>> Welcome to a new episode of the Azure Enablement Show.
In today's episode, we're going to talk about
an enablement scenario for data management and analytics.
Welcome back. My name is Thomas,
and I'm here with Mike to talk about
the enablement scenario for data management and analytics.
Before we go and speak about
data management and analytics, Mike,
can you explain to us a little bit about what scenarios are,
and why they are so helpful?
>> Think about where we are today,
enablement scenarios provide specific guidance,
and resources, and best practices for customers
to successfully adopt Azure long specific journeys.
We're not just talking about
individual products anymore when we talk about solutions.
Think about use cases such as SAP,
AI, Cloud-Scale Analytics,
Hybrid, Multicloud,
Modern app, the list goes on,
or you pivot to industries such as retail,
healthcare, and FSI.
They are all looking to
understand how they can on board their solutions,
their industries, with this specific guidance.
What scenarios are doing is
they're enabling scenarios principles such
as high value specific actionable assets, such as
Azure DevOps plan templates,
reference architectures, assessments,
Azure landing zone implementation and options.
The unique piece is it's one single concern location.
No longer do you have to search Bing over and over,
the content is all aggregated from a Cloud Adoption Framework,
the Well-Architected Framework, the Azure Architecture Center.
Also it gives you a plan on skilling and resourcing to
best implement these scenarios
without having to go to multiple document pages.
>> That's definitely super helpful
to have these different scenarios, and again,
pulling all of this information
together makes it definitely easier
for customers to get to the right information when they need it.
When we speak about data,
why do we actually need these scenarios,
and why do we actually need that guidance?
>> Let's reflect back a couple of years.
2019, the Harvard Business Review—the understanding
why analytics strategies fall short for some, but not for others.
In that report, 80 percent
report struggling to become mature users of data,
55 percent report data silos
and data management difficulties as roadblocks.
Then from the research that we then did with our customers,
an iconic customer base,
we see that customers have challenges in outcomes.
When we look at it and when we look through,
many companies are spending
substantially more on their data platforms,
analytics tools, and staff to manage them.
They're going through iteration V1, 2,
3, and 4
to try and get where they need to.
They're not thinking about scale,
how do I try and proactively leverage
the data and insights to realize the business value?
How do I go through the challenges that mount to
a complex barrier to achieve in
a modern estate and a governed estate?
Challenges that we heard very clear
was poor data quality, data silos,
shadow IT popping up because main IT cannot deliver,
so the business is creating their own shadow IT,
ever growing volumes of data.
Then how do you balance access and protection?
Now the desired study, for example,
is that we hear customers saying they want to protectively
use their data and insights to predict business drivers
but they question how they do that.
You've got unified data management and analytics,
buzzwords, how do you do that?
Then you've got governance that really,
they're looking to enable access and flexibility,
they want to be secure and compliant,
and have a fully integrated environment out
of the box to get away from these set shadow IT,
these spurs, these silos that they see today.
>> Absolutely. That's also what I hear from
many of our customer conversations is
data is obviously very valuable but also very challenging,
especially when it comes to compliance scenarios.
How do I actually build a data-driven enterprise?
>> There's multiple points that you need to consider.
First is your strategy.
Before you look at anything,
what is your plan and what is
your strategy for a data-driven enterprise?
How do I democratize data?
How do I have a modern data center strategy
on decentralized of data ownership,
and democratization of data ownership?
We want to enable teams to quickly deliver
that business value and to continue with the innovation.
We don't want them to be blocked
because there's a ticketing system that
you have to log a ticket,
one team deals with that,
and then another team deals with it,
and you end up in what we would term as ticketing how.
I've not been able to, really just
get your business driven forward.
Then there's that holistic governance.
Imagine, your data governance ensures your data is discoverable,
accurate, trusted, and protected.
When talking to our customers,
this keeps coming back.
If you imagine you've got data silos,
how I discover that I've got data silos,
I need to know where my data is,
I need to make it discoverable,
I need to have a strategy and
a process that sits over the top of it.
How I standardize my organization and
classification regardless of the line of business ownership.
Then there's that day-to-day management,
that centralized data management operation.
How do I manage that data governance on a day-to-day basis?
How I drive the consistency as a set,
and then increasingly we'll look in to mature those analytics.
Line of businesses want to be able to use
centralized services to build their own analytics platform
or they want to be able to have
the right data at the right time
to gradually use more advanced analytics tools
to gain insights into the business that
they are critically looking at every single day.
>> That's amazing. When it comes to this scenario,
how does it address this so that
organizations can set up for success,
and building the foundation for
these four pillars of modern data strategy?
