time,
instead of contrast, I have used the
concept of position. So all that I want
you to see is in the top left corner or the first two position also plays a very important
role when it comes to focusing the
attention of your audience. Let's play the game again. I will give you 3 s to count
the number of five trucks. Adding the tick
marks make it easy for the audience to
focus on what they want, what we want them to see. So, as I told you, Carlo, position and added
marks will make it easy for your audience to focus on what we
want them to see. I can also use other combinations like
position plus color, color plus added mass. Position plus Carlo
plaza, Denmark's. The choice is up to
you depending upon how comfortable you are and what
is that you want to use. But instead of
leaving the audience, going ahead and
finding it on their, on their own, we
should help them out. There is a concept on how
our human brain works. Technically, the brain looks. So there is a stimuli, which is a catalog Carlo, which isn't high contrast color. The eye has an optic nerve which immediately sends
the signal to the brain. If the contrast is prominent, the eye, the optic nerve, when send the signals quickly
to the brain for it to identify the difference or
to make it easy for it. Hence, it's important for
us to have a contrast. So do, do a quick recap. I can achieve a visual
contrast by using position, color and add it knocks. I hope you'll be
able to try this. And then let me know.
10. Customer Feedback Let us learn with Examples: Do you remember this graph that we used in my earlier class? Yes. This is called as a slope graph. Whenever you want to do a
comparison between two units, you can use a slope graph. Slope graph is nothing but dots on one side and on
the other side. The slope represents
whether it's a positive effect on the
area that we're focusing on. Is it a negative effect? As you can see, this is
an employee feedback for the overall organization and
for the various parameters. What is the scope? As I am the team leader
or off sales team, my focus is to tell
to my management that my team members enjoy
being with the coworkers. I have given a
very clear titles. Sales team loves
their coworkers. I can see that the overall
organization is at 81 and my team is at 95. What have I done? I have
made the attention of my audience go to the point
that I want them to see. Some of you would have noticed that my career development is dropped very significantly when compared to the
organizational feedback. It means that my
team does not have a good clear carrier
progression graph. But because I do not want
my audience to see this, I have not highlighted it. It could be easily missed out
if I do not talk about it. As a third or as a presenter, I can decide what
do I want to show. This is another example where I'm sharing the customer
feedback over time. This is 2014, was his 2015. You can again see
that the crisscross are happening in
some parameters. If this time my focus
is to talk about the reduction and the
action that I need to take. I have highlighted the
customer service which has dip from 49% to 33%. I have not taken the improvement of convenience from
80% to 96 person. So I, as a presenter, will decide whether I want to focus on the positive
aspect or do I want some action to be taken on the things which
are not moving well. So instead of using a bar chart, slope chart comes as a better rescue for you when
you're crafting your story. Thank you.
11. Cost per miles Let us learn with Examples: I welcome you to continuing your learning on
storytelling with data. As you understood that
these graphs can be built using simple tools like
Microsoft Excel and all. But you will not be able
to present it when, if you don't know the
art of storytelling. Most of the time, we think
storytelling is a soft skill which needs can be told only if you are in
marketing and sales person. We feel handicapped when it
comes to building stories, using drafts and using data. So biotin waiting,
we can get started. Let us learn with
this example, e.g. let's say that we manage the bus fleet and
want to understand the relationship between miles
driven and cost per mile. The scatter plot may look something like the figure
in the next slide. As we know that
whenever we want to establish a relationship
between the x and y-axis, we use scatter plot. X-axis represent the miles
driven per month by each bus. Because I have a fleet of buses, I have the data points. Each dot represents the miles driven per month by each bus. What is the average cost
per mile is on my y-axis. You have to be
careful what and on the x-axis and what you
represent on the y-axis. X-axis is usually the cause, and y-axis is the effect
that we want to understand. By looking at this graph, you can very clearly see that it is going down till a particular level and
then starts increasing. The average cost per
mile is 1.5 minus $1.5. So as we can see, that a certain number of leads or below the
average cost price, but certain buses are making, are charging, costing us hire. On a deeper analysis vegan, to understand that
our primary focus is that the cost
per mile anywhere, which is above the average, we need to reduce it. Now that is the problem
that we're trying to solve. But how can I show that
in a more effective way? So I use a very simple
technique using colors. I have drawn a dashed
line on the average cost. The green dots are
the one button. Okay, with as a business owner, the red dots are the
areas of concern for me. So what can we do? I have highlighted this
and I can say very clearly that the
cost per mile is higher than the average when we drive less than 1,700 mi per month or we drive more
than 3,300 mi per month. So we should ensure that
a bus should be driven anywhere 1700-3 thousand
or to be more safe, 2000-3 thousand mi a month. So if you have buses
which are running at less than 1,700 and some going
greater than 3,300. Can we use an operational model whereby excess can be
transported to the bus? We are looking for less, which are currently having.
12. Playthe game: Yes. To get started, as we've been playing the
game the previous room, Let's continue to play
a few more games, which clearly articulate
the contrast. So I will be giving you 30 s to count the number of threes. I will wait for you to complete. Yes. I guess you
would have counted. Can you type in the
discussion section, how many 3's did you count? Please do not pause the video because I want you to be
honest with yourself. Let's play one more time. This time, I'm only
going to give you 3 s. Isn't it much easier? If there are some free additive
attentive attributes like contrast and the color count, the number of sevens. You can very clearly see
that if I have a contrast, it makes it easy for me to see. What did you see that
because of her brain is hardwired to quickly
pick up differences. We see it in our environment.
