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BRUNO AZIZA: Hi, everyone.
I'm Bruno Aziza.
And welcome to another episode of "Data Journeys."
This is the place where we come and hear
from the leaders in data and analytics,
leaders that have achieved amazing results,
amazing growth, and have some great best practices to share
with us.
So today, I'm sitting down with Mike.
And Mike is the Chief Data Officer and Head
of Infrastructure at KeyBank.
He has spent a lot of time in the financial services
industry, but has been building his entire career
across multiple industries-- worked
at GE as well in the data and analytics space.
And so he's going to share with us his best practices.
Mike, thank you so much for taking the time
to talk to us today.
MIKE ONDERS: Thanks, Bruno.
Good to be here with you.
BRUNO AZIZA: All right, Mike, well, let's start at the top.
What does KeyBank do?
MIKE ONDERS: KeyBank is a regional bank.
We're in, I'd say, the top 20 banks in the US.
Full Service bank-- we support retail, small business,
corporate, wealth, and institutional investors.
We have over 1,000 branches.
We go from Alaska to New York in mostly northern United States,
over 1,500 ATMs, and we manage about $145 billion in assets.
BRUNO AZIZA: So you've got a lot of data to deal with,
and you've got data in traditional data warehouse
as well as in Hadoop environments.
You know, what was your first use case?
What did you start with when you started
migrating to the Google Cloud?
MIKE ONDERS: So our first use, we have two major data
platforms, as you mentioned, Bruno.
We have a Hadoop data lake, and we
have a Teradata data warehouse, high-performing analytics
environment.
We have about 40 marts on that Teradata environment, but about
40 analytic workspaces.
We have probably 400 analytic users that create
and author either models or analytics.
We have a legacy Cognos reporting environment.
We have Tableau for data visualizations,
and a lot of SaaS users still.
And so our first use case was, let's move Teradata to Google.
Let's move all of our marts.
Let's try to move all of our analytic users
because that's where they source most of their analytic data.
And let's try to get them all over to Google.
And then Phase 2 will be, let's lift and shift the Data Lake
and get that over to Google.
So we're running in parallel right now.
We've moved all of our marts over to Google.
We've got analytics users in the Google Cloud
and running in parallel right now with all of our data marts
right now.
BRUNO AZIZA: What was the justification for that move
in the first place?
I mean, many organizations listening to us
have a similar environment than you do.
MIKE ONDERS: We started looking at this two years ago.
The data warehouse in the clouds were really getting mature.
Both Google, Amazon, Microsoft, a small startup
called Snowflake were all kind of getting into the Cloud data
warehousing space.
And the advantage of Cloud data warehousing for us
was Elastic Compute.
And we're not a retailer that has a big Black Friday,
but end of month, end the quarter,
we run a lot of models, and a lot of forecasting models,
a lot of risk models.
And having that Elastic Compute is important.
Otherwise, you're going to buy a whole lot of compute that's
going to get used one week, and then it's
not going to get used.
And we were also looking at the end of life of our Teradata
contract, saying, hey, can we get to the Cloud
before we have to reinvest in a fixed compute model
and get into an Elastic Compute model?
So that the biggest use case, was
we knew when our end of life on these contracts were up.
We didn't want to go buy more on-premise hardware,
wanted to get into an Elastic Compute
model for our analytics users.
We did some RPs.
We did some POCs across a couple of different vendors.
And Google won.
BRUNO AZIZA: Mike, that's excellent.
So why did you choose Google?
MIKE ONDERS: We did a proof of concept
across multiple vendors, and what we saw from Google
was query times that were anywhere from 2 times faster
to 16 times faster.
But that wasn't really the key reason we chose Google.
It was the breadth of products, from data movement,
to data query, data storage, AI-- a total suite
that we could look at a full suite of products from Google.
And lastly, ease of working with Google,
which surprised us, actually.
But your ability to present corporate sales team
made it easy for our legal team, our contract team.
Negotiations went well.
It was really a differentiator and easy to work with Google.
BRUNO AZIZA: Well, we feel very fortunate to partner with you .
If you think about where you're going now
and what your future goals are, what
are you going to start with?
MIKE ONDERS: Yeah, my future goal
is get all my analytics users and Google,
take advantage of the new tools and techniques,
and build some really cool AI models that differentiate us
from our peers.
BRUNO AZIZA: Let's talk a little bit about these use cases.
I mean, 150 data sources, 80 downstream systems, and 250
models under governance--
I'm curious about how all that comes together
and what this looks like to the end users.
MIKE ONDERS: We do a traditional kind of-- we
source data from 150 systems into our data lake.
We move billions of records every night.
We organize that data.
We harmonize it.
We standardize it.
There's data quality dashboards, data
lineage moving into about 80 different downstreams.
So there's a lot of data movement, everything
from reporting and analytics downstream to General Ledger
to other core counting systems.
And then we provision that data for analytics into these marts.
And then we allow the users to access it.
But we're talking about three petabytes in the data lake,
a lot of data movement, and a lot of harmonization.
We've been on that journey for over five years, so the data
movement, and the data standardization,
the data quality, data governance,
all the stuff that a regulatory body wants
you to see from a banking perspective is all in place.
We viewed it more as a, hey, let's lift and shift this over
to Google, take advantage of the compute power
and the elasticity, plus take advantage of some of the new AI
tools that Google has-- not new necessary to Google,
but new to us as a bank--
new Python, new Spark, new clusters,
new machine learning capabilities that we really
want to take advantage of.
