Did you know that ninety percent
of the information processed by the brain is visual? Human brain takes 13
milliseconds to process an image, that's 60,000 times faster than processing text.
People also retain 80% of what they see, compared with 20% of what they read
and only 10% of what they hear. No wonder, then, that data visualization
tools are in high demand. Visuals provide us with an effective way to get our point across,
especially nowadays, when we're dealing with poor data than ever. And not all visuals are created
equally though, some help you uncover dangers or opportunities that you never even knew to look
for. I'll cover my favorite Power BI visuals I wish Excel had. You're going to see how easy it
is to turn your data into actionable decisions. Number one is the Ribbon Chart. This is great for
viewing data over time and also keeping track of rank changes. It's right here. Let's add it to
our report and we get an idea of how it's going to look, but let's bring it to life. We're going
to add total quantity to the y-axis and let's add an element of time to the x-axis. So here, I
just have three months of data in this model, I'll just add in the months. Now, I get
something that looks like a column chart, the ribbon is going to form when I add a category
to the legend. So, I want to take a look at the different article categories and how these
developed over time. Let's add that to the legend and now, our ribbon comes to life. The way
this works is that the highest range is always displayed on top. We can see our columns in the
background. We have now stacked columns because we have different categories. So if I hover over
this, we can see the total quantity for business, for casual clothing, party, and so on. And, the
beauty is the lighter areas in between. This is where we get to see the rank changes. Just like
in the music charts, where you get to see a change in rank, it's a similar concept here, and all of
this is built in. So our business clothing here, dropped two ranks. It was number one in February,
it became number three in March. And this one, right here, that's our casual clothing, it went up
one rank to first place. Now, the ribbon chart can be a bit difficult to read, so we can make some
adjustments here to make it easier. First of all, on the x-axis, we can turn off the title, let's
turn on the data labels, and let's also activate this Zoom slider. Here, if you want to get a
closer look at January for that bottom category, we can zoom in and we see, oh yeah, this is
our Sleep category and we sold 500 items. Now, this can get quickly overcrowded, if you have
many different categories. So, it could make sense to add a filter to the visual and just restrict
it to your top categories. So, for a filter type, we would go with Top N, we're going to base this
on total quantity and we just, say, let's take a look at the top three categories. Number two
is the decomposition tree. This is an AI Visual and it lets you visualize data across multiple
dimensions. You're going to find it right here, let's add that in. We get to pick what fields
we want to analyze, I'm going to go with profit. Now, we can decide what we want to explain this
by one field can be our article category another customer gender and perhaps customer City now we
get the Plus show up here we can choose how we want to split our data when I select this I have
the option between the different fields that I've added and also this part with high value and low
value. Notice the light bulb here, that's the AI feature. This might help you uncover issues that
you didn't even know to look for. So, let's say, I'm going to go with low value. The AI is going
to get to work and decide what category it should show me. It picked City, Garland. We have negative
profit here. Where is it coming from? Let's dig deeper. Low value, just from our sleep category,
and if we go again to gender, it's from other or NA. Now, once we've picked what we want to see,
we can apply that same pattern to different values here. So, for example, for LA, if I just click
here, it's going to find the low value, so it picked other/NA for gender, and it's coming from
the casual clothing category. To see the different categories for female, I just have to make a
selection here and I can see party is where we have the lowest profit. Now whatever you want you
can start the process again and look for something else. So you could decide to look for high value.
This time the AI picked the gender category. Now you also don't have to use the AI feature, you
could go and slice and dice as you want. So you're going to go with gender, let's drill down
for male, by category and notice here, the party category is making negative profit. And if I want
to see where it's coming from, I'm going to drill into City. It's coming from Seattle. If you want
to lock a field in place, you can. So notice, when you hover over these, you get the lock
icon, so if you always want to have your first drill down to be gender, you can lock it in place
and then leave the rest flexible. Okay, so this is a great visual for getting information about
your data that you didn't even know to look for. Next up is the scatter chart. I know Excel
has one but it's not as cool as Power BI's. Check this out, we're going to add the scatter
chart. Let's bring over the sales value to the x-axis, profit to the y-axis, total quantity is
going to go for the size of the legend. Okay, I just have one bubble now, but we're going
to split this by customer state, so I'm going to add State as the legend. Now, I can see my
different bubbles here. So this one is California, it's all the way up here. Now, what makes this
so different to Excel, well, it's this part, the Play axis. We can add a time factor here,
so I have my dates right here, in this case, I just have three months of data, so I'm going
to grab the month and drop it in the Play axis. This is also difficult to read with the legend up
here, so let's go and quickly format this visual, turn off the legend, turn on the category
labels, and let's also make them bigger. Okay, so California is all the way up here
in March, but was it always like this? Well, let's play this. We see January, February and
March. If you have data for longer time periods, you are going to be able to just play
this and understand the change over time. And last, we have infographics and custom visuals.
You see, the great thing about Power BI is that, you aren't even restricted to what you see
here. You can go and grab more visuals. From here, you get to search for what you want.
I'm gonna go and grab the infographic designer. Let's add it in, it was successfully
imported and I can see it on the bottom here. Each custom visual has its own settings but let me
show you something cool you can do with this one. Let's add it in, add in gender and total quantity.
I'm going to change up my measure to show values as percent of grand total. Okay, so now, let's
go to formatting options and see what we have. We just get chart, we can pick between different
types, I'm going to stick to column. We can turn off the X/Y axis, but where can we add our shapes?
Well, now here, we can add it in, right here. Okay, let me just expand this so we can see
the options better. I'll bring it up and we get specific designer options. This is where we can
add in the different shapes that we want. If you want each column to have a different shape, which
in this case I want, we're going to go to data binding off and select our data binding field,
this is gender. Now, we can select a different shape for each of these. When I select this, I
can pick different shapes from here. Under people, there's a female one, let's go with that. Male,
this one right here. Other/NA, I don't find what I want here. No problem, we can upload our own SVG
file. I happen to have one here let's select that, and add it in. Now we can apply this and we see
these update. But I want them to have the same height, so I'm going to go to layout adjust this
setting to be outer. Okay, so things are looking better. We want to add our filling based on the
percentage, so fill percentage is going to be based on percentage of total quantity. But notice,
female is fully filled in. I don't want that, so we're going to add a maximum value of 1. And
now, the filling is updated accordingly. Now, you have more options so you can adjust the color,
if you want. I'm going to turn this on and go with a light yellow color. You can also add the actual
percentage to this, so we can go and add text. I want the text to be dynamic, it should be a data
label based on percentage of total quantity, let's apply that. Go back and adjust the formatting,
let's make it really big, make it bold and add it to the bottom. Okay so, that looks good. Let's
make this smaller. Now, the great thing about this is that, it works similar to other Power BI
visuals. You can cross-highlight and cross-filter. Let's say, we had a bar chart in place that was
showing our profit for the different categories. When I select the different categories, my
visual is automatically cross-highlighted. I hope this brief introduction to some cool Power
BI visuals inspired you to be creative when you design your reports, and also to experiment
a little bit. It just takes two minutes, maximum, I guess, to add a new chart and tweak it
a bit to see what it's capable of. If you have a favorite Power BI visual, let me know what it
is in the comments below. Before you leave, like, subscribe and I'm going
to see you in the next video.
