>> Hello, I'm Dona.
>> I'm Sarah.
>> I'm Justyna.
>> Today, on Less Code, More Power,
something that's never happened before has happened,
Sarah and I are speechless.
>> It's true.
>> You are also going to be speechless when you learn how
data saves lives. Stay tuned.
[MUSIC]
Hello, welcome to Less Code, More Power.
Today's episode is unlike any of
the other ones you've seen before. I'm Dona.
>> I'm Sarah .
>> I'm Justyna.
>> Today, we've got an extraordinary guest,
the guru of Power BI.
>> Yeah.
>> Yeah. That's right. So I saw her
present at Ignite last November, and I was so
blown away at how interesting
her presentation was, and how fascinating she made Power BI,
a thing that I'm honestly scared of,
that we knew that we absolutely had to have her on the show.
>> You're too kind.
>> Oh, no. Not at all.
>> Yeah.
>> Sure.
>> All of this magic of Power BI and to showcase how
ordinary people like me who know
really nothing about dashboards and visualizations can do it too.
So tell us about you.
Why Power BI? How did you get into it?
How did they find you? How did you find it?
>> It's a pretty unique journey.
I was actually studying politics,
and I was planning on being a diplomat.
>> Wow.
>> Then I realized that that wasn't going to happen.
I somehow ended up in Microsoft,
but I was always really,
really into all the quantitative research and doing all the stats,
even when we do our projects,
they always be gravitating towards.
I wanted to do the number crunching.
So when I joined Microsoft,
Power BI was actually still an Excel plugin.
>> Wow.
>> No way.
>> You remember that?
>> Yeah. I think it was a product group,
I think about two months before we went to GA
with the Power BI desktop product.
>> Amazing.
>> So yeah, I knew that I wanted to get into data,
I wanted to do something with business Intelligence, so I
ended up on the Power BI product group, and it's been awesome.
>> I love that.
>> Literally been from day 1?
>> Yeah.
>> Literally.
>> Amazing.
>> Also, I guess the thing that I found
fascinating about this is the politics piece.
Because I guess from that side of things,
you actually understand how truly data can matter and influence.
As in especially because that's so ingrained in politics.
I love that you're still doing stuff that
you originate it [inaudible].
>> Yeah, totally. Power BI is actually
all about improving data literacy.
That's our mission statement.
>> I love that.
>> Improving data literacy.
>> Yeah. So it's all about replacing
opinions with facts, being data-driven.
So yeah, it's really, really aligned with.
>> That's cool.
>> That is fantastic. That is so cool.
So you've brought a very, very cool demo.
Would you want to talk to us about it?
>> Yes. So this is actually
a demo that originated from a Gartner BI bake-off.
>> A Gartner BI bake off?
We want to understand what we just said right there.
>> We did say bake-off.
>> Gartner BI bake-off?
>> Yes.
>> All right. Please explain what that is
because that is some news.
>> Yeah.
>> So every year, Gartner invites
the top BI vendors for their conference.
>> Yes.
>> A month before the conference,
they send all the BI vendors
a data set and a couple of questions and tasks.
We have a month to prepare
the best possible demo we can with this data set,
and then we all go into a bake-off.
>> You know you're really inspiring me here.
We should do an app off or
an app bake-off or something. We'll park this.
>> Give them a topic, everyone make an app and then present it?
>> Yeah.
>> We're going to park that.
You'll see that on another show.
>> Yeah. That's going to be funny. Then what happens?
So you prepare a demo.
>> You prepare a demo,
you show up at a conference,
all of the other vendors are there on stage with you.
You have a live audience,
and then it's like the data prep round or the dashboarding round.
>> Say, a prep rounds,
so that's actually you have to show them how you've done it.
>> Yes.
>> So some of this demoing what
you've already built, and some of this,
go build it from scratch right now, in front of everyone.
>> Wow.
>> So no smoke and mirrors here.
>> No. You literally should have to
build in front of thousands of people?
>> Wow.