BRUNO AZIZA: And when you talk about machine learning,
is that for the purpose of fraud or what are the typical use
cases where machine learning is helping you
from a business standpoint?
MIKE ONDERS: Fraud is a big one.
And that's one of the first use cases where we really spun up
Python and Spark clusters to do some new fraud
modeling, especially around smart
holds around check deposits and how
to use fraud algorithms to determine whether we should
hold this money or release it to you
when you deposit that check.
But we do a lot of modeling and marketing
for attrition modeling, pricing modeling, next best
offer, next best product, as well as, I'd
say, in risk modeling, in terms of credit risk modeling, market
risk modeling.
There's a lot of modeling in those spaces
to kind of predict what's going to happen in the economy.
Where do we have risk in terms of certain lending portfolios
and stuff like that?
BRUNO AZIZA: That's excellent.
So both macro goals and customer experience goals around fraud
and just overall better experience
with the next best offer is a great example as well.
So you have gone through this journey.
But this is not the only journey where
you've learned best practices for data analytics.
So I want to ask you, if you were to start over,
what would be the best practices and the worst practices?
Let's start with the positive.
Let's start maybe with three best
practices that you would point everyone to and say,
you must absolutely start with those.
MIKE ONDERS: Well, for us, moving--
we looked at Google in three different areas.
One was our data movement.
One, we were looking at Google for natural language processing
and voice and chat bots.
And we're also an early adopter of Google Anthos and container
technology.
So all three of those were kind of happening at the same time,
which was good.
And so best practice, I would say, for anybody, if that's
your first big movement to the Cloud,
get your cybersecurity team engaged early on.
We did a lot of work up front with Google and Google
engineers on, how do we engineer the security of our Cloud?
And then deeper, just for the data space,
how do we engineer the security for our data?
And obviously, being a bank, I've spent a lot of time
in front of our regulators explaining to them how we're
securing the data at Google, how we're tokenizing and encrypting
data, tokenizing personally identifiable information.
So early on, got our security team involved,
got them to be partners with us on this journey.
And you want to make sure Google Cloud is
different than your on-premise data center.
You're really not trying to rebuild on-premise security
in Google.
You're trying to take advantage of the services Google offers
and the way they secure their environments.
And ironically, we were presenting
to our executive leadership team that we
were moving the data to the Google in the same week
as there was a major data breach at Amazon
with a competitor of ours, another bank that
had a major data breach.
And we were walking in and saying, hey, oh, by the way,
we're moving our data to Google.
And so there's a lot that we had to educate them
on how we're securing it and why it was secure,
because it's a big concern in banks
and other industries right now is, is your data secure?
So that's one big one.
The other one is don't underestimate the change
management.
My team would say, hey, moving the data to Google
is the easy part.
Getting your users over to Google and helping them
learn the new tools is actually the change management
opportunity.
And so we've developed, like, 10 courses in conjunction
with Google that they can take virtual training on how
to get to Google, how to secure your data,
how to use the new tools, how to get your project stood up,
and all that.
And I'd say, the last best practice that we've learned
is, we've got to make sure you've
got enough time for your users to straddle both environments
for a while, right?
And depending on how big your environments are
and how many users, that could be a quarter.
It could be two.
We're planning on three quarters that we're running parallel
now.
And we plan to shut down the old environment in Q4 this year.
And that forces them to get over to the new environment,
run their new models or their dashboards,
make sure they work the same way.
There's a few minor nuances that they
have to make a few minor adjustments based
on a few minor query differences.
But that's three of the things I'd
say I'd recommend to people.
BRUNO AZIZA: So security, internal change management,
and, of course, running in parallel,
so there's no disruption.
And then, at some point, deciding
that you're cutting off and moving to the new, more
modern capability.
There's this one, I'm sure, is the flip side
of the worst practice is not doing that,
is not running in parallel.
What are other maybe worst practices that you're seeing?
MIKE ONDERS: I'd say, we ran into a few bumps
with our BI tools.
And it is different, connecting to Teradata or other on-premise
versus connecting to Cloud and different drivers
and different SQL drivers.
So we're a big SaaS Tableau shop Cognos.
So make sure your BI tools are already connected
and have certified connections to Google.
And then as Google releases new releases,
make sure those partners are staying current
with their drivers to stay current with the new releases.
So we ran into a few bumps in the road there.
And I think we were early on, and so some of those partners
have caught up and are probably good now.
But we ran into a few bumps there.
BRUNO AZIZA: But I think it's a good assumption to think that.
And it's true for BI applications.
I'm sure it's true for custom applications
as well, is not starting with the assumption
that because you fixed the underlying infrastructure,
everything else at the top is just
going to behave the same way.
And in the end, these stacks are different stacks.
So it's expected they would behave differently.
Well, Mike, you have given us a ton today.
I mean, I'm taking away things like,
make sure security is on top of the list
when you're evaluating solutions.
You've talked about including cybersecurity teams early.
And I think that's a really critical point.
And how you choose your Cloud as well--
you talked about change management.
You talked about running in parallel.
And of course, thinking about your deployment as an ecosystem
deployment, so you don't just assume all the parts
are going to work equally--
so thinking about it as an ecosystem environment
deployment.
Mike, I hope people are going to reach out to you.
You've got a lot more that you can share.
I know I could talk to you for hours.
Thank you for taking the time to talk to us today.
MIKE ONDERS: Thanks, Bruno.
It's been a pleasure.
And yeah, it's been a great experience so far with Google.
And we look forward to a successful production rollout.
BRUNO AZIZA: Thank you very much, Mike.
Until next time, I'm Brian Aziza.
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