13. Declutterconcepts: I welcome you to this class of storytelling with
data declutter. It's one of the
most important step when it comes to storytelling. Let us understand this
in a five-step approach. Before I go further, I'm going to tell you
some important things. The first principle is
called as proximity. How many groups do you
see on the left side? Let me turn my laser. Well, how many groups do
you see on the left side? Yes. The answer is three. How do you see the data dots arranged in the middle graph? Please write it in the
discussion section. How do you see the dots
in the right graph? Yes, you are right. These are appearing
as verticals. These are appearing
as horizontally. This is happening
because of the spacing. Spacing plays an important
role in the human mind. As you feel that the
distance here is less. These are all vertical bars. As you feel the
distance here is less. We feel that they
are horizontal lines as the dots are close together and the distance
between these dots and this is more psychologically, or in our mind, we classify
them into three groups. For horizontal, for
vertical lines, and for horizontal line. Keeping your data, keeping in mind about proximity
is an important thing. Similarity. What do
you see in this graph? Yes. The blue dots. What do you see in
the middle graph? Yes. The cross and the
text of squares. What do you see in this? You would say yes,
the blue dots. So what happens is your
human mind tries to understand and group things into similarity, similar things. But here, instead of
seeing it as three groups, we saw it as blue dots and green dots beside
cross and squares. And we saw as blue dots. So we try to group
things based on the similarity and segregate them based on their difference. The next principle
of similarity, you can see one more example. This dance up separately. Right? Enclosure. What do you see on the graph? On the left side? They're the same dots
which you saw earlier. But now because I
have an enclosure, you feel that there is a
story that these 4 bar or this gray box has. I can use this when I want to compare the actual
versus forecasted. Having this light enclosure gets the attention
of the audience. Very easy. Closure. What is this thing that
you see on the left side? Most of you would
have seen a circle. Are you not seeing they're
actually dashed lines. But because they are so
close to each other, the proximity is in such a way that above mind
easily forms a so-called. Similarly, if you
see this graph, I have used the
concept of enclosure. So getting An enclosure or setting the data in
such a way makes it easy. Continuity. You see this
as tall rectangles. So your mind is actually visualizing the
deepest separate them. They will look like this, but the reality can
be something else. So in continuation to
the previous topic, you would understand
that how our mind works. How can I use that when
I'm using some chats? Yes, the sequence of the
bus is very important. Connections when you're
trying to connect the dots, the fields from
how is it moving. From big to small, from something that is
important to focus or from a to D. So in short, showing dots in this format. If I connect them using a line, then I'm able to understand
the trends and patterns. I'll see you in the next lesson.
14. Declutter example: Let us understand
the five steps to declutter their very
simple principles. This will help you
understand how you can declutter your graph
to make it more effective. Wind Number one is
liberate how people see, as we covered in our
previous lesson. Use the concept of similarity, grouping, proximity, spacing. And these will help you identify and get the focus of your audience to
what you want to seek. Employment. Visual orders. A human brain sees left to right
and top to bottom. We will be taking up
many examples of it. Why number three is to
create a clear contrast, we'll have a separate
chapter which will completely explain you about what is the importance
of contrast. Number four is due
not over-complicate. Keep it simple. The first one is stripped
down and build up, meaning. Remove everything that is not important and build
your stories on it. So as we go further, let us see this example. Can you identify and
remove clutter? For me? These borders are not
adding any values. These grid lines are not
adding any value to my story. So the step first would be
to remove those flutters. I have removed the borders. I have now removed
the grid lines. Can I improve it further? See there's already a
difference that we can see. But can I improve it further? Yes. I have removed those tick marks, which was not adding any value. Let us improve it further. I do not need the
legend at the bottom. Can I make it more easier? What if you look at the
x-axis? Can I enhance it? A human height has difficulty in slanting down and
reading the x-axis. I have just made it easy to
read format of the x-axis. Now, I will work
with the legend, and I have placed the
legend over here, rather than straining
the eyes of the audience to identify
which color presents. What the last cell in the last point with the same
color tells me what it is. So the red color talks about the process number and the blue color talks about
the received numbers. Clearly stating that
the received numbers are higher than the
process numbers. So you can see how
far we have been from completely
traditional graph to a graph which is
D cluttered, right? So I removed the borders, I align the x axis. Can you also see the logins, the y-axis legend where having two decimal points
and all of them are zeros. I have just trim them
as well. Ticket volume. Ticket volume was
a common keyboard, so I removed that and I just
said with C versus process, I'm still going to cover about
the chart title and all. But you can see that how
I can declare two micro and get to focus on what I
want the audience to see. Let's take this further. I will see you in
the next class.
15. Discovery Method Learn with Example: Is this discovery journey story, as I call it now,
is the reason why. So let's see how that
data became a story. First of all, it was
structured like a real story. All the background of my project and the
early data I found, that was the context, the beginning of the story. And the challenge
came when I found this strange relationship
between sales and profits that changed in 1983. And I obviously
wanted to know why the conflict was all the work I did to solve that mystery. It was thinking up
hypotheses and testing them out and finding that
they didn't seem to work. Then there was thinking
up another solution and finding out it
didn't work either. The resolution was the
discovery of the right answer. Then I transitioned out of the story to the
lessons which were the conclusions of the
analysis and finally to the strategy
recommendations at the end. Just like it should be for storytelling instead
of at the beginning, like an, a typical presentation. Second, notice this twist. When I got to the conflict, instead of just telling them about my struggle to
find the right answer, I let them struggle
with it themselves. And I let them continue to struggle until they
found the solution. I'd found. I gave them the gift
of discovery I'd had, and that's what turned my recommendations into
their recommendations. Third, notice the
emotional impact of the dramatic pause
while I let my audience assess the data in
the scatter plot. Stories or emotional,
or they're not stories. If you want to tell
stories with data, there has to be an
emotional moment forth. Notice the element of
surprise at the end, when someone finally got the right answer to the mystery
of what happened in 1983. Just like with
emotion, great stories have an element of surprise, even Storytelling with Data. And last, notice that instead of telling my audience what their conclusions and
recommendations were, I let them come up
with their own. Again, that's what you
do in storytelling. When you're done
telling the story, you pause and let
the audience react. Give the story a chance to work. If they're smart as
you and they probably are likely to come up with
the same conclusions you did. And if not, you can
always tell them. Yeah, I thought about that too. I came to a different
conclusion and here's why. Now, use this method. You certainly don't
have to give your audience all the
data that you had or take them through
all the wrong turns and dead ends you went through. Just give them enough of
the wrong turns for them. You struggle a little. And just enough of the data
for them to struggle a bit themselves before
finding the right answer. And you may have to help
them along like I did. Another book which I would
recommend you to read with us. It's like balls met
and video which you saw belongs. Thank you.
16. Whatdidyousee: Let's proceed further on
the storytelling with data. I will require you
to take some action. By watching this. You will have to type in
the discussion section. What did you see?
