SPEAKER 1: All the major global improvements for
the last 20 years has been access to electricity.
This map shows the population's access
to reliable electricity,
from the lightest color where
less than 20 percent of the population have
access up to the
darkest where almost everyone has access.
We filtered down to just Africa and Asia.
We see at the start of the millennium,
many of these countries are very light in color.
Then the first 10 years of the millennium,
the global method number was pretty steady.
We're looking here the number of people without access to
electricity through 2005 and through to 2010.
We start to see some darkening,
particularly around the Indian subcontinent.
But in 2011, there's this weird bump.
What could cause 100 million people
to find themselves without power?
We can also probably try to help augment around
analytics and explain the increase for us.
Probably I uses AI to go and
look at the data and find out that the
eight percent increase in
population without access to electricity,
over 112 million people were predominantly in India.
It turns out that there were some massive problems
in India with coal supply and
the grid infrastructure that
many huge portion of the population
went without power for about a year.
But in the 2010, we see
a massive improvement in
access to electricity around the world.
In 2015, for the first time since it was invented,
less than a billion people didn't
have access to electricity around the world.
Through to 2019, we see some marked improvements.
For example, by around Sub-Saharan areas,
some of these areas are starting to get darker.
But if we look at India and
some of its surrounding countries,
we can see some major improvements.
On the right hand side here,
we see places like Afghanistan
where the rural population in 2005,
only seven percent of them had access to
reliable electricity versus by 2019,
that was up in 97 percent.
Now, with great power comes great responsibility.
We see that the continents who have poorest access to
electricity also are the ones
that have the lowest GDP per capita.
Africa with 41 percent access to electricity,
only $4,000 per capita.
But conversely, the places with largest
GDP also have the highest CO_2 emissions.
A stark difference is really brought to
life when we start to look at this over time.
As I run through this, you'll see that
these countries down the bottom here,
the developing nations, their GDP starts to grow,
but the CO_2 emissions don't go up too much.
It's when they turn this corner
around $10,000 per capita,
the emission stops increase significantly.
We're now in this place where in the world
we have two major energy problems.
You've got countries in
developing nations, predominantly across Africa,
where they're held back in
terms of GDP growth by poor access to electricity.
This is in places like Europe where we've got
much higher CO_2 emissions as the GDP is growing.
It's on the very richest nations,
places like Sweden or Ireland,
where they've been able to get hold of
those CO_2 emissions and reduce them in recent years.
Building visuals in Power BI is as simple as
asking questions using natural language.
If we ask for access to electricity by date,
Power BI will understand, hey,
the best way to show this is on the line chart over time.
Conversely, if I say GDP per capita by country,
it's going to know that, hey, we probably want to see
that as a map and plotted properly.
I've wanted to add some analytics to this.
I could have forecast on my line chart just in one click.
I can change the forecast and say, hey,
actually let's ignore the last five
years and supply that.
We see some interesting things here,
that about 95 percent of the population
should have access to electricity by 2039.
But actually, if we look in
the last five years, that's been accelerating.
We're doing even better than we thought.
Now over here, I'm going to add a smart narrative visual.
This is going to give me an
automatically generated textual summary
of all of the visuals that are on this page.
I can see the trends,
I can see how it jumped up over the last 19 years,
I see that Luxembourg has the highest GDP per capita,
and I can see the split between urban and
rural in terms of access to electricity.
But, of course, all these are linked together.
If I was to brush over
these countries in the Indian subcontinent,
then I get a filter here.
I get the same filters, I get new forecast,
and I get news summaries that tell me that
Sri Lanka had the highest GDP per capita there.
If I go over look on this tab,
I wanted to take a look at
the key influences for CO_2 emissions.
I'm going to do that using a key influences visual.
Just let me do some analysis
using a bit of machine learning.
I'll look at our CO_2 emissions.
That's going to be our main thing to analyze.
But I'm going to do it by country by year.
I want to know what influences
CO_2 emissions to decrease.
As I had these fails to explain it,
we're going to build a little machine learning
model behind the scenes.
I can see that when access to clean
fuel for cooking increases,
CO_2 emissions decrease by up to four tons per capita.
If I had some of these other indicators,
particularly these socio-economic indicators,
I see something really interesting.
At a global level,
there's actually as much of a decrease
in terms of CO_2 emissions,
where electricity from coal has
decreased as there is when there's
a literacy rate increase,
they both have the same impact on CO_2 emissions.
These are the same all the way around the world.
If we look at this close to three,
this is the developing nations here.
