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On the other hand, I think we 
definitely have lost some of 

2
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their discipline of analysis, in
the name of analytics. 

3
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Right now. 
You are seeing a lot more 

4
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scaling from. 
I would say, our Tech first 

5
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mindset rather than a decision 
first mindset and over a period 

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of time. 
I think it will come find its 

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own balance and settle it out 
when I think about Talent 

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scarcity. 
And kind of all these challenges

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that we talked about. 
It's about the biggest challenge

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is getting those bilingual 
books. 

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Hello, and welcome to data, 
Shadow the podcast on all things

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data. 
This podcast is a series of 

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conversations with experts and 
Industry leaders in data. 

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At each week. 
We aim to unpack a different 

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compartment. 
Data suitcase. 

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I am your host Catholic charity 
that I'm a blogger newspaper. 

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Columnist book author and a 
former data and sides 

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inconsistent at currently head 
analytics and business 

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intelligence for delivery. 
One of India's largest logistics

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companies. 
You can follow me on Twitter at 

21
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Karthik s that is Kar Phi. 
K s and read my blog at no 

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Intruder.com that is n and ph.d.
A.com or opinions expressed in 

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his podcast, belong to me and my
podcast is and I do not reflect 

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the views of any organizations. 
We might be Associated. 

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Nothing discussing this podcast 
should be taken as been achieved

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from the letters. 
Over the last decade, we have 

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seen tremendous advances in Big 
Data data science, artificial 

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intelligence and machine 
learning. 

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Every company wants to be a tech
first company now and wants to 

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do data science companies can 
probably double their valuations

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by just adding a DOT AI to their
names companies that actually 

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use artificial intelligence and 
machine learning. 

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Maybe have an even higher 
premium on their valuations. 

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Is data science. 
Worth the hype is a, I going to 

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take over the world. 
And why is data science being 

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eaten by computer science over 
the years and what happened to 

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Classic analytics, operations, 
research, statistics, Etc. 

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To answer all this. 
We have someone who worked in 

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data science, even before the 
phrase had been coined. 

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I'm rich, 3 Part. 
D is s v bi and analytics 

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business leader at genpact till 
recently. 

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He was a partner with PWC 
leading, the firm's data and 

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analytics Consulting and help. 
The 500 million dollar business,

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previously, embraced confounded 
and collect the information and 

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analytics practice for Diamond 
management and Technology 

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consultants. 
And also serves as an Adjunct 

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professor for data science and 
business analytics at the 

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University of North Carolina, 
Charlotte. 

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Let's start with the 
Fundamentals by your definition.

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What is data science? 
What is analytics? 

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What's machine learning? 
What's actual intelligence? 

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Yeah, Gothic. 
I I actually don't even think of

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any of those things. 
All these terms have been so 

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bastardized where one ends and 
the other begins or what are 

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overlapping is even hard to get 
your head around, but I really 

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think about it is How do you 
make better decisions? 

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Like what is the, what is the 
decisions? 

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How do you make that more 
efficient? 

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And to do that? 
You have to think about like 

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Data Insights outcomes and 
process, right? 

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Or workflow of some sort and 
open data Insight, workflow 

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actions and outcome. 
In some ways that said, I felt 

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it and that's what matters. 
And then there are two go from 

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data to in size. 
There are many, you said of In 

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case you need to do drive, you 
could apply all caps, all kinds 

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of techniques, organs of 
terminology, but that's kind of 

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all the things that you talked 
about kind of somewhere falls 

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into that but somewhere like in 
artificial intelligence of the 

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automation stuff also, probably 
40 to that in some ways doesn't 

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really matter. 
I really start thinking about 

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decisions like kind of work 
backwards. 

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Everything else is pretty much a
marketing term know. 

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So the recently actually, what's
happening is I was rereading. 

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This is famous article by Thomas
Davenport in a DJ but 2012, HP 

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are so I'll link it in. 
So gnomes. 

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This is that Data scientists 
expected job of the 21st century

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or something like that. 
And I was rereading and I was 

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like, okay this mentions all 
about hypothesis testing 

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designing experiments. 
How do you collect data and so 

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on as nothing about machine 
learning? 

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But in the industry, I found 
that some people sort of like 

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use these to interchangeably. 
Somebody had once even told me 

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that you're not doing machine 
learning. 

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Then how do you call yourself a 
data scientist? 

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So, it's a why, why why? 
Because machine learning and 

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programming in general taken 
center stage in in data science 

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over the last 10 years. 
Yeah, you're worth really 

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interesting. 
I'll just go back to that same 

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article. 
I think the article it except 

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coat but I think Hal Varian 
rules boobs Economist, right? 

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And he was an economist to start
with and talks about it from his

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vantage point and sometimes you 
kind of go and you you hear the 

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whole notion of design of 
experiments and everything that 

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you kind of refers to there. 
But I mean, what is what has 

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happened? 
Is those of us who've been in 

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space of a takes for whole bunch
of years right now? 

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It used to be statistics shins, 
econometricians, maybe 

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operations research, people who 
used to do this, right used to 

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used to do this in like the 
initial analytics kind of 1.0 

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wave which essentially was those
folks. 

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And then obviously you bunch of 
things happened in the world of 

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computer and you kind of heard 
the word big data and then a lot

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of it is has Around less around,
I would say more and it does 

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mean less and less around 
thinking about the decisions and

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thinking about the art of a pit 
order of accuracy because you 

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could compute your way through 
it. 

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You could breed force your way 
through it and getting all 

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getting more and more into like 
numerical and mathematical 

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techniques that will allow you 
to brute force and kind of solve

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the problem. 
Which is care, which is a good 

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thing in some ways because it 
creates access to some of these 

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techniques in the very, very 
way. 

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And essentially, Analytics that 
there's evolved is more of 

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discipline of computer science, 
and Computing than some of the 

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earlier things we talked about, 
and which is why you see machine

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learning and you can see 
artificial intelligence and in 

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those terms being thrown around.
But essentially it's, I would 

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say the march of and computer 
science on this whole field and 

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that has a huge set of 
advantages in terms of, I think 

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it democratizes it kind of scale
set up in a very different way. 

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You could use it in all kinds of
things because I still remember 

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there are experiments. 
Of things that I would run, that

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would go on for like code, the 
SAS code that will go on for 24 

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hours and you don't see series. 
You don't see right now as many 

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of those but at the but the on 
the other hand, I think we 

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definitely have lost some of 
their discipline of analysis, in

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the name of analytics. 
Can you elaborate a bit on that 

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that with that we have lost 
memory if you thing about 

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analytics? 
And if you think about decision 

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making I mean I'd be. 
Why are we doing any of this is 

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fundamentally to change some 
Behavior, some decisions and you

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make it more active. 
Got to go and do that. 

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You need to start thinking about
you. 

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Basically, you're trying to mod 
mathematically model reality in 

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form of an equation. 
And reality is pretty damn hot. 