You might want to say the name and then
what did you see first, I would request you
to please write. What did you see immediately? Because of a human brain is hardwired to pick up
something that is different. Let's take some more examples. I haven't been giving
you 3 s per image. And you will have
to quickly tell, take a pen and a paper and write down what
did you see first? Be honest, because
this will help you when you're crafting
your storytelling with data. So what did you see? First? The orientation image. Okay. What did you see first? What did you see first in
the line length? What did you see first
in the line width? What did you see first
in the curvature? What did you see it first
in the added marks? What did you see first
in the enclosure? What did you see
first in the size? What did you see
first in the density? What did you see first? What did you see first
here in the motion? What did you see
when you saw that? You please ensure that you participate and
write this answer in the discussion section because we will pick this up
and proceed further. So as I told you earlier, also, the visual perception, there is some
stimuli which we see in the graph on any image. And that's been caught by your eyes and your
brain transplants it. And we know that we
are hard wired to see the difference rather than
seeing all the other things. How we can leverage it. So I will give you
a project file. The Excel sheet isn't
the project section. You please pick up the data
and see what you can apply. 20 min it will take, it will not take more than that. Some of the exercises which my participants have done
has been on the screen. It very clearly says they
have used very simple text, highlighting it in bold pink because we are
talking about female. Only 35% of the
passengers were feeding. This was the week when
one of the participants, the other participant
wrote it in this way, only 35% of the passengers
where females, they show. The shoulder Nikon telling that only 65% of the
passengers were made. Not only 65% of the
passengers were made. Sometimes you can
also show it in terms of the size of your image. This was 30 and this was 62. The size is different, right? It makes it easy for
the brain to work. I hope you will take the break. You'll take time to complete your project
work and upload it. Sometimes we have a mind of
seeing it as a pie chart. When you're using the
pie chart in the title, you can use the same
color that you are using. Food present the pipe. So 65% of the
passengers were male. It is blue and the blue color to Pi is what
is it presently? Or on a contrast, you can see 35% of the passengers
were female. And that's what we have present. As a data analyst, as a data scientist, as a person who loves
to work with numbers. By two, that is not my favorite, but we can use it very rarely. You can use enclosure like this. Main passengers
were approximately highest in the third group. So this enclosure gets your attention directly
to the third section. The same graph, the
same information is presented by others
using the slope diagram, very clearly showing that
the females were the least, both in first, second,
and third category. Compared to them, the
male were higher. But if you see the
proportion difference between male and female, the third category, we can
see a very slanting slow. Yes, you can use all these ideas when you are
building up your graphs. Most people will
consider a long life. We'll represent a greater
value than a short line. When you have lines, the bigger line shows higher value and the smaller length shorts or smaller way. But how can you see that? Which color is greater? The red or the blue? We cannot think about things
like this in the CMB. Gunners cannot help
you magnetize. Cannot represent the magnitude. Yes, it's a meat. It isn't, it's not a
meaningful question. So we will take an important aspects that
is preattentive attributes. This is extremely
useful and do things, drawing our audience attention quickly to where you
want them to look. Creating a visual
hierarchy or conformation. What did you see first? Let's take this. What did you see
first on the screen? Please ensure you type in the discussion section that
one did you see on the road? Most of you would have seen the stop simply
because it is red. It is of the big size and it's coming right in the
center of the image. Let's take it further. Do we have other things? It did not take our attention. What did you see first? For many of them, they would
have seen the sun falls. And for many of them they would
have seen the play first. When you look at the plane, the sun is still dragging
your attention to the corner. When you're looking
at the sun will have something that is calling
your attention to the plane. The reason I'm giving
you these examples, it is for you to understand
that how our brain works. What do you see first? If you're like me,
the first thing would have grabbed
your attention is the perineal sales because it's in pink color and
it's in the center. There are others which
are of same size font, but the color is not
grabbing my attention. And later on I would say, the first thing came
this and then this and then my mind is going in, it's exactly but
I don't think so. We would have gone red
Family Dollar immediately. Some of you would be different than they
would have send it. Yes. I read that first
and then I came here. But it's a very
small percentage. So most of my audience will need something that they can
easily point out too. So I need to keep that point in my mind when I'm
cutting my stories, preparing my slides, and
delivering my presentation. What do you see first? You will see lots of
crowns with lots of color. Nothing, not one piece
is grabbing match. Exactly. So when you have too many
colors on your screen, nothing graphs that tension. Everything looks or
gifts equal importance. So we must avoid
situations like this. Most of the time we end up seeing graphs like
this where we have too many colors and we end up not taking up a focus to
any of a single place. We are distributing
our audience and we're losing the message
that we have to give. If I would have shown
it in this way, it's more easy, right? The orange color crayon
is standing out from all the rest cabinet. This to my previous lesson. Contrast is very important. So to summarize our learning, colors that grab attention. Colors signal where to look. Color should be used sparingly. Very rarely. Colors can carry quantitative
value, but very rarely. Color. Gary Stone and
meaning red, alert. Green means calm nature. But remember one thing, many people cannot see colors. I'll be showing
you some examples. And color should be
used consistently. If you're using a
particular color to represent something, you have to use the same color throughout your presentation. Let's go for the menu. Read this paragraph.
What do you get? You feel it's such
a big paragraph. You will not even put
effort to reading it. Some of you might
read it quickly, but it's not taking
my attention. If I use contrast like this. Even in a paragraph,
it becomes easy. It's best in their class, problems are resolved from you. Customer service
exists expectations and keep up the good work. Right? Now. It makes up that, oh, it was talking about a survey and we are doing a good job. I can use contrast and
colors as required. E.g. let's say that he managed
the bus fleet and want to understand the
relationship between miles driven and cost per mile. The scatter plot can, can look like something that I'm going to show
you in the next slide. We can see that as my cost per
number of miles increases, my cost per mile decreases. But after a certain point, my cost per mile
starts increasing. So how can it's not a direct positive or a
negative relationship? There is a difference
that we have to do. My average cost per mile
is somewhere around $1.5. Now, instead of showing a graph like this and
losing my audience, can I do something different? If we want to focus primarily on those cases where the cost
per mile is about average, least slightly modify
the scatter plot to draw our eyes more quickly to
what we want them to see. See this example.
