[MUSIC].
>> Welcome to another wonderful
customer Power BI session
at the Microsoft Business
Application Summit.
My name is Lauren Faber,
and I'm super excited to have Dhiraj
from the Wonderful Company
here with us today.
Dhiraj if you could start
us off by telling us a
little bit more about who
the Wonderful Company is,
what we'll be talking about today
and your role at the company.
>> Thanks, Lauren. Thanks, everyone.
Let me just give you
a quick background
about the Wonderful Company.
Wonderful Company is a
$4.6 billion company.
It's a privately held company.
The company's mission is to provide
high-quality healthy brands and help
consumers make better
choices everyday.
Wonderful Company's products
you would find in clubs stores,
grocery chains, e-commerce
stores, retail outlets.
Wonderful Company harvests,
packages and sells fruits and nuts,
flowers, water, wine and juice.
Primarily based out of California.
They employ over 10,000
people across the globe.
These are some of
the Wonderful Company brands
that you may be familiar with.
I'll give you a quick overview
of some of the products that
you may be familiar with.
Wonderful Company is the world's
largest grower of tree nuts.
They are North America's
largest citrus grower as well.
North America's largest wine nursery,
and the largest flower
delivery service with
Teleflora network florist are
part of Wonderful family of products.
My name is Dhiraj Chhajer.
I am IT Director in
the Wonderful Company.
I've been with the Wonderful
Company little over 12 years now.
In my current role, I am
responsible for data analytics,
enterprise technology solutions and
financial reporting
and consolidations
solutions for various
wonderful business units.
My team is responsible
in two key areas.
Enterprise software
solutions delivery.
This includes integrating, building
integration solutions across
various enterprise applications.
Customer applications, which involves
building customer applications
to support features,
gaps in existing
solutions and building
complete off the shelf
solutions as well,
and then database and
systems maintenance.
On the data analytics
and reporting side,
we are responsible for
the entire data pipeline,
which includes: building data models,
building data pipelines that
involves transforming data
from transactional system,
building data analytics and
data analytic solutions,
which involves delivering reports,
dashboards and applications to
fulfill enterprise BI needs.
>> That's perfect. Thank you
so much for that introduction.
I know that the Wonderful Company was
facing a challenge that's
very common to a lot of
different organizations that are
trying to find a single
source of truth and trying to
unify everyone on a single platform
to be able to have the
best reporting possible.
Could you dive into that
challenge a little bit?
>> Thanks, Lauren. Within
the Wonderful Company,
each group is run
independently and that
obviously leads to siloed
systems and data as well.
There is lack of single
source of truth.
Some of the are not able
problems as you can imagine.
There are multiple reporting
tools that are being
used across these
different companies.
With all of this, there was
a growing frustration
in the business users,
we're also looking for
a single platform,
and they also were looking for
self-service and ad hoc reporting.
To address these issues,
we evaluated various products
sometime during last
year and prescribed
a reporting architecture
that addresses
Wonderful's BI and
data analytics needs.
The key focus for the solution was,
it has to be enterprise class.
It has to be scalable
and flexible and agile
enough for changing
business needs altogether.
I'll give you a quick walk-through
with the reporting architecture
that we prescribed,
and we'll get into the details.
Key aspects being we
wanted to address
both transactional and business
intelligence reporting.
As you can see here,
the solution highlights
both real-time
which is the transactional
reporting and the BI needs.
Let me just walk you
through from here.
Down here, these are
the various sources through
a host of data pipelines
that we built.
We get the data in the data lake
that serves the foundation for
both real-time reporting and also
for the data warehouse as well.
Data warehouse is hosted in Azure,
and we use Power BI as
the visualization tool.
Some of the key highlights,
Power BI has provided the
lowest total cost of ownership.
We are able to address both
real-time and analytical reporting.
It also is able to cater self-service
and IT governance as well.
Or the last year also we expanded
this footprint and we have
these various additional
sources such as IoT devices,
various Cloud systems,
plant automations have
also been added in our
reporting architecture.
The system has proven
that it is flexible
and we are able to
scale as we adopt more.
>> What are some of the use
cases for this architecture?
>> This slide shows what
the use cases have been
delivered through
the BI architecture.
For all of these use cases,
we have used Power BI as
the reporting solution.
Notable ones here's being
the sales, farming,
financials, the sales analytics
where we get data from
Salesforce and so on.
