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ANGELA SOARES: Hi, I'm Angela Soares, a Product Marketing
Manager here at Google Cloud on the Data Analytics team.
Today, you'll hear from Hank Roark, Director of Analytics
at Boeing, where he'll share about Boeing's data journey
with Google Cloud.
Organizations need a modern approach to enterprise data
in order to fully realize its value.
A data cloud that easily supports
a wide range of workloads and offers access
to machine learning that everyone can leverage can help
data-driven organizations truly transform their business,
from better customer experiences to more
streamlined decision-making and efficient operations.
Organizations choose Google Cloud to build their data cloud
for its ability to fuel data-driven transformation
like no other solution on the market today.
At the core of Google's data cloud is BigQuery.
Customers analyze gigabytes to petabytes
of data with BigQuery, a fully managed serverless data
platform with built-in machine learning and business
intelligence.
BigQuery offers organizations the ability
to manage, analyze, and operationalize data
at any scale.
Some of the key features of BigQuery
that data teams benefit the most from
are the ability to scale storage and compute as your data needs
change so you can focus on the data
rather than managing infrastructure, full ANSI
SQL for a seamless data analysis experience
across your organization, BigQuery's robust security,
governance, and reliability controls that help protect
your organization's data with encryption by default,
all with high availability and durability with a 99% uptime
SLA.
Now let's hear from Hank to share more about Boeing's data
cloud journey.
Over to you, Hank.
HANK ROARK: Thank you, Angela.
I really appreciate the opportunity
to be here at Google Next to share part of Boeing's data
analytics and cloud journey.
My job is to make all the teams within Boeing
that help to optimize the business
and to serve our customers and stakeholders through data,
business intelligence, data analytics, and AI
as productive and happy as possible.
I lead a centralized platform team and center for enablement
as part of making that goal a reality.
And as anyone can imagine, like any significant enterprise,
this journey is a multiteam collaboration.
So while I get to be the point person today at Google Next,
there are lots and lots of teams of Boeing
that have been making this a reality.
And I'd like to recognize those teams
and also make a special call-out to all my users
that provide valuable feedback along this journey.
Thank you.
Boeing has a long history of using data analytics
for all kinds of use cases, some of which
I'll go into more detail a bit later.
To help set the stage for what we are looking
for in our next evolution, I thought
it might help to share some of the scale and impacts
of our enterprise data platform that were
the baseline for any evolution.
First, our enterprise data platform
is made up of more than a dozen data warehouses,
a Hadoop-based data lake, and a very large number
of data marts.
These process and serve data for just about every function
within the company, including engineering, manufacturing,
supply chain, finance, human resources,
and aftermarket services, to name just a few.
As to be expected, these are systems for 24/7 reliability,
and high data reliability have been
standard for quite some time.
Further, like lots of companies, we
have global laws to comply with that drives complexity
into any solution in this space.
Another way to understand the scale of our enterprise data
platform is to look at the amount of data process.
The data warehouses alone process
tens of exabytes of data per year, running at jobs
that range from extract/load/transform
processing to serving that data through to applications,
dashboards, and direct to data analyst, for example.
Some of that data is used several thousand times per day,
and some of it is used rarely.
And given the business we're in, we also
have very long retention periods of this data,
typically on the order of decades for some scenarios.
As mentioned before, these systems
serve every employee and many external stakeholders.
The data warehouses and data lakes
alone have a few thousand direct-to-database users,
the majority of which are in our engineering,
supply chain, finance, and other business functions and not
in our centralized IT department.
After that, nearly.
2/3 of employees, about 100,000 users,
consume data through the various dashboards, reports,
visualizations, and data products
served directly from the enterprise data platform.
Once we extend the impact to reverse ETL jobs,
we see the enterprise data platform
serving every employee and many external stakeholders
of the company.
And then a final way to understand
the impact of the baseline enterprise data platform
is to look at the direct financial impacts
of the company.
While I won't get into those numbers here,
suffice it to say that it is significant.
With all of that, new opportunities
are challenging what we were doing.
In particular, demand for supporting machine learning
and AI workloads is increasing rapidly.
When we looked out into the near future,
we were facing significant growth of sensor data
from all facets, from engineering
to manufacturing operations, all the way out to our products.
New business opportunities were being presented,
where we needed a digital twin of our products
throughout their entire lifecycle.
And then finally, but not least, as Boeing continues
to innovate, there were new business opportunities
that were putting new pressures on our platform
to be more responsive and agile.
A few years back, a group of stakeholders
from across the business gathered
to set the stage for Boeing's second century.
I was on the team focused on developing
the objectives for the evolution of our data and AI platform.
Through a series of interviews and workshops,
we landed on the following four big areas
for objectives for evolution-- digital twins,
AI, driving down unit cost, and exceeding all compliance
and security requirements.
I'll dig a little deeper on each.
Digital twins and digital threads--
the objective here is to provide the company
a single authoritative source of data covering
the entire product lifecycle for any data analytics and AI use
case.
That authoritative source of data also needed
to have fine-grained security to be able to address our growing
global user needs.
Also, we are seeing more and more opportunities that
required integration of external-to-the-company data
sources, like weather and radar feeds,
with internal-to-the-company data.
One of the bigger drivers was the objective
of evolving our enterprise data platform to being a true data
and AI platform.
Generally, I believe AI and ML will be embedded
in many, if not all, solutions.
And as the technology matures and becomes easier to use,
we are looking to help everyone that
wanted to use machine learning to do so.
Already, machine learning was being
used in many of our functions, but we
were seeing more demand driven by increasing business
opportunities within every sector
of our business for manufacturing
and industrial internet of things use cases,
through to computer vision-based solutions
and a large and growing number of predictive analytics
opportunities.
