[MUSIC PLAYING]
STEPHANIE WONG: Hey, everyone.
This is Stephanie Wong, Cloud Developer Advocate.
And today, I have the pleasure to sit down
with Bruno Aziza, Google Cloud's Head of Data Analytics,
to talk about what customers have taught us
about the future of data.
And today, we are going to be talking
about how some customers have achieved incredible results
with data, the best and the worst practices,
and touch on topics like how many data people should
your company hire, how should your data
and analytics department work and who should they report to,
and what do most modern architectures look like?
But first, for those of you who don't know you yet,
Bruno, can you give us a quick intro
of what you've been up to?
BRUNO AZIZA: Well, thanks for having me here, Stephanie.
You're right.
I specialize in everything data, AI, and analytics,
and this world of data has been part of my life
for the last three decades.
Before working here at Google, I worked
at companies like Business Objects after it went IPO
and before it was acquired by SAP.
I most recently was at Oracle when
my team led one of the fastest turnarounds in the business
intelligence industry.
I had the opportunity to launch startups
like Alpine Data Labs that was bought by TIBCO, Scisence,
AtScale, and I also helped Microsoft grow its business
unit for data to a billion dollars.
STEPHANIE WONG: Great.
So we all know that you were very busy before and now
at Google Cloud.
So tell us a little bit about your role here
and what you do here at Google.
BRUNO AZIZA: That's true.
I'm busy, but it's busy good.
And simply, my team really is split into three
main responsibilities.
The first one is engaging with customers and partners.
We spend about 60% to 70% of our time within the community.
The second is our product strategy and execution,
our launches, our new products, and I
know we're going to talk about a few here today.
And then, of course, everything related
to go-to-market globally-- you know,
how do we make it easy for our customers
to onboard and consume the value of our services,
transform, and innovate?
You know, I've been here now about a year,
having a blast working with a lot of the people I've
worked with previously in the industry.
STEPHANIE WONG: Right.
So you have spent a lot of time with customers.
So what are some of the key trends that you've been seeing?
BRUNO AZIZA: There are lots of problems.
But that also means there's a lot of opportunities
in this field.
One of the biggest challenges that data executives have today
is how do they turn into competitive advantage
this immense, fast-growing, fast-moving amount
of information that their organization
and their customers and their partners and the ecosystem,
in a way, is creating?
Now, of course, you know about data growth.
You know according to IDC, 175 zettabytes of data
will be created in the next five years.
If you're wondering what a zettabyte is, it's 21 zeroes.
So it's a lot of data.
You know that our data production
is increased by more than 10x over the last eight years.
And in fact, 90% of the data in the world that has been created
has been created over the last 10 years.
So when it comes to turning this data into business outcomes
and business values, the situation is quite alarming.
You look at stats in the industry--
close to 2/3 of the data produced
is actually never being analyzed, and almost 70%
of organizations are unable to realize tangible and measurable
value from data.
And 90% of employees say that their work
is slowed down by data they just can't rely on.
STEPHANIE WONG: So Bruno, we've known each other
for a little while now.
And one thing that stands out is that I
know that you eat, breathe, and sleep data and analytics.
So I want to ask you more of a philosophical question,
since you're so ingrained in it.
What is one thing that CDOs or CIOs or other leaders
in this space should keep in mind when
thinking about the future in the data space?
BRUNO AZIZA: Well, thank you, Stephanie.
Actually, there are at least three things
they should keep their mind on.
The first one-- we talked a little bit about it before--
this idea of multi-cloud.
You got to build your system assuming
your data is going to be across a hybrid environment on-prem as
well as multiple clouds.
The second aspect of your strategy
should incorporate real-time as a foundational piece
of your architecture.
No longer are organizations going
to differentiate themselves by working with data in batch.
And then, finally, embedding machine learning
is an important way that you're going to democratize access
to data for everyone.
STEPHANIE WONG: If you are a CDO, or chief data officer,
or a CIO, a chief information officer,
how many data people should your company
hire in order to stay competitive?
BRUNO AZIZA: Yeah, this is a really tough question,
because leaders often say that the source
of their competitive advantage comes from their people,
not just their services or their products.
And if that's true, then the best way
to tell what your company's core competency is
is to look at the footprint of your employees
and their functions.
