[MUSIC PLAYING]
XAVIER RILEY: Standard Industries
is a holding company with eight individual operating companies
underneath its umbrella.
The two largest operating companies
represent the world's leading waterproofing and roofing
companies.
GAF represents North America and BMI represents
the rest of the world for us.
My role here at Standard is bifurcated into two--
digital strategy and innovation.
For digital data strategy, I help
define the corporate strategy and goals
around data, governance, and use.
We also manage and maintain our central repository of data
vis a vis our data lake.
SEAN YAZDI: Considerations for data integration challenges
we were facing as an enterprise could be categorized
in three distinct ways.
First, the consolidation of disparate,
siloed data that existed across our enterprise.
Consistency is realized when consolidation of data
takes place and it unlocks the value of our data.
Within specific subject areas, we
harmonize data, and amongst diverse data elements
from varying subject areas, we integrate and link data
together to enable sophisticated analytics and insights.
Secondly, we address challenges to streamlining
the ingestion of data, an accelerated delivery
of both internal and external data into our data lake.
We take a use case driven approach
to building our data lake and quick time to value
was critical to supporting our approach
and delivering value to our enterprise stakeholders.
And finally, working with our Google team,
establishing an ingestion framework that's
reliable, maintainable, and supportable,
and enables the scale and complexity
to support the analytics and data science
use cases for our business.
XAVIER RILEY: Why did Standard Industries choose Google?
I smile because we went through the typical exercise
that most enterprises do, looking
at all the public clouds out there,
and we had our metrics to evaluate them.
But what really stood out for Standard was security.
We're a security first organization.
Simplicity of the solution.
Some of the public clouds out there,
they have so many application solutions
that it's sometimes confusing what to use and when to use it.
And the last part was--
well, there's two more parts, really.
One was around Google at its core has a pure engineering
ethos and I wanted my team to emulate
much of that engineering at its core as possible.
My team was also not going to be a very large,
so I needed to make sure that the solutions that we were
going to deploy from Google Cloud
were going to be simple to use, easy to use,
but also extremely powerful.
And really, the last thing when I considered Google
as a solution space was their commitment
to open source technologies.
Many of their tools were co-opted from the open source
space and just integrated into their overall schema
to work naturally with some of the other Google-based tools.
So for us, Google was a fantastic choice
and it has been and I'm sure it's
going to be for many years in the future.
How does Data Fusion help Standard Industries?
I remember seeing when Data Fusion was launched at Google
Next 2019, I leaned over to our CTO
and I said, that's the easy button.
I can't wait until that becomes generally available.
And when Data Fusion became generally available,
we were one of the first companies
to deploy the solution at scale within our enterprise.
And really, what does Data Fusion do for us?
It makes ingressing data and doing
the first initial transformations of data easy.
I just can't put it any simpler than that.
Some of the transformations that your engineers or developers
would do using other tools or other means are simplified.
They're-- I don't want to say that there's a drop-down menu,
but it's almost as if there's a drop-down menu.
Data Fusion has been the key to keeping my team tight
in our data and our data pipelines flowing smoothly.
So Data Fusion.
SEAN YAZDI: Next for our data journey
is to focus on data literacy, enabling
better and faster access to data to further enable
data-driven decision making.
For producers of data, exploring new ways
for optimizing the sharing of data throughout our enterprise,
and for consumers of data exploring, new ways
for streamlining the search, understanding, and utilization
of data across our enterprise.