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BRUNO AZIZA: Welcome to another episode of "Data Journeys."
This is the place where we come to learn from leaders
in data and analytics.
I'm Bruno Aziza.
And we've got a great leader today.
This is Harish.
Harish is director of engineering and analytics
at Sunrun.
You probably have seen his great detailed presentation
at the latest Google On Air event.
If you haven't, we're going to put the link down for you,
so you can take a look at it.
He is at Sunrun today.
But before, he was at Levi's.
He was also at Electronic Arts.
He was Synopsis.
And he built amazing data analytics platforms there.
So today, we want to learn from him and his dos and don'ts, his
story, and get you guys some learning so you can try
and replicate his success.
So Harish, thank you so much for taking the time
to talk with us today.
Tell us a little bit about how you
got here and the types of results
that you're getting at Sunrun.
HARISH RAMACHANDRAIAH: Thanks, Bruno.
I mean, it was a combination of various points
at Sunrun's juncture.
We certainly were dabbling on a legacy environment
with Oracle stack.
And it had served its purpose, but we
were looking futuristically.
Five years from now, 10 years from now,
what are the combination of stack we would like to do?
But also, more importantly, we were
trying to solve the business side of the problem.
So it was very important for us to come back and address
some of those analytical needs for the company.
And we took this opportunity to say, hey,
let's look out for the best technology which is out there,
and also pick and choose the best of the breed
within those technologies in order to enable us.
And that's how we landed up with GCP,
and that's how we started the journey to migrate
from on-premise into the cloud.
And essentially, what we undertook
was pick the best-of-breed from an ingestion
standpoint, modeling standpoint, as well as from a reporting
standpoint.
And we were able to undertake this journey and being
successfully implemented.
BRUNO AZIZA: So you took an environment
that was on-prem into a cloud environment.
How does a traditional company think about that?
How do you modernize, essentially?
Do you essentially lift and shift, and replicate everything
you've got?
Or you take that as an opportunity
to re-architect your processes?
HARISH RAMACHANDRAIAH: When it came to cloud,
I mean, there was certainly a lot of unlearning what
we had done in the traditional data warehousing world,
where we were driven by the star schema
or the dimensional modeling aspects of it, versus what
we get to see in the cloud version, right?
It's a different architecture.
It was not a lift and shift versus we
had to re-engineer the pipelines, the models,
as well as the reporting aspects of it--
and purely because of taking advantage of what it can scale,
which you could traditionally not
do it in the on-premise world.
But it also means upgrading your skill set,
upgrading your architecture the way,
and a very simplified version of it, right?
The way we operate was purely in a two-stage manner--
Stage these datasets, try to go towards a normalized approach,
and take advantage of some of the platform which can provide,
where datasets are kept separate,
whereas the compute power comes from separating, and get you
the best ability of BigQuery, for that matter, as well as
some of the performance which you gain purely from an auto
scale perspective.
Those are some of the key things which we did.
And then Looker was, again, a very futuristic-looking
reporting tool.
It takes advantage of the back-end standpoint
and gives you the flexibility of logically modeling it
in terms of writing a SQL code in order
to stitch these datasets and be able to expose in a very
quick turnaround time.
And so overall, we could scale the platform
in a very short duration of time.
Close to nine months, we could pretty much port the systems
as well as reduce some of our development cycle
on a day-to-day basis, on a projects basis, significantly.
Close to 50% to 60%, we were able to reduce the dev load.
BRUNO AZIZA: So before we get to the skill set question, which
I want to help people understand,
what do they need to hire for, talk to me about the use case.
What is the use case today or the use cases today?
And how did you prioritize them?
HARISH RAMACHANDRAIAH: Surely.
When we looked at the broad brush of business [INAUDIBLE],,
we were looking at various aspects.
But some of the key things were, what
are the subject areas we need to go after?
And certainly, the sales was one of the key things.
Anything to do with sales would have had major implications,
not only from a usage, but the mileage and the implications
of what it could bring around from a certain standpoint.
So we took that specific one.
We knew that if we did it right, got it right into the system,
picked the right components and make it happen.
And that's how we went about.
And it certainly got our backing from the business as well.
And it pretty much went [INAUDIBLE] soon after that.
Anecdotally, we went on a very low-budgeted one
initially, got the success story,
and then got back to the business,
asking for additional budget to be
able to expand the footprint of the environments.
BRUNO AZIZA: And the sales use case,
you picked it because it was high-demand
and it was particularly in need of modernization,
or because it was connected to revenue, or both?
HARISH RAMACHANDRAIAH: It was certainly connected to both.
And at that point of time, it was demand generation
for the company.
It was a high-growth potential situation.
And so what we thought was that the best way.
And it was.
To a large extent, that was the main funnel
which would feed the rest of the company.
And there was a lot of analysis being
done in terms of the quality of the market lead
generation coming into the system, the quality
of convergence, which are happening with realization,
the cycle time associated with those sales.
And you could easily double-click
on any of these metrics and drill down, as well as
come back with the good diagnostic aspects of it,
in order to make some good judgments
against these numbers.
And that fed into the rest of the ecosystem.
And the executives would take advantage of it.
So that was one of the key reasons, I would say.
BRUNO AZIZA: And it's also a high-visibility project,
I'm assuming, sales.
So how does this new architecture
solve some of the sales issues?
I mean, if you could describe kind
of what the pipeline looks like now,
as it compares to what it was before.