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Damn hard to model. 
There are all kinds of things 

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that you don't understand. 
You don't know what's going on 

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and everything. 
There's all this is not part of 

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it, through the whole process 
and how you, what you what you 

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think about. 
All the we talk about least bias

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and explain ability as if they 
are new like they're like 

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invented yesterday. 
I am going to be asking is to 

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this tournament even thinking 
about that from the very, very 

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beginning and that notion around
the thinking about the 

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mathematics. 
It's a mathematical 

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representation of reality. 
There is a there is a not Then 

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you actually start thinking 
about in those contexts. 

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You don't think about automating
and putting all the data 

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together and do all the feature 
engineering at one go and try 

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all of it in an extra boost to 
model and then kind of do an 

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ensemble model and find an 
answer. 

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That is like, okay. 
I kind of think think I'm 

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putting kitchen sink throwing 
the kitchen sink at it and yes, 

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some version of reality it is 
there but there is a little bit 

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of a structured process around 
thinking when I bought the 

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sister tell you what is actually
means a real life. 

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Would it actually made me even 
make any things around decision?

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That I think is what I talked 
about. 

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There is a not process and they 
used to be. 

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It used to be through an 
apprenticeship model. 

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You learn that thing over a 
period of time in the world, 

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which is a Computing. 
First word. 

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You think in terms of calling 
functions from Sky kit, and, and

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replacing that. 
So I think this is the sort of a

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constant sort of hat. 
I might be able to call it back 

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that I have with some of the 
younger people in my team 

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because I have come from a 
conventional sort of. 

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I learned my first. 
In statistics, then I learnt 

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econometrics and then start 
applying it in business and so 

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on. 
So for me, it's very much like 

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you need to understand the data.
You need to know what your 

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modeling the, why do you want an
extra boost here and not a 

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random forest, for example, 
because what is it about this 

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data set? 
That is that suits it for extra 

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boost, but sort of the younger 
people in my team. 

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They're like, there are a lot of
them, their approaches like here

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are 20 different models here. 
Is this data set. 

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Let's just apply this on 
everything and then like see 

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which gives the gives the best 
accuracy. 

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And we'll just pick back. 
Yes. 

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Yes. 
I'm in a tournament in the world

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of carrier Lauren that it's all 
a competition about accuracy, as

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like, accuracy towards what and 
for why it's sometimes, that's 

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lost, ya know. 
They actually cattle remind me 

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of that. 
I mean, living huge is, is the 

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disservice that it is done. 
I mean it done some good work. 

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It's put out some interesting 
problems out there. 

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It's Allowed lots of people to 
work on interesting things and 

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showcase their work in public 
and so on. 

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But I think what's happened, is 
it just become an accuracy race,

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00:10:17,300 --> 00:10:19,000
right? 
It's almost like you're trying 

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for the best R square or 
whatever, right? 

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So so think that's one problem 
with Carol, where you just go 

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00:10:26,600 --> 00:10:29,300
for the highest accuracy rather 
than let's say hi. 

195
00:10:29,300 --> 00:10:32,200
Have best model that you can 
understand. 

196
00:10:32,200 --> 00:10:37,100
And things like that. 
I think we're going with it is 

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when you start modeling. 
Reality. 

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00:10:40,300 --> 00:10:42,400
And you start thinking in terms 
of decisions. 

199
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You actually, you actually force
yourself not to go after the 

200
00:10:46,500 --> 00:10:49,800
accuracy, but correct, the 
pragmatism in the practicality 

201
00:10:49,800 --> 00:10:52,700
of getting something dark. 
And and by the way, there's a 

202
00:10:52,708 --> 00:10:54,600
psychology of decision. 
We have an economics and 

203
00:10:54,600 --> 00:10:56,900
everything is a whole host of 
other things just by that really

204
00:10:56,900 --> 00:10:58,700
matter to how you were. 
You can might have the best 

205
00:10:58,700 --> 00:11:01,800
answer but you still won't make 
major break based searches with 

206
00:11:01,800 --> 00:11:03,800
a whole host of other things 
that are going on inside, which 

207
00:11:03,800 --> 00:11:06,700
is what white too big. 
And it makes the whole world was

208
00:11:06,700 --> 00:11:08,400
over. 
So so our whole disciplines of 

209
00:11:08,400 --> 00:11:11,000
fascinating there. 
Economics that has been met with

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00:11:11,000 --> 00:11:12,700
economics and experimental 
design. 

211
00:11:12,700 --> 00:11:14,600
That is statistics is operations
research. 

212
00:11:14,600 --> 00:11:18,700
There is Computing and but it's 
one of the 15 things that 

213
00:11:18,700 --> 00:11:21,100
actually are important. 
And then, but everything kind 

214
00:11:21,100 --> 00:11:22,200
of. 
It sucks. 

215
00:11:22,200 --> 00:11:24,600
The oxygen out of a whole lot of
other things. 

216
00:11:24,600 --> 00:11:26,800
Sometimes you just have to be 
careful about. 

217
00:11:27,500 --> 00:11:31,100
So and I think what's happened 
is, I think you are particularly

218
00:11:31,100 --> 00:11:33,600
in a good position to answer 
this. 

219
00:11:33,700 --> 00:11:37,200
So if you think about it, like 
analytics has sort of scaled 

220
00:11:37,200 --> 00:11:40,700
massively over the last 10, 15 A
year since since you joined the 

221
00:11:40,700 --> 00:11:43,700
industry and what's happening 
slots, lot, more companies are 

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00:11:43,700 --> 00:11:46,500
using it, which means that we 
need a lot more people who need 

223
00:11:46,500 --> 00:11:51,100
to deliver this and so on. 
So when you scale, so massively,

224
00:11:51,100 --> 00:11:55,100
how do you do it without sort of
dropping quality, without 

225
00:11:55,100 --> 00:11:59,600
without dropping your standards,
or like, making sure that you 

226
00:11:59,600 --> 00:12:05,000
don't do bad data analysis. 
Yeah, it's, it's hard because 

227
00:12:05,000 --> 00:12:08,300
because it's seen, as, okay, if 
you learn three Tools in your 

228
00:12:08,300 --> 00:12:10,300
kind of getting it. 
But are two things that are 

229
00:12:10,300 --> 00:12:12,800
happening, at least, my 
fundamental belief has always 

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been, you will have to 
understand the context of 

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decision-making to understand 
the context of decision-making 

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have to actually understand the 
workflow, right? 

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And not a whole lot attention is
paid to the process part of it 

234
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of that equation. 
Like you, are you, you always 

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make decision in context and you
make all this decision in as a 

236
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sequence of things that is going
on. 

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And if you don't understand 
that, or if you don't understand

238
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the content, whether you are 
doing your making a A delivery 

239
00:12:40,300 --> 00:12:45,100
route choice or you're making a 
choice around which application 

240
00:12:45,500 --> 00:12:50,900
to except for giving a loan or 
whether you understand which, 

241
00:12:50,900 --> 00:12:53,400
what is the forecast that you 
are going to lab? 

242
00:12:53,500 --> 00:12:56,800
But put your product plan, your 
production to these are all 

243
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decisions. 
And these are like very real 

244
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live, very candid. 
David like very practical 

245
00:13:00,400 --> 00:13:03,200
decisions and you need to 
understand how all of this thing

246
00:13:03,200 --> 00:13:05,400
works together. 
There's a sales force is a sales

247
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person who's trying to kind of 
forecasted and he's trying to 

248
00:13:08,300 --> 00:13:12,900
protect his Birds or the process
that he follows when he's 

249
00:13:12,900 --> 00:13:15,800
talking to the to his clients or
his her clients. 

