¿Cómo se crea la innovación? ¿Cómo desarrollar una estrategia sólida de Inteligencia Analítica? Federico Lage, Líder de Data Hub en Consubanco te lo explica en este episodio,
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Innovation today is vital for any organization. Artificial analytics and intelligence are building blocks
of business strategies to transform your businessor to give assertiveness to key decisions.
In this episode we will talk withan expert and passionate of analytics, Federico
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Lage, leader of tatahot En withhis bank I am Elizabeth Bautista, and
this is digital decision. Of thosestay and listen to the vision of an
expert on the subject. Welcome todigital decision, where analytics and business converge.
What is analytics, how to useit in your business, how my
industry benefits, how to develop theday- to- day you are taking
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full advantage of opportunities. Who benefitsfrom it SAZ' s digital decision.
It' s time to add scienceto your business and grow. Innovating not
only requires decision or need to transform. It also involves knowledge and experience,
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but with a great deal of curiosityto find the right solutions at the right
time. Today we' re goingto talk to Federicolaje. He was part
of SAS for almost ten years,so he knows us well, but also
almost six other years he was partof Banco Santander, running the Data Science
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area and almost two years ago hejoined the ranks of his bank, a
multi- bank institution. He islicensed to act by ITAM and has a
master' s degree in analytical intelligence. It also has a specialty of data
mining both by Anahuac University and otherimportant data. He is also choreographer of
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the Research Center. I think graphiconus for the imba certainly a good combination
and I think I have this doseof curiosity for various worlds and knows how
to combine it. All right,Federico, welcome to this episode how biny
thank you so much for the invitation. Thank you, Federico, and well,
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the truth is that I' mgoing to take advantage of this confidence
to meet you to call you asmany as we call you very colloquially here,
because that' s how he likesto be called. So, then,
let' s start this talk thatI' m sure is going to
be very interesting for those who arelistening to us. Tell me e fic
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how you consider that you can innovatein a business and in your experience in
the financial sector, without really puttingthe value of the business at risk.
It' s a big question whyinnovation is now an extremely fast- paced
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situation that doesn' t sometimes confuseus with what you innovate first and then
not within your business. In fact, what we have to do here is
to get to know the objectives ofthe business, not only those of today,
but those that will have tomorrow and, depending on that, to strategically
review what the points should be toinnovate in order to arrive and adjust to
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the strategy of the day of tomorrowand then align innovation with the growth and
evolution of the business. It soundstrivial, but it' s very knowledgeable
how it works today, how itworked yesterday and how you want tomorrow to
work. In your experience, thisis an easy strategy to define, good
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the bigger the institution, because morecomplicated to define is the business and what
are its objectives in general, becauseusually you know the individuals and then,
how to lead, in general toan institution to be better technologically, speaking,
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more efficient, more able to communicatewith its customers, to know better
how it can optimize its functions,because it has a lot to do with
communication, that there is within allits leaders and understand where all these leaders
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want to go to then dispose ofthe technology and that magic happens, not
agree and good. I just wantto ask you how linked you are the
technological advance that this organization is reallyhaving, be it very big or much
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smaller in relation to this value ofthe business, because currently I could tell
you that in its entirety already thedata is obviously generated everywhere, Ahorita,
the challenge we have is that theyare generated in a useful way, because
of that they exist, exists andeverywhere. He used to be sons and
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how we do to get this data, Ahorita, it' s given and
it' s somewhere. No.The thing is, you have it and
it' s useful to you forwhat you need to accomplish. So yes,
to propose the evolution of a company, without considering the evolution of its
data strategy, because nowadays it isno longer conceivable. I don' t
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agree it has to go totally handin hand. In the end the data
are really active and important from anycompany. But today we no longer go
beyond big data concepts. I mean, we really have an excess of information
and, of course, financial sectororganisations are something that is flooded with data.
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How it is really possible to makean innovation process effective by properly using
data. Well, look, we' re in the middle of two big
worlds, the world of innovation,the technological infrastructure, that there forces us
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to think about big projects, bigchanges, that is, today this my
infrastructure, that name my tomorrow Iwant it to be in the cloud,
because the thinking of these strategies,because it is not small, is a
big thought and it has to havesome order, especially because of the costs
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that this means, that is,the movement is slow by nature, but,
on the other hand, the datahas to be useful for the areas
that are in the business, thatis, it has to be agile,
it has to be timely, ithas to be as updated as possible and
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good and super democratic to the areasof business that are implementing it and that
are using it to make decisions.So these strategies of data innovation, because
we have to put these two worldstogether as this issue, which can be
slow and very strategic and a lotof design to be able to integrate the
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correct architectures, be able to connectit with the use cases that we need
to have to queen immediate and alsoimmediate results for the Board of Directors.
What is it and how much moreI gained, how much I saved why
I increased my usefulness. No,then, yes, it' s a
big challenge to put those two worldstogether. I think analytics today is that
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great translator between the evolution of infrastructureand the evolution of decision- making.
Indeed, it is how you translateexactly the relevance and value of this data,
so that the entire management team canactually take them in the right direction.
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Now, tell me a little bitabout the relevance of because a little
bit of the data cycle seems tome to integrate, exploit, store this
data. How the truth is thatI think and imagine that you, who
are a choreographer, really mount achoreography. A good dance needs synchrony to
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make it look clean, to makea show really stand out in terms of
data, how to make everyone goorchestrated and go, at the same time,
to the rhythm of what the businessreally needs and the market itself,
because, because you are absolutely right, it is a choreography and as such,
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first we have to be clear aboutthe space, the technological space,
the space of the processes and thespace of the people who interact with these
processes. It' s amazing howcompanies institutions, which have been doing the
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same thing for many years, talkinside and there are times when people don
' t really know what the processesare for and what the results they expect
from each of the processes may alreadyhappen automatically, both in people and in
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systems. So, the first greatchallenge is to know within people the use
of processes and to know within processesthe objective of the role that people must
fulfil and, consequently, how thedata that integrates this situation, these situations,
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how the data that they have totransform so that the processes are effective
and timely. And this in balancewith what we talked about earlier on the
evolution of architectures. Today I havethe obsolescence scheduled. It puts us in
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a great challenge, because we haveto synchronize the planned obsolescence with the business
objectives, with the capabilities of thepeople who occupy all these roles. So,
yes, an evolution in some datatechnology, because it does require us
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to orchestrate a synchrony in all theseelements so that this change or this technological
evolution will have an impact on acertain result for business, whether it was
efficiency in a personal way or thatwe see a change in income or in
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non- expenditure. That' sright, but tell me a little bit
more about how to do it whenyou really have a heterogeneous ecosystem. In
technological terms, what are these challengeslooks at the first thing that nothing is.
