(bright music)
- Coming up, we take a closer look
at Microsoft's Intelligent Data Platform
that integrates databases, analytics
and governance into a single solution.
We're going to show you how this can solve challenges
like today's supply chain issues
by removing the silos around data
to bring near real-time insights
augmented with predictive AI and also built-in controls
that protect your data, wherever it goes.
I'll also show you how you can easily scale
and build out new intelligent experiences around your data
to modernize your customer-facing apps.
And to walk us through everything,
I'm joined today by Sharieff Mansour
from the Data and AI team at Microsoft.
Welcome.
- Thanks, happy to be here to help unpack things further.
- Thanks so much Sharieff.
We've been working with you and your team for a while now
and keeping track of all the updates
across all of our database services,
analytics and governance solutions.
And they are are great options on their own,
but together they form
the Microsoft Intelligent Data Platform.
- That's right they do.
And it's really by design.
We've been working on providing
best in class individual solutions
as well as providing deep integration
across our technology stack for some time now.
So, let me explain how a single intelligent data platform
can improve the ways you work with data.
Providing a deep level of integration
not only brings different types of operational
and analytical data together,
wherever it resides and in near real-time,
it also allows for greater understanding
and discoverability of your data
at pretty much limitless scale.
And silos are also removed at the people level.
You know, your data professionals, developers
and end users can more easily work together
on common data and insights
and they can use the tools they're already familiar with
without having to learn new skills.
- And from an IT perspective,
not only are we breaking down the silos
and helping people work better together,
there are also foundational advantages here as well.
So underpinning all of this
is the shared identity and access management
through Azure Active Directory
that allows your users and your apps
to safely access your data.
And we have end-to-end security through Cloud Defender
and also Microsoft Sentinel,
as well as unified policy controls
that extend to all of your data
whether that's in Azure, on premises,
hybrid or across cloud.
So, why don't we make this real though for everyone
with a tangible example.
- So Jeremy, why don't we do this together
with a scenario that's top of mind for many of us today,
supply and demand.
And this is more challenging than ever,
because it's not just about
operating at scale and meeting demand,
there's also tension on the supply side
as supply chains are disrupted.
Ultimately, this is a data problem.
In this case, we're a manufacturer
of solar charge controllers.
The device that distributes solar energy
between the battery units and the power grid.
And there's lots of variability.
Starting with the demand side,
this is impacted by different government incentives
starting and stopping all the time
and people considering their options
based on events like brownouts.
On the supply side, we rely on just-in-time supply
so that we don't overstock,
but we often struggle with things like
component shortages for manufacturing
as well as shipping delays and unexpected facility closures.
So this is the app our sellers currently use
to look up units available for sale.
Now, so that we don't impact our operations,
we do daily batch runs,
which means we never have an up-to-date perspective
of inventory levels.
In fact, as you could see here,
the last batch run happened more than 19 hours ago.
And as a rule of thumb, we never sell
more than half of our current stock levels
so that we don't promise units
that have been already accounted for from sales orders
that are not yet represented in this view.
I think this is something
that we've probably all been feeling
with the recent pressures on both supply and demand.
- That's right, it's a common pain point today.
So let's see what happens when we take the same app
and apply the intelligent data platform to it.
Here's our new app.
- [Jeremy] Such an improvement.
- Such an improvement.
And instead of looking 19 hours in the past
for my last batch run,
as you could see, inventory is updated every two seconds.
So I know immediately what I have available to sell.
On the left, I can see the number of units in stock
and we know this number doesn't tell the entire story.
To solve that, on the right,
I can see what's available for sale
with a data-driven calculation
of what will be available a week from today.
It's a dynamic and current perspective.
Let's dig into the details.
A lot of factors impact what's really available to sell.
Behind the stock level,
I can see what's already accounted for,
the cancellation rate, the manufacturing run rate,
shipping delays, returns, parts required for servicing
and I have a predictive view of demand surge,
which I'll explain in a moment.
The point is, before, using my rule of thumb,
I would only commit to selling around
half of our current stock level,
which is 430 units.
And I'd be leaving money on the table,
because I actually have around 1200 units available.
And if I flip over to Power BI,
I can get a macro level view of supply and demand
with AI-driven insights.
I can see a spike in demand in shipments
in places like South Africa,
but I know there are a lot of government incentives there
so that's not surprising.