All of this has been
delivered at present.
This slide explains in detail on
what KPIs we have
been able to deliver.
These are the KPIs that the business
had deemed critical for delivery.
Some of the notable
ones over here are
inventory roll-forward
where we are able to do
snapshot of inventory over
a period of time and
look back and be able to
do much more data-driven decisions
on future inventory on hence.
Similarly, financial
analytics and be able to
do a drill down to
individual datasets at a
much more effective pace.
We have been very
appreciated by the business.
We will get into a demo
very shortly and that will
cover some samples of what we
have been able to
deliver to the business.
>> Yeah, that's perfect.
It's pretty amazing how
your solution has been
able to scale across
multiple different
aspects of your company.
I know that the demo you've prepared
is pretty extensive and in-depth,
so you'd rather jump into that now.
>> Thanks, Lauren. We'll
get into the demo aspect.
Just to set the context for the demo.
All the data that you see has
been randomized and these numbers
have been scrubbed to quite
a good extent altogether.
These numbers are just random
numbers across the entire demo.
The first dashboard that I
wanted to show over here,
and these dashboards are
part of applications.
For the purpose of this demo,
I'm going to show you
sales analytics first,
and let me work you through.
On the sales side you
have in any business,
you have two major measures,
one being units sold,
and then the other part
being the dollar sold.
On this particular page,
you're able to see last
four weeks of sales,
last 13 weeks of sales,
and last 52 weeks and comparison
with prior year as well.
You can see it by
either the dollar values or
by the units sold altogether.
If I look at loaded amount
that basically shows by unit,
and you can actually change
the dates to whatever
dates you want to compare.
Let's say we focused on
a certain customer and we
wanted to see more details
on how the customer has purchased
from the various
locations altogether.
Then if I wanted to
look at individual,
let's say region, and I
want to see what they have
purchased I can see that as well.
Then I can go into the individual
product and I can see what
various customers ship to
sides or distribution
centers are purchased.
You are able to do that analysis
by week, by different dates.
You can do it with
various filters as well.
You can do it at an
individual sales rep as well.
The business appreciates
this and able
to use this for making lot
of their business decisions.
Moving on, this may have
a lot of data over here,
but this data shows
a certain purpose.
So what you see over here
is sales quantity and
the average price for
each sales altogether.
So these are the various
varieties that we sell,
these are the by customers,
the sales quantity,
and the average price
for that particular customer.
So now if I select a
certain customer here,
this customer changes
my sales variety,
down here I see
the pack codes and the video
sizes that are available.
So I am able to see all of
that data changing by
the specific customer.
As I move across customer,
my data changes as well,
and I'm able to make more
meaningful decision.
Now, if I go for a
certain customer and
I just want to select a
certain variety altogether,
and then I can actually do
further product
attribute level analysis
just for that particular customer
and that product altogether.
I can do further analysis
for a specific size,
and now do an analysis across all
customers for a certain size,
and this applies for every
other filter as well.
So what has happened
over here is that
the individual fields have become
selectable filters for other
visualizations altogether,
and you can obviously
change dates and
other filters which
are available as well.
This particular dashboard shows
top 20 customers and how they have
purchased versus the rest of
the customers altogether for
a certain time duration.
So you can see KPIs
like order counts,
sales units, the various
amounts as well.
You can look at them from a
visualization perspective,
or you can look at it
from a table standpoint,
and you can obviously export
and do drill downs as well.
So I can show you how
drill downs work.
So if I'm interested
in a certain customer,
I can actually select
that particular value
and do a drill through
to sales detail,
which will give me the
detailed breakup altogether.
So moving on from the
top 20 customers,
we'll take a look at
the ad hoc analysis.
So what is ad hoc analysis?
This feature allows you to look at
a specific measure and do a slice
and dice by various dimensions.
So in this particular case,
we have a certain dollar
measure over here,
and you can now slice it
by various customers.
So let's say you want to see what are
the top customers who made
this particular number.
Now I'm looking at let's take Kroger,
and I want to see what varieties
we have sold to Kroger.
I can take a look at the various
varieties that we have sold.
I can then look at
particular variety,
and let's say I'm interested
in a product attribute,
and let's say I want
to look at sizes,
I can take a look at
the various sizes.
So this is good, I got my
business analysis for Kroger,
I want to now do the
same analysis for Ahold.