The first thing that is done isto understand the business dynamics, what
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their objectives are and where they haveto go. The second is to observe
how current technology or infrastructures serve theseobjectives, especially to identify deviations. No,
and those deviations are often given withwhat I do first, do the
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operative or do the transformation. Andit' s actually how we transform the
operation. They' re not reallytogether. But if we think about it
in time, which is the mostlimited resource we have, that is,
that I dedicate time to the securityand continuity of the business, or to
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venting situations of risk with some transformationsand once you have clear these you begin
to have clear these struggles. Otherwise, in order to innovate. So if
you already look more precisely at howthe technology is, how the architectures of
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the current processes are armed and youget involved with a couple depends on the
size of the company, but witha couple of very disruptive and creative people
to see how we are going tobreak the paradigms of the process, without
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breaking it, but integrating innovation tothe current, so that then, consequently,
all the people who participate in thisprocess request the evolution they are observing
that they need and then take advantageof and make the developments that are going
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to seek this evolution and then theusers are going to see themselves in a
completely different situation. But happy becauseresistance to change, because it is one
of our most important challenges in thisinnovation, certainly, certainly, and especially
in organizations, because they have perhapsbeen many years, that many roles,
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because they really need to understand thenew ways, and how it is that
we can speed up without any fear. Don' t tell me, I
want to ask you a little more, precisely from this part, how to
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really integrate these new technologies and allthis new or. Which is not that
they are no longer necessarily hypes,like generational artificial intelligence, in addition,
but that they are already well-founded and ready to adopt technologies. But
I want you to help me understandand to help you listen to them too,
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how to really look inside the organizationor how to have inside the organizations
a recognition for the figure in someof the CHIF Data Analytics, the Chif
Analytics Sophiar And I love it becauseyou are the leader of a data hop.
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How is it to tell me alittle bit, how is it that
the concept of this data Hob ariseswithin this bank or is a tendency to
tell us Exo a little bit moreabout how it generates the areas of systems
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that move data from one place toanother have already existed for a long time.
It is sequele is a reference thateven today is used for everything in
question to move data from one placeto another, to analyze them from start.
Then data has always been needed forthe data. What has also happened
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is an exacerbation of the amount ofcalculations that we can do with this data.
In addition to how data can begenerated, what has not evolved is
the ability to make all this datauseful, because even if the machines go
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very quickly, because it is humanbeings who make decisions and, moreover,
generate new ideas around the insights ofthe data insights that they generate. So
what has increased, because it isthe amount of calculations that we can do
and the amount of data that wecan make all these changes with. However,
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what do you want to calculate andhow are you going to apply it
and where are you going to focusit, because there you do return again
to the speed, maturity or evolutionof the institutions. So here' s
what the objective of the data is. Ho ah, well, and before
for technological reasons, because you couldn' t save the data, because you
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had nowhere to save them. No, and that caused a lot of data
threads to be generated. No,I mean, the risk area has its
data and you' re solving yourtechnological needs. How they were getting to
technology. The truth. We're talking about data lakes, data mage,
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we' re talking about that,and the ubiquity of the data isn
' t, that is, notonly are hundreds of data generated by everyone,
billions of data everywhere, but youcan already have them anywhere, I
don' t say in quotation marks, and anywhere there' s a lot
of security that needs to be takencare of. Harden ning remains a great
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challenge for any institution. But thedata already starts to make it much more
democratic, to be in more places, and this legislation where the data is
and why it is there, howthey will treat it, I mean already
the data, the ubiquity of thedata does not. So, the goal
of a datajop is to maximize themaximum value of the asset that is the
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data for an institution. And whatdoes it mean, then, that is,
to identify the threads that were formedin the past by necessity and to
begin to integrate them into a singleversion in order to democratize the knowledge of
some areas with others. No.And that, as it is, through
access to data, the use ofdata democratisation today. Before democratisation was,
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therefore, designed qpeís and show themin an orderly way in a visualization,
which is that what you do wasthe VII, the business interim. Or
already the interaction with the data iscompletely different. No more Apple got his
goggles you play with with the data. You touch us already the interaction nor
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a conversation with data, because itis already while I talk to you,
I can be showing you data,I can make capsules Can I make videos?
I can do that what I'm going to is that democratic is
already a definition of v evolved.Already value all that data, because it
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is already a topic of communication andintegral communication between how many will arrive this
video that you are doing explaining yourdata, because you have to start before.
You were thinking, this is aboard for the area, for a
specific area. Today it is likelythat a video will be seen by anyone.
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So your communication strategies through the dataand the storytelling of the data,
because it already becomes a whole strategy, in a whole much more sophisticated topic
that we have to connect with theevolution of the architectures and the needs of
business, because I can do astorytelling really, but it doesn' t
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serve business to move the needle,because nothing happens, it doesn' t
make sense. That' s wherethey stay. Maybe it' s been
a long time we' ve heard, even from the side of those we
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' ve given to some study aboutfifty percent of the analytical model projects stay
in the way. It' sjust because they don' t finish concrete,
because maybe they weren' t reallyaligned to the business priority to and
only sometimes they might shoot, tosay, innovate takes the new, but
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as for what it is, yes, totally and is. And that is,
therefore, an important complication, becausebasically the analytics we live from the
confidence that people have in mathematics,not to say yes if it gives if
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an area like data hob tells alie, because it loses all the work
that has made integrating data and beingable to lead them to a modeling and,
after modeling, be able to generatea metric with which to make a
decision. So, if it's very important to align science with the
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need for business and the ability tocommunicate science to business. Much of what
has demanded of me a constant creativitylately is to realize the immediate quantitative and
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qualitative benefits of an analytical implementation,that is, either of a model or
of an analytical platform, of oneinvolving several models. At the same time,
what are the immediate benefits. Because, in addition, all this has
to do with new working methodologies.Not all of us are already, already,
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all the institutions are trying to maketheir version of the Agile, this
framework of work where for us,the analytics, because it is very interesting,
because, on the one hand,we have to comply with the scientific
method, but we are nothing.No. But, on the other hand,
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towards business, no, it's not anymore, it' s
not even a no method. Andso how is it that we achieve the
balance between YALE and the scientific method, because it has to do with speeding
up the connection of business needs witha formal process of knowledge production through data.
