>> Let's start by looking at the global number of
cases and vaccination roll-out per 1,000 people.
The color bars on the left here show
the seven-day rolling average of cases.
It's colored green as the caseload is going down,
it's colored red as the caseload is going back up again.
We can see the black line shows us
the population who have been vaccinated,
8.6 percent globally shows we've got a long way to go.
There have been some real success stories though.
In Israel, with over 60 percent
of the population getting vaccinated,
the case numbers have come down
significantly to practically zero.
It's a similar story in the UK,
47 percent of the population had been
vaccinated and the cases have dropped dramatically.
In the US, it's a slightly different story.
The vaccination roll-out has been accelerating,
but you can see the number of
cases hasn't come down as dramatically.
In fact, in the last couple of weeks,
it started to pick up back up again.
We can drill through to find out more to
try and analyze if we can find out any reasons for this.
Maybe we can learn some lessons from Israel.
Does getting people fully vaccinated
have an impact on the number of cases?
I've sorted this table here by
the number of people who are fully vaccinated,
select the bottom three and the top three,
New Mexico, Rhode Island, and South Dakota.
We can animate this data to really bring it to life.
Animations have been a staple of Power BI
and Microsoft BI for over 10 years now.
On the left-hand side here, if we roll back to
the start of 2021 as the vaccines began to roll out,
we're looking at the change in the number of cases,
as more people get vaccinated per 100 along the x-axis.
In the first few weeks of 2021,
we'll see those cases drop dramatically.
In fact, as we get into the last week of February,
all of these states had seen
a decrease in cases by 50 percent.
Let's select those and have a look at how
those paths have tracked over time.
As we roll through into March and April,
we see something interesting.
The green states, those with a high number of people
fully vaccinated actually start to pick back up again,
Rhode Island and South Dakota.
Whereas the states with
a smaller number of people vaccinated,
Utah, Georgia, and Alabama,
those cases have stayed down.
There must be something else at play here.
Actually, of all of the different factors,
whether it's vaccination rollout, lockdowns,
everything, something we really spotted was just climate.
We can see the small multiples
on the left here are showing us
all of those different cases of vaccinations by state.
If we look at just the warmer states,
the ones in the south,
we see places like California
where the caseloads have come down and stay down.
In Texas the same and in Arizona.
Contrast that with some of the cooler states.
You see places like New York, Illinois,
Pennsylvania where the cases are starting to
creep back up again, and look at Michigan.
These cooler states, if we
compare in aggregate to the hotter states,
although the cooler states have managed to
vaccinate a higher percentage of the population,
the caseload have been going back up in
recent weeks compared to the hotter states,
with smaller number of people vaccinated,
but the caseloads have stayed down.
Summer's just come early in those warmer states and
the COVID virus really
doesn't like the warmer temperatures.
Building visuals in Power BI is as simple
as asking questions using natural language.
Let's start by asking for cases by date,
and cases by state.
The Power BI suggest the most appropriate visuals for me.
A line chart from my time-series data
and a map for my geographical data.
Let's start to do some analysis on that chart though.
I'm going to use our anomaly detection capability to find
anomalies where data points
are outside of what we would expect.
There are three points here,
I can click on them to get explanations.
For example, I see Florida and Tennessee had
a higher number of cases on
this day than the other states.
What about forecasting?
I look at the vaccine distribution per 100.
I can add a forecast at just one click.
Decide how far into the future I want
to forecast and add some seasonality.
The data here says that the CDC wanted
to distribute vaccines to everybody by the 1st of May.
When I ran this on the start of
April, it looked like actually,
the target was going to get hit on the 1st of May,
we're hitting a 100 percent then.
I wanted to make
some space now by just
merging all of these things together into a group,
just like I would do in PowerPoint.
It's really simple to lay things out in a Power BI report
and I'm making some space for
our smart narratives visual.
This gives me automatic natural language insights
across all of the data on this page.
I see the anomalies get summarized,
I can see the trends,
I can see the highest and lowest distribution of
vaccines by state as well, and it's all dynamic.
When I click "New York",
a new set of anomalies get found,
I get a new forecast,
and new automatically generated
insights for me in this text.
I wanted to look at the factors that are
driving the number of cases in different countries.
I am going to use our key influencers visual to do that.
We have lots of metrics here so I'm going to
focus on just the health indicators.
I'm going to analyze
the total cases per million by country.
I'm going to add those health metrics one at a time.
As I add them to the visual, watch what happens,
we're creating a machine-learning model behind
the scenes to rank the biggest influencers.
As I add more fields into the visual,
we retrain that model,
recreate it to take advantage of that new data.