Here we see that the
primary or the highest correlation is actually
around fertility rate decreasing and
around life expectancy increasing,
dealing with those absolute basics.
Then for these countries where they started to turn
this corner and CO_2 emissions
are increasing a little bit.
Actually, here we see that it's
poverty ratio decreasing,
that highest correlation with CO_2 emissions.
Those countries who are dealing with the basics
making sure that population is fed and housing closed,
then they can start looking
at their environmental impact.
All of these insights are interesting.
We can capture those at bookmarks to capture the state,
the selections, the filters that I've made in my report.
But when we think about how people
go and tell stories with the data,
half a billion people around
the world use PowerPoint to tell those stories.
I'm really excited that we can now insert
Power BI data into our PowerPoint slides.
This is a securely embedded
live Power BI report here and PowerPoint,
and it's just like any other object in PowerPoint.
I can move it around,
I can resize it to whatever I need to.
I can do things like inserting shapes as call outs.
I want to put something in here that lets me say,
hey, there's a slide over here you can play with.
But because all of these reports are live,
I can change the selections,
I can change the filters,
I can go back and replay these animations
right here securely with real time data.
Now, if I wanted to take a snapshot
of that, I can do that as well.
I can say, hey, show that as a saved image
so that I know, hey,
my data isn't going to change if I go
and present it to my executives in future.
But Power BI is all about telling stories with data,
and PowerPoint is a place people do that and works with.
It makes sense for us to bring these two worlds together.
I've got this data from this website where I have
population density and flags about these countries,
and I want to use that in Power BI.
But because it's not a nicely formatted table,
I can use Power BI's AI capabilities
to suck in that data.
We have over 200 connectors,
everything from access databases through
to Zucchetti HR Infinity.
But in this case, we're going to use our web connector.
Now, what we're going to need to do is put
in the URL of the web page.
This is going to go connect to the web page,
and it's going to look through the HTML to find
any tables of data in there.
Now, this page doesn't actually have any tables,
but what we can also do is provide
examples just so that we can
tell Power BI what it is that we want.
I'm going to say Afghanistan,
choose from that little prompt.
Now, the second country here is Albania.
Linear, select that one,
and having given it two examples,
it can follow the pattern and
identify all of the rest of the countries.
Let's do the same thing with our population.
Afghanistan's population is 38,041,754
and it's already figured out the pattern.
I only need to give one example here.
Let's do density again. You'll see 58.3257.
Boom, it gets this straight away.
Scraping data from the web has never been so easy.
Now, I'm not actually going to load this data.
Here's one I've made earlier.
It saves a few seconds.
What I'm going to do, though, is try and
split this country column up.
I want to do some more transformations on this.
I've got country name and the code here.
Now, I could use the hundreds of transforms that
we've got out of the box here through
the GUI but actually,
I'm just going to give some examples again.
In this case, I'm going to type in the name
Afghanistan, I do that here as well,
and it figures out for me that
what I want to do is use this text before
delimiter function to just
show the first word in that column.
Really easy ways of cleaning up the data as well.
I can do the same AI things on numeric data.
I've got consumption-based CO_2 emissions here.
I can provide an example here of the ranges
that generate based off that column,
so no to five, run.
It figures out what's
the range for all of these other countries
based on their annual consumption-based CO_2 emissions.
Add that as a new column and then I
can use that in the rest of my analysis.
I get a preview here
of all the data that I'm being loaded,
whether it's got any errors or
any empty values in it as well,
so I get that before
everything gets loaded into our models.
If I look to merge tables,
merge queries, I can do that here.
I can say, I'm going to use
that country name column match up with a short name,
and I've got fuzzy matching options here as well if I
wanted to if the names didn't quite match up.
But I actually don't need to
flatten all of the states out.
But I can also do over in Power BI.
I can load all of those tables data individually
to keep the star schemas or whatever format it is in.
I have multiple fact tables,
I can create relationships between them,
and then I've got a rich semantic model
here so I can create calculations,
I can create hierarchies,
I can even do things like running
these relationships through an
automatic detection algorithm,
so I don't need to go and define them myself.
As we adapt to today's hybrid work environment,
it's more important than ever for businesses to bring
data to their people wherever they're working,
and that's why we've integrated deeply into
the world's most popular productivity platform,
Microsoft Teams.
Alongside my chart, I've got
this rich Power BI application
here inside Teams that gives me
access to all of my reports,
reports that other people have opened or shared with me.
In fact, any of the datasets that are
available in my organization and I
can see things like whether they've been promoted or
certified to know that I can trust that data.
More than this, we actually let you embed
Power BI right into Teams chats or channels.