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And now yes, tell me howit is that this ability of data
hop to really give and defend itsown analytical reputation, to really give results
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of data integration at the right timehow it can do it with technologies today
like artificial intelligence riding on the cloud. Tell me a little bit about how
these emerging technologies are impacting the taskof analytics on a maybe once you'
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re clear about the business goal andwhat your analytical actions should be, to
say, to develop in order tomove the business differently. There' s
a super fun stage coming up.I' m fascinated by all this.
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Not super fun, because it's knowing the technologies, that is,
knowing all the technologies with a certainlevel of detail. It doesn' t
have to be too much, butit does. You must like this to
understand the logics within tools and theevolutions of tools over time and find a
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way to make them an enabler ofchange. If you use technology to enable
change, it' s better thanif technology requires you to change, because
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you use technology exactly not and then, for example, it doesn' t
right now. Artificial intelligence is superfashionable. But the truth is, it
' s artificial intelligence, because it' s how you use the technological advances
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we' ve had since the 1960s. No, I mean, we'
re making up the black thread.The thing is, we have a lot
more computing capacity and, therefore,calculation, and then we can automate a
lot more processes and generate decisions inautomatic. And then artificial intelligence becomes an
enabler of certain changes within a company. And then, today, because everyone
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like that hears I' m notapplying artificial intelligence. I feel, I
feel old, I don' tfeel back, not perfect. That feeling
is I help you get out ofyour comfort zone in some processes that if
we apply analytical anteligency, because wemodernize them, modify them, make them
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more precise, that is, wereview them with another point of view.
And then there' s your processwith artificial intelligence. And because the sum
between that you are more technologically capable, but besides, you know better your
processes, because magic comes, theredoes not come a major change in your
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results. Exactly I plan to askyou a little bit about your vision,
about how these technologies are being adoptedand how they will continue to be adopted,
mainly in the financial sector. You' ve already told me a little,
but based on your experience, youthink we' re going at a
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pace, that is, the financialsector is going at an appropriate rate of
this adoption of these new technologies.Or what a challenge it takes to work
first. Or state looks interesting thefinancial system, because it is a great
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consumer of technology, either for regulatoryissues or suddenly you have to biometrics.
Not why we are adapting them,because the regulator demands that we adopt ourselves.
On the other hand, the financialsector, because it has capital to
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invest in technological evolution, not inother words, it is the financial world,
Yes it is clear that the speedand efficiency of your calculations can place
you in a more advantageous position withreference to the competition. So it'
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s interesting to see how all theproviders feel about evolving the tools for the
financial system. So how do Isee all these evolutions, because to me,
in the personal way, because Isee them amazing, because they give
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me a soda. I don't mean, I started as a statistician
who knew how to use the computer. Then they turned me into a data
miner. Then they made me adata scientist and now it scares me to
be told that I am an artificialintelligent, but of course the financial system
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is watching how to leverage all thesetechnological enablers to be much more efficient in
delivering their financial services. The caseof being able to serve the customer through
artificial intelligence, as it gives banksan opportunity to improve their customer service.
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And that' s amazing, notbecause the bigger the bank, the more
work it takes to have a personalizedlistening to people. And so, to
me, personally in this technological evolution, it seems like a great opportunity to
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bring banks or the financial system muchcloser together with their customers in a personalized
way. So that' s whatthe consumer demands today to be and understand
the data itself more to technology itselfand that' s got a lot to
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do with what you were saying aboutdemocratizing it. But now, tell me
a little bit about what you thinkthese paradigms might be then to break in
the sector, because first look thatnothing is afraid of collective change, not
that it is when you have avery big institution, because you say don
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' t wait for them to changeand then I' m going to or
first, I' m going totalk. Then and there I settle down.
As before, technology evolved at veryspecific points. Today technology is advancing
at all points at the same time. So even it' s already overwhelming,
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I don' t mean, whereI upgrade, I don' t
mean what I update is which bookI read first, no, because now
it' s two hundred manuals,I don' t mean, now,
on the contrary, you have oneto make economics make very good decisions in
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which book you' re going tobe reading and why and what juice you
' re going to get out ofthat that in what you' re training,
then this causes us to do.The big challenge is how we communicate
institutionally, how we align the objectivesinstitutionally, and so we align the technological
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enablers that are going to help us, break the paradigm and improve the results
that we are giving. That's right now, as we get a
little closer to closing this episode,I want to ask you what these recommendations
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would be. First, for thosemathematical statisticians who are on the way,
who are really coming to the market, to the world of work with very
different technologies, with which you arriveda few years ago today, as an
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analytical leader with experience in the financialsector, what would be first the recommendations
to those generations that are and that, eventually, they will be the ones
to give all the decisions, becausefirst they congratulate them because they are making
a good decision to do mathematics andmake computers and prosecute them for the benefit
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of an institution, of a company, of a government is to give them
the right way to technology. Ithink that, especially for the subject we
were talking about, a long timeago, there are times when there are
developments that can be incredibly creative,but that they don' t match with
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the need for business and dilute thatknowledge that is diluted and wasted I think
it' s a lot that inschool, universities, courses, everyone talks
about developing models and that if theywrite and that it is better and that
you do the best, and thateveryone can say which one is the best.
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But there is no discussion in thedevelopment of a model, in a
modeling process that, in addition towhat is required of you by the mathematics
teacher at the University, in schoolyou do not hear mathematical birds. They
apply them well, because now themachines do everything. No, I mean,
he did good. The machine didit well You really did the machine
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well. You can well interpret theresults that the machine pulled out. That
' s where we have to focusa lot on school. But there'
s a big part that doesn't teach you to school that they teach
you your life already working, andit' s implementation. How I implement,
how this model is implemented, thisanalytical process, these developments in front
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of the computer, which can bevery weird, because it' s me
and the computer and nothing else,but we do have to think after that,
the next day, how it works, that' s going to work
with people, with processes, howit' s going to interact with society,
with my society, with my community, all that I' m producing,
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and then surely there' s goingto be many adjustments to my pro
a, my modeling strategy, becauseit turns out that the way I think
things are going to work isn't necessarily. It' s true.
