[MUSIC].
>> Hi everyone. I'm Lauren Faber.
I stay on the Power BI CAT team.
I'm super excited to have
Meha Saxena here with us
today from Electrolux.
Welcome, Meha.
Before we get started into
your challenge and solution,
could you tell us a little bit more
about what we'll be going over today,
your company and your
role at your company?
>> Hi, Lauren. Sure. Let me start
by a little bit about
my introduction.
I have been working in Electrolux
as Reporting Lead Architect.
I have around nine plus
years experience in
analytics in SAP BI
HANA business objects.
Recently I shifted
my focus from SAP to
Azure for this particular initiative
which we will discuss today.
This is a little bit
about our agenda today.
I'll give you a company overview.
What challenges we had, the vision,
how we have structured
our organization,
a little bit about the solution,
and we can look at a demo,
and then finally the results.
Electrolux is a leading
global appliances company.
It has been around for
more than 100 years,
and we have around 51,000 employees,
60 million products sold annually.
These are some stats
which you can see
on the screen about the company.
>> Perfect. Thank you
for that introduction.
Electrolux, I know it was facing
a certain challenge that led to
the Power BI and Azure solution.
Do you want to tell us a
little bit more about that?
>> Yes. Sure. We started our
journey last year in 2019,
and we had a couple of challenges.
These are some of the
challenges which held us
into looking into an
alternative solution.
There were some issues
like if we need a product,
an analytics report or something,
it takes very long
time to prepare and be
available in a traditional
ID structured format.
Of course, with
not a powerful system employee
time wasn't optimized.
They spent a lot of time
downloading data and doing
certain manual tasks which were
not adding any value to the business.
These were mostly manual task.
We have a lot of systems and
there was no platform to
integrate all of these systems
and bring the data together.
Of course, if the data is
not present in one platform,
we cannot really find the data
and consistency so easily.
There is a lot of different type of
data and people work
in a different manner.
Also, not everyone can bring
the whole product and customer
dimension in one view,
which was always the concern
because the data volume is so much.
Finally, there was no platform
to democratize the information.
Since everything is
in different places,
there's no one platform to look
at everything in one view,
in a holistic view.
All of these caused inefficiencies
in the analytics and the way
the people were working.
>> That's a super-big challenge.
Even though it's got complex one,
it's one that's very familiar
to a lot of businesses.
What solution did you
end up coming up with?
>> Based on all of these issues.
We started looking into
different solutions.
Our vision was we wanted to transform
the business by
democratizing the data,
and of course embedding
some AI or machine learning processes
in the key finance systems.
Because we have a lot of solutions in
the market and without
using those properly,
we cannot bring value
to the business.
This is the structure
of our FP&A CoE team.
We have business analysts who
have thorough knowledge
about business,
management information
specialists are the people who
have understanding of
business and technology both.
They work as an
interface and make sure
the systems are up and
running in the right manner.
Then we have IT skill
people who are working
mainly in bringing the technology
closer to the business.
This particular team is
managed within business and
that's why it's quite easy to work
and bring the results faster.
While designing the solution,
we looked at a lot of
different analytics tool,
and based on few of the
principles in the company,
we chose Power BI.
We wanted to create
a data-driven culture so
that people don't have
to wait for anyone;
they just have the data
and they can work with it.
Also we have a lot of knowledge in
our organization related to Excel.
They are all finance users and they
have a very vast experience in Excel.
We would like to leverage
those skills rather
than bringing in a new
technology in the system,
so they can work with it easily.
Of course, we wanted to have
this particular ideology,
discipline at the core and
flexibility at the edge.
We wanted to make sure
that the thing should be
properly maintained and there
should be a governance.
But then there has to be
some flexibility if someone
needs some information on
trying to make some changes,
they can do it themselves.
With all of these principles,
we thought we'll have Power
BI as the branding tool
and bring more flexibility
in the environment,
we chose Azure as the backend.
>> Could you share a
little bit more about
the data architecture and how
the services work together?