It shows me that ranking these influencers,
the female smoking rate is the biggest influence.
When that goes up 10 percent,
the average number total cases per
million increases by 18,000.
Is that the same around the whole of the world?
If I select Asia in this other visual,
it filters down my model and shows me actually
the diabetes prevalence was
the biggest correlator in Asian countries.
I'll add some variance analysis.
We can all agree that lockdowns and
other government mandates had an impact on GDP,
but how much of a change did it cause?
I'm going to use our decomposition tree to break
this value down by any number of different factors.
I'm going to start off by choosing continent.
I can see that our Central and South American countries,
that's where the GDP went down the most,
8.4 percent, but Asian countries
only went down by 3.6 percent.
I can use the AI in Power BI again here to help
me analyze which of the factors drove the biggest change.
The highest change in
Asian countries was workplace closures,
countries that had less than a 100 days of closures
actually saw their GDP grow by 4.5 percent.
What about the other end of the scale?
What drove the lower values
in Central and Southern America?
Well, here it was domestic travel restrictions.
Countries with over 300 days of restrictions,
saw their GDP shrink by 11 percent.
I can select that to see
exactly what countries it was on the right-hand side.
Power BI helps me find this with AI out of
the box for free for every Power BI user.
If I want to share these insights
with other folks in my team,
I can just create bookmarks and
capture the state of the report.
I can add a new one to capture what I was looking at
and I can play them back just by selecting them,
in team meetings, or wherever it might be.
I even get to select specific values in my charts so I
can highlight particular countries
to share particular insights that I found.
Here I have a website full
of great things about flags and
different countries population growth
rate like that sort of thing that I want
to use as a data source.
The only problem is, it's not in a nicely
structured table for me to pull in.
Luckily we've got AI and
the data prep parts of Power BI that can help as well.
First I need to connect to the data and
Power BI offers over 200 different connectors.
All the way from Excel, flat files,
Access databases to Zucchetti HR Infinity
and for this task I'm going to use the web connector.
The first thing I need to do is
connect to that flag website,
just put in the URL and it's going to
suggest a couple of tables to me
of data that's found on the page.
But if it doesn't find exactly the right tables,
I can provide examples as well.
That way I don't need to structure the data beforehand,
so let's start with country name.
Just type in an example of the data I want
from the page, Afghanistan AFG.
Let's do another one Albania
and Power BI will figure out the rest of the data for me.
It picks up all the rest all the country names
from the CSS on the webpage.
Let's do another one, so let's do the population.
The population Afghanistan 38 million,
I just provide one example
and it picks up all of the rest for me automatically.
Let's do one more, let's do the density as well.
It's super simple to do this,
just type in what you need, select
from the suggestions that pop
up and it automatically figures out the rest for me.
Scraping data from the web has never been this easy.
But this data still needs a little bit of cleaning up.
Specifically, here I don't need the country code,
I just want the country name.
Now I could choose from hundreds of out of
the box transformations but
rather than try to figure that out,
I can just give Power BI examples
and it'll figure the transformations out for me.
I'm just going to type in Afghanistan again
without AFG and it's automatically figured out,
you need the text before delimiter transform and it's put
the expression in there for me too.
I've got a table, can I get a preview
of the distribution of my data.
I get information about whether there
is blanks or any errors,
I get all those statistics in here too.
It's just as easy for me to
manipulate quantitative data as it was for text.
I've got a column here for the people
who vaccinated per a hundred.
Let's say I want to group that up.
I could just provide examples again,
so I'll just type in the range that I want 0-5.
It automatically figures out
what are the intervals that we need to apply
across the whole of this range of
data to get that grouping column.
Now for merging tables, I can join them here.
Just choose the columns, the tables that I want.
I could specify the type of join I want to
do and whether I want to do fuzzy matching as well.
If the column values don't quite match up,
we'll figure that out as well.
But I don't need to flatten everything out like this.
Over in the model view, I can keep
my data in a star schema.
Power Bi gives me a complete semantic model
with relationships, hierarchies,
calculations and it can
even auto-detect the relationships and
tables of data based on
the columns and the underlying data types as well.
With companies shifting to
remote work to truly maximize productivity,
you need to bring data to where your business users are.
That's why we've deeply integrated with
the world's most popular productivity platform: Teams.
Instead of accessing my Power BI content on the web,
we've embedded the full rich
Power BI experience as a Teams app.
I've got access to not just all of my data
and my favorites, frequently used items,
but also the reports that were
shared with me and even all of
the datasets from around the organization,
including information about whether
they've been certified or promoted,
how they've been endorsed and sensitivity labels as well.