I have to go out and integratewith the teams, go out with the
sales teams, the promoters and seehow they sell to make a propensity model,
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buy go with the customer support ones, how they serve the customer,
how their calls are so that laterthey can make a care model, a
sentient analysis and then the recommendations don' t stay with the math books under
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their arm. Yes, read them, but also go and see what it
' s like in real life,how it' s like in a business
process, how the numbers that wegenerate are integrated and we consider them useful
in a population that, best ofall, doesn' t like math,
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but that is susceptible to data andinteract with them, that we need them,
but we need them to be interpretedto us. And now, your
suggestions, your recommendations for your peers, for these analytical leaders who, well,
in this podcast listen to us fromall over Latin America. Then what
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would be like two three points youcould suggest, for patience and discipline.
Today that you sleep well and sleepenjoy the weekend, let your patience and
discipline rest. Today, and Idon' t know, it' s
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happened to everyone. My mom missesthe Internet and calls me she doesn'
t tell me she' s failingthe Internet. Better the company. But,
well, I have days, butit' s just that you already
run like the so I have tocheck everything around that there' s Internet
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in my mother' s hiring company. Well, there' s something similar
going on with the data today.No, I mean, there' s
already a problem in some system.And so, how do you realize,
then, of the giving through thedata? And if you realized through the
data, that it doesn' thave to solve, because the data one
doesn' t and not necessarily,that premise is fulfilled. It may not
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be necessary to fix something in theprocess, it is necessary to align something
in the communication, but because itcomes through a data, it does touch
on the data, because to beginto discover where the problems are, in
order to go to give them asolution. And in that sense, patience,
because now they ask us of allthis by name, because you are
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the data one, because then youhave to solve it. So, well,
there' s patience. So watchme take advantage of those crises to
see ok then what new data I' m going to have access to,
not how I' m going tobring it and help you solve those problems
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that you have. And the disciplinepart is not to lose faith in your
initial plan, because if you understandthe business well and build and understand the
technological difficulties your institution has, whathappens in the middle is all the work
you have to do and many timesyou pose all that transformation, those jobs.
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But, on the other hand,the institution has immediate needs that command
from the past and then you enterinto a good dynamic, so what do
I do, do not sustain thebusiness, how are you or transform it
not And so, then, disciplinein the sense of when I am transforming
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and when I am already tending tothe business in its daily needs and not
lose the focus of where we aretaking this transformation through the great technological enablers.
And finally, tell me what thesteps are, these points that you
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don' t have to lose bytwo thousand twenty- four inside your bank,
what challenges they' re in,where they' re going. We
have a lot of personalization, communicationand interaction challenges with our customers. We
have no ambitions to be their mainbank, to be their solution in financial
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services. Therefore, one of thegreat challenges is how to get to know
them better in order to care forthem as they deserve, and in that
sense we are very much looking forwardto it. Another big challenge we have
is the journey to cloud. Inthe end, if we see an opportunity
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in the cloud, but at thesame time we see great risks of ending
the budget in a single exhibition.So, the challenge that the journey to
Cloud is putting us in is toaccurately know the data flows, because because
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we don' t want to takethe trash to the cloud, we really
need to bring the useful data.And this is forcing us, as data
hop, to integrate, on theone hand, the operational and technological parts
and, on the other hand,everything that has to do with attention and
product design and etcetera, etcetera.So it' s super interesting,'
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cause now it' s dating.You are really looking at the business transversally,
from start to finish so you canachieve this journey and to Cloud No
and above all great implement all these. I' m happy things and we
have a lot of juice to takeout that tool. That' s just
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what he thinks it' s toknow, to make good use of technological
resources for how to integrate assertively andtake advantage of the very little budget that
you have within your institution. It' s vital and I' m saying
that because you' re a memberof a multi- bank institution. I
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think it is very relevant, perhapsfor listeners from other countries who are also
not necessarily in the ranks of thebiggest banks, not but really these strategies
are really made for everyone. Havinganalytical implementation is really for everyone and it
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is yes, perfect fico. Ireally appreciate this conversation. I think there
are still many more issues. Sothere I' m going to want more
time with you to discuss a littlebit more about other very interesting topics within
this sector, like this part ofthe regulation, which I think is super
(43:37):
interesting. But for now, then, I thank you very much for your
time in sharing a little more ofyour experience and vision. And so,
finally, some comment that you wantto leave here at the Many thanks for
the invitation, thank you very muchfor listening to them and therefore feel like
(44:01):
positioning data scientists, not as asexy job, but as a group of
people that generate the difference in theresults. Amazing, amazing, incredible quot
to close. So, thank youvery much for listening to us in this
episode and listening to us in aforthcoming one. You want to know more
(44:29):
about our offer for the banking sector. We have many success cases that may
interest you visit the sas Com diagonalname of your country. The world is
not changing. The world has changed. We live a unique world, a
fantastic time, A critical point inyour hands is the possibility to transform your
(44:53):
company, your organization or the institutionyou lead using big data, artificial smart
analytics and take off big or staylike this and let everyone overstep your digital
decision. It is a production ofsad Latin America. Rights reserved. If
you want to know more subscribe andshare it, visit us in digital decision
(45:21):
sas Com and on our social networks
Innovation today is vital for any organization. Artificial analytics and intelligence are building blocks
of business strategies to transform your businessor to give assertiveness to key decisions.
In this episode we will talk withan expert and passionate of analytics, Federico
(00:24):
Lage, leader of tatahot En withhis bank I am Elizabeth Bautista, and
this is digital decision. Of thosestay and listen to the vision of an
expert on the subject. Welcome todigital decision, where analytics and business converge.
What is analytics, how to useit in your business, how my
industry benefits, how to develop theday- to- day you are taking
(00:51):
full advantage of opportunities. Who benefitsfrom it SAZ' s digital decision.