HARISH RAMACHANDRAIAH: Yeah, I think
for folks who are aware of the traditional pipelines,
essentially you undertake a number of hops
before you arrive at the final solutions.
And what I mean by that is, you not only
state the raw data sets, but then you
get into the various operational data stores
and then the star schemas, and then
subsequently aggregates, data marks,
and then subsequently do the reports.
In the newer platform, the way we've stated
is the raw datasets comes in stages in the BigQuery
directly, and then subsequently, we
do something called a drive table for some of the elements
where you need some level of transformation.
So between the stage and the drive table,
you pretty much solve the business metrics.
So all the transformation rules, the business rules
are pretty much taken care of between the stage and the drive
table.
And we stitch it together at the Looker platform, which
is, again, tapping into these prebuilt, normalized datasets,
and take advantage of the compute power
in order to expose the metrics, the reports.
And it also has very less engineering thought
process in our architecture.
We are not into partitioning it, although those features are
there, or indexing these datasets,
building aggregates, or engineering
the dimensions of any change.
We don't have to go and make changes in multiple places.
It's a very surgical way.
As when there is announcements coming in,
or there is a bug being introduced,
it is very easy for any of the developers to go,
literally do a surgical problem, solve it, come out of it,
and there you go.
And the sales or any of those departments
have ability to go and start using it.
BRUNO AZIZA: You know, I noted that you
talked about how obviously the adoption of the platform,
but also the consumption of the platform
has gone up between 30% to 50%.
What is that due to?
Is it due to this factor that the platform is more agile,
it's more real-time, and then people get engaged with it?
What's creating this increase of engagement?
HARISH RAMACHANDRAIAH: In that, I
think there are multiple assets to it.
I think certainly the confidence which
we've garnered with this backend platform,
with the GCP turnaround, the reduction of the dev cycle.
Business doesn't hesitate to come back and ask
us, saying that, hey, can you get me this or by tomorrow,
by next week?
And there's a lot of assurance for them
that they will get those metrics.
So that has infused some level of confidence
within the business community to come back.
But also what has helped us is the Looker
way of building these reports and dashboards have also
scaled up.
What I mean by that is, it's a very easy process for us
to build a hub-and-spoke process.
So to determine the single source
within the core engineering team,
but also have the ability to spread it
across a spoke model towards the business.
And they have the ability to customize additional metrics
or bring in their own datasets, and yet
take advantage of the central functions
of the central business roots which are being established.
We have a very nice protocol in the governance system.
And so with that, what it gives is authority
on the quality of the data.
The business roots which all the businesses have agreed upon.
The metrics, what one business sees from a marketing
standpoint is agreed upon through the sales,
through the operations, through the customer operations,
and so on and so forth.
So that gives you a lot of confidence.
And along the way, it is also not only a bottom-up approach,
but a top-down, right?
When all the execs giving their board meetings,
they pretty much refer to our Looker report.
70% of the reporting now comes out of Looker.
So you have the metro market leaders, the regional leaders,
or the branch office leaders, who
are referring to these numbers.
And the conversation just bleeds itself
into a much wider audience.
And the single source of truth happens
to be your Looker, not an Excel sheet or a Google Sheet, which
has its own version of the truth.
BRUNO AZIZA: You've been able to build
both the combination of a governed
and the self-service system, a bottom-up and a top-down model.
So you are engaging more users, which
means they're consuming more data, which
means they're adopting it more, and they stay more engaged.
Just maybe one last question.
You talked earlier about upgrading the way you
think about what's possible.
Did you have to hire new roles?
How did you transition?
While you transitioned the approach and the technology
itself, did you also have to bring
new skill set into your team?
HARISH RAMACHANDRAIAH: In our case,
I think we did not have the choice.
So what we did was we underwent a very serious POC,
and we course-corrected our thought process
about how we used to build versus what needs to be built.
And we certainly hired Google's consulting firm
at that point of time.
So we certainly learned that part in the POC.
But beyond that, we essentially retooled ourselves,
since we were coming from an enterprise mindset.
And then here, the cloud technology combination of that
was fairly lethal for us to come back with a very nice solution.
But along the way, we certainly course-corrected
certain modeling aspects of it, certain protocols of it,
and so on and so forth.
But along the way, we course-corrected,
and then we made certain mistakes.
But absolutely, we came out in flying colors.
The performance is phenomenal in terms of the reporting,
and so on and so forth.
So there's a little bit of retooling within the team.
All of us got certified in the respective areas.
We have the enthusiasm to learn new things, new ability,
because that's the future, right?
You have to go through to undertake that
in order to be successful in the data platform technologies.
BRUNO AZIZA: Well, Harish, thank you so much
for your time today.
We learned a lot.
And the first one is, of course, don't just lift and shift.
Use the opportunity of moving to the cloud to modernize
your processes as well.
We also learned how you can marry
both the governed and the self-service environment
to drive engagement.
And you got over 1,000 users.
You got 30% increase in adoption.
You got 50% increase in engagement,
which is related to this tactic of both governed
and self-service.
And I know that's very unique, so people
should reach out to you if they want
to know more how you did it.
And of course, how do you accelerate
is by adding certified skills into your team
and then bringing Google Talents as well
to be additive to your existing capabilities.
Again, I know you're very busy, and I want to invite people to,
one, connect with you, two, make sure that they take a look
at your great detailed presentation on our Google
On Air event.
And then, of course, if people want to hear stories just
like this one, make sure that you
click on the link down below, where we got more Data journeys
with leaders just like Harish.
Until next time, I'm Bruno Aziza.
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