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BRUNO AZIZA: Welcome to another episode of "Data Journeys".
I'm Bruno Aziza and this is the place
we come to learn from leaders.
Today, we're going to talk to Luis from Accenture.
Luis, thank you for being with us today.
LUIS POLANCO RAMIREZ: Thanks, Bruno.
BRUNO AZIZA: All right, well, we think about Accenture
as a partner, but today we're going
to talk about Accenture's internal deployment
on a data platform that sealed six times faster in executing
high volume queries.
We're talking about 300% increase
in client support with the same cost
and 90% reduction in incidents.
So obviously, a lot of great success
you can share with us today.
But first, tell us, what does Accenture do?
LUIS POLANCO RAMIREZ: Well, Accenture
is a global professional services company.
And today we have like 675K people around the globe.
And our people is working in more than 7K
plus clients in 50 different countries.
And I am telling you this not because I'm
trying to do some marketing, but imagine all the information
that we have around that, all the information
that we generate when we're running our businesses.
So every area of Accenture is today
generating some kind of data and relaying all of the features
that we have in data, because we're truly
a data driven company.
So if you think about Accenture in this perspective,
you can understand that our data challenges are massive.
Imagine all the information that we generate every single day
over the years.
And that amazing amount of information,
we are using that to empower our teams and leaders to make
real data driven decisions.
My team has the opportunity to be
in the middle of that challenge.
The data platform that you're talking about
is the forefront of the innovation of the analytics
that we're doing internally at Accenture.
And we're responsible for putting all this data and all
this analytics in the hands of Accenture people for business
insights and actions.
So we are responsible for providing our people
the architecture, the infrastructure, services,
all the stuff that is really enabling
Accenture to serve ourselves and to serve better to our clients.
BRUNO AZIZA: That's outstanding.
And you do have a lot of projects,
with over 260 projects, 60 plus data science projects,
and 75 predictive models that you've built.
And the numbers are growing.
All of that as BigQuery is the center
of the universe for your platform.
So take us through some of the use cases
that you've deployed at the company.
LUIS POLANCO RAMIREZ: Yeah, we have great examples.
One that I really like is ALICE.
This is probably the first project
that we did when we started working with you guys.
ALICE is a great collaboration example
between our IT global team and our legal team.
Accenture houses 1 million contracts in its record systems
and we're adding thousands of new contracts.
So in our contract with the clients, our attorney,
they need to understand Accenture rights
and obligations across all the different contracts.
ALICE, that it is Accenture Legal Intelligent Contract
Exploration, that is the name of the tool,
it's using natural language processing
and artificial intelligence to translate all these contracts
from all the different languages that we
have across all these countries into English,
and within our analytics on top of that,
so we extract useful metadata from all this information.
To give you some numbers, last January, we
have processed over 20K documents.
ALICE is using the translate API from you guys
to move documents from Japanese, Italian, Spanish or German
with very, very high accuracy into English.
On top of that, we are analyzing the content
to generate useful metadata.
So we're making all that information
more accessible than ever.
So our legal employees, over 2,800 professional,
they're working globally.
They're able to deliver faster because we're unleashing
the data that was previously not easily
accessible for these guys.
So we're now enabling them all the knowledge in the moment
that really matters.
To give you an example, ALICE was
supporting our legal leaders to quickly evaluate
the COVID-19 impact on all the existing contract that we
had at that point in time.
BRUNO AZIZA: So that's a great example, ALICE, Accenture Legal
Intelligent Contract Exploration.
Over a million contracts, almost 3,000 professionals
globally get value from the integration
with the Translation API.
That's really an amazing project.
What else are you doing today?
LUIS POLANCO RAMIREZ: So we have other good examples
of what we're using, in this case BigQuery, right?
This Manage myBusiness.
Manage myBusiness is a self-service analytics
dashboard that is enabling Accenture business units
and client accounts with real-time easy and secure
access to vital information that we're
using to manage all the business performance internally.
The application is using BigQuery
to blend cross data information that used to be in silos,
and we're using that BigQuery to manage
all that stuff in one place.
So with these multiple sources, we
applied artificial intelligence on top
to provide a very interactive experience
that it is allowing our businesses to analyze
performance indicators, connecting to different sources
so they can diagnostic and drill down really
in the things and the data that we have around our business.
BRUNO AZIZA: Now, you also do a lot of data science work,
particularly around anomaly detection with your data.