It' s time to add scienceto your business and grow. Innovating not
only requires decision or need to transform. It also involves knowledge and experience,
(01:11):
but with a great deal of curiosityto find the right solutions at the right
time. Today we' re goingto talk to Federicolaje. He was part
of SAS for almost ten years,so he knows us well, but also
almost six other years he was partof Banco Santander, running the Data Science
(01:34):
area and almost two years ago hejoined the ranks of his bank, a
multi- bank institution. He islicensed to act by ITAM and has a
master' s degree in analytical intelligence. It also has a specialty of data
mining both by Anahuac University and otherimportant data. He is also choreographer of
(01:57):
the Research Center. I think graphiconus for the imba certainly a good combination
and I think I have this doseof curiosity for various worlds and knows how
to combine it. All right,Federico, welcome to this episode how biny
thank you so much for the invitation. Thank you, Federico, and well,
(02:21):
the truth is that I' mgoing to take advantage of this confidence
to meet you to call you asmany as we call you very colloquially here,
because that' s how he likesto be called. So, then,
let' s start this talk thatI' m sure is going to
be very interesting for those who arelistening to us. Tell me e fic
(02:45):
how you consider that you can innovatein a business and in your experience in
the financial sector, without really puttingthe value of the business at risk.
It' s a big question whyinnovation is now an extremely fast- paced
(03:06):
situation that doesn' t sometimes confuseus with what you innovate first and then
not within your business. In fact, what we have to do here is
to get to know the objectives ofthe business, not only those of today,
but those that will have tomorrow and, depending on that, to strategically
review what the points should be toinnovate in order to arrive and adjust to
(03:35):
the strategy of the day of tomorrowand then align innovation with the growth and
evolution of the business. It soundstrivial, but it' s very knowledgeable
how it works today, how itworked yesterday and how you want tomorrow to
work. In your experience, thisis an easy strategy to define, good
(04:03):
the bigger the institution, because morecomplicated to define is the business and what
are its objectives in general, becauseusually you know the individuals and then,
how to lead, in general toan institution to be better technologically, speaking,
(04:25):
more efficient, more able to communicatewith its customers, to know better
how it can optimize its functions,because it has a lot to do with
communication, that there is within allits leaders and understand where all these leaders
(04:46):
want to go to then dispose ofthe technology and that magic happens, not
agree and good. I just wantto ask you how linked you are the
technological advance that this organization is reallyhaving, be it very big or much
(05:08):
smaller in relation to this value ofthe business, because currently I could tell
you that in its entirety already thedata is obviously generated everywhere, Ahorita,
the challenge we have is that theyare generated in a useful way, because
of that they exist, exists andeverywhere. He used to be sons and
(05:30):
how we do to get this data, Ahorita, it' s given and
it' s somewhere. No.The thing is, you have it and
it' s useful to you forwhat you need to accomplish. So yes,
to propose the evolution of a company, without considering the evolution of its
data strategy, because nowadays it isno longer conceivable. I don' t
(05:56):
agree it has to go totally handin hand. In the end the data
are really active and important from anycompany. But today we no longer go
beyond big data concepts. I mean, we really have an excess of information
and, of course, financial sectororganisations are something that is flooded with data.
(06:18):
How it is really possible to makean innovation process effective by properly using
data. Well, look, we' re in the middle of two big
worlds, the world of innovation,the technological infrastructure, that there forces us
(06:41):
to think about big projects, bigchanges, that is, today this my
infrastructure, that name my tomorrow Iwant it to be in the cloud,
because the thinking of these strategies,because it is not small, is a
big thought and it has to havesome order, especially because of the costs
(07:03):
that this means, that is,the movement is slow by nature, but,
on the other hand, the datahas to be useful for the areas
that are in the business, thatis, it has to be agile,
it has to be timely, ithas to be as updated as possible and
(07:29):
good and super democratic to the areasof business that are implementing it and that
are using it to make decisions.So these strategies of data innovation, because
we have to put these two worldstogether as this issue, which can be
slow and very strategic and a lotof design to be able to integrate the
(07:54):
correct architectures, be able to connectit with the use cases that we need
to have to queen immediate and alsoimmediate results for the Board of Directors.
What is it and how much moreI gained, how much I saved why
I increased my usefulness. No,then, yes, it' s a
big challenge to put those two worldstogether. I think analytics today is that
(08:16):
great translator between the evolution of infrastructureand the evolution of decision- making.
Indeed, it is how you translateexactly the relevance and value of this data,
so that the entire management team canactually take them in the right direction.
(08:43):
Now, tell me a little bitabout the relevance of because a little
bit of the data cycle seems tome to integrate, exploit, store this
data. How the truth is thatI think and imagine that you, who
are a choreographer, really mount achoreography. A good dance needs synchrony to
(09:09):
make it look clean, to makea show really stand out in terms of
data, how to make everyone goorchestrated and go, at the same time,
to the rhythm of what the businessreally needs and the market itself,
because, because you are absolutely right, it is a choreography and as such,
(09:37):
first we have to be clear aboutthe space, the technological space,
the space of the processes and thespace of the people who interact with these
processes. It' s amazing howcompanies institutions, which have been doing the
(09:58):
same thing for many years, talkinside and there are times when people don
' t really know what the processesare for and what the results they expect
from each of the processes may alreadyhappen automatically, both in people and in
(10:20):
systems. So, the first greatchallenge is to know within people the use
of processes and to know within processesthe objective of the role that people must
fulfil and, consequently, how thedata that integrates this situation, these situations,
(10:41):
how the data that they have totransform so that the processes are effective
and timely. And this in balancewith what we talked about earlier on the
evolution of architectures. Today I havethe obsolescence scheduled. It puts us in
(11:07):
a great challenge, because we haveto synchronize the planned obsolescence with the business
objectives, with the capabilities of thepeople who occupy all these roles. So,
yes, an evolution in some datatechnology, because it does require us
(11:28):
to orchestrate a synchrony in all theseelements so that this change or this technological
evolution will have an impact on acertain result for business, whether it was
efficiency in a personal way or thatwe see a change in income or in
(11:52):
non- expenditure. That' sright, but tell me a little bit
more about how to do it whenyou really have a heterogeneous ecosystem. In
technological terms, what are these challengeslooks at the first thing that nothing is.