And we needed to do all of this while driving a step
change in our operational cost per unit work.
We looked to do this primarily by drastically simplifying
our platform by moving to a data foundation that
would service most, if not all, of my users' workload needs.
We set the path for this objective
to be solved by both technical simplification and data
duplication reduction, or even elimination.
Finally, but far from least, we needed
solutions that would meet or exceed our compliance
and security requirements.
We knew these requirements would continue
to change both externally and through our global growth.
In addition, we needed to transition
to a DataSecOps approach that would empower teams and enable
security throughout the entire data product development
lifecycle and software supply chain.
And we knew we had to do all of this while
making data access as simple as possible within those security
and compliance requirements.
These are quite ambitious objectives
we set out for ourselves.
They are challenging both technically and from a change
management perspective.
And like in all good journeys of this magnitude,
it is best to go at it surrounded by partners
that can share in your goals and where
everyone involved achieves their objectives along the way.
So finally, to the Google Cloud part of the story
and why we are here today, we have
built a strong relationship with Google Cloud while
on this journey.
I feel Google has been there with us,
helping us achieve many of the objectives we set out.
And I believe we have helped Google
with new and demanding scenarios that then can go on
to help companies, industries, and individuals
across the globe.
I want to highlight some benefits
that I have seen in our relationship with Google.
First, the Google team has really
gotten to know many facets of our business.
This is a big deal, as aerospace is a rather complex arena,
and being able to work with a company that could speak
our language has been key.
And with that and Google's background in using data,
they have been able to help us map our opportunities back
to scalable and impactful technology-based and
data-driven solutions.
Second, Google leaned in to help with early wins
as a way to better understand our needs and requirements.
Key use cases were selected to test against the objectives.
Early proofs of concepts were conducted against some
of our hardest problems.
That then provided us and Google engineering and product teams
key feedback as to particulars of our needs.
Google's engineering and product teams
have provided many improvements into their products that
were specific to our demanding requirements,
and that continues today.
In terms of step changes in unit costs
as we continue our adoption of migration,
we are seeing the benefits of simplifying to a few key data
technologies, like BigQuery and Google Cloud.
Further, many work processes that
rely on central teams and manual processes
have been automated and driven into our DataDevOps
teams, which is increasing our agility and ability to respond.
This was no small feat, as the automation of these processes
required understanding detailed requirements that
were distributed across multiple existing teams,
as well as helping our teams understand modern data product
development processes.
We continue to work with Google to improve in this area.
Finally, many of Boeing requirements
are driven out of our compliance requirements.
We deal with some of the same things of just about
every company, like the growing requirements
around the handling and use of personally identifiable
information and things that maybe not every company
has to deal with, like data that has export
and geographic sovereignty requirements,
just to name some examples.
We have been working closely with Google
on the shared workloads so that we
can provide our users a common data and AI cloud experience,
no matter the export control needs.
This has been beneficial, as we can
place data and workloads into the proper shared workload
regime.
And by and large, the user/developer experience
is the same.
Further, the fine-grained security
enabled by identity and access management,
BigQuery fine-grained access controls,
and authorized views has enabled us
to simplify data access while maintaining governance
and security.
OK, well, no presentation at Next
is complete without talking a little about technology.
I've mentioned it briefly earlier
but want to go a little deeper into one
of the hubs of Boeing's data cloud, BigQuery.
BigQuery's serverless architecture
has been a big unlock for us by letting
us put more and more power into the hands of developers
and data analysts while still achieving
our service-level objectives and cost goals.
It took us a while to understand what a truly serverless cloud
data warehouse could enable, and so far so good.
A few areas where I'm seeing BigQuery enable our business--
data can now be shared with data teams and data product teams
throughout the company without duplication.
The decoupling of processing and storage
is changing the way we manage data,
and it's really driving us to new ways
to serve data to enterprise users.
We have grown to appreciate and use many, if not all,
the security knobs of BigQuery.
The fine-grained security is enabling
us to more easily serve data to our global users
while giving us confidence in protecting and governing
the data.
As we have grown in our use and understanding of BigQuery,
we are starting to transition to more and more scenarios where
we treat data as a product, being measured
on common product performance indicators,
like adoption and user satisfaction.
Combined with our data catalog, we
are creating an efficient marketplace of producers
and consumers of data.
This is something we didn't anticipate when we started,
and early use cases that are taking this approach enabled
by BigQuery are reporting back positive results.
We are now taking this a step further and experimenting
with how BigQuery will play a pivotal role in our adoption
of a data mesh approach.
Stay tuned.
Finally, the embedded machine learning capabilities
are something I knew teams would use,
but I had no idea that it would be at this level.
These are really powerful algorithms presented in the SQR
SQL language to developers.
I already have several teams, primarily
those working with sense data as part of product engineering
and operations, using machine learning
to gain new and impactful insights from our data assets.
BigQuery has made scaling that relatively simple,
and we anticipate growing adoption of BigQuery ML
into more and more use cases.
Tying this all back to our objectives
and enabling a digital twin and digital thread,
embracing AI and ML, reducing unit operating costs
to decrease complexity, all while
exceeding compliance and security requirements,
we see a direct line between those objectives
and how we can and are realizing them using BigQuery.
Well, that's really it for me today.
Again, thank you to all the teams at Boeing
making this a possibility.
And thank you to Angela and the Google Next
team for this opportunity to share a slice of Boeing's data
cloud journey.
ANGELA SOARES: Thank you so much, Hank,
and thank you very much to our viewers today
for tuning into this session and the rest of the sessions
at Google Cloud Next.
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