More than that though, we bring this right into
the Teams channels where you
work with the rest of your group.
The reports here are not just screenshots,
there fully interactive experiences.
It's very simple for me to consume the data,
analyze it and then also
discuss the insights I find with others.
You can see on the right, I've started
a chat and I've tagged people into the information.
Chris shared a report back with me
that I can open from here and I
can subscribe to it and get
alerts when the data changes too.
But we go even further than this.
Business success is not just about sharing data,
it's about sharing a mission.
It's about aligning everyone
and holding people accountable to
business goals and I'd like to introduce
Power BI goals that lets you do just that.
I am playing the part of someone who runs
the mass vaccination site in downtown Seattle.
My key goals, my key business metrics are
vaccinating as many people as possible per day.
We've got public target of 22,000 people a
day and maintaining staffing
to administrate all those vaccines.
If I want to add a new goal,
it's as simple as giving it a name.
I want to minimize the number of wasted
doses and then assigning
an owner and this accountability
is so important with business goals,
which why we make it front and center.
I can add a target and a current value and
the status and update it. I'm creating
the dataset behind here as I go, it's very simple.
I do actually have a report
tracking this patient check in data.
If I want to connect that to my goal,
I don't need to write a query,
I don't need to go find the data.
I just find the report that
shows me the information that I need,
so I can see check-ins per hour by
date here I just point to the data point.
A Power BI figures out what's the latest value
and how do I track this over time.
When I hit "Save," it's going to add it back to my goal.
I'm going to see a little spark line
to show me how I'm performing against
that and it's always got that link back to
the report so I can see how that number's being driving.
But they choose only part of the story.
We also let you check in any progress on these goals.
I can come and say, for
this operational staff availability,
I'm going to make new check in.
I can update the value and
put in a status whether
it's on track or not and when I save this,
you'll see the data gets updated automatically.
But crucially down here, I could have added notes
to this as well to explain what's happening.
I can see the steps I've taken towards this goal.
That's how we make sure that the whole organization is
aligned with data to back them
up on those business goals.
Earlier we looked at how I can use
Power BI goals to track key business metrics.
But as the person running the vaccination center,
I don't want to have to come back to my desk every
time I want to go and open one of these reports.
I'm tracking the overall check-ins
by time here and I can see
a diagram at the bottom of
the lines as people come into the center.
But I can check this on the Power BI mobile app
on my mobile device too.
I can see the same report here,
so I can be looking at it whilst I'm on
the ground working, helping with patients.
But more than this,
we can actually tag this report and place it into
the physical space to have it
located exactly where the check-ins are happening.
Now I've been volunteering
down at the vaccination center,
so I recorded some footage down there.
This is what we're looking at
here is where people actually come
in through the doors and they come in
through these types of barriers.
This lady at the end checks them off,
make sure that that they're at the right time,
they've got their appointments scheduled,
got all their ID and everything else we need.
The device that she's using is what's
generating data is going into that report.
We use Azure Spatial Anchors
to put this report into the physical space,
I can drag it, resize it, move it around.
As I move my phone again,
we're using the AR capabilities to
lock it into that space.
I'm placing this report
exactly where the data is being generated,
that is going into it.
I get this live updated preview that I can walk up
to and see whenever I'm working on that report.
It detects the surroundings,
all the objects in the architecture and everything to
identify exactly where it needs to be placed.
It's got that context whenever I need to come back to it.
Let's say I come back the next day and I
click the little "Scan" button
at the top of my mobile app.
Then through the camera,
I moved my phone around and again
it picks up the location where
I'm and what am I looking at and places
that report back into space.
But take a look at the bottom of the screen here,
you'll see this little radar with three dots on it.
The one in front is the report I'm looking at,
but the two dots over to the left,
are other reports that somebody
else pinned into the same physical space.
If I pan across you can see this is where
people are actually getting the vaccinations.
These are lines leading up to
the desks with clinical staff
waiting to do it and you can see these two reports here.
These reports that somebody else created are telling me,
have we got enough doses?
How many vaccines are actually being administered?
How many people are waiting in those stations?
You can see the filters that are applied to
the report: the date and
the line at the bottom of this report.
Of course I can click into this report to
view at full screen to do any other analysis that
I want to do right on my mobile device
and this is marking something really new.
This is really changing the way that people
use BI in context,
the ability to present this data
in the physical space tag these reports into
the physical location exactly where
the business process is generating or needs that data.
Whether it's in a vaccine center,
on a shop floor,
in a manufacturing environment,
anywhere that somebody has any mobile device,
they can use this data in that context.