17. Data Analysis using Excel: So let's dive with our first basic data analysis
practically on an X sheet. So I have this data which is a generic data about products. I am a sales manager who
is selling thread mills. I have three types of product which I'm selling in my shop. That is M 195,498.799
These are some codes. Now the data is given to me and I would want to
understand from scratch. Let's tell you that
your stakeholder has not given you any questions, but just given you a data dump. And said go ahead and analyze. What I would do is the
first thing as I've known, what is the data types, I'm going to first
identify the data types. So let me just write down over here that I know what type
of analysis can I do? Age is numeric. It is continuous because I
can measure age in years, numbers, months,
days, and so on. Gender is nominal, education, number of years of education. I would treat it as
discreet as whether you complete one year of education or you
drop out in between, that year is not counted. If I drop out just
before my exam still, I'm not called as a graduate, I'm considered as
a college dropout. If I have an education
of 14 years, it means the 14th year of education exam I have given
and I'm not a dropout. I have from, from 12, that is 12 people
who have completed 13 years of education and
so on till the age of Phd. Marital status, again, it is a nominal field because whether you call it as a
single and partnered, or partnered, a single does
not make any difference. How many days in a week
do I use the treadmill? Now, here I call it a discrete. Whether I use the treadmill
for 5 minutes or for 5 hours, I'm still going to consider
it as one a many a times. We go to the gym and we get a call and we leave
the gym and come back, we either say, yes,
I went to the gym today but could not exercise
for the full 1 hour. I got a call and I had to
come back in 10 minutes. You still consider going
to gym as yes or no? There is no gray so that's why we consider it as
a discrete data. There is nothing called is
3.5 or 2.5 in case of age. Income is again
continuous because your income can have decimal points after
taxes and before taxes. Miles, number of miles you
run is also continuous. Because I can say that I
ran for 2.5 miles in a day. That's a valid number
product, it is nominal. Fitness is a score which we are giving from a rate
of one to five, that how fit am I as a consumer. Again, this is going to
be a discrete number, or I can call it
as ordinal number, because a person who is having
a fitness score of one is not considered fit versus a person having a
fitness score of five. Now as I have an idea about the data types that I have now, I should just go ahead
and frame some questions. So the questions, could we how many products did
I sell or did we sell? Right? How many
products were bought by men and similarly by how many of. I can go ahead and say, do I have a pattern
of married customer versus single when it comes
to buying our product? I want to understand if there is a pattern of married
versus unmarried. Does income have an
influence on Miles Run? Does gender have an influence on Miles? Does Have an influence on the product type bot I've just
listed on a few questions. You are free to
add more questions because once you
have the questions, you will go ahead and
start analyzing the data. How many products did we sell? I can just keep my
cursor on the data. Click Insert, And it's
already in a table format. I'm going to click
on Pivot Table. Click on the new
worksheet and say, okay. Now I wanted to understand how
many products did we sell? I take product in rows and product in values.
Very beautiful. I have got the analysis, but as I know that a
visual is more better. So I just come to insert
and recommend a chart. I have my chart in front of me. I remove the grid
lines and the Gens and go ahead and hide
on field buttons. I can add the data
labels and remove the axis in the vertical front because it's very clear
that what product, how much did we sell. So I'll have my
first cup writing which says that I
have sold lot of TM 195,498.798 comes at the second and third
position respectively. My next question is that how many products were bought by men and similarly by female? I can go ahead and replace this. I just copy this and
paste it over here. And I want to
understand that how many of them were
bought by gender? I'm going to add up the
gender element over here. Remove the grand total. And remove a grand total. I can go ahead and
insert the chart again. Can you see it's
very clear pattern. Again, let's do the simple
math that I always do. I remove the legend, I
remove the grid lines, I have removed the
vertical axis, I have added the data labels. And increase the size of the data labels to
a viewable size. Okay, You might say that why
did I remove the legend? So let's keep the legend
back and let's get it here because we will know
what is male and what is fee. Now if you see by
default Field button, by default it has not
taken the correct color. Female is taken as blue
and male is taken as red. I can either come
here and say move to the end and you
will find that male is blue and
female is orange. I think that's a better way. Orange. I can select
this blue color, orange color and
make it as pink. To say that this is female and men are always
represented as blue. I can go ahead and add a chart title which
says very clearly, Sales by Sales Right now, what is a clear thing that you have identified when
you look at this chart? Let's take a little closer
look at this chart. Yes, when we see 79, I have equal representation
of male and female for 195 When it came to 498, It's just a number difference of one which could be by chance. But when I come to 798, there's a clear distinction between a man prefers
to buy a 798, whereas a woman would
not be interested. I is not convinced
to buy this product, and hence we are
not able to sell. It's a difference of almost
four x 11 versus 46. I'm not going with an exact
um, but an approximation. You can see that because I
have questions in place, I'm able to analyze data
better. Let's come back. Let's go. So I have
answered this. I will say yes, yes. Do we have patterns of married customer when it comes to buying our
product? Very good. I come back over here, I take the same graph, paste it again
after a few space, show the field list. This time instead of gender, I'm going to take up
as marital status. I'm going to go ahead with
my recommended graph. As usual, I will increase
the size of this, get the legend up, hide all field buttons. I remove the grid lines and I remove the grid
lines because it's cross, a lot of confusion. I add the data labels
because it's easier to view because I want to
remove retenant information. I had the vertical. Can you see between
single and married, we have more number of
married customer across product who are buying from us. It's more easier to convince a person who is married
versus a person. We have good representation
even from single. Because if I go ahead
and show the subtotal. Yeah, I can see that. Now I can go ahead and add the
chart title and I can very clearly say customers buy. Now, because I want
to hide the legend, I'm just going to go ahead and
make it more clear, right? Or else prefer to buy or we
are able to convince them. Very good. Now, let's
go to the next part. I have answered this as well. Does income have an influence on Is because I have
income and my I can just go ahead and select both these lines and draw
a correlation diagram. I can just come to insert and draw my
correlation diagram, added trend line, and
remove the grid links. When you look at this graph, what do you understand here? Obviously, I'm not
going to remove the. I can go ahead and format this. I want to start with 20,000 Yes. What do you find? That
in the range of 20 to 60,000 Income and miles
have no correlation. But after this, there is
a positive correlation. Maybe I need to
divide my data into two segments and then
try to analyze it. All right, so I can go ahead
cut this and keep it next over here so that I have
all my data over here. I'm just going to mind versus. Right. Let's go to the next
question that we have. You have seen that when
it's a correlation diagram, I have to pick up the
whole data and not a pot. Now, does gender have an
influence on miles run? I can go ahead. Click