The first thing that is done isto understand the business dynamics, what
(12:18):
their objectives are and where they haveto go. The second is to observe
how current technology or infrastructures serve theseobjectives, especially to identify deviations. No,
and those deviations are often given withwhat I do first, do the
(12:43):
operative or do the transformation. Andit' s actually how we transform the
operation. They' re not reallytogether. But if we think about it
in time, which is the mostlimited resource we have, that is,
that I dedicate time to the securityand continuity of the business, or to
(13:09):
venting situations of risk with some transformationsand once you have clear these you begin
to have clear these struggles. Otherwise, in order to innovate. So if
you already look more precisely at howthe technology is, how the architectures of
(13:31):
the current processes are armed and youget involved with a couple depends on the
size of the company, but witha couple of very disruptive and creative people
to see how we are going tobreak the paradigms of the process, without
(13:56):
breaking it, but integrating innovation tothe current, so that then, consequently,
all the people who participate in thisprocess request the evolution they are observing
that they need and then take advantageof and make the developments that are going
(14:18):
to seek this evolution and then theusers are going to see themselves in a
completely different situation. But happy becauseresistance to change, because it is one
of our most important challenges in thisinnovation, certainly, certainly, and especially
in organizations, because they have perhapsbeen many years, that many roles,
(14:41):
because they really need to understand thenew ways, and how it is that
we can speed up without any fear. Don' t tell me, I
want to ask you a little more, precisely from this part, how to
(15:03):
really integrate these new technologies and allthis new or. Which is not that
they are no longer necessarily hypes,like generational artificial intelligence, in addition,
but that they are already well-founded and ready to adopt technologies. But
I want you to help me understandand to help you listen to them too,
(15:24):
how to really look inside the organizationor how to have inside the organizations
a recognition for the figure in someof the CHIF Data Analytics, the Chif
Analytics Sophiar And I love it becauseyou are the leader of a data hop.
(15:46):
How is it to tell me alittle bit, how is it that
the concept of this data Hob ariseswithin this bank or is a tendency to
tell us Exo a little bit moreabout how it generates the areas of systems
(16:07):
that move data from one place toanother have already existed for a long time.
It is sequele is a reference thateven today is used for everything in
question to move data from one placeto another, to analyze them from start.
Then data has always been needed forthe data. What has also happened
(16:30):
is an exacerbation of the amount ofcalculations that we can do with this data.
In addition to how data can begenerated, what has not evolved is
the ability to make all this datauseful, because even if the machines go
(16:52):
very quickly, because it is humanbeings who make decisions and, moreover,
generate new ideas around the insights ofthe data insights that they generate. So
what has increased, because it isthe amount of calculations that we can do
and the amount of data that wecan make all these changes with. However,
(17:17):
what do you want to calculate andhow are you going to apply it
and where are you going to focusit, because there you do return again
to the speed, maturity or evolutionof the institutions. So here' s
what the objective of the data is. Ho ah, well, and before
for technological reasons, because you couldn' t save the data, because you
(17:41):
had nowhere to save them. No, and that caused a lot of data
threads to be generated. No,I mean, the risk area has its
data and you' re solving yourtechnological needs. How they were getting to
technology. The truth. We're talking about data lakes, data mage,
(18:07):
we' re talking about that,and the ubiquity of the data isn
' t, that is, notonly are hundreds of data generated by everyone,
billions of data everywhere, but youcan already have them anywhere, I
don' t say in quotation marks, and anywhere there' s a lot
of security that needs to be takencare of. Harden ning remains a great
(18:29):
challenge for any institution. But thedata already starts to make it much more
democratic, to be in more places, and this legislation where the data is
and why it is there, howthey will treat it, I mean already
the data, the ubiquity of thedata does not. So, the goal
of a datajop is to maximize themaximum value of the asset that is the
(18:56):
data for an institution. And whatdoes it mean, then, that is,
to identify the threads that were formedin the past by necessity and to
begin to integrate them into a singleversion in order to democratize the knowledge of
some areas with others. No.And that, as it is, through
access to data, the use ofdata democratisation today. Before democratisation was,
(19:25):
therefore, designed qpeís and show themin an orderly way in a visualization,
which is that what you do wasthe VII, the business interim. Or
already the interaction with the data iscompletely different. No more Apple got his
goggles you play with with the data. You touch us already the interaction nor
(19:48):
a conversation with data, because itis already while I talk to you,
I can be showing you data,I can make capsules Can I make videos?
I can do that what I'm going to is that democratic is
already a definition of v evolved.Already value all that data, because it
(20:08):
is already a topic of communication andintegral communication between how many will arrive this
video that you are doing explaining yourdata, because you have to start before.
You were thinking, this is aboard for the area, for a
specific area. Today it is likelythat a video will be seen by anyone.
(20:30):
So your communication strategies through the dataand the storytelling of the data,
because it already becomes a whole strategy, in a whole much more sophisticated topic
that we have to connect with theevolution of the architectures and the needs of
business, because I can do astorytelling really, but it doesn' t
(20:52):
serve business to move the needle,because nothing happens, it doesn' t
make sense. That' s wherethey stay. Maybe it' s been
a long time we' ve heard, even from the side of those we
(21:15):
' ve given to some study aboutfifty percent of the analytical model projects stay
in the way. It' sjust because they don' t finish concrete,
because maybe they weren' t reallyaligned to the business priority to and
only sometimes they might shoot, tosay, innovate takes the new, but
(21:37):
as for what it is, yes, totally and is. And that is,
therefore, an important complication, becausebasically the analytics we live from the
confidence that people have in mathematics,not to say yes if it gives if
(21:57):
an area like data hob tells alie, because it loses all the work
that has made integrating data and beingable to lead them to a modeling and,
after modeling, be able to generatea metric with which to make a
decision. So, if it's very important to align science with the
(22:22):
need for business and the ability tocommunicate science to business. Much of what
has demanded of me a constant creativitylately is to realize the immediate quantitative and
(22:44):
qualitative benefits of an analytical implementation,that is, either of a model or
of an analytical platform, of oneinvolving several models. At the same time,
what are the immediate benefits. Because, in addition, all this has
to do with new working methodologies.Not all of us are already, already,
(23:07):
all the institutions are trying to maketheir version of the Agile, this
framework of work where for us,the analytics, because it is very interesting,
because, on the one hand,we have to comply with the scientific
method, but we are nothing.No. But, on the other hand,
(23:27):
towards business, no, it's not anymore, it' s
not even a no method. Andso how is it that we achieve the
balance between YALE and the scientific method, because it has to do with speeding
up the connection of business needs witha formal process of knowledge production through data.