>> If you go over time, there's a buzzer,
they buzz you off, and then
everyone has to vote for who they like the most.
>> Wow. That is an intense amount of pressure.
>> X-factor.
>> Yeah, X-factor for dashboard.
>> For dashboards and visualizations.
>> Absolutely.
>> I did not know about this until right now.
I'm so amazed at this.
>> Yeah.
>> So you're competing in this thing?
>> Yeah. So we've got us,
we've got Tableau, we've got Click, we've got SAP.
>> Wow.
>> Because some real challenge is there.
>> Absolutely.
>> These are some hard hitters.
So tell us what's the word?
We're getting a sneak peek of what they're showing, by the way.
This conference was months ago.
>> Yes.
>> No spoilers.
>> Yes, no spoilers.
>> Yes.
>> So every year, it's about data for good.
>> Yes.
>> This year, it was all about health and life expectancy,
and what governments and the private sector can
do to improve the population's life expectancy.
>> Amazing.
>> So we got some World Health Organization and World Bank data,
and then we had a month to prepare whatever we could.
>> Amazing.
>> Wow. It's actually helping people at the same time.
>> Yes.
>> It has that angle too.
>> I like that the whole conference is focused on that,
data for good. That's really interesting.
I like how none of us knew about this until right now.
Now we know. So tell us.
>> Yeah. So let's dive into the demo.
>> Yeah.
>> So what you're seeing on the screen over here
is the world life expectancy in the year 1800.
>> Wow.
>> Things weren't looking too good in 1800.
>> Bad.
>> Yeah, not too great.
>> Under 30?
>> Yes, I would probably be
in the middle of my retirement at the moment.
>> Wow.
>> You know what? If we go and look at it,
let's say, a hundred years into the future,
so 1900, not much changes.
Life expectancy is still pretty bad.
>> Pretty dire.
>> We increase it to 34,
the map is still old by [inaudible].
>> The oldest people are 60? Not even.
>> No, 45-59 is the best case.
>> Wow.
>> The best case scenario.
>> Fifty nine? So you don't even hit retirement?
>> No.
>> Really?
>> I think retirement might have been a little [inaudible].
Retirement is like retirement.
>> That is so bad. Wow.
>> But take a look what happens.
So let's fast forward to 1918 over here.
Life expectancy drops to 23.
>> No way.
>> What?
>> Yeah. So initially,
I thought this was a data quality issue,
and I started researching into it,
and no, this is real.
>> This is real.
>> Did you find the cause? What actually happened?
>> I did. It was this Spanish flu.
>> Spanish flu. We are talking about a very weird disease.
>> Spanish flu.
>> Yeah.
>> Hits the world. It infects 27 percent of the world's population.
>> Twenty seven percent?
>> It have that impacts on the life expectancy.
>> Yes, it wiped out 50-100 million people.
>> My gosh.
>> I did not know that.
>> Yeah. Bigger impact.
Over here, you can see World War II.
>> Yes.
>> Bigger impact than World War II.
>> That's insane.
>> That amazing and interesting
because I guess you would never have already found that out,
if you hadn't done this.
>> That's right.
>> You hadn't had this visualization, you hadn't had this task.
>> Yeah. So it was actually a really fascinating task
about just exploring all this data because I learned a lot.
>> Yes.
>> Yeah.
>> Absolutely.
>> So then we went forward into the '50s,
the '60s and things actually started to improve.
We discovered penicillin, got antibiotics going.
>> Antibiotics.
>> But this number is also pretty deceiving
because things improved a lot in countries like,
let's say, Norway, who jumped to 70 years of life expectancy.
But in the developing world,
a lot of countries were still actually
living [inaudible] to the 30s.
>> That's right.
>> But if we actually prosper today,
let's just take a moment to celebrate
this amazing progress that has happened.
>> That's a short space of time thought.
>> Yeah, that's short space in time.
>> That's not even that long.
>> Yeah, we know life expectancy jumped with 73.