on Insert Pivot. In my existing chart, I can come down over here
and replace one more pivot. The question was that, does gender have an
influence on miles? I'm going to take up
gender over here and I'm going to take up miles
over here. Total miles. Obviously men have
run double the miles, then female, I'm
going to add miles, one more type to get an average, because I remember that
I have less number of females in the overall thing. Right. So I'm going
to just select this, put a comma to get it. Yes. The total miles run by
female is half that of male. The average miles run
by female is still, is still lower than of male. So I can make an assumption
that based on my data, the customers who
come to our workshop, to our shop, men tend to use more and females
tend to use less. Now I can go ahead and try to understand that isn't
influenced by the product. Let's take product over here and let me take
gender over here. Let me just remove the subtotals and just
look at individual. Okay? So just by moving the values up and
down and getting both the averages
next to each other, and both the totals, if you see the total for male
versus female, not a major difference. A total of male versus female, not a major difference. But when it comes to the total, it is different because I have more number of
males buying my product. But let's see the average, if you now see the average
on an average a man. Use the vehicle. 88, 89 miles. But when it comes to
798, we have 1608169. Females are using less
for a lower end product, but if you see female, have bet the men. When it comes to this
high end product though, the total is less. The average miles run
by a woman picking up 798 is much higher
than the main. This helps us driving
our strategies that if a woman is convinced or she's in
a better health grade, I can go ahead and
send her this. She's definitely
an athlete type. It's not about looking at
the numbers individually, at a total, but also
look at it individually. I can go ahead and add visuals which makes it much easier for me to
do the comparison. Now in a slide like this, it becomes a problem. What I'm going to do,
I'm just going to copy this here and
paste it down. But this time I'm going to remove the total
miles, because for me, average miles was
something that has given me an insight here. I will go ahead and
take this fourth one. Okay, I right click and
say selling the data, and I say switch
rows to columns. If you now see, you will find a pattern. Now the pattern says that when it comes male and female are
almost at the same average, but here there is a
dip for a male, right? So I can go ahead and add
data levels which clearly shows if I go ahead and remove
this decimal point, I think the numbers will
come out more easily. Right now it's easier. I'm going to go ahead and
hide the field buttons. I'm going to click here and
remove the vertical axis. With the horizontal axis that this is female and this is male. I might make it more, very clearly that
when it comes to a lower end product, I can. Now you might say, I have
removed the legends. I'm just going to
add this format, the data label here. I'm going to say the
category, series name. May I will click
here one more time. Can you see there
are six white dots, which makes it only one
data label to change. If I do it over here, both of them will change. I want it only at
a particular one. I click one more time, I have more eight white dots. So I go ahead and
say series name. It very clearly says that for 798 females are doing
better than male. There's a dip for others. Male are at an average of 88, 89 women have a gradual right. I can go ahead and add the
chart title, not access title. Chart title is I have raised is by Anita. So good I have analyzed
the data more. Now let's go to the next point. Let me highlight, does fitness have an influence
on the product type? I want to understand fitness and the product type
that they're buying. As usual, I can copy this, come down pasted instead of gender and taking
up the product. Here I want to take up how
many products did the by. I want to understand, if the person is on a
higher fitness range, does he tend to buy more of my high end product versus when the customer is
on a lower fitness range, he prefers to buy off? Medium or low end product. This is a lower end product. This is a medium end
product for 498. Also, the customer are in
the score of 3.4 for 195. Also the customer in
this range of 3.4 we can clearly say that if a customer is rating
himself as three, the chances of him buying
195.498 is higher. And if the customer is
in the range of five, then his chance of buying the higher end
product is higher. So this is what I'm able to analyze or disper
from this pattern. Now you might say dimple. We have the number
two over here. It means that my sales
team was not able to convince this customer to
buy the high end product. So this is an opportunity
loss for me here. I would say it's an
opportunity gain though. The customer said, I'm
an average customer, but my salesman was able
to influence him to go for a higher end product at
a fitness scale of four. There is not a defined pattern
that I can find from here. Now you might say
that how did you understand it's more
the data you see, the more you will
understand the pattern. Now, drawing this
pattern visually might not give you
such a great impact. We might just decide to need it. I see over here I'm not able to, so I'm not able
to get the zeros. All right, so it's
not very clear, so I will leave it as
a table format itself. With that, we have
completed the analysis of six of the questions
that we have over here. 15567, Right. So I would request you to play with your data and then
go ahead with this. Okay. Before I let you go, just keep your cursor here. You have the Analyzed
Data button. This is available
in Microsoft 365. It the minute you click on this, it starts analyzing the data and it has shown some points. It says, Education 16
is noticeably higher. Miles usage of 3.4 have higher income
show 29 more results. This is a frequency
distribution. Age and income appears to be
in two different clusters. Education 16 have noticeably
higher income frequency of age fitness scale of 3.5 have
higher frequency of miles. I can get my lot of analysis pre done for
the product, 195. Fitness three counts for
the majority of income. It has done a complete analysis. If I click on Insert Pivot, this gets as a pivot. It has taken the ten, the fitness and said that, okay, Fitness three accounts for the majority of the
income for 195 product. But if I go ahead and
change this product, you will see that the
income is least for fitness is maximum for fitness five and not
for fitness three. You might get some
idea from here of how to print your date
if I select all, then again, fitness three
accounts for the majority. But because here it
has analyzed for this, it is, given the graphs. In this way, I can go ahead and ask questions
over here about my data, total miles and product, which product for
marital status. I can go ahead and
get my data analyzed, or I can go ahead and
write my own questions. Ask a question. Let's pick up one of the questions
that we have asked. Does gender have an
influence on my It will immediately give me gender
influence on miles. The minute I click over here, it will go ahead and
do the port for me. It has done the sum of gender. I can go ahead and add the data. Show the field list
at the Mis here. I can go ahead and
see the average Mis, so I can get the help
from here as well. Let's say I have a question. This gender have an influence on minds product. I'm asking two questions. So it says yes. If you now see the gender wise thing
it is showing in this way is by gender
and product abate mind. If I'm writing my question
in English still, it will generate and give
it to me separately. Right. I can click on and
remove the decimal point and I can have my data labels
remove the grid lines. I can go that. It's very set. I can move this with a pig and you'll find that
it's all getting updated. This is male, this is female, and these are the average mice. It can quickly draw
things for you. Remember, the data
is kept separately. It is doing these suggestions
for us separately. Great. I would request
you to please practice on your data and come back to me with any questions
that you have. Thank you.