And so if you look at companies like Wayfair, for instance,
you know, recently in the press, they
claimed that they had 3,000 engineers and data scientists.
Now, out of their total employee base, that's close to 18%.
Now, you're probably sitting here and thinking, well,
that's not a traditional company.
Wayfair is an online digital data-first organization.
But then you look at banks, and more traditional organizations
like DBS Bank, for instance.
Their CEO recently was quoted in the press saying that we're now
increasingly seeing or thinking of ourselves as a technology
company offering financial services, rather
than a traditional bank.
And the fact that we have twice as many engineers than bankers
is perhaps a testimony to the shift in the nature
of the company that we are.
So if you think about that, you have to ask yourself,
how many data employees should you
employ as a percentage of your total employee base?
And here, what we've learned from the community-- and here,
I've got to give credit to Kirk Borne.
He's a chief scientist officer at DataPrime,
and has come up with some really interesting and useful ratios.
According to him, approximately 100% of your employees
should be data literate.
And that makes sense.
That's the ability to recognize, understand, and talk data.
We want everyone to be literate when it comes to reading data,
if you will.
A third should be data fluent.
So what that means is the ability
to analyze and create arguments and present results with data.
And about 10% should be data professionals.
So now that means they are paid to create value from data.
So now roles like chief scientists, data analysts,
BI specialists kind of fit in that category.
So what I would tell your customers here
is that if you're listening here,
what is your percentage today according to these ratios here,
and what do you think it should be
so you can innovate with data?
STEPHANIE WONG: Yeah, that's great guidance, Bruno.
And while we are on this topic, another
question that we often get is around reporting structure,
right?
So who should data leaders work for?
Who should own the data and the analytics?
What are your thoughts, there?
BRUNO AZIZA: There's a lot of questions within the question,
here.
The first one is, who should data leaders work for?
This is particularly a difficult one,
because you've got lots of choices, right?
You could be having the data teams report
under the CFO, the chief financial officer, or the CPO,
the chief product officer, the CTO,
if it's a technical function, or maybe even
the respective business unit leaders.
And here, everyone's afraid of answering the question,
because it's difficult to prescribe based on roles,
right?
If it goes under the CFO, does it
mean, that, then the initiative is around cost reduction?
If it goes under the chief product officer,
does it mean that we're going to focus on product analytics
only?
And so when we ask customers at scale the answer,
typically, it's under the CFO or the CTO.
Now, while I think that makes sense,
I think the question behind the question
is also to think about how should you approach data
so you can truly enable your company's ability
to innovate with it?
And there, the trend that we hear
is more around data fabric or data mesh.
And what I mean by that is the ability
to basically centralize data and decentralize analytics.
There's a consensus there of companies that basically
realize they want to centralize the metadata management,
the policy, and the security, and then they want to create
landing zones-- some customers call them data neighborhoods--
that they can distribute access to those users.
In those neighborhoods, people can transform the data.
They can augment it.
They can analyze it.
But they can always do that in a way that
is connected to the centralized data strategy,
and it abides by the corporate policies and the rules.
So I'd advise people to read up on that.
There's a great testimonial from the head of data at Fidelity
who created this idea of neighborhoods, rationalized
100 plus data warehouses into a platform looking
at Universal IDs, single customer
profiles, central taxonomy, over 3,000 data elements,
and a strong governance function.
So neighborhoods is a way that I would look at that.
STEPHANIE WONG: So Bruno, as you and I know, and many people do,
data analytics, data integration,
data processing-- it can be a very complex space, especially
as we continue to modernize, right?
So what is the one gotcha that you've
seen for people in this space?
BRUNO AZIZA: So as you plan for change,
one of the biggest gotchas is probably data sharing.
It's a very hot topic in our industry today.
But data sharing the old way has been problematic and difficult.
Data sharing historically has been
about delivering data and then having people work on it.
And in a way, it's kind of like giving a gift card.
It's a great thought, but you have to do a lot of work
in order to be able to consume the value of it.
And so the people that are innovating with data sharing,
they think about analytics sharing.
They think about putting together
rich data applications that are combining data sets,
multiple data sets, as well as dashboard analytics solutions
so then people can consume the value almost immediately
once the asset has been shared.