Tell us a little bit about that too.
LUIS POLANCO RAMIREZ: Yeah, probably one capability that we
excel is the anomaly detection.
Our data scientists, they have really
expertise and when using analytics tool to automate
the detection of anomalies and outlying behavior,
such as non-compliant business expenses,
or processing contracts, or procurements, or [INAUDIBLE]
activities.
So the volume of information that we have about expenses
is over 25 million lines annually.
And this number is growing because Accenture is growing.
So every report gets analyzed by business rule,
that they are trying to check if the expense is compliance
or not.
So the challenge here was, how can we
reduce the amount of false positives and all the anomalies
that we have, but also improve the efficiency,
reducing the time?
So we have every focus, the big focus
only on the questionable claims.
So we decided to develop an anomaly detection solution
for Accenture, an expense reporting system,
to augment our existing rule based analytics.
So the objective was to help address
the high volume of expenses reports
that they were triggering false positives.
So our data scientists, in collaboration with our time
and expense organization, they took the situation
as an opportunity to improve Accenture's
current rules-based system and we created an AI solution
that it is super accurate and it is
identifying compliance expenses and reducing false positives.
BRUNO AZIZA: So we're obviously not going
to have time to cover all 260 projects,
although I would love to know every single one of them.
But you're doing some great work on data mesh and data fabric.
Can you tell us what that's about?
LUIS POLANCO RAMIREZ: Accenture is a multi-cloud company
that has operational and transactional data hosted
in different cloud vendors.
Our data is produced by application teams
and the app streams of the data link
and then we have different flavors
of data that is produced at different speeds,
scattered across different cloud providers.
Our business data platform is right in the middle
of all this data.
And we're now improving the way we're
connecting all this collection of data
in a cohesive and self-service manner.
We have in a very advanced vision about data
fabric we're building.
So there's one unique and unified management
of data integration where we all can consume data
coming from all these sources.
All our data will be connected data fabric,
optimizing all the integration that we have across systems,
and improving all the data structure
that we have from the sources.
On the other hand, we've seen that Google
like to explore the data pattern,
because, especially for data exploration, where
data scientists are interested in understanding
if this information is interesting for us,
should be available in the data, should
be production ready, right?
We need a flexible way to make this data available for data
sources, data available for data scientists
before it is really moved into production into the public.
So with Google, with you guys, we're actively
collaborating with BigQuery Omni and Databricks product teams
because we would like to understand,
what is the art of the possible and if these services may help
us in a future, implementation of these multi-cloud data
means pattern that we're looking for.
BRUNO AZIZA: Yeah, that's outstanding,
and you've got some really great work to look forward to.
Now, you've been in this space for a long time
and you've learned a lot along the way.
So we always like to ask best practices and worst practices.
So I'm going to start with the positive part of the equation.
What are some of the top best practices that you've witnessed
and that you would advise people listening to us to follow?
LUIS POLANCO RAMIREZ: Yeah, well,
the first practice that probably was learned to me
is know your customer.
When we kick off analytics in 2014,
it was all about a bunch of technologists doing technology
and we thought that data analytics was
about high tech, big volumes of data,
and complex libraries with weird names,
and we were completely wrong.
Data analytics is about a skill diversity and culture
and it is not about technology.
When you have to go for innovation and creativity, that
is required to bring in bring in analytics to your organization,
you need really a cultural mix, where
you bring business experts, data scientists, and technologists
in the same room.
You cannot deliver value to the arc if you don't understand
that this is required to feed your customer needs.
So know your customer is probably
the foundation of delivery building analytics
and bringing this multi-culture delivery cells,
where business and technology are working together,
is probably a very consistent way where
you can deliver data analytics in your organization
and really make it successful.
BRUNO AZIZA: And we always forget about that
because we think about it as a logical problem,
but there's a huge human equation
in succeeding with analytics.
But what else do you have, did you observe?
LUIS POLANCO RAMIREZ: I was going
to say the second one is probably fail early, fail safe,
and I'm a giant believer in we deliver always be Scrum.
This is probably one of the first thing
that we applied on top of what we're doing.
So the innovation lives and dies with the ability
to learn quickly and you may fail in your learning.
So you need to measure your experimentation.
So we, as an agile organization, we truly
believe that the fail early, fail safe concept is great when
you're running experiments.
And analytics is all about experimentation.
During our processes, especially when
we're doing the inception phase, that
is the very beginning of the project,
right, we track and we measure everything.