250
00:13:15,800 --> 00:13:19,300
And how does the whole process 
work that there is a richness 

251
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around that if you understand. 
Okay. 

252
00:13:20,700 --> 00:13:23,000
What are you influencing in the 
in that whole system? 

253
00:13:23,300 --> 00:13:26,300
That becomes the starting point?
And actually, it's in someone 

254
00:13:26,300 --> 00:13:27,800
it's not very hard. 
It's these are these are 

255
00:13:27,800 --> 00:13:30,600
practical set of things that go 
on and you remember you work 

256
00:13:30,600 --> 00:13:33,000
backwards from there. 
You will go backwards from that 

257
00:13:33,000 --> 00:13:34,600
and try to understand. 
Okay, what do I need to 

258
00:13:34,600 --> 00:13:35,700
influence? 
And what did I do? 

259
00:13:35,700 --> 00:13:38,000
I need in our techniques. 
I'm either going to do that. 

260
00:13:38,700 --> 00:13:41,200
It's basically kind of almost 
like going left all the time. 

261
00:13:41,200 --> 00:13:42,900
The more you are able to do 
that. 

262
00:13:43,200 --> 00:13:48,600
And and if I mean, I would say 
companies or organizations that 

263
00:13:48,600 --> 00:13:52,600
are more who think like that you
can scale up pretty effectively.

264
00:13:53,100 --> 00:13:56,200
It's just damn hard to kind of 
do that to that that process, 

265
00:13:56,400 --> 00:13:59,100
especially because it's easier 
to just say, listen, you need, 

266
00:13:59,100 --> 00:14:01,600
you need to understand these 
three tools. 

267
00:14:01,700 --> 00:14:06,000
These two techniques, this one 
certification, and you go. 

268
00:14:06,400 --> 00:14:08,300
So it's about that balance. 
If you're if you're doing, you 

269
00:14:08,300 --> 00:14:11,100
can So you guys, so I just don't
like to answer questions, like 

270
00:14:11,100 --> 00:14:11,900
that. 
Scale. 

271
00:14:12,000 --> 00:14:15,900
You absolutely can scale 
analytics, data science, the 

272
00:14:15,900 --> 00:14:20,600
whole thing, but to ski I think 
was the mindset is the right one

273
00:14:20,600 --> 00:14:23,000
to do scale or not. 
And I think as an industry we 

274
00:14:23,000 --> 00:14:25,500
will kind of get but right now 
you are seeing a lot more 

275
00:14:25,500 --> 00:14:27,100
scaling from. 
I would say, our Tech first 

276
00:14:27,100 --> 00:14:31,300
mindset rather than a decision 
first mindset and over a period 

277
00:14:31,300 --> 00:14:32,400
of time. 
I think it will come up. 

278
00:14:33,000 --> 00:14:34,800
Find its own balance and settle 
it out. 

279
00:14:35,200 --> 00:14:36,300
Yeah. 
Because I think what's also 

280
00:14:36,300 --> 00:14:37,800
happened? 
Is that like TV? 

281
00:14:38,500 --> 00:14:40,100
Especially in some older 
organizations. 

282
00:14:40,100 --> 00:14:43,100
What you have obviously, you'll 
have some sort of a few old 

283
00:14:43,100 --> 00:14:45,900
timers late say, who would have 
been like sort of deficient 

284
00:14:45,900 --> 00:14:49,700
first and they are somehow able 
to percolate their way of 

285
00:14:49,700 --> 00:14:51,800
thinking through the 
organization and that works 

286
00:14:51,800 --> 00:14:54,400
great. 
But what, but what I also see 

287
00:14:54,400 --> 00:14:57,700
now is like, interval, or 
usually sort of younger 

288
00:14:57,700 --> 00:15:00,400
companies. 
We're like pretty much everybody

289
00:15:00,400 --> 00:15:02,100
who's, let's say working in 
analytics. 

290
00:15:02,200 --> 00:15:05,800
Data science has graduated 
fairly recently in the tech. 

291
00:15:05,800 --> 00:15:11,200
First word, post 2010. 
Because I think if you think 

292
00:15:11,200 --> 00:15:14,900
about it, when he then was a 
sort of a around this, a massive

293
00:15:14,900 --> 00:15:17,500
inflection point for the 
industry, that because I think a

294
00:15:17,500 --> 00:15:21,900
combination of big data and 
cloud and faster Computing, 

295
00:15:21,900 --> 00:15:24,800
which enabled better machine 
learning like things. 

296
00:15:24,800 --> 00:15:28,900
Like, when I did my undergrad, a
and squirted nobody was working 

297
00:15:28,900 --> 00:15:29,700
on. 
He didn't say. 

298
00:15:29,800 --> 00:15:32,400
So my generation of computer 
science, people has been like 

299
00:15:32,400 --> 00:15:34,600
completely. 
I don't think anybody really 

300
00:15:34,600 --> 00:15:39,200
works in core data science. 
No, Exactly. 

301
00:15:39,200 --> 00:15:41,900
I agree. 
I mean it was the AI winter. 

302
00:15:41,900 --> 00:15:44,500
That was the term. 
Yeah, so I think 2010 was when 

303
00:15:44,500 --> 00:15:46,800
like you had all these all the 
data sitting in the cloud 

304
00:15:46,800 --> 00:15:49,700
sitting in everything sits in 
one day in Google cloud and all 

305
00:15:49,700 --> 00:15:51,700
over awsm everything kind of 
some of those things. 

306
00:15:51,700 --> 00:15:54,800
Become a lot more mainstream in 
care of these these tools are I 

307
00:15:54,808 --> 00:15:56,900
think we came this way and a lot
of the tooling came out. 

308
00:15:57,000 --> 00:15:59,800
I've been done with like I just 
made it a lot easier to. 

309
00:15:59,900 --> 00:16:02,500
That's why I think I normally 
think about have you started, 

310
00:16:02,500 --> 00:16:06,000
they have you started in this 
world after 2010 or pre-2010 a, 

311
00:16:06,000 --> 00:16:08,200
that kind of tells you how your 
mindset a little bit of what are

312
00:16:08,200 --> 00:16:10,900
you? 
Yep, that's what I found. 

313
00:16:10,900 --> 00:16:14,500
Is that like especially I mean, 
I am grossly generalizing here 

314
00:16:14,600 --> 00:16:17,500
based on a few data points. 
I'd if you have a, let's say if 

315
00:16:17,500 --> 00:16:19,800
you have a team actually mostly 
post 2010. 

316
00:16:19,800 --> 00:16:22,800
I mean it's now very poor who 
have sort of looked at data 

317
00:16:22,800 --> 00:16:25,100
science from a very thick first 
perspective. 

318
00:16:25,500 --> 00:16:27,700
Then it's possible for the 
entire organization to sort of 

319
00:16:27,700 --> 00:16:29,100
get lost in it. 
Take ten. 

320
00:16:29,100 --> 00:16:33,100
Not really, not really look at 
the decision part of it, right? 