STEPHANIE WONG: So Bruno, I feel like data analytics, as a term,
is thrown around a lot, and it's a topic at many organizations,
whether you are a new startup or someone who
is very well-versed in the area and has a lot
of legacy systems in place.
But what are your thoughts and advice for people
who just simply want to get started?
BRUNO AZIZA: Yeah.
There's a lot of theory around this, right?
So when you get started and you plan for change,
what should you be doing?
Should you be pointing at new use cases
and then build a new baseline for performance?
Or should you take existing use cases
and then see the change from the old to the new?
And the reality is that it's neither.
It's the use cases that you find have strong sponsorship, where
the definition of success is shared.
And the average tenure of a chief data officer
is about 1,000 days.
And the reason for that is because we're often not
very clear about what we expect from our data leaders.
And so what I would advise you to do
is forget the notion of new and existing,
and focus on business value from day one.
How are you going to measure that,
and how are you sharing that with leaders that
are supporting your initiative?
STEPHANIE WONG: OK.
So we have talked about organizational blueprints.
What about the technical blueprints?
There are a lot of people talking about the modern data
stack and the modern data architecture.
So what do you see out there?
BRUNO AZIZA: Well, first of all, we
do see an incredible amount of companies moving to the cloud--
probably it's the fastest speed that we've ever
noticed in the past.
I think about companies like PayPal--
20 plus petabytes serving over 3,000 users in its migration
from Teradata.
I think about Verizon, with 200 terabytes ingested daily,
100 petabytes stored in BigQuery.
And then traditional retailers like Crate and Barrel--
10x more information sources compared to a few years ago,
doubled their return on ads, and with only increasing investment
by 20%.
So really, an incredible amount of innovation going on
in the space.
And what do they all have in common, these organizations?
They have a modern approach, just like you mentioned here,
to their data practices and their data platform.
So what are the components?
The first one is it's an approach that embraces
the old and the new, right?
Every single one of the most important brands on Earth
has legacy systems.
And they have developed leadership
over decades and systems before the cloud came along
that carried them there, right?
So take the example of an organization
like ATB Financials, where it saved millions,
generated millions by moving from SAP to the Google Cloud.
They built a system they call DEEP.
It's an acronym that stands for Data Exposure Enablement
Platform.
It's a system they built on BigQuery for real-time data
acquisition, enrichment, self-service, analytics,
and automation.
And so that's really kind of attribute number one.
Don't ignore or disrespect, if you will, what got you here.
Second is don't discard what's going to take you there.
And the big trend there is this idea of multi-cloud, right?
If you look at research, more than 80% of businesses
reported using multi-cloud this year.
And more than 90% have a plan in place today
to be multi-cloud in the future.
So the bottom line here is that companies
run a multi-cloud world today, and they
are going to be running in a multi-cloud environment
tomorrow.
And so modern architectures are multi-cloud by default.
And then finally, data is no longer a stagnant asset, right?
Organizations that win with data--
they think about data as part of an ecosystem.
The head of Data Products at Lloyd's of London
shared this picture with me.
I don't know if you cam see it well here.
But you can see the dot on the top here-- the dark dot-- is
your data, and the rest is the world's data.
And so what that means is this small dot
that I showed you here is just the data that you're
struggling to see today.
We talked earlier.
You know, 2/3 of the data in your company
is not being analyzed.
But you have the opportunity to see the world's data, which
brings us to another trend that's really important-- it's
this idea of data sharing.
The most progressive companies look
at data as part of an ecosystem, and data sharing
is really important.
They build data networks.
I mean, consider this.
Gartner predicts that by 2023, organizations
that promote data sharing will outperform their peers
on most business value metrics.
But only 5% of data sharing programs today
correctly identify trusted data and locate trusted data
sources.
So if you're listening to us, you've
got to consider multi-cloud, taking
into account your legacy, but then think
about where you're going next.
And really, my big question for you
is what is your data sharing strategy?
STEPHANIE WONG: Nicely said, Bruno.
That's excellent.
And I completely agree with that.
We talked about both organization optimization
and technology optimization, so I love that.
Thank you.
I hope people will reach out to you for more insights,
and I also hope that many of those watching
did find this useful.
So thank you to everyone.
And thank you to Bruno for sharing your thoughts.
BRUNO AZIZA: Thank you, Stephanie.