>> Let's look at the
system architecture here.
We have SAP BI as the
main reporting system.
Then we also have other systems where
we have product attributes,
marketing data,
some local files which
clusters or countries use,
and we wanted all of
them to be combined
into one place in one
data lake, for example.
It's a bit tricky to
connect to SAP BI,
and we had a couple of options.
We have explored connection
directly from Power BI to SAP,
and then various EPL tools.
The best one, which we found
was Azure Data Factory.
It's very easy to implement
and it's very secure.
We have data factory here which
extracts the data from
SAP and other systems,
and we bring everything
together in block storage,
that's the first layer.
By using Databricks, we have
transformation logic and we
upload the data in Azure Synapse,
previously known as Azure
Sequel Data Warehouse.
Then of course, the final model
is in Azure Analysis Services.
We have Power BI
Premium capacity, P1,
which we use for sharing and
maintaining the reports.
Because of Azure Analysis Services,
we how central data model,
very secure based on the data access,
people will see the data,
otherwise they will not see if they
don't have access to
the data or report.
This makes the whole democratization
of the data very easy.
Also keeping in mind that
we have the security in
place so that no one can look
at any data which
they're not supposed to.
Then we use a lot of works.
We have workspaces,
European workspace,
and we work in different
clusters here within Europe,
and all of them have their
particular workspaces,
giving them the freedom to
work with the same data,
but can create their own views
and share among their users.
>> What are some of the benefits that
you've been able to
see from the solution?
>> Yes. We saw some benefits here.
First of all, it's all
Cloud-based platform,
so it was very easy to
spin up technologies,
check if they're working
the way it should be.
If not, we can move
to the next option.
During the whole implementation
project we did a lot of testing
and checked if those technologies
work for us and in
the right way or not.
As I explained before,
we have a centralized and
secure data platform.
It's available centrally, no one has
to think about the refresh
of the data or anything.
Also, they don't have to
worry about the security.
Everything is secure based on
the access that we have
granted to everyone.
People can work with
very large datasets.
We have more than a
billion records in one of
our data module and people are
working every day with that.
Of course, flexible cost management,
because we can switch on,
switch off technologies
when we don't need them,
we can switch it off.
That helps in managing
the cost as well.
It was a very small development team,
which we will put
only three or four people who
are actively involved
on the solution.
Of course, it was a very
quick implementation
that took us around
two or three months,
and within that we did couple of
redesigns as well and
thought it was available.
We do frequent and automated
data refresh every day.
We refresh four times a day,
and it's almost 30 million
records every time we refresh.
It's very quick to
get the data on date.
>> Perfect. I believe you've
prepared a demo to show
us some of the solutions you've
been able to come up with.
Could we get into those?
>> Yes. Let's look at the demo.
I have prepared a very simple example
of some of the things that
we use here in Electrolux.
Of course, the numbers are completely
wrong because it's fictive dataset,
so please ignore the number.
This is a simple profit
and loss data model,
and we use a lot of bookmarks here.
Here we can see growth versus
previous year, growth versus budget,
and we can see all the different
KPIs that we focus on.
This is simply net sales on
different product groups,
and you can then see more
information when you go here.
I use tooltips, etc,
and there are a lot of
people who like zebra,
and it is quite a good visual,
and we use it quite often
in our environment.
Also it gives a lot
of different views to
the data which is very
easy to work with.
This is how people can
use and even though this
is a fictive dataset,
our actual dataset looks
very similar to this,
and people have the possibility
to look at all the transactions
along with thousands of
product attributes and
customer attributes in one go.
The performance what
we can see right now,
is very much similar
to our actual dataset.
We use a lot of decomposition
tree in the environment.
It's very simple and gives
a quick view of the things
that you want to look at.
For example, if I want to see
how the data is
progressing or changing,
I can simply look at this,
and reuse on this particular visual
quite a lot in my environment.
Then we have comments.
We have a feature which we
are using here for comments,
writing comments, and getting
the insights from the data.