321
00:16:33,100 --> 00:16:36,900
Like so worrying. 
I mean, considering that given 

322
00:16:36,900 --> 00:16:38,300
the distribution of Ages and 
sir. 

323
00:16:38,500 --> 00:16:42,700
If most teams are sort of of 
this nature, how do you sort of 

324
00:16:42,900 --> 00:16:46,200
make sure that you don't end up 
doing what I would just call is 

325
00:16:46,200 --> 00:16:49,700
bad data else. 
Yeah, it's not bad is good. 

326
00:16:49,800 --> 00:16:53,200
It's fun in some ways. 
I mean, they have some fantastic

327
00:16:53,200 --> 00:16:56,100
data scientists after 2010. 
Also, I'm just going to H over 

328
00:16:56,100 --> 00:16:59,800
generalizing it, but you need to
be, you will know very quickly 

329
00:17:00,000 --> 00:17:02,800
is whether you have a menu 
controller career experience. 

330
00:17:02,800 --> 00:17:04,400
I look at organizations all the 
time. 

331
00:17:05,099 --> 00:17:09,700
Do you have relevance India? 
And because Designs, I mean 

332
00:17:09,700 --> 00:17:13,000
unless you are even if you had a
wedding in the product and the 

333
00:17:13,000 --> 00:17:16,000
platform of the company, in most
of the times in the business 

334
00:17:16,000 --> 00:17:20,900
decision-making, set of things, 
do people care about what you're

335
00:17:20,900 --> 00:17:22,099
doing. 
Do they use what they are 

336
00:17:22,099 --> 00:17:26,300
actually use any of it, right? 
And that automatically Tells A 

337
00:17:26,300 --> 00:17:27,400
lot of it. 
And some of it. 

338
00:17:27,400 --> 00:17:29,700
I mean, you see, like you've 
data, what's really amazing is 

339
00:17:29,700 --> 00:17:31,800
you did at evaluating sources 
becoming so prevalent. 

340
00:17:31,800 --> 00:17:35,300
So quickly item is as a 
function, but also, you can see 

341
00:17:35,300 --> 00:17:38,200
the growing pains essentially, 
what, which essentially, they 

342
00:17:38,200 --> 00:17:40,000
are. 
The cio's of the 1990s. 

343
00:17:40,000 --> 00:17:42,100
I was like, get through the 
Growing Pains of how do you 

344
00:17:42,100 --> 00:17:43,700
establish? 
What, what these folks are? 

345
00:17:43,700 --> 00:17:45,500
What do they do? 
How do you make themselves 

346
00:17:45,500 --> 00:17:46,700
relevant? 
In an organization? 

347
00:17:46,700 --> 00:17:49,000
So we see some of that, some of 
the challenges. 

348
00:17:49,200 --> 00:17:54,600
So, yeah, I would say, it's not 
about like, clean ants, but the 

349
00:17:54,600 --> 00:17:58,200
system kind of works itself out 
because some of those things in 

350
00:17:58,200 --> 00:18:00,900
the larger business analytics, 
kind of a context, those teams 

351
00:18:00,900 --> 00:18:04,700
have less, and less relevance, 
and things change and a kind of 

352
00:18:05,300 --> 00:18:07,900
the next version comes along, 
make since Nixon. 

353
00:18:08,700 --> 00:18:11,400
Changing tracks a little bit. 
I think we've spoken about what 

354
00:18:11,400 --> 00:18:15,100
what data science has been like 
and how its evolved. 

355
00:18:15,100 --> 00:18:18,500
I think direct from us selling 
perspective, how have things 

356
00:18:18,500 --> 00:18:22,000
changed in terms of like, 
organizations more receptive to 

357
00:18:22,000 --> 00:18:23,600
this? 
I think, 10 years back. 

358
00:18:23,600 --> 00:18:25,200
They used to be the huge 
concerns about. 

359
00:18:25,200 --> 00:18:29,400
Can I give my data to a third 
party to kind of get them to 

360
00:18:29,400 --> 00:18:31,900
give us insights? 
And so on the, so how has that 

361
00:18:32,300 --> 00:18:33,900
changed over the years? 
Yeah. 

362
00:18:33,900 --> 00:18:36,800
I mean, I mean, I've always been
on kind of the Professional 

363
00:18:36,800 --> 00:18:39,800
Services and selling piece of it
and And ours is selling that and

364
00:18:39,800 --> 00:18:44,400
then the selling of internally 
selling of analytics group 

365
00:18:44,400 --> 00:18:47,100
internally to their 
stakeholders. 

366
00:18:47,200 --> 00:18:48,700
This is stakeholders. 
I mean, I think I've learned 

367
00:18:48,700 --> 00:18:52,800
this in both of that side of it.
I think do things have happened.

368
00:18:52,900 --> 00:18:57,800
Number one. 
This discipline was I would say 

369
00:18:58,500 --> 00:19:01,100
a back office discipline in like
early 2000s. 

370
00:19:01,700 --> 00:19:04,400
Now over a period of like the 
good part of. 

371
00:19:04,400 --> 00:19:08,100
It is like whether it was Big 
Data, a high, all the hype, and 

372
00:19:08,100 --> 00:19:11,700
the buzz words that came along 
and it kind of changes every few

373
00:19:12,000 --> 00:19:14,300
years. 
What becomes more important or 

374
00:19:14,300 --> 00:19:17,200
less. 
It has become a very business 

375
00:19:17,400 --> 00:19:18,800
discussion. 
Right? 

376
00:19:19,000 --> 00:19:21,300
When I had like clients, who 
would ask? 

377
00:19:21,300 --> 00:19:23,700
Okay. 
Like, how can I get AI faster 

378
00:19:23,900 --> 00:19:28,300
and whatever that means? 
But the point is it is where 

379
00:19:28,600 --> 00:19:31,000
there are there's an education 
Journey that actually happens. 

380
00:19:31,000 --> 00:19:33,700
But there is a lot more 
awareness that we need to do 

381
00:19:33,700 --> 00:19:35,100
that. 
And this is why it's important 

382
00:19:35,100 --> 00:19:37,100
and everything. 
But there's a massive, I would 

383
00:19:37,100 --> 00:19:42,000
say, between 2010, to 2015. 
I mean before that, they were 

384
00:19:42,000 --> 00:19:44,700
like early movers but I believe 
that there was like a massive 

385
00:19:44,700 --> 00:19:47,900
education and awareness that has
happened in the corporate world 

386
00:19:48,000 --> 00:19:50,200
around these topics. 
And I would say even even before

387
00:19:50,200 --> 00:19:53,700
that to do a little bit and that
actually has opened up around 

388
00:19:53,700 --> 00:19:57,300
people. 
I would say there are vast. 

389
00:19:57,300 --> 00:20:00,400
Majority of people are we 
absolutely need to do this and 

390
00:20:00,400 --> 00:20:02,700
then like a why do you need to 
do is exactly what you need to 

391
00:20:02,700 --> 00:20:03,000
do. 
This? 

392
00:20:03,000 --> 00:20:06,100
We are still, I think working at
myself so out and now there's 

393
00:20:06,100 --> 00:20:07,400
this new transformation, 
whatever word. 