So I just move over to Ahold and
the entire decomposition tree
also changes accordingly.
If I want to look at for
other customers, so on.
So this is great.
Now, if I wanted to just
look at my export customers,
I can do just select "Export" and
look at the export
customers altogether.
Now, if I want to now move
on and basically say,
"I want to do the
analysis on my dollar
by sales reps." So I can
look at my sales reps,
and I can see various sales
reps and see their sales.
I can do it for a specific date,
I can change my time period,
or I can do further
analysis on who are
the top customers for
a certain sales rep,
and I can do that
analysis further as well.
So this allows you to do
a decomposition tree,
this is basically a
decomposition tree visualization
that allows you to do
ad hoc analysis and get business
answers very quickly altogether.
Moving on, this is an
example of a report
that actually gets data from
transactional reporting,
this is a live report
and this is used
for order fulfillment needs.
But just as an example,
this is getting from
our live database and
the data constantly gets refreshed.
So now from sales analytics,
we will move to financial side.
I'll give you an example of what
we have done on the financial side.
So here, financials being
a lot of numbers-driven,
we have used matrix visualization
quite a bit with the financial side.
So this is EBITDA,
which is earnings before interest,
taxes, depreciation,
and amortization.
You can slice it by individual
location, by department,
all the values within
the chart of account,
you can do it at a
product level as well.
So this current team stays
across the entire
financial analytics app.
You can look at prior
year period to date,
you can look at current
year period to date,
you can look at year to date,
you can look at variances
against the budgets,
and you can drill to
the lowest level,
or you can go all the
way to the top level,
you can look at individual
numbers altogether,
and you can do a drill through.
Now on the drill through,
we have connected it where you
can get at a balance level,
which basically gets you data at
a much faster pace at
an aggregated level,
or if you want to do a drill
through further at a lower level,
you can actually do a drill through
and do a sub ledger level detail,
which actually goes to
the data warehouse and
gets the reporting out.
This is compounded
annual growth rate,
obviously these numbers
are randomized numbers.
So this is not the CAGR
for Wonderful Citrus,
but you can slice it
by various product.
I can expand it to include new
data and your changes as well.
You can again do further
lower level analysis as well.
This is another template
for revenue growth,
this is specifically focusing
on the revenue side.
As you can see over here,
we had specific
request on revenue and
expenses as two separate
aspects altogether,
and this gives you a quick
visual insight into what
the various revenue has
been over the years,
or by the various department,
or the various accounts altogether.
In all of these places,
you can actually drill
down to the lowest level,
and this is where the
columnar database and
the Power BI multipack engine
gives us quite a good
speed and agility for
the users as compared to how they
used to do reporting before.
On the expense side,
very similar to revenue here,
this is expense, let's say
Wonderful has their own cafes.
So if we look at just
specific department,
let's say cafe, you can
actually see the revenue and
expenses just for that
particular department.
You can actually do a drill down,
or you can look at period
by period comparison,
and see how this has actually moved.
Moving on, this is Adhoc Analysis.
This gives an accountant
all the data points around what
were the opening balances,
what was the periodic
activity ending balances,
period to-date, quarter to date,
year-to-date, actuals and budgets
for pretty much the
accounting segment.
On this particular case,
we are looking at balance sheet.
Up until now, we were looking
at the revenue accounts.
Now this is the balance
sheet accounts.
I can change from balance
sheet to income statement with
just a different hierarchy
and this basically
changes from balance sheet to
income statement altogether.
Right here, now this is an
income statement hierarchy.
Like we discussed before,
you can actually do drill-down.
All of these numbers,
they allow you to do a drill-down to
balance level or sub-ledger level and
this helps them reconcile
their month end close
activities and so on.
This one is
Multi-dimensional Analysis.
This gives the users
an Excel-like functionality where
they can change the rows and columns.
In this particular example,
we have the income statement
hierarchy that is in the rows.
Then in the columns,
we have locations.
Now, if I wanted to see my
income statement hierarchy,
instead of locations, I
want to see it by product,
I can change it and now I see
the various products that we
sell and the income statement
hierarchy by the products.
If I wanted to see it
at a department level,
I can change it to department
and then I would be looking at
the various department level
income statement hierarchy.
This allows the accountants and
the CFOs and the controllers
of The Wonderful
Company to actually see
by various chart of account
segments altogether.
This is again another income
statement Adhoc Analysis.
