All language subtitles for 5. Power BI Demo Decomposition Tree Visual

All right.

It's time to talk about my all time favorite A.I. visual and powered by the decomposition tree.

Now this one still a preview feature.

So you're going to want to make sure you have the latest version of power by desktop installed.

And once you open it up you need to head to your options and settings open up the options window and

then we can do is drill into your preview features.

Just give that checkbox at check where it says decomp position treat visual and press.

OK so you'll probably be prompted to restart power by go ahead and do that and you'll see this nice

little icon here.

Kind of looks like a flowchart or a process diagram with that a light bulb.

That's our decomposition tree.

So if you'd like to follow along with me go ahead and open up the visuals file and we're gonna create

a new page here called D composition tree.

Let's go ahead and give it a click drop in under a canvas resized things a bit.

Looks good now you know me.

Before we start tinkering I always think it's important to talk about the why.

So why are we using this visual.

What's the objective and what I love about the decomposition tree is that it's great for ad hoc data

exploration.

It's a great way to understand how your data is distributed and it's an excellent tool for root cause

analysis.

And what I love about it is that it's smart it's intelligent it's a.i. Driven but it's also really intuitive

and really user friendly.

So let's go ahead and give it a click and you'll see kind of two fields that should look familiar from

our key influencers visual we've got and analyze well and then explain by well.

So it's important to note is that anything we pull in the analyzed field here needs to be a quantitative

metric so we can pull a categorical field in here and project outcome but it's going to get aggregated

either as account or distinct count.

So really what this visual is designed for is for continuous or quantitative fields things like the

amount pledged in U.S. dollars for instance.

You can see that by default.

This was aggregated to a sum which in this case makes sense.

So not really impressive quite yet.

Right.

We've got a little bar here showing the total amount pledged five hundred and forty four million now

where it gets interesting is when we start populating the explained by fields and think of these explained

by fields as the ways that we'd like to break down these dollar amounts this amount pledged.

So I kind of like to think about this like a hierarchy right where I want to break things down at a

high level maybe like category first and then a little bit deeper like subcategory and then we'll go

all the way down to the project name and you can actually drop these in in any order.

It's just kind of the way that I think about it my head and you'll notice nothing really changed here

except this tiny little plus icon appeared which was so subtle that you might have missed but what that

does is allows us to build our composition tree and we've got two options here with light bulbs.

These are the A.I. driven options where we can find out what makes our metric the highest or the lowest

or we can manually select one of the fields that we dragged in ourselves.

So I'm going to start there because it's really clear really intuitive and also a really powerful option.

So I'm going to take my amount pledged I mean to break it down by category.

Boom there you go.

Instantly I can see that game's design and technology products make up the bulk of my amount raised

or amount pledged.

There's a big big drop off before film and video publishing.

And then I can actually scroll down a bit to see dance here at the bottom.

So from here I can keep building my tree.

I can select any one of these nodes and break it down at another level.

So subcategory for instance.

Here we go within games tabletop games.

Clearly the biggest driver here.

And let's drill one level deeper to project name.

And there you have it.

We've built a decomposition tree in about 30 seconds and it looks great.

And check this out it's totally interactive.

You can select any of these nodes totally dynamic.

You can see exactly how your data is distributed where you'll find the bulk of the volume.

It's just a great very user friendly tool.

And honestly if they left it at that it would still be a pretty valuable addition to the visualizations

pain but where it gets really cool is with the A.I. features.

So let's close those out and let's go back to a plus sign.

Now what if we choose the A.I. driven high value.

OK.

We see the same result that we did manually pulled in the category field and it's showing games at the

top.

Same order here kind of descending in volume.

Now what we didn't really realize happened is that power b I looked at all of the available fields in

order to find the one field with the biggest chunk of volume that happened B category with games here

which drove one hundred and forty four million dollars.

But if for instance we had country as well now all the sudden US is the top driver and it's really just

based on the ratio of that four hundred fifty four million out of the total.

And that's what determines the rankings here as well as which field was selected to be displayed.

And that's nice but it's not really artificial intelligence.

It's not that smart or that impressive but where it gets more interesting is if we go into formatting

pain drill into analysis you can see that by default the analysis type is absolute.

In other words it's based on volume.

That's why we see always the kind of descending by volume we see the biggest categories pop up first.

If we change this to relative watch what happens.

Everything changed.

Now that A.I. driven high value is showing project name which is the lowest most granular field that

we have and it's showing this top project The Seventh Continent has the biggest relative factor that

contributes to the amount pledged.

So the simplest way to explain kind of the rationale here is that basically power by saying OK.

Given all of the project names what would I expect a project to drive in terms of pledge amount.

In other words it's the average across all projects in the entire table.

And the reason why this project here The Seventh Continent is appearing at the top and why project name

is appearing at all is because the difference between what this project drove and the average among

all projects that difference was bigger than any other difference in the dataset.

It's bigger than what the US drove as opposed to the average among all countries and it's bigger than

what games drove compared to the average among all categories.

And that's why A.I. has determined that project name should appear here and why The Seventh Continent

is technically the biggest relative driver towards the amount pledged.

So really interesting findings here that would be very tough to kind of realize without doing some pretty

heavy analysis on your own so that let's go ahead and clear that out.

I'm going to kind of recreate that initial tree that we built because I think it's a pretty helpful

one category subcategory and project name.

Note that if you hover over the headers here you can lock specific categories.

So if you always want to start at the category level but you'd like users to be able to rearrange these

or pull them out you can go ahead and lock one category or one column here of your tree but leave the

others flexible now.

Last thing I'll call out here that makes these visuals so great is that you can treat them just like

any other visuals.

You can format them.

In fact Microsoft just released a lot of new formatting options for this composition tree visual specifically

so you can change the look of your data bars if you want.

You can change the connector lines all sorts of formatting options here and perhaps the best thing of

all is that you can filter and cross filter these visuals based on things like sliders or other visuals

in your report.

So check this out.

It's as simple as dropping in a map for instance or use a filled map here put country in and now check

it out.

R D composition tree is now dynamic it's tied to our map we can look at the UK France Canada the US

and everything updates dynamically.

Again really powerful tool when it comes to ad hoc data exploration data distribution and root cause

analysis.

So there you have it the new a.i. Driven decomposition tree visual.