(23:56):
And now yes, tell me howit is that this ability of data
hop to really give and defend itsown analytical reputation, to really give results
(24:17):
of data integration at the right timehow it can do it with technologies today
like artificial intelligence riding on the cloud. Tell me a little bit about how
these emerging technologies are impacting the taskof analytics on a maybe once you'
(24:44):
re clear about the business goal andwhat your analytical actions should be, to
say, to develop in order tomove the business differently. There' s
a super fun stage coming up.I' m fascinated by all this.
(25:07):
Not super fun, because it's knowing the technologies, that is,
knowing all the technologies with a certainlevel of detail. It doesn' t
have to be too much, butit does. You must like this to
understand the logics within tools and theevolutions of tools over time and find a
(25:29):
way to make them an enabler ofchange. If you use technology to enable
change, it' s better thanif technology requires you to change, because
(25:53):
you use technology exactly not and then, for example, it doesn' t
right now. Artificial intelligence is superfashionable. But the truth is, it
' s artificial intelligence, because it' s how you use the technological advances
(26:15):
we' ve had since the 1960s. No, I mean, we'
re making up the black thread.The thing is, we have a lot
more computing capacity and, therefore,calculation, and then we can automate a
lot more processes and generate decisions inautomatic. And then artificial intelligence becomes an
enabler of certain changes within a company. And then, today, because everyone
(26:41):
like that hears I' m notapplying artificial intelligence. I feel, I
feel old, I don' tfeel back, not perfect. That feeling
is I help you get out ofyour comfort zone in some processes that if
we apply analytical anteligency, because wemodernize them, modify them, make them
(27:06):
more precise, that is, wereview them with another point of view.
And then there' s your processwith artificial intelligence. And because the sum
between that you are more technologically capable, but besides, you know better your
processes, because magic comes, theredoes not come a major change in your
(27:30):
results. Exactly I plan to askyou a little bit about your vision,
about how these technologies are being adoptedand how they will continue to be adopted,
mainly in the financial sector. You' ve already told me a little,
but based on your experience, youthink we' re going at a
(27:56):
pace, that is, the financialsector is going at an appropriate rate of
this adoption of these new technologies.Or what a challenge it takes to work
first. Or state looks interesting thefinancial system, because it is a great
(28:22):
consumer of technology, either for regulatoryissues or suddenly you have to biometrics.
Not why we are adapting them,because the regulator demands that we adopt ourselves.
On the other hand, the financialsector, because it has capital to
(28:44):
invest in technological evolution, not inother words, it is the financial world,
Yes it is clear that the speedand efficiency of your calculations can place
you in a more advantageous position withreference to the competition. So it'
(29:07):
s interesting to see how all theproviders feel about evolving the tools for the
financial system. So how do Isee all these evolutions, because to me,
in the personal way, because Isee them amazing, because they give
(29:30):
me a soda. I don't mean, I started as a statistician
who knew how to use the computer. Then they turned me into a data
miner. Then they made me adata scientist and now it scares me to
be told that I am an artificialintelligent, but of course the financial system
(29:56):
is watching how to leverage all thesetechnological enablers to be much more efficient in
delivering their financial services. The caseof being able to serve the customer through
artificial intelligence, as it gives banksan opportunity to improve their customer service.
(30:23):
And that' s amazing, notbecause the bigger the bank, the more
work it takes to have a personalizedlistening to people. And so, to
me, personally in this technological evolution, it seems like a great opportunity to
(30:44):
bring banks or the financial system muchcloser together with their customers in a personalized
way. So that' s whatthe consumer demands today to be and understand
the data itself more to technology itselfand that' s got a lot to
(31:06):
do with what you were saying aboutdemocratizing it. But now, tell me
a little bit about what you thinkthese paradigms might be then to break in
the sector, because first look thatnothing is afraid of collective change, not
that it is when you have avery big institution, because you say don
(31:30):
' t wait for them to changeand then I' m going to or
first, I' m going totalk. Then and there I settle down.
As before, technology evolved at veryspecific points. Today technology is advancing
at all points at the same time. So even it' s already overwhelming,
(31:55):
I don' t mean, whereI upgrade, I don' t
mean what I update is which bookI read first, no, because now
it' s two hundred manuals,I don' t mean, now,
on the contrary, you have oneto make economics make very good decisions in
(32:20):
which book you' re going tobe reading and why and what juice you
' re going to get out ofthat that in what you' re training,
then this causes us to do.The big challenge is how we communicate
institutionally, how we align the objectivesinstitutionally, and so we align the technological
(32:42):
enablers that are going to help us, break the paradigm and improve the results
that we are giving. That's right now, as we get a
little closer to closing this episode,I want to ask you what these recommendations
(33:04):
would be. First, for thosemathematical statisticians who are on the way,
who are really coming to the market, to the world of work with very
different technologies, with which you arriveda few years ago today, as an
(33:27):
analytical leader with experience in the financialsector, what would be first the recommendations
to those generations that are and that, eventually, they will be the ones
to give all the decisions, becausefirst they congratulate them because they are making
a good decision to do mathematics andmake computers and prosecute them for the benefit
(33:54):
of an institution, of a company, of a government is to give them
the right way to technology. Ithink that, especially for the subject we
were talking about, a long timeago, there are times when there are
developments that can be incredibly creative,but that they don' t match with
(34:20):
the need for business and dilute thatknowledge that is diluted and wasted I think
it' s a lot that inschool, universities, courses, everyone talks
about developing models and that if theywrite and that it is better and that
you do the best, and thateveryone can say which one is the best.