18. Practical use case for Storytelling using Data: Hello, friends. Today, we are going
to explore with a real life Kay study how we can do
storytelling with data. So let me share my screen and explain to you what data I have. As you can see on
my Excel sheet, I have data for FANS, and this data is for
two financial use. I have different
types of products. I have the invoice month. I have the data for KG, taxable value, material food, where it is done. What is the value? What type of channel, and so on. Now, I need to create a
story from this data. I'm going to teach you a mix
of how I can use queries, plus create dashboards using
slicers and build my story. The first thing I'm
going to do is I know where this file is saved
and I'm going to close it. Now I have opened a blank Excel sheet where
I want to do my analysis, create my storyboard, and communicate my stories to the senior management from here. I go to the data
in the maneuver, click on Get data from File
and from Excel Workbook. So because data is an
Excel workbook format, I'm going to say
from Excel Whoo. If I had the data in JSON PDF or it is a
multiple files in a folder, I would be using appropriate
item from the dropdown. You can also connect
to the Azu data lake, Power platforms, and other
sources like ODBC and web. So this is the beauty of the queries that are
available in Excel. So as my data is an Excel sheet, I'm clicking over here. So my data is sales
data for Dashboard. Because I have multiple sheets, it is asking me which sheet
do you want to pick up? Let me just click on each
of it to see which data I have in case I have
forgotten, right? So I'm going to use
a safe data sheet. I'm not going to use a
sheet two and sheet three. I'm going to click on transform data because I
want to delete few records, delete few columns, whatever
is not matching my criteria. And I don't want
to sit and write any MCOs to do that work for me. So the year of sales I need, the invoice month I need, the plant number I need, unit sold I need, invoice number, I don't. So what I'm going to do
is I'm going to come to invoice number
and say remove. I don't need the packing bit, so I'm going to say remove because I see there
is a lot of null. Number of bags I want to remove. I'm not interested in doing analysis of pride at
this point of time, I click on remove. I need the quantity
that was invoiced, plus I see that net
weight in cages. I have the weight in cages, plus I have quantity. I'm going to keep the
net weight in cages, but remove the invoice
quantity because remove any unnecessary
information. Taxable value in INR, material code, material group. I see that there is
a proper group over here which says that what
type of products do we have? And this is the SKU code. So I don't need the SKU code, so I'm going to RIM I have
the region description, the customer city,
brokerage value, final amount in INR, final value, inclusive of taxes. So I'm going to
remove broker value. I'm going to remove final
value, including taxes. I'm just going to keep
the final amount in IRR. I don't need the price per unit. At this point. Okay,
let me keep it. What is a sales order quantity? Because I already have
the quantity earlier, I don't want to keep that. I have bulk customers. I have the names of
the salesperson. So I have AR, units, its hounds as, et cetera. I have some categorization
as by product, packing material, and so on. I don't need this so I can go ahead and remove it
or let me keep it for now. I have also created a field
call as week or I have a field call as week
number which says at which week of the
year it was, right? So I will keep that. Now, I'm just going to go
ahead and say save and load. Excel is running
the query for me, and it is loading
the records I have. It's almost more than
101 lack records. It's still loading.
Let it complete. Quite a few. Ten
lack records I have. Let's just see quickly what's
there in those records. Otherwise, I'm going to
update my query to do it. When I see here, when I'm
clicking on column A, it is telling 45,696. But here I'm seeing
ten lack records. This could be because
it is also taking the blank records
into it, right? So I don't need
the blank records because it's unnecessary. Do I have to manually delete it? No, I'm going to click on query and I'm going
to edit the query. And over here, I'm going
to take the drop down. I want to say load more. Is loading some more data
from the sales data. And it is showing me the null. Now, what I'm going
to do is I'm going to untake the null
and say, Okay. So it has filtered the row. If I come here, it is telling in the rows where is
not equal to nu, keep that so that if the
next year data comes in, that will be available,
but if there are any blank rows, it
will be deleted. I don't even have to worry
about writing my formulas. Monoclcon save and
load the query. Now let's see how many
records are coming up. So sometimes it's
showing records, sometimes it's showing
us MB of data. So I have now 45,000
records which are available. It's loading. If I keep my cursor over here, you can see that it's
showing a pop up. It's telling that
the data source is still getting loaded.
Let it finish. I'll wait for a few seconds
while you enjoy the music. What is more important for
me as a storyteller for influencing is that now I need to come up with questions
that I want to know. What I'm going to do is
I'm going to list down the questions that can help me. What is the sales by region? What is the I'm going to
just make it more easier. What is the sales? By region, B salesperson trend
by e, if possible, compare the two years by month, by weeks so that we know how it is by product and by quantity. Now, do I need? These are the questions which I am
getting immediately to my mind. Obviously, I'm going to
increase the number of questions as I'm going
to start analyzing it. Sorry for the spelling mistake. What I'm going to do, I'm
going to click on SS data, keep my cursor here, click on Insert pivot
table and say, Okay. So my first question, what
is the sales by region? So I'm going to take region description and
total taxable value in IRA. I see that I have sales
across all the regions, I'm just going to make the
format theater easier. So by region, I have the data. Let me just see there
is no duplicate Cities or yes. My concern is places where
it is either zero or black. So what I'm going to do is
I'm going to come here, see the region description, pick up the zero and pick up the blanks
and see what's happening. Okay, so here it is
telling us Colombo. So it could be
something that I don't want to get in because
it's not the region, so it could be the export data. So I'm going to leave it. I can go ahead and
do one more thing. I can update my
query to not have any regions which does not have wherever I have the bland. I go to query, edit. So you are seeing it so easy for me to go back and
update the query. So I'm going to come here, take the filter,
remove the blanks. I'm going to say load more because there was one more place where
there was zero, so I don't need
the zeros as well. So I'm going to untick four. I'm going to select everything. I'm going to untake
zero and blank, rest while I'm keeping. So if there is a new region
with x put up over there, so it will automatically
include that in future. So it says table Seleco. I don't have to worry
about writing the formula, the coding is
automatically done. Close and low. Right?