394
00:20:07,400 --> 00:20:11,600
You use in that broader context,
people follow early realize, I'm

395
00:20:11,600 --> 00:20:13,800
going to use the technology in 
my ecosystem. 

396
00:20:13,800 --> 00:20:16,100
In a very different way to 
interact with my customers, 

397
00:20:16,100 --> 00:20:18,900
interact with my employees, as 
or interact with my partners in 

398
00:20:18,900 --> 00:20:22,000
the ecosystem and and all of 
that stuff is happening and with

399
00:20:22,000 --> 00:20:24,700
covid-19 with Stacy when it's 
like, which is crazy. 

400
00:20:25,100 --> 00:20:27,700
Everything you can realize you 
can do it over the river in in 

401
00:20:27,700 --> 00:20:30,800
very different ways. 
So, there is this, the whole 

402
00:20:31,800 --> 00:20:35,800
that awareness is creating a 
huge pull towards it. 

403
00:20:37,000 --> 00:20:40,200
The data side of it, by the way,
pre-2010 was actually a lot 

404
00:20:40,200 --> 00:20:41,400
easier discussion. 
There. 

405
00:20:41,400 --> 00:20:43,700
It was I okay you want to do 
these are data do but whatever. 

406
00:20:43,700 --> 00:20:45,200
Yeah, give me the stick right 
now. 

407
00:20:45,200 --> 00:20:47,100
The data discussion is a lot 
more, your data has suddenly 

408
00:20:47,100 --> 00:20:48,400
become an corporate asset, 
right? 

409
00:20:48,400 --> 00:20:50,700
And so everyone is starting to 
think about how do I manage the 

410
00:20:50,700 --> 00:20:53,200
data and everything sedate? 
I would say the people that want

411
00:20:53,200 --> 00:20:56,300
more, it's but there's a lot 
more progress being made also on

412
00:20:56,300 --> 00:20:59,000
how to manage the data, how to 
kind of, like, do you identify 

413
00:20:59,000 --> 00:21:01,000
how we gonna do that? 
So this event that thing is 

414
00:21:01,000 --> 00:21:03,900
progressed. 
Well, so yeah, it's a, it's a, 

415
00:21:03,900 --> 00:21:06,600
it's a, it's a supply side. 
It's a supply curve. 

416
00:21:06,800 --> 00:21:08,900
Market right now. 
She's like last 24 months. 

417
00:21:09,000 --> 00:21:12,400
Maybe that's 48 months and will 
continue for I as I see next 

418
00:21:12,400 --> 00:21:14,900
body at once, which is very 
supply side and Urban Market 

419
00:21:14,900 --> 00:21:17,100
order to Mandarin Market. 
There's a ton of demand out 

420
00:21:17,100 --> 00:21:18,600
there. 
So from a from, from our 

421
00:21:18,600 --> 00:21:21,300
perspective, the the rubber 
meets the road. 

422
00:21:21,300 --> 00:21:24,400
However is yes, I mean, it's 
there's a lot of things 

423
00:21:24,400 --> 00:21:26,900
happening. 
How many of them are being used.

424
00:21:26,900 --> 00:21:31,300
How many decisions are being? 
Are you making better? 

425
00:21:31,300 --> 00:21:36,900
Because of this that maturity we
are still like in They were 

426
00:21:36,900 --> 00:21:39,100
saying that the cricket 50 over 
credit of allergy. 

427
00:21:39,100 --> 00:21:42,200
We are still in like probably 
the first 20 overs there. 

428
00:21:42,200 --> 00:21:45,000
Now that that the other again is
that like one of the other 

429
00:21:45,000 --> 00:21:47,000
guests on the podcast had 
mentioned this, you know, that 

430
00:21:47,000 --> 00:21:49,400
her financial services investing
back in context. 

431
00:21:49,500 --> 00:21:51,200
He's back for investment Banks. 
Right now. 

432
00:21:51,200 --> 00:21:55,500
It appears that the purpose of 
having a data science team is 

433
00:21:55,500 --> 00:21:58,700
not to do data science. 
It's to have data science team. 

434
00:21:59,600 --> 00:22:02,200
There's a little bit of that is 
like I need one of the why I 

435
00:22:02,208 --> 00:22:04,800
need one of those and then I'll 
figure out what to do with them 

436
00:22:04,800 --> 00:22:09,500
yesterday. 
I mean I would say at least a 

437
00:22:09,500 --> 00:22:13,000
feeling the last 24 months. 
If nothing else. 

438
00:22:13,400 --> 00:22:18,000
We are getting from the toll 
earlier doctors to kind of 

439
00:22:18,000 --> 00:22:20,800
scale. 
You just sense it in like 

440
00:22:20,800 --> 00:22:23,800
everyone wants to do it at scale
and and a few areas which has 

441
00:22:23,800 --> 00:22:25,500
being right relatively proven 
out. 

442
00:22:25,500 --> 00:22:27,200
So you kind of got us to do 
that. 

443
00:22:27,500 --> 00:22:31,000
So yeah, there is a little bit 
of like, I'm vanity impact in 

444
00:22:31,000 --> 00:22:32,800
business for having things like 
that. 

445
00:22:32,800 --> 00:22:36,600
I see less and less of that dog.
Yeah, because I think there's 

446
00:22:36,600 --> 00:22:39,500
only so far that vanity can go 
right because IMA get to find 

447
00:22:39,500 --> 00:22:41,600
it. 
A fun of my old clients who was 

448
00:22:41,600 --> 00:22:44,800
like, okay, what you've done is 
fine, but can you put in a 

449
00:22:44,800 --> 00:22:46,500
little bit of machine learning 
into this? 

450
00:22:46,500 --> 00:22:48,600
It's be good. 
If you are investors. 

451
00:22:49,400 --> 00:22:52,500
It is it is that, by the way, 
that is the other big piece of 

452
00:22:52,500 --> 00:22:53,700
it. 
They invested emits. 

453
00:22:53,700 --> 00:22:56,800
A lot of it is driven by 
investors and overnight. 

454
00:22:56,800 --> 00:23:00,300
Everything became a, I write. 
I mean, sometimes my well the 

455
00:23:00,300 --> 00:23:04,800
jokes is like, yeah, if you're 
able to sort of column a minute.

456
00:23:05,200 --> 00:23:09,300
Let's say I started function, 
isn't it? 

457
00:23:09,300 --> 00:23:12,300
I function. 
So this brings me to the other 

458
00:23:12,300 --> 00:23:13,500
one other thing, you do that 
again. 

459
00:23:13,500 --> 00:23:15,600
I think we reached the talking 
about business impact and things

460
00:23:15,600 --> 00:23:19,500
like that. 
So, how do I, I guess you deal 

461
00:23:19,500 --> 00:23:22,300
with a lot of business, folks, 
who are looking to sort of 

462
00:23:23,500 --> 00:23:27,600
introduced AI or whatever into 
that process, slightly how to 

463
00:23:27,600 --> 00:23:30,200
burn business people are what 
the equation between the 

464
00:23:30,200 --> 00:23:32,700
business, guys, and analytic, 
guys, like nowadays, in terms of

465
00:23:32,700 --> 00:23:35,900
like, because one view that I 
have seen, In the past is that 

466
00:23:36,000 --> 00:23:38,600
either the business doesn't 
understand it and just let them 

467
00:23:38,600 --> 00:23:42,300
do what they want or they just 
assume that whatever the 

468
00:23:42,300 --> 00:23:45,500
analytics team has done is they 
come it great. 

