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BRUNO AZIZA: Hi, everyone.
I'm Bruno Aziza.
And welcome to another episode of Data Journeys.
This is where we come to learn from data leaders, their dos,
their don'ts, their best practices,
and their exceptional results as they're migrating to the cloud.
Today, I'm really excited to have
Juan, who is the Chief Data Officer at Rackspace.
We've done a lot of work together,
and he's got a great story to share with us.
Juan, thank you so much for your time today.
Welcome to the show.
Let's get started.
Tell us about yourself, and the company, and your journey.
JUAN RIOJAS: First of all, Bruno, thanks for having me on.
I'm really excited about this.
I mean, this is one of the topics
that I absolutely geek out about, I'm
fairly passionate about.
So my name is Juan Riojas.
I'm the Chief Data Officer for Rackspace.
My responsibility is looking at data analytics
internally for the company and really truly
taking and elevating data as a strategic asset.
I'm not sure if you're familiar with Rackspace,
but if you're not, we are a global multicloud solutions
provider.
We have over 140,000 customers.
We are in multiple geographies.
And we ultimately help enable transformations
all around data services, multicloud, private cloud,
security.
BRUNO AZIZA: And so now you've been at Rackspace
for a couple of years.
Now, previously to this, you were at Informatica.
You also worked at Accenture.
So you have a really diverse perspective
on this world of data analytics.
You've been in the space for a long time.
So tell us a little bit, when you started at Rackspace,
what were the use cases?
What were the problems you were brought in to kind
of solve or optimize for?
JUAN RIOJAS: My background is what I think helps kind of look
at data holistically.
You saw my first half of my career--
I called out Accenture, but my first half of my career
was all in supply chain.
In supply chain, you always have to look
at how all the dots connect.
Because everything has an effect.
For every action, there's a reaction.
So I had a little bit of consulting,
I went into the consulting world.
It helps you look at problem-solving.
It helps you look and really refine
how you look at opportunities.
I fast-forward now into what we're doing at Rackspace,
and I can certainly see how we wanted to go ahead and take
our data seriously.
And when we started investing into that,
I think one of the biggest tenets
that we saw was really looking at how
do we start driving better customer
experience through NPS.
That would be the result. And then
secondly, in this quantum world where everything's connected
and it's this subscription-based economy,
it's how do we mitigate our churn.
If we can do those things and you can get it right,
that's really what sets you apart.
BRUNO AZIZA: And they created the role for you.
There was no chief data officer at Rackspace before.
So can we talk more about this?
One, the customer experience and churn, how is that measured
and how do you construct an effective data infrastructure
around these two business objectives?
JUAN RIOJAS: This data office did not exist.
What you had these multiple organizations
that were decentralized.
And we brought them all together.
And what we had in churn was, for example,
there was 14 different versions of how we measure churn.
On NPS, there was multiple different versions
of how we look at NPS.
And on those two things, how data and the infrastructure
helped us get to a significant improvement
was by bringing the data together.
At Rackspace, when I first came in,
we had 70 ODSes and four data warehouses, a data lake.
It really wasn't being manager-governed
to the best of its ability.
And by bringing all the data together,
we were effectively being able to go ahead and, one,
harmonize the data, get it in near real time.
We were able to go in and standardize on a definition.
So we had integrity around our metadata.
And then third is we exposed it in a way that
was easy to go and understand, to look.
And when you measure, you improve.
So what we saw was, quickly, when
we brought the infrastructure together on GCP
and we were using, for example, ingestion with-- we
started with Alooma, and then it went to Airflow,
and we went to Dataflow, we started
seeing how we could actually stream this information.
So going from trying to go ahead and talk
about the integrity of our information,
we were able to go ahead and get this information
near real time.
And more importantly, we start seeing improvements.
As an example, NPS, we grew 10x of where it was.
We continue to see improvements.
That fundamentally shaped our entire predictive analytics
strategy to start looking at churn not in a reactive state,
but a proactive state.
By incorporating all the data and having it in one place,
we were able to accelerate our journey
to using TensorFlow to start using decision trees,
to start bringing the modern data
together to start looking at customer experience
and really translate that to operational sense,
whereas we are looking at how we can actually go and drive
improvements in churn by looking at 40 different variables.
BRUNO AZIZA: So let's talk a little bit
about these business results.
You started talking about the definitions
and the centralization of data and so forth.
So did you go from a very decentralized
way of looking at data to a centralized data warehouse that
was getting feeds into in real time?
And if that's the case, then how much time did that take you?
And what changes did you make?
Did you have to hire new people?
How did that all work?
JUAN RIOJAS: Yeah, we did.
I mean, I think one of the biggest challenges
is I think data was a liability for us at the beginning.
So when I first came in and we were averaging about 80 defects
in production a month--
a month-- which is unprecedented.
But because we had no standards, there was no quality,
even though we had data warehouses,
they were built on on-prem environments
and they were fairly archaic.
The code hadn't been updated in years.
The data model was fairly antique.