So my safe d updated. Let's come here and
refresh my driver. I think I just need to
refresh my queries, so I'm going to come
to the queries and say refresh. It's fresh. Let's just wait for a few
seconds for it to refresh. But as you saw, my
first question was, I want to meet
this sales region, salesperson, and trend by year. So let's solve one at a time. So I'm going to
refresh so you can see the zero and the
blinds are them. I'm going to click on
Insert recommended chart. And as you remember, we said, whenever I have more categories, I'm good to use it
in this format. Right click sort
largest to smallest. Right click, hide all feel
buttons, remove the legend. I'm going to include
the data labels. I will click on
the axis and make this value in millions
or maybe in crows. Let's keep it as lax thousand
10,000 hundred thousand. That is lax. I'm going to
change this text flux. Okay? Now, this is a little difficult for anybody to
do if I'm elongating it, that I have the
information of all of it. Right click sort,
small too large. In this way, I'm
getting the order. Instead of calling it as total, I'm updating the chart title as sales across in. Now, you remember there is a problem over here.
What is the problem? I have data for two years. So that is why I don't know
which belongs to what. I can either complicate it
by adding the year over here and distributing it or else I can do a
simple thing. What? I can go ahead, insert
the slicer and say that I want to know
by invoice moment, by plant, by material groove, by region, by salesperson. And I think this is
good enough for you. I have got enough slices, right? So I'm going to take those
slices to my dashboard page. Let's select all of it. And my graph, click
on Control X, go to this sheet and paste it. I have data for two years. Go to the slide, make
it as two columns, change the color, make it do. So first is my year. Then I have the plant number. I have four plants, so I'm going to make it as four. The size is little
uncomfortable, so I changed it. Change the cl. Now invoice month, there
are obviously 12 months. I have an option to make it as 4360 and good enough
for me to have a look. So this piece, I'm going to
keep it over here or maybe extend it till here
and make it as zero. I'm going to change it as
six and reduce the size. Change the color to orange, and even if it is SEPT or DEC, we understand what it is. Now I have some material group. Let's see if it's coming. Oh, there are way too many
material in my group. Let's see if I make it
as three good enough. I'm going to make it as three columns and
keep it over here. I have the names of
the salesperson, so I keep it over
here next to the S, the beauty with the
slices or the dashboard that we are creating is to ensure that you are able to
do the analysis quickly. S at the same time, it becomes easy for you to run your queries and
explain it to them. Now we see the plank and
the zeros are still coming. It's natural because the slices were created on the total data. Let's make it as four
and reduce it trick. So I have my menuar over here. I'll change the color. Let's make it a little vibrant. And I have the data
for sales by region. So I have done what is
the sales by region. Now I want to get the
sales by sales person. So I'm going to come here, copy my table, leave you
rose, paste it over here. Instead of region, I'm going to take the names
of the salesperson. Right? So sort it, the largest to smallest, insert recommended chart,
and I have the performance. Remember, I had taught you that whenever your XX is category, you are not going to
use a line chart. Right click hide
all feel buttons. I don't need the legends, I don't need the grid lines. Obviously, these
numbers are very big, so I'm double clicking on it, making it as lags. And I don't even want to p this because I'm going
to add the data label. Right? And I'm going to say, this is the sales
performance by Sames person. Right? Again, I have
the data for two years. I would suggest, in this case, I'm going to keep
the year over here, making it easy for me to
do the comparison 2023-24. It's very sad to see that 23
is more this year is less. I'm going to add
the legend because I don't know which
color repres spot. One of the rules for
storytelling with data is 2024. Is to remove the legends, remove things which
are not important. So I'm going to come here, make this as gray. This is my current year, so I'm going to keep it as
this then delete the legend. Color will and this one filled. So I have a comparison
of 2000 versus 35, sales and lack by salesperson. I'm going to cut this four here, and paste it over here. So I have got my cut by
region by salesperson. I want to do a
comparison of two years. So simple, copy
this, thumb down, paste it, right click
Show field list. Now, instead of salesperson, I will put it as
the invoice month. Oh, I can see that there is no data for some of the months. Maybe the company is
following the financial year. What I'm going to do is click on January move to
the end February, move to the end and
March, move to the end. So I have the data
from April to March. I have data for 2023. I have data for 2024, but I don't have the full data. So this is why it is showing
a different numbers. I'm going to click on
insert recommended chart, and this time I'm going to
chart tie out my e chart. Cut, go to my dashboard, and paste it over here. Right, click. Hide all button. Obviously, I'm going
to use the same title. Instead of B salesman by month. And 2024, I'm going to select and change
the line to be this. And 2023, I'm going to select and change the
line to be create. I'm going to remove
the grid line. I'm going to remove the legends. The amounts are huge. Come here, make it as lax. Come down to the
labels and say, none. I have the data. I'm going
to sell in the last bit, right click, and I'm going
to add the data label. Selling the last bit, add the data logo. Right? So I have the dat trend for
now I can understand why the performance for 2023
is higher than 2024. What I'm going to do is
I'm going to pick up those months which are not available for 2024
and remove it. I don't have data for
Januar March and December. So now it's a proper comparison. How is the company doing? Let me put on my thank
you for your patience. So I have got the
data, by region, by trans person, trend by year, if possible, compare by month. I've got by month also,
and I've got a total. So now for the total year, what I'm going to do
is a simple thing. I'm just going to take
this a little differently. Let's keep the salesperson
name over here. I'm going to use this
data to get a total for 2023 and 2024. I'm going to say equal to go to my sales data
and performance for 2023 and go here and get the data for 2044. I want to also get the high
so I'm going to do this. Minus this divided
by the base amount. So there's a 16% interest. Numbers are looking huge. It's very difficult
for me to read it. So what I'm going to do is I'm going to take this and keep it over here and I'm going
to say equal to this. So I'm going to
just round it up. And divided it by
one lag thousand, 10,000, one leg up zero. So I can go ahead and save. Now, when I'm old to do this, it's going to be a problem,
so I'm going to leave it. The data is going to
refer to 2024 and 25. So amount in. Right? So I want to understand how let's increase
the font site where it's clearly visible
and highlight it. I'm going to click on view
and remove the grid line, remove the form lover. Just having a quick look, is it looking proper? Yes. If I get it on one screen, I have the full data. Now, what else is missing? So I have B region,
bisson if possible. This is done by month. Can we get it by
B and by product. So this same thing, I'm going to coffee. Paste it over here. So we have covered by