California on the other hand, stands out as a big anomaly.
And AI gives me a possible explanation
by linking the blip to widespread power outages.
Now let's look at the supply side.
I can see status of shipments en route to our factories.
The map on the right shows me
where supplier factories are offline
and below that, I can see the areas
where inbound supply is stuck.
For example, in LA and San Francisco,
where the ports are gridlocked.
And a lot of our inbound supply goes through those routes.
This level of insight is night and day
compared to what we had before.
So Jeremy, why don't we unpack what's going on
with the tech under the covers?
- Sounds good, let's have a look at
what makes this app tick.
So behind the app was SQL Server 2019
and we've since upgraded that to SQL server 2022
and this helps extend our on-premises database to the cloud,
but keep the data where it is now.
And we've also connected it to Azure Synapse
using Synapse Link for SQL Server.
Now, this has opened up more data sources
across all the various dimensions for supply and demand.
So let's see how this comes together.
Just to show you how integrated everything is,
I'm going to start in Synapse Analytics.
And using integration with Microsoft Purview,
right here from Synapse started a search
for predictive inventory.
Now it returns results across my entire data estate.
I'll select this table for predictive inventory,
which is our join table.
And through automated data classifications
right here in the schema tab,
sensitive information is already flagged.
Now, this lets us protect the data
using classifications to ensure that neither our apps
nor our reports will surface sensitive data.
Now, the data lineage tab provides us
great left to right view of the Azure Synapse table
showing that it originated first in Cosmos DB
and a couple of SQL databases on the left.
It's being all the way used through our Power BI report.
And not only are these databases discoverable
in Microsoft Purview,
they're also fully integrated in Azure Synapse
with all the tools necessary
that you need to work with the data.
And to be clear, not only does it unify data access,
but it also brings together the various people
and their skillsets to work with it.
Now, our data engineers they've brought in all the data
and we can now pivot that.
And we need to make sure here
that our inventory on hand is always current
not those 19 hour legs like you showed.
So our Synapse Link connection makes it possible
to query operational inventory data in our SQL database
and it's also instantaneous.
Now our pipelines, they're also bringing
in IoT telemetry data from Cosmos DB.
Now, this is globally distributed
and coming in from thousands of devices simultaneously,
which are installed around the world at customer sites.
And this data is also aggregated so that we can calculate
and allocate the correct amount of inventory for servicing,
for things like parts in the field.
So what we've shown is how all the data
comes together in a single view
across our operational and analytical sources
as well as the various people involved.
And as you saw, the new app
also has AI predictions built into it.
So let's unpack what's behind those.
- Sure, so those also need to be calculated in real time
and this is where we can help
multiple teams work together even your data scientists.
So, right now, I'm in the Azure Machine Learning experience.
My data science teams can apply
predictive machine learning models using the same data.
And there are models here to predict
order cancellation rates as you can see at the top
and then shipment arrival times
as well as units to allocate for parts and servicing.
And the one here at the bottom,
it's looking at localized demand spikes.
Now these are all regional phenomena
that indicate a surge in demand.
So I'm going to click into this one here
to access our notebook.
And here, we're looking for things
like outliers and demand by region,
and we're also doing anomaly detection.
So in certain cases, this is going to find things
like those power brownouts
that you mentioned before in California.
And if I scroll down a bit further, there we go.
I can also see predicted demand
and I can see weekly predictions,
which will inform how we plan our manufacturing
for all of our controllers.
- And by the way, these models
are how we are able to forecast
up-to-the-minute numbers and predictions in Synapse
that show up in our new app.
- Right, so now all of that machine learning data
needs to be merged with all the operational data
to be able to inform the predictions
that were surfaced in your app.
So right here in Azure Synapse,
we can access and leverage the models
that we just saw to score the combined data table
that I showed earlier from the operational side
and expose all those predictions
and adjust the totals as needed.
So these here are the mappings actually from my source data
along with my prediction data here in this middle column
that's called Model Input.
And now, I can upload the model to my table,
which will surface up all the predictions that we saw.
And this SQL script here
will score the data behind our updated app.
And this data is also behind our Power BI dashboards
so that our business analysts
can also plan out ordering and manufacturing.
And if I log into SQL Server Management Studio,
because what data demo would be complete without that,
I can see that all of our Synapse connected data is here
and it's written back to our SQL Server data store
that's on premises.