We just looked at it before.
This one is Maintenance Spend.
Wonderful being in a
capital intensive industry,
we do look at maintenance
quite significantly.
This one is specifically only the
maintenance related accounts.
How they have done period to date,
prior year period to
date, two years ago,
three years ago, and compare it by
various locations and see
at the various plant level.
>> Perfect. Thank you so
much for that demo, Dhiraj.
You can really tell how much thought
and strategy went into planning
those reports to make them look
so clean and have such
high performance,
even though there is a ton of
data that's being funneled into them.
I know with any solution
that you develop,
you hit challenges along the way.
What are some of the lessons that you
learned from developing the solution?
>> Lauren, from a delivery
approach perspective,
we were on a time crunch.
Management wanted to have a lot of
these reports and dashboards
done in an aggressive timeline.
We found agile methodology works
for data analytics projects.
We identified the KPIs with the
business users ahead of time.
The key learnings are,
you work with the key business users,
they have to be identified within
the business unit and the department,
work with the business users on
mock-ups that helps clarify what
you want both from
a data model and also from
a visualization standpoint.
Having a risk mitigation
strategy is always helpful.
Data validation is definitely a
challenge with many data projects.
As most folks know,
the reporting is the shiny object,
but the key for a lot of
the data analytics projects is
around building the data pipeline,
having data validation done,
having the training
and documentation with
the users on the data
models is very critical.
Focus on documentation.
Having a plan for training
users at various levels is
also critical for our
data analytics project.
>> It's amazing to hear how much you
accomplished in such a
short amount of time,
talking about that time
crunch that you were on.
But that last bullet point there
where you were talking about
planning for how to train users,
oftentimes, getting people to adopt
what you've built can be one
of the most difficult parts.
How did you go about doing that
adoption and training those users?
>> I'll talk about
two key drivers here.
We worked on multiple
projects at the same time.
One of our business unit,
the top-down approach
from management,
they identified KPIs and
we had those KPIs clearly
defined by the individual
line-level managers and directors.
That worked. On the other side,
we also worked with, let's say,
folks on the line who had
specific reporting needs and work
it all the way to the top
level management as well.
So both the top-down or bottom-up
approach works quite well.
Having a clear goal and purpose,
that always helps with
other projects as well.
In this case, with Citus,
the management was very clear
on what they wanted with
respect to KPIs and what kind
of reporting they wanted.
So that always helps.
Having the alignment between
various stakeholders is very
critical because you're talking
about multiple players here.
You've got a business user,
you've got a C-level executive,
you have developers
who are developing,
bringing specialized skills, you have
a UI/UX person who just brings
the UI/UX aspect of it.
Having that clear alignment
also helps in getting some
of these projects
workout at rapid pace.
As we were able to deliver
multiples sprints one
after another on these KPIs
in four-week turnaround time,
we adopted some of the
learnings from the past,
some of the mistakes that we had
with respect to data validation,
soliciting user feedback early.
As we embarked on some
of the later sprints,
we worked with users hand in hand on
data pipelines and data models
and clearly solicited their feedback.
In our case, at this point,
from a data analytic standpoint,
you have three types
of data velocities,
one being real-time needs,
such as the transactional reporting,
analytical needs, which
are the BA aspect of it.
Then the streaming needs, which is
where you have streaming
datasets like temperature,
sensors, and other data or
website traffic data, so on.
That's the streaming part.
Identifying dataset ahead of time
and working with the users on
what they would like
to with respect to the
data velocity is critical,
and then having a training
plan is critical as well.
>> Yeah, that makes a lot of sense.
Thanks for sharing that.
As we're getting to the
end here and wrapping up,
is there any final last
thing you'd like to add?
>> Sure, Lauren. I'd like to give
a big thanks for both
Nikhil and Sakthi.
Nikhil from Microsoft and Sakthi
Kannan from iLink Digital.
We worked with Nikhil
during our evaluation
of the various visualization
tools and Sakthi
has been helping us out
on building some of
the reports and dashboards
for The Wonderful Company.
>> Perfect. I just want to say
thank you so much, Dhiraj,
for being here today and
for putting together
that amazing demo and being
able to showcase that today.
I really appreciate it.
>> Thank you for providing
the opportunity to share
our journey in transitioning
into a modern,
data-driven organization
and happy to have
any follow-ups as well, Lauren.
It has been my pleasure.