The biggest improvements actually came from
developing countries who've really
closed this gap of health inequality.
>> From 30 to almost 70.
>> Yeah.
>> That's amazing.
>> So really, really cool progress.
>> I'm in love with this dashboard that [inaudible].
>> Sarah's like, "I want this dashboard at my house,
I want it in my wall."
>> Are you seriously in love with this?
Can you do that and say again?
>> I really love this because the thing is,
it really makes probably what look like quite boring data that you
originally received and it really brings its life.
It's like a real tool for storytelling.
>> Yes.
>> Yeah, absolutely.
>> Exactly.
>> So it's all about, a lot of people just think, "Okay,
I'm just going to throw a couple of visuals onto our page."
>> Charts and graphs on this and call it.
>> Yeah.
>> But there's such an arch to actually taking this all together.
You should see him, by the way. I work with a very,
very talented report designer.
When I threw this report to him,
it was literally like, "Okay,
here's a bunch of pages with charts.
Can you just put this together?"
So shout out to Miguel, thank you so much.
>> Thank you, Miguel.
>> Yeah, this is amazing.
>> For the data storytelling,
it's so important to have something that has been thought
out and trips of how everything integrates together.
So yeah, I was so excited when I saw this for the first time.
>> This is beautiful. You can explain this to anyone so easily,
without them being at all confused about like,
"Wait, what about that row?",
or whatever it is.
>> Yeah. So then the next question was, "Okay, well,
what actually happened in that last decade that
improved the life expectancy so much?"
So one of the things that happened
is less children are dying at a young age.
>> Okay.
>> That's good.
>> Yes.
>> That's good.
>> So if we actually go back and look at what
the world looked like just 30 years ago.
I'm not going back 200 years, 30 years ago.
What you're seeing over here is
life expectancy by child mortality,.
You can immediately see that in
the areas where we have high life expectancy,
child mortality is very, very low.
>> Yes.
>> But then there are also countries here on
the right-hand side where
one in three children are dying before the age of five.
>> Just three?
>> Just 30 years ago.
>> Before the age of five?
>> That's scary.
>> It is. When I actually saw this data,
it took me a moment, and I had to reflect on
it because it was pretty crazy.
But then take a look at what happens in the past 30 years.
There are a couple of exceptions where things go a bit weird.
So actually observe at the beginning,
this thought over here which is for Rwanda.
You can see as we move forward,
Rwanda actually goes all the way to the right phase.
>> Oh wow, it did.
>> Yes.
>> It made this weird shape, right?
>> Yeah.
>> That was a genocide that happened.
>> Yes. That's right.
>> Wow.
>> So you can see how these factors
can just have this huge impacts.
But generally, everything's moving to the left.
>> Yes.
Everything's moving up.
>> Yes.
>> That dot over there that flash before,
those are the Haiti earthquake.
>> Oh, wow. Interesting.
>> So you can actually pinpoint this stuff.
>> Events in history.
>> Yeah. It really brings history to life.
>> Yes.
>> Then you look at reasons and with that data like,
I love this stuff. This is amazing.
>> Yeah. This is very cool.
>> Here the great news is that,
in the countries that saw these very high child mortality,
things actually got reduced to cases of one in 10.
>> Wow.
>> So huge progress that happened in
just a very short space of time.
If we actually want to dig into one of
these countries and see what's been going on,
we can just click on the dot,
we can see the journey it went on, and we can see
how all of these top indicators have been changing.
So in a country,
in this case, this is Sierra Leone that we're looking at,
you can see how much immunizations have actually increased.
>> Wow.
>> They're still below the global average, but they've
gone into this amazing journey in the past three years.
>> Turns out science works.
>> Wow.
>> Yeah.
>> Me and Dona are just like.
>> We're amazed to this too.
>> I think we have said wow and amazing like 100 times now.
>> We're not speechless not much right now.
We are like, wow, look,
science works and the data actually shows it.
>> This is amazing. Wow.
>> Yes.
>> This is cool. I love this.