(34:42):
But there is no discussion in thedevelopment of a model, in a
modeling process that, in addition towhat is required of you by the mathematics
teacher at the University, in schoolyou do not hear mathematical birds. They
apply them well, because now themachines do everything. No, I mean,
he did good. The machine didit well You really did the machine
(35:06):
well. You can well interpret theresults that the machine pulled out. That
' s where we have to focusa lot on school. But there'
s a big part that doesn't teach you to school that they teach
you your life already working, andit' s implementation. How I implement,
how this model is implemented, thisanalytical process, these developments in front
(35:32):
of the computer, which can bevery weird, because it' s me
and the computer and nothing else,but we do have to think after that,
the next day, how it works, that' s going to work
with people, with processes, howit' s going to interact with society,
with my society, with my community, all that I' m producing,
(35:59):
and then surely there' s goingto be many adjustments to my pro
a, my modeling strategy, becauseit turns out that the way I think
things are going to work isn't necessarily. It' s true.
I have to go out and integratewith the teams, go out with the
sales teams, the promoters and seehow they sell to make a propensity model,
(36:21):
buy go with the customer support ones, how they serve the customer,
how their calls are so that laterthey can make a care model, a
sentient analysis and then the recommendations don' t stay with the math books under
(36:42):
their arm. Yes, read them, but also go and see what it
' s like in real life,how it' s like in a business
process, how the numbers that wegenerate are integrated and we consider them useful
in a population that, best ofall, doesn' t like math,
(37:04):
but that is susceptible to data andinteract with them, that we need them,
but we need them to be interpretedto us. And now, your
suggestions, your recommendations for your peers, for these analytical leaders who, well,
in this podcast listen to us fromall over Latin America. Then what
(37:27):
would be like two three points youcould suggest, for patience and discipline.
Today that you sleep well and sleepenjoy the weekend, let your patience and
discipline rest. Today, and Idon' t know, it' s
(37:52):
happened to everyone. My mom missesthe Internet and calls me she doesn'
t tell me she' s failingthe Internet. Better the company. But,
well, I have days, butit' s just that you already
run like the so I have tocheck everything around that there' s Internet
(38:14):
in my mother' s hiring company. Well, there' s something similar
going on with the data today.No, I mean, there' s
already a problem in some system.And so, how do you realize,
then, of the giving through thedata? And if you realized through the
data, that it doesn' thave to solve, because the data one
doesn' t and not necessarily,that premise is fulfilled. It may not
(38:37):
be necessary to fix something in theprocess, it is necessary to align something
in the communication, but because itcomes through a data, it does touch
on the data, because to beginto discover where the problems are, in
order to go to give them asolution. And in that sense, patience,
because now they ask us of allthis by name, because you are
(39:01):
the data one, because then youhave to solve it. So, well,
there' s patience. So watchme take advantage of those crises to
see ok then what new data I' m going to have access to,
not how I' m going tobring it and help you solve those problems
(39:22):
that you have. And the disciplinepart is not to lose faith in your
initial plan, because if you understandthe business well and build and understand the
technological difficulties your institution has, whathappens in the middle is all the work
you have to do and many timesyou pose all that transformation, those jobs.
(39:49):
But, on the other hand,the institution has immediate needs that command
from the past and then you enterinto a good dynamic, so what do
I do, do not sustain thebusiness, how are you or transform it
not And so, then, disciplinein the sense of when I am transforming
(40:10):
and when I am already tending tothe business in its daily needs and not
lose the focus of where we aretaking this transformation through the great technological enablers.
And finally, tell me what thesteps are, these points that you
(40:31):
don' t have to lose bytwo thousand twenty- four inside your bank,
what challenges they' re in,where they' re going. We
have a lot of personalization, communicationand interaction challenges with our customers. We
have no ambitions to be their mainbank, to be their solution in financial
(40:58):
services. Therefore, one of thegreat challenges is how to get to know
them better in order to care forthem as they deserve, and in that
sense we are very much looking forwardto it. Another big challenge we have
is the journey to cloud. Inthe end, if we see an opportunity
(41:22):
in the cloud, but at thesame time we see great risks of ending
the budget in a single exhibition.So, the challenge that the journey to
Cloud is putting us in is toaccurately know the data flows, because because
(41:43):
we don' t want to takethe trash to the cloud, we really
need to bring the useful data.And this is forcing us, as data
hop, to integrate, on theone hand, the operational and technological parts
and, on the other hand,everything that has to do with attention and
product design and etcetera, etcetera.So it' s super interesting,'
(42:09):
cause now it' s dating.You are really looking at the business transversally,
from start to finish so you canachieve this journey and to Cloud No
and above all great implement all these. I' m happy things and we
have a lot of juice to takeout that tool. That' s just
(42:32):
what he thinks it' s toknow, to make good use of technological
resources for how to integrate assertively andtake advantage of the very little budget that
you have within your institution. It' s vital and I' m saying
that because you' re a memberof a multi- bank institution. I
(42:53):
think it is very relevant, perhapsfor listeners from other countries who are also
not necessarily in the ranks of thebiggest banks, not but really these strategies
are really made for everyone. Havinganalytical implementation is really for everyone and it
(43:15):
is yes, perfect fico. Ireally appreciate this conversation. I think there
are still many more issues. Sothere I' m going to want more
time with you to discuss a littlebit more about other very interesting topics within
this sector, like this part ofthe regulation, which I think is super
(43:37):
interesting. But for now, then, I thank you very much for your
time in sharing a little more ofyour experience and vision. And so,
finally, some comment that you wantto leave here at the Many thanks for
the invitation, thank you very muchfor listening to them and therefore feel like
(44:01):
positioning data scientists, not as asexy job, but as a group of
people that generate the difference in theresults. Amazing, amazing, incredible quot
to close. So, thank youvery much for listening to us in this
episode and listening to us in aforthcoming one. You want to know more
(44:29):
about our offer for the banking sector. We have many success cases that may
interest you visit the sas Com diagonalname of your country. The world is
not changing. The world has changed. We live a unique world, a
fantastic time, A critical point inyour hands is the possibility to transform your
(44:53):
company, your organization or the institutionyou lead using big data, artificial smart
analytics and take off big or staylike this and let everyone overstep your digital
decision. It is a production ofsad Latin America. Rights reserved. If
you want to know more subscribe andshare it, visit us in digital decision
(45:21):
sas Com and on our social networks
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