I've got a tab here for this channel where
I've embedded the Power BI report and it's
a report so I can slice and filter
and do all that analysis that you'd expect.
I can talk about it with
my colleagues here on the right-hand side as well.
In fact, somebody shared another report with me here,
which I can open or subscribe to
directly from the channel.
In the case of subscriptions,
we let you put in the e-mail addresses for
the people who you want to have this delivered to.
Even people outside of the organization
who don't have access to Power BI.
That obviously get it as a snapshot in
an e-mail on a daily basis.
We're really trying to surround people
with data wherever they go.
In fact, Outlook is another good example of that.
Here I've just got a draft e-mail
that I've been typing out to somebody.
I can even format it however I want to.
In order to embed some Power BI content to here,
I can just attach a Power BI report as well.
It gives me a list of
the recent reports that I've been using,
and I get the same experience with that ability
to open or subscribe to it right from within the e-mail.
But business success is
not just about sharing reports or sharing data.
It's about sharing a mission and
aligning people behind that mission.
That's where Power BI goals come in.
I've used Power BI goals here to attract some of
our energy sustainability targets.
I've got goals around becoming carbon negative,
increasing the campus recycling, hardware reuse.
Adding a new goal is as simple as just giving it a name.
Crucially, the first thing we ask for is who's
the owner who's accountable for this goal.
Having somebody responsible is really important.
I can type in data if I want to in a target.
I can build the data set as I go
or I can connect it to some existing data.
Now, I don't need to go and write
a query to go and get this data.
I just say, hey, here's a report I know
about this that shows me what I want to track.
Select the data point I'm interested in,
and Power BI will figure out the right filters to
apply to get that data into my dashboard.
I get a nice little sparkline,
and I can go and track it over time.
But it's more than just having the data available.
Most important thing when you're
tracking these little goals is to
allow people to check-in and give commentary around it.
We let you put notes on things to say, hey,
here's what I've been doing to
address this over the last quarter.
Here's what we're doing,
what the actions that we're taking,
and that's how we allow people to get aligned behind
the business goals with
data to back up the decisions they're making.
Earlier on, we looked at how we could use
Power BI goals to keep track of
some of our main business objectives.
Let's say I'm responsible for
the energy sustainability goal,
particularly around heating and cooling efficiency.
How much energy is our campus
using on different buildings?
I might have a report like
this that shows me temperature and
humidity over time with
energy consumption for
the air conditioning in different meeting rooms.
But looking at it in the abstract here,
when I'm sitting at my desk,
I lose some context.
I don't really understand where this is in the building.
Now, I could use the Power BI in
mobile to take this on the go and
maybe work with facilities managers
to walk around the campus and understand,
well, is there
any problems in the different meeting rooms?
Do we need to raise any service requests?
I've got access to exactly the same reports here,
and the report creators might
have made mobile-specific versions
that better reflect the form factor
of these mobile devices.
But more than that,
I can actually take these reports
and pin them into the physical space,
into the physical location where these meeting rooms are.
When I do this, what I'm going to do is,
we use the AI capabilities of
the device as I move the camera around the area.
We're going to learn where the architecture,
different objects in the environment and drop
that report into the meeting room.
I can scale it and resize it.
Then as you move the camera around again,
we're locking it back into space,
just using standard AI capabilities on any mobile device.
You don't any fancy VR headsets to do this.
What it means is that these cards then pins
so when I come back at a later date
I can open a Power BI mobile app
again, bring up the scanner,
and then I move the camera around,
you'll see that these reports
pop back into the physical space because we're
picking up that space of where
was I and what reports around.
Then as I walk towards it, you'll
see I get a live update
of the data refreshing into
this report from this meeting room
where it's being generated.
Let me draw your attention to
the bottom right-hand corner, another radar.
I see two dots here.
The one in the middle is the thing that I'm
looking at the report right in front of me.
But that one over to the left is another report,
somebody else pinned in space here.
As I pan around, you'll see
those reports pop in as a couple more
here for each of
the different elevators, and as I walk towards these,
you see this is going to be showing me things like
the energy usage of those elevators,
the number of rides they've taken up and down,
the number of people who were in them,
and I can see that filter down to
the specific elevator that they're pinned in front of.
This really makes a difference that I
can now see in context.
Again, those reports where the elevators are I
know according to which one.
Of course, I can open it up in
full screen and go and do some more analysis.
This really marks the start of something new for us
that you can now take these reports
and pin them in the physical locations where
those business processes are generating those data,
so you've got the data at your fingertips
where you need them in a physical context.