469
00:23:45,500 --> 00:23:46,900
It's like it's artificial 
intelligence. 

470
00:23:46,900 --> 00:23:48,300
So we ought to use it kind of 
thing. 

471
00:23:48,300 --> 00:23:50,100
So how has that evolved over 
time? 

472
00:23:51,100 --> 00:23:54,000
Yeah, actually the second I 
don't see a whole lot Sia. 

473
00:23:54,000 --> 00:23:57,800
The a number one is the whole 
notion around business guys. 

474
00:23:57,800 --> 00:24:01,100
And analytics, guys right now in
that divide that the way we 

475
00:24:01,100 --> 00:24:04,300
actually even talk about it, 
which actually is the way that 

476
00:24:04,300 --> 00:24:08,500
actually, I didn't go away. 
It's a matter of, I mean, 

477
00:24:08,500 --> 00:24:12,300
because if you go and look at, 
like, I mean, the, the business 

478
00:24:12,300 --> 00:24:16,200
school courses of today and the 
classes of today, right now, 

479
00:24:16,200 --> 00:24:18,800
whether it's MBA classes or 
whether it's like marketing 

480
00:24:18,800 --> 00:24:22,900
classes, whether it's studying 
to become, even a social 

481
00:24:22,900 --> 00:24:26,200
scientist, you have literally 
analytics and data science, 

482
00:24:26,200 --> 00:24:28,200
course, in pretty much every 
curriculum out there. 

483
00:24:28,200 --> 00:24:31,700
I don't know, we're driving, at 
least most of the universities, 

484
00:24:31,700 --> 00:24:34,200
you will kind of go and do that 
and then you have data science 

485
00:24:34,200 --> 00:24:37,100
courses, also. 
Oh, they're so so I think that 

486
00:24:37,100 --> 00:24:40,300
whole barrier of understanding 
of what, some of these 

487
00:24:40,300 --> 00:24:41,700
techniques are. 
And what does it mean? 

488
00:24:41,700 --> 00:24:46,500
And everything are very rapidly,
kind of going away on one on one

489
00:24:46,500 --> 00:24:50,500
end of it on the, on the flip 
side of it right now. 

490
00:24:50,500 --> 00:24:53,900
It is seen as though this these 
are the technology people and 

491
00:24:53,900 --> 00:24:56,400
this is like the business person
and the technology people are 

492
00:24:56,400 --> 00:24:59,600
continue hyping up and newer new
things that are coming in and 

493
00:24:59,600 --> 00:25:01,500
the business guys are, what are 
you telling me like this thing 

494
00:25:01,500 --> 00:25:03,600
makes no sense. 
Like, you don't understand the 

495
00:25:03,600 --> 00:25:06,200
pragmatic aspect of it. 
Which is this whole notion right

496
00:25:06,200 --> 00:25:08,500
now, right now, at least, what 
is I would say, next? 

497
00:25:08,500 --> 00:25:11,500
That's a few years, this whole 
notion, what I call bilinguals 

498
00:25:11,500 --> 00:25:14,100
becomes very people who aren't 
who can understand analytics, 

499
00:25:14,100 --> 00:25:16,700
and who can understand, who can 
understand the business of the 

500
00:25:16,700 --> 00:25:19,000
town, or the context of the 
domain part of it. 

501
00:25:19,000 --> 00:25:20,900
And how do they get of, how does
it all interact? 

502
00:25:20,900 --> 00:25:22,800
And you can go from one to the 
other, right? 

503
00:25:22,800 --> 00:25:25,300
You could be a very good 
marketing person who understand 

504
00:25:25,500 --> 00:25:29,000
more text acts, and understand 
the idea a little scope for 

505
00:25:29,000 --> 00:25:31,700
portion of it, or you could be a
very good analytics person or 

506
00:25:31,700 --> 00:25:34,900
you or pick first person in who 
was picked up marketing, but You

507
00:25:34,900 --> 00:25:37,000
need to be bilingual to be 
successful right now. 

508
00:25:37,000 --> 00:25:39,700
When I think about Talent, 
scarcity and kind of all these 

509
00:25:39,700 --> 00:25:43,200
challenges that we talked about.
It's about the biggest challenge

510
00:25:43,200 --> 00:25:46,000
is getting those bilingual folks
in place and the moment you get 

511
00:25:46,000 --> 00:25:48,400
that you will be able to kind of
bridge that and that's get that 

512
00:25:48,400 --> 00:25:51,600
and that is going to be probably
the mechanism to Bridge and 

513
00:25:51,600 --> 00:25:55,000
drive value in next few years 
until I think a whole lot of 

514
00:25:55,000 --> 00:25:58,500
bilinguals show up because 
that's what we have in training 

515
00:25:58,500 --> 00:26:01,000
people. 
Yeah, the way because I know I 

516
00:26:01,000 --> 00:26:05,200
have what I think I was a sort 
of a early adopter in some Being

517
00:26:05,200 --> 00:26:08,100
a bilingual of somebody who's 
probably better in business than

518
00:26:08,100 --> 00:26:11,500
most data, people better in data
than most business. 

519
00:26:11,500 --> 00:26:13,400
People. 
I found myself in this sort of 

520
00:26:13,400 --> 00:26:16,700
bucket in 10-15 years back. 
And then, like, I found it is 

521
00:26:16,700 --> 00:26:20,600
difficult to sell in some sense 
because, like, it's, because 

522
00:26:20,600 --> 00:26:24,300
there are very few bilinguals 
and it's and even now I don't 

523
00:26:24,300 --> 00:26:27,200
see a whole lot of them. 
I either have to kind of take 

524
00:26:27,200 --> 00:26:31,200
business people and sort of 
teach them programming or take 

525
00:26:31,300 --> 00:26:34,900
take data people and sort of try
to make the make sure they First

526
00:26:34,900 --> 00:26:38,200
and the business and so on. 
So it's a bit of a that's I 

527
00:26:38,200 --> 00:26:40,800
still see that as being a 
challenge for the next few 

528
00:26:40,800 --> 00:26:44,400
years, at least it is. 
And if you have bilingual set 

529
00:26:44,400 --> 00:26:47,700
scale that becomes a very big 
differentiator, how do you 

530
00:26:47,700 --> 00:26:52,000
create bilingual set scale? 
If you, it's probably, if you 

531
00:26:52,000 --> 00:26:55,500
start from a business mindset 
and go to technical, they have a

532
00:26:55,500 --> 00:26:59,300
better chance by my sense of it 
versus. 

533
00:26:59,600 --> 00:27:03,200
If you start as a, just from a 
technical impact on the 

534
00:27:03,208 --> 00:27:05,200
business. 
Because if you have it, This 

535
00:27:05,200 --> 00:27:08,100
goes back to the, my process, my
decision making in the context 

536
00:27:08,100 --> 00:27:11,300
of something. 
If I understand that, and the 

537
00:27:11,300 --> 00:27:13,400
tools are becoming easier and 
easier to take. 