regen by salesperson, by trend by year, if possible by week by month. Now I need to get it by week
to do a proper comparison. I'm going to speak
up the month data, copy it, roll down
a little, paste it. I'm going to show the field
list instead of the month. I'm going to take
the week number. So I have a proper comparison
between the two weeks. Insert. I'm going to take the fourth chart so that I
can do a proper comparison. Remove the grid lines, make the amount in
flag for consistency. Hide all buttons. Maybe this chart
makes more space. And I'm going to cut from here, take it to my dashboard
and paste it. So now you can see, right, beautiful it's
coming next to it. H. So I have the data
very nicely put up. I need to add the chart title, click over here in the plus
sign is a chart title. I'm going to save sales
performance by week. Save performance
by week and change the colors or the orange ones and the blue ones
are now with them. Oh So I'm doing a comparison 2023-24
by weeks in terms of. I'm not going to add up the
data labels because it's going to get very messy, right? So now I need it by product. Either I can create
a separate group or I can use the slicer to
understand the product. I need the total
quantity, come here. Copy this, roll down
frost, paste it. Now, instead of showing
the taxable amount, I'm going to take
net weight in cases. I don't need it by one. I just need the totals. There's a very simple option. I'm going to do the
same thing that I did over here of fight in 2023 2024 and writing down
the quantity over here. Let me just move this
left and increases. So I'm going to say
equal to come here, take the 23 value, and I'm going to come here
and take the 24 values. Now, this isn't Kg. If you divide, so we know right, 100 Kgs will make one, and thousand kgs
will make 1,000. So I'm going to make it
as pans to take it up. I think I have hidden the formula bar to show the
formula bar and come here and say round this divided by thousand
who get the amount. I can just pick this format
and put it up over here, take it as center. I have the amount in tons
with the title right. Increase the font. God no. Let's just tb a letter. Increase this font. So I have information of
lax, quantity and weight. Let's do a quick test. I'm going to remove
all the filters. Yes, I can see
there's a decrease in the volume because I
don't have the full data. I want to see only for 2023. Now I have a problem. It's showing an
error, hash hashp. I don't need this error. So what I'm going to do is I'm going to play a little game. Want to move this inside, and I'm going to say equal
to If error for this value. If there is an error, say. And here, I'm going to say I
error for this, then you say And this I don't want to show, so I'm going to
change the fill color as white and the
pot color as white. I just played a
little gimmick to get my information in the
most easiest spot. So this is protein. So let's just let everything
and make it as Extra. Right? So let's
do a quick check. I'm going to select
this unselected months. I'm going to remove the filters. This is not come out properly. I'm going to make it
as a percentage good. If I'm doing it only as 2024, one year, then
percentage is not. I want the details
only for say, bread. So what I had a
requirement by product. So for product, by
month performance, by week performance,
by region performance, and by safe person. I can see Suresh is the person who is selling bread mostly. I want to understand
who is selling dow. All right? So I have
all these details. Quantities are also
getting updated. So I'm going to now go ahead
and delete these columns. I'm going to hide my formula. I'm going to go to
the full screen. So are you ready for the action? My screen is looking
pretty good. Easy for me to do the analysis. I want to know how
is the performance. So I have the data
for all the years, all the months, all the plants, all the regions, all the
people all the material. I want to understand how is the performance of bread across. So is the person who
is selling bread maximum and others are not contributing to
the sale of bread. So what is that
Punit is selling? I can take on the material, come here and select Punit. Punit is responsible
only for two regions, that is deli and MMP, and he's selling a
variety of products. We can see that there
was a sudden jump in August in the performance
of SNY last year. That is why the gray
color has spiked out. Maybe there's something that
happened during that time. I'm going to remove
the August data and see how is he performing
for the two years. We can see that forNE there is an improvement
in sales performance, and soon as I remove August, you see that the region
also has reduced only one. I'm going to untick all and
just select August for Punib. So during August,
Punip has sold to both the region that
is MP and Deli. The amount is really in 2024, during August, the
ceiling have been very down as compared to 2023. But we also saw when I'm
seeing the overall data, there is a sudden
spike in performance. So I'm going to untick the data for which I don't
have the comparisons. Like multi select, take the
data where I don't have comparison and remove the data for August to see the
performance of Sui. Get the August back. I want
to see how did Suraj perform. So Surig has seen only a 5% jump in sales
between two years. Now I have the data of Sur. Now it's showing the
comparison of years. The numbers are almost the same. There's only a 5% growth, and we can see more
or less his week by week performance is
hovering around six. I want to understand
a simple question. Who is serving deli? When it comes to deli, only Ritu and doing
the major sale, the sale tu is not consistent. That is why Ritu has picked
up the momentum in 2024. In 2023, the sale is higher, but it is not consistent, and that's why my data
is in broker line. Okay. Let's see what is unis doing? Huniil is very nominal. That's why it is
almost like o like, so maybe some nominal scenes a. I want to see
the performance. Who is selling most in Odisa. Again, in Odisa I have Ritu and Shura who
are contributing. RipoPerformance. 2024 for Odisa has improved. The products have been Atta, caster, bread, and dough. I want to see with all
regions that Suresh serve. Surase is serving in
a variety of region. Major thing is coming
from Karnataka, followed by Masta and all
the other regions phenomena. Where does Sunil serve? Almost same bad as Ritu. So I can see Vitus focus is on under and Karnataka is
almost at the bottom. We're doing a nominal scene. Whereas if you come to Sunil, he's focused on
MP and Telangala. Suraj is focused on
Karnataka, Ma, and guija. Amr is focused on
Karnataka, Agra, and so on. So I'm having a pun view. I can click on each of it to see things in a more
specific detail. By the same person, buy the year, buy the money, by the material group, by the region, and how
is the improvement. So in a simple way, I'm able to get a complete
view of my performance. Now I can drill down and ask questions specifically
to my salespeople. Let's say I want to see the
performance for November. So during November last year, this is how you had performed. But this year during Amer, there were no sales
in the 44th week. I can go ahead and question
Amer what happened at yours. Overall, there's a 50% jump because he was focusing on
the marketing activity. He may give this explanation. Amor is focused only on three
regions. What about Pune? Pune, we can see that
Ritu, 32 person jam. Sin eight person
jam, Sura, 13% jump. So who is the best
performing person in the month of November? It is obviously say maybe
he has a bigger portfolio. He's a experienced guy
he's a head of sin, right? So you can create
dashboards like this with real data and
do a lot of learning. Thank you.
Dimple Sanghvi, Master Black Belt, Data Scientist, PMP