>> Yeah. It really brings it to life.
I can not say that, and you're right.
I can't stop thinking about when you said, people
just throw some chats in Power Bi,
and I have [inaudible] .
I'm guilty of this,
so I'll just throw some stuff on
a dashboard, and I'm like, that's the Power Bi [inaudible] .
>> Yes. Absolutely.
>> Whereas I think this demo really shows the capability of-
>> The power of storytelling.
>> Yeah. It's like storytelling, it's visualization,
and really to gather true insights from that data.
>> Yeah.
>> Yeah. There's always insights I'm telling you about,
I just discovered by playing around with the data myself
and it was just a fascinating journey to go on.
>> Yes. I love that. That is fascinating.
>> So one other thing I wanted to show you is we
also have some built-in AI visualization into Power BI,
can it actually help you do a lot
of this just analysis automatically for you.
>> Make you look good to your boss without you doing the work.
>> So over here I've dropped in what we call the Key
Drivers or a Key Influencers Visualization.
What I've done is, I was given like literally 100 of indicators.
So I was like, which ones actually matter?
So I split them out into these different categories
that you see over here like, health expenditures,
socioeconomic, lifestyle,
and then I just threw all of them into the visual and I said,
tell me what's important.
So what this visual does is it runs a machine-learning model,
starts to ranks all of these different factors,
and just automatically tells me what matters.
So the top factor that's
associated with increasing life expectancy,
that you can see over here,
is actually access to safe drinking water.
>> Oh, fascinating.
>> Populations which have higher access to safe drinking water,
in this case, 85 percent of the population or more,
on average lives, 13 years longer.
>> Wow.
>> You can see-
>> Safe drinking water?
>> Yeah, and then you can see
your data actually on the right-hand side.
That's representing this particular insight.
>> You actually had to see why it's that high,
like is it why the dashboard or why.
>> Yes.
>> Not like there's a person in dashboard or anything, but in
terms of why is it that the top commands of the others.
>> Exactly. That was actually the reason why we built it this way
so that we don't just give you a black box model.
You can "Click" on that factor and actually
see the underlying data that's explaining that insight.
>> What I love even more about this, I am blown away.
>> We are very amazed at what we are seeing here.
>> [inaudible] Dona and I are just like at
the screen with our mouth like, "Oh my God, this is so good."
>> This is very interesting.
I think this is a sign that you and
I need to do better at Power BI.
>> We probably should to be fair.
>> [inaudible].
>> You can still show us.
>> Yeah.
>> The thing I really love about this,
is that you didn't have to make
any decisions about what data you chose.
Because the thing is like, all this time,
you're not an expert on the data,
you get the data to present it visually, to put into the tool.
To make those decisions is quite hard.
>> Yes.
>> Like you said, you have no idea
which ones rather than what should I put in there?
Whereas this capability has enabled,
you to put everything in there and let the tool do it for you.
>> Actually, I mean, literally, if we're into this
[inaudible] pane,
you can see I have just put in,
I want to analyze life expectancy.
It was this easy to build.
I just drag and drop these field, and I
said just analyze all of these different fields.
I'm just going to drop everything that's
socioeconomic related, and then it just stack rank them for me,
So it's that easy.
But this visual is also fully interactive, so if I only want to
look at indicators that impact females,
for example, I can click on females over here and the.
>> Oh, fascinating.
>> Regression model reruns.
>> Wow.
>> Okay.
>> Now we're seeing the top factor that comes up is education.
Education is super important when we
have higher levels of
education, or females are able to go to school,
then again, that impacts life expectancy as well.
>> Wow.
>> I would never assume they are related.
>> Why?
>> I know they're both important. I didn't know they are related
>> Yeah. I'm really big fan of education, right?
>> That's right.
>> We've had a number of guest talking about.
>> Yes.
>> Like why education is important?
>> Why education is important.
>> Yeah, the fact that it's now linked to
life expectancy or that we've learned is like.
>> Literally saves lives.
>> Yeah,.