538
00:27:13,800 --> 00:27:15,800
And think those things are 
becoming easier easier to 

539
00:27:15,800 --> 00:27:18,000
understand that. 
I know how to apply the right 

540
00:27:18,000 --> 00:27:19,900
way. 
There are some things you have 

541
00:27:19,900 --> 00:27:22,600
to be careful about. 
But so so I think you would you 

542
00:27:22,600 --> 00:27:25,400
do bilinguals at scale which I 
mean if you think about the big 

543
00:27:25,400 --> 00:27:27,400
of the dusty buzzword around is 
data literacy. 

544
00:27:27,500 --> 00:27:30,200
This there's some value in it. 
I mean, if done well, that has 

545
00:27:30,200 --> 00:27:33,800
probably the best Roi because 
you met, you start doing 

546
00:27:33,800 --> 00:27:35,900
bilingual that scale. 
Gail for any within any 

547
00:27:35,900 --> 00:27:38,300
organization and they are. 
Again, they're able to kind of 

548
00:27:38,300 --> 00:27:41,700
put this to rest of it. 
Through the, if you are able to 

549
00:27:41,700 --> 00:27:45,000
solve the whole problem. 
We, we actually because most of 

550
00:27:45,000 --> 00:27:46,700
the approaches has been black 
kind of. 

551
00:27:46,800 --> 00:27:50,100
It's at, for a discipline that 
has grown by worshipping. 

552
00:27:50,100 --> 00:27:53,100
The Brute Force Technique. 
We try to Brute Force more 

553
00:27:53,100 --> 00:27:56,300
technology through this and 
rather than doing that. 

554
00:27:56,400 --> 00:27:59,200
If you start kind of solving for
the pool problem is why, what, 

555
00:27:59,200 --> 00:28:01,100
how would you create the pull on
the other side, rather than 

556
00:28:01,100 --> 00:28:04,200
creating more push? 
I think you'll have much better 

557
00:28:04,200 --> 00:28:07,300
results. 
And why did you say that? 

558
00:28:07,300 --> 00:28:10,700
Like you think it's better to 
kind of teach technology to 

559
00:28:10,700 --> 00:28:14,200
business people and teach 
business technology because 

560
00:28:14,500 --> 00:28:17,400
fundamentally because it's 
becoming simpler, right? 

561
00:28:17,400 --> 00:28:21,100
I mean if you looked at and of a
SAS code versus of python code 

562
00:28:21,100 --> 00:28:25,000
or in that direction of tell you
to tell you a little bit about 

563
00:28:25,000 --> 00:28:28,500
hahaha easy, this is that if you
think about libraries that you 

564
00:28:28,500 --> 00:28:33,100
have to write in kind of a c or 
Java to what do you want like 

565
00:28:33,100 --> 00:28:35,500
Sky kit and python that you can 
learn on that thing? 

566
00:28:35,700 --> 00:28:39,200
You keep you think about really 
date myself, Crystal Reports, 

567
00:28:39,200 --> 00:28:42,500
where you require the programmer
to kind of do they do to put a 

568
00:28:42,600 --> 00:28:45,800
table and chart together to what
you what you can do in the 

569
00:28:45,800 --> 00:28:47,700
power. 
Bi interval that problems on the

570
00:28:47,700 --> 00:28:49,900
basic stuff. 
You can learn a few Arts things.

571
00:28:49,900 --> 00:28:52,500
I just becoming a lot more 
simpler and democratize and 

572
00:28:52,500 --> 00:28:54,200
technology companies. 
Fundamentally want that. 

573
00:28:54,200 --> 00:28:58,500
They want a lot more Democrat. 
They want to, these are the, 

574
00:28:58,700 --> 00:29:01,600
these are the arms, the arms 
race and they want everyone to 

575
00:29:01,600 --> 00:29:04,000
have a God. 
I will it's basically that. 

576
00:29:04,000 --> 00:29:06,300
And once you have that, then it 
becomes Becomes a lot easier. 

577
00:29:06,300 --> 00:29:10,300
That's why I think fundamentally
you are going with that flow, 

578
00:29:10,300 --> 00:29:13,100
which is weird. 
We're all kind of creating the 

579
00:29:13,100 --> 00:29:15,400
environment to make more of that
thing easier. 

580
00:29:15,400 --> 00:29:18,300
It's of the technology barriers 
little bit less, even though 

581
00:29:18,300 --> 00:29:21,300
they're very confusing for other
things, but it's a little bit 

582
00:29:21,300 --> 00:29:23,200
less of them right now. 
You don't have to be big 

583
00:29:23,200 --> 00:29:25,300
programmer right now, but 
because all of these things up, 

584
00:29:25,900 --> 00:29:27,400
yeah. 
And but I guess the one of the 

585
00:29:27,400 --> 00:29:30,100
downsides at least like if you 
ask the more technical people 

586
00:29:30,100 --> 00:29:32,500
what they'll tell you is that 
over to you, if you have people 

587
00:29:32,500 --> 00:29:34,900
who don't have a match 
background who are just applying

588
00:29:34,900 --> 00:29:36,800
these things, as Blackbox 
technique. 

589
00:29:36,900 --> 00:29:41,000
Then that will increase the sort
of likelihood of bad analysis in

590
00:29:41,000 --> 00:29:42,800
some sense. 
That is absolutely right. 

591
00:29:42,800 --> 00:29:45,300
But if you think about it's a 
great analogy I used to do a lot

592
00:29:45,300 --> 00:29:48,900
of work and I and Motorola 
versus Motorola versus Apple. 

593
00:29:48,900 --> 00:29:52,400
I bought rather radio I think is
still was was a fantastic radio 

594
00:29:52,400 --> 00:29:54,400
and you could hear a lot better 
and everything. 

595
00:29:54,400 --> 00:29:58,100
I couldn't do a win but because 
it had a better voice reception 

596
00:29:58,100 --> 00:30:01,600
a bit of girl quietly life it 
one because it was more usable, 

597
00:30:01,900 --> 00:30:04,500
it was and it created an 
ecosystem in a very different 

598
00:30:04,500 --> 00:30:06,300
way. 
So Yeah, I mean, there are 

599
00:30:06,300 --> 00:30:09,800
dangers of what you were just 
talking about that and they 

600
00:30:09,800 --> 00:30:12,100
would be rad analysis. 
You have to figure out ways to 

601
00:30:12,100 --> 00:30:15,300
protect bad analysis and they 
could be liabilities around bad 

602
00:30:15,300 --> 00:30:16,500
analysis. 
They could be divided with 

603
00:30:16,500 --> 00:30:19,200
regulations, in this space. 
I think in the next few years. 

604
00:30:19,700 --> 00:30:23,100
So all of that stuff, 
notwithstanding, I think the, 

605
00:30:23,300 --> 00:30:25,900
the volume, and the number of 
decisions that you are going to 

606
00:30:25,900 --> 00:30:30,000
influence, I think in Balance 
would be, that would probably be

607
00:30:30,000 --> 00:30:32,500
the packet of. 
And were told that the side we 

608
00:30:32,500 --> 00:30:35,500
will go towards and we have to 
figure out manage the arrest of 

609
00:30:35,600 --> 00:30:37,400
As we go along. 
Okay, I think I'm going to take 

610
00:30:37,400 --> 00:30:40,000
another big jump right now. 
So I think we will talk about 

611
00:30:40,000 --> 00:30:41,500
organizations business 
everything. 