So by bringing it all together-- and when I say all together,
we effectively migrated a petabyte of information, the 70
ODSes, the four data warehouses, the one data lake
that we had on Hadoop.
We structured the team, we all learned how to ramp up on GCP,
we all learned how to use SQL and Python,
and we effectively migrated everything
within six months, which for me was fairly impressive,
because that really started driving the business outcomes.
And typically what you see in transformation, especially
in data transformation programs or modernization efforts,
if you're not quickly showing value,
the energy starts fizzling out.
So one of the biggest tenets was really planning effectively,
getting everything migrated, getting everything migrated
with quality, to the point that every single dashboard that
comes from us, every single data source,
has our seal of approval.
Kind of like-- think of this like an iso seal.
We're called GDO, so Global Data Office.
And every dashboard has our seal in it.
If it doesn't have our seal, it's really not going to be--
I wouldn't say trusted, but it's not
going to be representative of what we have.
Our seal, we use a traditional supply chain concept
of a perfect order.
And we converted that to data.
If we have a definition for the metric, if it's defined,
if it's secure that it's encrypted
at rest and in transit, and that we check for completeness
or accuracy, integrity of the information,
if it has those three things, then it receives our seal.
And that's what we expose.
So there is no ambiguity of how you use that information.
BRUNO AZIZA: So there's a lot here.
The dos and don'ts is the next section we're going to get to.
Before we get to that, I want to talk about the concept
of certification.
If I want to certify data the way you're doing it, Juan,
where do I start?
Do I hire new people to do it?
Is this automated through algorithms?
How does this work?
JUAN RIOJAS: It's not automated yet,
but it is a manual process where we
take a simple approach of making sure that we have the three
tenets of quality, that we have security,
and that we have a definition of governance around our metrics.
It is manual right now.
Whereas we don't have to invest in it.
It's really the same team members.
Our BI team as an example, all the dashboards,
they ensure that those three tenets are clearly defined,
they're all aligned.
And then once it's done, then we will put our seal
on the dashboard as an example.
Or when we pass data are, our team
will go and share that specific seal.
It is manual right now.
But we do want to get to the automation piece.
And I firmly believe that that was the catalyst for us
to go from less than 200 users of our information, our data,
to over 2,000 concurrent users.
So much so that I think the average user
of even our dashboards, before they used to go and look
at it in less than a minute.
Right now, the average person looks at it for 26 minutes,
meaning that they are going in and they're going deep
in thought or going deeper.
And now we're using those analytics
to go ahead and help improve that experience
and improve more insights to the organization.
BRUNO AZIZA: So you've got a full cycle here.
Not only are you increasing the number of people adopting,
but you're increasing the attention.
And all of that is based on the seal of trust,
in a way, that your team is applying to this data
based on key principles.
Let's talk about some other dos before we get to the don'ts.
What is the one thing that people listening to us here
that are trying to follow you in a similar journey,
an exceptional journey, what should they absolutely do?
JUAN RIOJAS: I highly encourage you all
to really look into planning.
We created something fairly unique
in our organization, which is we created a product office.
We start with three tenets--
data as a service, data as a platform, and data management
as a service.
So data management as a service, our governance, is a platform.
And then our infrastructure data as a service
is how we enable, how we consume our information,
how we keep on driving the insights.
So investing into an organization like that
is going to pay dividends.
Because at the end of the day, you
want to take out all the requirements, all the business
partnership, all the technical specifications
so your team can really focus on what they can do right.
It is on really building the models,
taking advantage of the next-gen analytics,
really bringing together optimization strategies.
And I think that is absolutely pivotal for what
I would consider doing.
That and we incorporate also scaled agile.
So being able to go in and have a defined methodology,
and then coupling that with the planning.
I think that, for me, was a recipe for success.
BRUNO AZIZA: So both process and planning and doing
that early as you move to your transformation journey.
Now, what about the opposite of that?
What is the mistake that most people do, or that you see,
or maybe that you've done yourself,
that you want to make sure everyone's avoiding.
JUAN RIOJAS: I think the last thing
is I think everybody has great ideas.
There's always going to be edge cases.
Sometimes those edge cases, you may just
want to go and leave them to the right.
Put them into a parking lot.
Because when you spend a significant amount of time
to go and solve for that less than 1% of the issues,
that's when it just drives frustration, it drives delays,
and I would rather go ahead and focus
on getting the bigger pieces right, start driving value,
and then look upstream as to why those exceptions are happening.
I think that's something that I would recommend
do not go do right now.
BRUNO AZIZA: Juan, thank you so much for your time today.
We learned a lot.
We learned about data certification and trust.
We learned about the importance of planning
and having a process to migrate to the cloud.
And, of course, we learned not to optimize for exceptions.
This was an exceptional data journey.
I hope people are going to reach out to you to learn more
about your journey.
Thank you again for spending the time with us today.
If you want to find out more about stories just like this,
make sure the click, down below here,
to the link of many more stories like this.
Juan, I want to thank you for your time.
I hope people are going to reach out to you and learn from you.
Until next time, I'm Bruno Aziza.
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