>> Wow, I did not know.
>> We can also just do a quick search,
so I think, you're from the UK?
>> Yeah, Okay.
>> She has a British accent..
>> Our life expectancy UK.
Let's see it Sarah. This is all your fault.
>> I'm a little bit worried
>> So if we typed in the Great Britain over here,
you can see again the visual just updated.
Here's actually to do with
how much income is
shared by the lowest 20 percent of the population,
so the poorest 20 percent of the population.
>> Wow.
>> When they have more income coming to
them that again impacts life expectancy.
>> Wow.
>> Because in the UK probably safe drinking water
has been something that's been
probably taken for granted for a long time.
>> That's right for a long time.
>> There hasn't been much variation in that.
That did come up as an influencer.
>> Wow.
>> How much of the income is shared by the first 20 percent?
>> I love how it changes based on
the location as well because it doesn't just,
again, it's very dynamic.
So you can easily pinpoint what is affecting
the world in the most way. Is it drinking water?
>> In a most numerical way.
>> Yeah.
>> Per region, per country.
>> Yeah. The machine learning model
actually just reruns on the Fly.
So throws away the old one,
builds you a new one literally on the spot.
>>You could just just set up a booth at the UN.
They'd combine, say,
"How do we get our life expectancy up to here?"
You're like, "Let me run this for you,
water, educate your women."
>> Yes.
>> Yeah. Wouldn't that be really fascinating.
>> You know, maybe not a booth at the UN.
But I really want to think about what more can we do this data
because now that it's built,
I just feel like I want get
into the hands of other people as well.
>> Other people who can actually go make
environmental changes happen in their countries.
>> Yeah.
>> That's so fascinating for just like
the minister of health and education and stuff to know.
>> Yeah, so actually because we're all about
identifying problems and creating apps to solve them, right?
>> That's right.
>> But it's about, this is like helps you
identify those problems, this kind of stuff.
Because it's full-circle.
You identify the problem she built up,
she get the data, you identify
the problems you built up, she get the data.
>> Yes. She get the data. Yeah.
>> Yeah. It's like this.
I'm blown away.
Like, I am pretty much speechless.
Well, nothing can really make me feel better.
>> Close. Yeah. That is awesome.
Thank you so much for sharing that.
>> Yeah.
>> So we never let our guests leave
without assigning homework to all of you.
>> So Justyna, we're sure
you've got homework assignment for our audience.
>> Yes, and hopefully, this should be an easy one.
Please go ahead and download the Power BI desktop.
You can download it for free,
it stays for free forever.
You can just use it, play around with it,
gets some data into it,
build out some reports,
try out some of these AI visuals.
They're just built into the product.
You can get started with Power BI super, super quickly this way.
>> That was amazing. I just want to make this even easier.
So what do you recommend any free data sites?
I know there's some governments and some countries do that.
Would you recommend any good ones?
Because we're not going to let you
have the excuse of, "I've got no data."
>> "I have no data, my data is boring." No.
>> Yeah. So a really good one that I
use very often myself is called kaggle.com.
That's actually a site for
data scientists and data science competitions,
but they provide a ton of the data sets.
You can just search for any topic you're interested in.
Download the CSV, load it into Power BI,
and you're off building your first dashboard.
>> You're off in business.
I think even we can figure this out today.
>> Yeah.
>> I'm very much an app person or virtual [inaudible] person, and
I've avoided dashboards because they're hard. But this was-
>> I'm trying to change your mind.
>> It's actually working.
>> Wow, you have.
>> This is interesting. It'll be so cool to
see social media trends,
which of our shows are doing well, etc.
That would be fascinating.
>> Yeah, you can even do for your retail business, right?
>> I could even do it for my retail business.
There's so many use cases I can think of.
>> All right. We are, of course, out of time
because we don't know how to shut up.
>> Thank you so much for tuning in. Till next week.
>> I'm Dona.
>> I'm Sarah.
>> I'm Justyna.
>> We are Less Code More Power. Thank you.