This particular task was being
done by the users manually,
and it takes a lot of time
because the data is
changing every minute.
It's very difficult to
keep the comments updated.
What we have done is we
have looked at this add-in,
which works in a simple
manner with Power BI.
We just need to bring the add-in,
and write the code, and
that works with the data.
It is also flexible.
As per our selection,
it changes as well.
If we have selection on the screen,
it changes as well.
Also we have Q&A,
which is very useful.
We have people using this every time.
They can write the question,
and get the insight very
easily from the data.
This is quite a big hit as well among
the business users because
it's really easy to work with.
Finally we have the full P&L,
and here as well,
tooltips are used quite a lot.
Here you can see there's
even more information
if you will hover over
the different data bars.
This is about different technologies
that we use here in our system,
and it's a very rough
introduction of what we do,
but it's an example report for you.
>> That was awesome.
Thank you so much, Meha.
I really love the use of
conditional formatting on the
first dashboard that you saw,
and the fact that you're using
Q&A and the decomp tree.
It's really cool to be able to see
those things and
production and being used.
Thank you so much for that demo.
It's one thing to be able to put
all of these things together,
but it's another to actually see
results and things come from it.
What has been the
result of the solution?
>> Yes. Let's look at the results.
There have been a lot of benefits
from this solution
we have seen so far.
Our CFO agrees with it as well.
As you can see, he has
mentioned the same thing.
We have the possibility to analyze
the data on all dimensions.
As I explained in the demo,
we have around thousands
of product attributes,
we have customer attributes,
and we can look at everything
together in one place.
Some of the things with
limited possibilities used
to take three days earlier,
now it just takes 10 minutes.
Some of the reports
which used to take
two days to create every month,
now they don't have to create them,
they can use it everyday.
As part of this solution,
we have a very easy
integration with SAP,
which was not available before,
and we can download or
extract millions of records
in a very easy manner.
It also helps us combine
the SAP data with other dataset
which we have been working with,
and now we have
one particular environment where
we can see all the data together.
Of course, as I mentioned earlier,
we have a lot of skill set
available in our company,
and we wanted to enhance that.
This particular product or Power BI
actually helps us give
that particular chance
to the business user,
they are enhancing their skills,
and they are getting rid of
these manual task which
they used to do before,
and they can look at some value
added task for the business.
Of course, data is available to all,
it's central data platform.
Anyone can access
the data, of course,
as per their access because
that's very important.
Also with all of this,
we have got a chance to look
at some of the things which
can be included in our
reporting portfolio.
Because earlier,
everyone used to spend
a lot of time just
doing the manual task,
which is free now,
so we can shift our focus from
the operation reporting to
some advanced analytics.
We have started some of
those initiatives already.
For example, determine the
drivers for high margin products.
We have thousands of attributes
related to a product,
and instead of just looking
at product profitability,
we are now looking at all
those attributes and see
which attribute is driving
the profitability.
For example, energy class.
That's one particular initiative.
Also we have a very long budget
process around three months.
We want it to automate and
see how much those tasks can
be automated and remote so
that the time can be reduced,
and this is what we are
looking at as well.
Sentiment analysis is one of the
major focus for any company.
They look at the consumer ratings.
Now we can look at
the consumer ratings along
with the financial data,
and we can do the
analysis in a better way.
This is also in progress.
Chatbots, we have a lot
of people who answer
the same questions or provide
the same information every
day, including myself.
If we can remove those
particular questions and
bring in chatbots so that people
can get the answers even quickly.
This is one more initiative,
and automated commentary which
I showed you in the demo.
We have already completed that.
This helps quite a lot in
terms of getting rid of
all those time spent writing
the comments which was
not really necessary.
We have started with all
of these initiatives
and just because we have
the data available.
This is it from my side. Thank you.
>> Perfect. Meha, thank
you so much for being here
today and for the work that you've
put into this presentation.
The information was super beneficial,
so I just want to say huge thank you.
>> Thank you.