If it is a success or if it is a failure.
But that is giving us meaningful instrumentation
so we can change ourselves and improve
with some really good information that
is coming from our POVs, POCs, and our experts.
So with this approach, the experiments
are feeding back our infrastructure,
are feeding back how we deliver things on top of the platform,
that it is making us cheaper, faster,
and more agile than ever.
BRUNO AZIZA: That's outstanding.
What about agility?
So what is your take on that?
LUIS POLANCO RAMIREZ: Well, and agility is probably
the third one.
We've learned that data scientists and analytics
and everything around this new world
is about provide the right tools at the right moment, right?
The case, for example, that I was talking before with ALICE,
right?
The ability to have all these documents at the right time
when we were suffering COVID globally, right?
It was huge for us.
So we decided to move our platform into the cloud
because we are now running the data platform as a utility.
The data can be offered as a product
and can be consumed as a service.
So the data analytics in the platform today
offers a very simplified model where
team can sell service capabilities,
they have full control over all the resources
that they are running, and that is leading us
to a better model, where everybody go in the pay
as you go.
So when we really need the infrastructure, when we really
need the services, we have the agility to deploy everything.
We have the agility to give that access,
that real access to our analysts to the information.
And then when we need to close all that stuff,
we delete and destroy.
But it's given a lot of flexibility
plus a lot of cost savings, because we really
using the infrastructure when we really need it.
BRUNO AZIZA: That's outstanding.
So now we've talked about the positive part of the equation
and we like to know about the negative part,
the lessons learned along the way, things
that might make sense when you get
started, but in retrospect, there might be mistakes
that people can avoid.
So what have you seen?
LUIS POLANCO RAMIREZ: Well, the first one sounds weird,
but it is a lesson learned.
It's too much data is a problem.
It took us a little time to understand that it is not
a problem of data volume when we're doing analytics,
it is about collecting meaningful data that
really makes sense for your business cases.
One of our early principles of the data lake
was to store everything.
And we did.
Our ability to collect data now is bigger than the ability
that we have to support, to filter, to manage the data.
So sometimes I feel we're on the top of a big amount of data
that it is hard to mine and to understand.
So big data, it is true that it is about big volumes of data,
but it is about big, useful volumes of data, right?
So now that we're switching to this smart place
where with the business, with the business with us,
right, we have our data scientists
and our data experts really shaping
what is the data that we have in the platform
that is fully focused to resolve our business problems.
BRUNO AZIZA: Gotcha.
So this is really not so much about the volume itself,
but when you have a data problem or a data opportunity,
it's about storing it, but also coupling it
with a smart platform so you can use artificial intelligence
and the power of data science to zero in on the data that
is most useful to your people.
What about other mistakes that you've seen other folks
make in the space?
LUIS POLANCO RAMIREZ: The second one,
and I am a geek so my heart is always in the architecture
side of the world, but now I understand that sometimes
the tech hype is a mistake.
Because architects and data scientists, especially
these roles, at least in our org,
we are always looking for the next shiny toy.
The new technologies, new libraries, new algorithms,
are something that really excite all of us.
So when we start building an offering using this tech
hype without thinking through what we really are using
and what is really available in the organization,
sometimes we bring new technologies
that requires new operations on top of that
and requires new effort to make it available to the end user.
So we've learned to be patient and look
further ahead in the future.
And now we have a formal process to onboarding new technology
that it is supervised by a committee of architects,
security guys, data persons, and other groups of experts
that, when we really want to put the focus on our technology,
we have this committee that is really helping us to,
first of all, does it make sense to put it on board?
And second, ensure that when we put it in the hands of the end
users, it is really compliant with all
the essential requirements that we
have about security, governance, and everything else.
BRUNO AZIZA: I love the diversity
that you have with the platform architects, the enterprise
architects, security experts.
So you really get a 360 view of, OK,
is this new concept actually something that's useful
or is it hype?
Those are great pieces of advice and I
hope people are going to reach out to you, because you
obviously have amazing success here
and this short interview doesn't do justice to it.
So I want to invite everyone to, of course, reach out to Luis.
And, of course, if you want to know stories just
like this one, make sure you click
on the link down below for more amazing "Data Journeys."
Thanks for the time, Luis, today.
LUIS POLANCO RAMIREZ: Thank you.
BRUNO AZIZA: Until next time, I'm Bruno Aziza.
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