>> If we actually have to take a look here,
we're able to really call out
at least 4-5 pillars
that we need to consider in our plan and our strategy,
and especially in our ready phase as well.
Think of democratization,
let's look at each of the businesses and
understand and prioritize the line of business needs.
Let's also evaluate that current ownership, create and
implement, and iterate upon that decentralized plan,
and let's give guidance on how that can be done.
When we look at the governance,
how are we going to put that strategy
together to map the data in environment,
catalog, scan, and classify data?
How do we connect data across a distributed data landscape?
At an enterprise level,
you can have companies with many different locations,
you need to be very aware of those compliance around
where you're holding that data.
Equally, you look at the authorization of data management,
you want to implement
agile data management and build
upon top of what we call Azure landing zones,
which are part of the Cloud adoption framework.
We want to make sure that they're fast, they're scalable,
and we can always deploy a foundation of
security in governance and compliance best practices.
We also want to
secure and strengthen our data security with authentication,
authorization, automation,
and then integrate data for consistency across the business.
Then moving on,
there's always that plateau of self-service,
but as we look towards the Cloud and we look
towards the hybrid scenarios,
we really are hearing from our customers that we're looking
for self-service enablement to understand,
distribute data across the data domains,
to bring them closer to the users,
and drive that business value to empower the line of businesses
to deliver powerful analytics with advanced analytics tools,
effectively creating data assets as a service.
>> Now, this is fantastic, and again,
mentioning the Hybrid scenarios and multicloud scenarios,
where you want to get all this data doesn't matter where it's
actually located all together
and use these management tools is great.
Now, you also talked about landing zones,
can you tell us about how this integrates
in Azure Landing Zones as well?
>> Absolutely. I think before we just look at how
the Azure Landing Zones really comes together,
let's take a bit more of a higher level look
at the data management analytics scenario.
As a whole, you can see this all
encompassing circle that we have here.
We've talked about the things on
the right-hand side here, the scale,
the distributed architecture,
Cloud model, and reduce time for deployment.
But what we're trying to bring here you see is
we don't want to just talk about writing the next book,
the next document,
it's how do we make it real for yourself?
We're looking at reference architectures,
those assessments there,
deployment templates, skilling, well-architected,
hence why we are calling these enablement scenarios.
We're building on top as we've already
said of these landing zones,
Cloud Adoption landing zones,
Cloud Adoption Framework,
and the Azure Landing Zones that we have there.
When we scale this out,
we think of a scenario into elements for these landing zones.
We think of them as a data management landing zone,
which is very much along managing
the governance of the environment of the whole of the platform.
Then we're thinking that as we scale
our analytics and we have our use cases that we have,
we might have different regions that we
need to have data landing zones.
These are very much for our data products,
our data domains,
our analytics work close that are working in this space.
But this is great in
from a point of view that you can look at a pretty picture,
we want to just take it a pause from that.
As such, just to dive in at a high level,
where we think of the governance,
we think as a have you really
drive in that insight, that management,
the ability to scan sources.
We're also thinking of how do we have data contracts
so that we know where our data is flowing through to,
and we think of standardization of automation,
whilst trying to incorporate into these Azure Landing Zones.
This is one landing zone that looks after
your governance across the whole of the platform as I keep saying.
Then we come to the next part,
this data landing zone.
This data landing zone is all
about our standard services that are in there,
ability then to have
this snapshot drive in your analytics workloads,
and your use cases.
Then our data consumers that are also taking from this.
But equally, we want to make
sure that if I've got one data landing zone,
it can communicate with another data landing zone.
Then finally, when we look to bring it all together,
we want you to go and play with this.
We actually want you to more than play with it,
but actually try and use it as well.
We've tried to take this reference implementation as
a simple one click and DevOps
deployment options that allow you to go run this.
Go and deploy to Azure.
Go and look at DevOps pipelines
and GitHub actions that allow you to set this up.
This has all been tested and validated with customers,
so you can be assured that what we've got
in those particular repos and
DevOps options is what
Microsoft would define as the standard for that security,
for that governance, and
really aligns to that well-architected framework.
>> It's awesome, and again, I love that you're making it very
easy for customers to implement that.
Now, if I'm a customer now and I want
to learn more about this, where do I go?
>> You've got three tip locations that you can go and do this.
Go and look at the Microsoft Docs that are being
published and they've been available for some time now,
and we put in data management and analytics scenario,
there's a short link that's available for that,
and equally, you can go and look at
these sample templates as well,
where you can do that exact thing of deploy to Azure,
and get it spun up, play with it,
understand what is going on with the scenario.
>> Awesome. Thank you very much, Mike.
Thank you, everyone for watching.
As always, check out aka.ms/adopt.
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