Now, this allows our operation teams to query it directly
as you can see as I make this select statement here.
- And just to put this into context,
what we just saw is the reduced churn
in connecting operational and analytical resources
as well as a layer of governance
and understanding of the data.
Something that would take months
is now achievable in just days.
- Right, and it's also kind of blurring the lines
between all the different services.
You've got Synapse, you've got Purview,
you've got all the different data sources
and also Machine Learning.
And it's also blurring the lines
in terms of how various teams can work together.
- That's right.
And that's really the point here.
We're breaking down both tech and people silos
across various processes
to intelligently make your data work for you,
but let's even go further.
Let's fast forward in time.
We want to expand from being a B2B wholesaler
to sell direct to consumer.
So here, you're seeing our new public website
that allows almost anyone around the globe to buy direct.
As you could see, I'm visiting the site in the US.
Everything here is powered
by the same near real-time, predictive,
intelligent data platform backend
we built for our internal app, but now it's public.
We've also added recommendations
for the highest ranked models
in our lineup using availability,
compatibility and local service provider data.
And as you can see, this is all location specific.
- Right, and under the covers it's actually
looking at a few more things.
It's looking at the units that are out in the field,
the servicing records, and also customer feedback
to really recommend the best possible unit.
So why don't we take a look
at what makes all of this possible?
So, behind the customer facing web app
is a massive Cosmos DB.
Since everything is already live
and available globally, here we can see
just the scale of the request,
the throughput, the data and also index size.
Then as you can see from the map,
some massive dataset flowing in
from around the world in real time.
And it's leveraging the supply and demand insights
that we built along with the data
from our order and fulfillment processes.
Now, ultimately we want to also light up
new experiences for our customers.
In fact, in addition to our usual text-based chat bot,
we've also added voice interaction
for personalized real-time feedback
on things like shipment status and product recommendations
using Power Virtual Agents,
which is integrated with the Intelligent Data Platform.
Let's try it out by calling the support number.
- [Virtual Agent ] Thanks for calling
Rela Cloud Customer Support.
In just a few words, tell me how we can help you today?
- I'm calling about my order status.
- [Virtual Agent] Sure, I can help you
check the status of your order.
Do you know your order number?
- No, I don't have it.
- [Virtual Agent] No problem.
Let's find your order another way.
What's the name of the company on the order?
- Contoso.
- [Virtual Agent] Thanks.
Now, can you give me the last name
of the primary contact on the order?
- Vance.
- [Virtual Agent] Great.
Finally, can you tell me the date the order was placed?
- Last Wednesday.
- [Virtual Agent] Good news.
Your order was dispatched on July 3rd
and is currently estimated to arrive on July 8th.
Is there anything else I can help you with today?
- No, thanks.
Notice the intelligence baked in there
beyond voice recognition itself.
The bot was able to verify my request
and look up the information I needed
even though I didn't have my order number.
- And all this is made possible through conversational AI,
which by the way we covered on a recent show
that you can check out
at aka.ms/PVAMechanics.
I'm going to show you how this works as well.
So I'm here in the Power Virtual Agents authoring canvas
for the topic that gets triggered
when a customer asks about order status.
So in this action here, you can see
that we're calling Power Automate,
which seamlessly connects to Cosmos DB
using the pre-built connector.
And there are lots of connectors available
to connect to the Intelligent Data Platform
as well as common third party data.
Now, in this condition, you can see
how the bot can even escalate to a live agent
if there's a problem finding the order.
The voice integration by the way,
is using Azure Cognitive Services
and Azure Communication Services are used
for voice and telephony.
- And what we went through today
is just the tip of the iceberg,
because your teams don't have
to integrate all these technologies.
You can use your existing skills.
Governance is baked in throughout.
You can respond faster, work together more easily,
unlock new insights,
and modernize the way you work with your data.
- It's really great seeing the possibilities
that the Microsoft Intelligent Data Platform brings.
So, for anybody who's watching right now
looking to get started or learn more,
what do you recommend?
- The cool thing is everything we've demonstrated today
is available right now.
And to learn more, you can go
to aka.ms/MicrosoftIDP.
Thanks so much Sharieff.
And of course, keep checking back to Microsoft Mechanics
for all the latest updates
and be sure to subscribe if you haven't already,
and as always, thank you for watching.
(bright music)