612
00:30:41,800 --> 00:30:44,700
So coming back to sort of for 
analytics itself, right? 

613
00:30:45,200 --> 00:30:47,900
So, I mean, as you mentioned, 
like, I when it started, or it 

614
00:30:47,900 --> 00:30:51,100
was mostly like stats, people 
who are people, who are into 

615
00:30:51,100 --> 00:30:53,500
this, its own. 
But now, it seems like, I mean, 

616
00:30:53,500 --> 00:30:57,900
I know a few friends who started
off as over hardcore over 

617
00:30:57,900 --> 00:31:02,000
people, but now who do nothing 
but machine learning, it's 

618
00:31:02,000 --> 00:31:06,100
almost machine learning has 
eaten up every other Kind of 

619
00:31:06,500 --> 00:31:08,000
branch of data science and so 
on. 

620
00:31:08,008 --> 00:31:11,400
So what's happening as in? 
Why is it that it's taken over 

621
00:31:11,400 --> 00:31:15,200
so much and you live, is this 
sustainable in terms of? 

622
00:31:15,200 --> 00:31:17,100
What are you? 
It's in some ways, is like 

623
00:31:17,100 --> 00:31:19,600
technology, whatever replacing a
mice imperishable. 

624
00:31:19,600 --> 00:31:25,300
Hypothesis is is Tunic, right? 
The, the tooling available to do

625
00:31:25,300 --> 00:31:28,000
machine learning is a lot more 
democratized, then the tooling 

626
00:31:28,000 --> 00:31:34,500
available to do extra design of 
experiments or to do operations 

627
00:31:34,500 --> 00:31:37,700
research, right? 
I mean, if you really think 

628
00:31:37,700 --> 00:31:41,700
about seaplex to grow beyond the
oven and I'm an older guy. 

629
00:31:41,700 --> 00:31:44,500
So the yeah, they have been a 
few things too few things that 

630
00:31:44,500 --> 00:31:47,900
have happened, but a lot more 
Innovation, a lot more thinking 

631
00:31:47,900 --> 00:31:51,600
has happened to make the machine
learning, kind of using the 

632
00:31:51,600 --> 00:31:54,700
tooling there, and has become a 
lot easier on one side of it. 

633
00:31:54,700 --> 00:31:57,400
I think that's why s the number 
of use cases. 

634
00:31:57,400 --> 00:32:01,800
I mean at the end of the day 
seek sorting and sequencing 

635
00:32:01,800 --> 00:32:05,400
certain things in a Smart Way, a
lot more use cases. 

636
00:32:05,500 --> 00:32:07,800
I mean in the, for all the and 
all the regulation you have in 

637
00:32:07,800 --> 00:32:11,100
regular regression and logistic 
regression just application. 

638
00:32:11,100 --> 00:32:14,200
I mean you there are just a lot 
more use cases to kind of do 

639
00:32:14,200 --> 00:32:15,600
that. 
Did this are easier to kind of 

640
00:32:15,600 --> 00:32:18,400
go and upload machine learning 
or basically like more for I 

641
00:32:18,400 --> 00:32:20,600
said, I mean, if you're doing 
regression, are doing supervised

642
00:32:20,600 --> 00:32:22,500
learning. 
And some sort, there's just a 

643
00:32:22,500 --> 00:32:24,000
lot more use cases of 
optimization. 

644
00:32:24,000 --> 00:32:28,400
There are fewer of those use 
cases comparative and I mean, 

645
00:32:28,500 --> 00:32:34,200
and third, the it's easier to 
take the art part out and do 

646
00:32:34,200 --> 00:32:36,000
that. 
I mean, the other big Next thing

647
00:32:36,000 --> 00:32:38,700
about simulation. 
I'm in simulation and system 

648
00:32:38,700 --> 00:32:42,000
dynamic as a big Navy, for 
instance, as a super powerful, 

649
00:32:42,200 --> 00:32:44,800
especially when you're in sports
data world and you have a lot of

650
00:32:44,800 --> 00:32:47,800
expertise and you have to 
building data, very powerful 

651
00:32:48,900 --> 00:32:52,500
doesn't scale, doesn't scale 
that woman doing is they're 

652
00:32:52,500 --> 00:32:55,300
still clunky. 
So part of it is dueling, part 

653
00:32:55,300 --> 00:32:57,200
of it, is we have the use cases,
are part of. 

654
00:32:57,200 --> 00:33:00,500
It is like where this very 
quickly you can industrialize 

655
00:33:00,500 --> 00:33:02,600
certain things. 
So it has gone. 

656
00:33:02,600 --> 00:33:06,900
The machine learning way for a 
reason and over and and over a 

657
00:33:06,900 --> 00:33:09,200
period of time, there is where 
the demand is, you will are ya 

658
00:33:09,200 --> 00:33:12,600
even much of our problems you 
start to start thinking in terms

659
00:33:12,600 --> 00:33:15,200
of organic and then can I make 
them prediction problems rather 

660
00:33:15,200 --> 00:33:17,400
than optimization problems? 
And there are ways you kind of 

661
00:33:17,400 --> 00:33:19,300
think about that and that's what
people are doing. 

662
00:33:20,200 --> 00:33:22,700
I mean, you you work in a huge 
essentially working in our 

663
00:33:22,700 --> 00:33:25,500
company, and I think you, you 
probably use more whereby, we 

664
00:33:25,500 --> 00:33:26,700
would do it. 
When you are chatting, you're 

665
00:33:26,700 --> 00:33:30,100
telling me you probably use more
machine learning and you have 

666
00:33:30,100 --> 00:33:34,200
made a lot of those problems. 
Prediction problems versus 

667
00:33:34,300 --> 00:33:36,000
opposition problems. 
Yeah. 

668
00:33:36,000 --> 00:33:39,300
No, I mean I do try to I mean I 
do have some were people in my 

669
00:33:39,300 --> 00:33:42,200
teammates one because there are 
some something, if you I mean, 

670
00:33:42,200 --> 00:33:44,300
I'm still liking if you think 
about it old school life, where 

671
00:33:44,300 --> 00:33:47,600
I'm like, if you kind of 
formulate the problem in a nice 

672
00:33:47,600 --> 00:33:51,000
and elegant - then like, you'll 
be able to solve it far more 

673
00:33:51,000 --> 00:33:54,400
cleanly in an even know what 
perspectives rather than sort of

674
00:33:54,500 --> 00:33:57,800
throwing the kitchen sink at it.
But even what I find is even as 

675
00:33:57,800 --> 00:34:02,300
a sub problem to the war, we end
up like sort of we end up solve 

676
00:34:02,500 --> 00:34:05,400
doing some machine learning 
models to to be inputs. 

677
00:34:05,500 --> 00:34:09,100
To the over thing. 
And then it's not one or the 

678
00:34:09,100 --> 00:34:11,900
other at the end of it, but it 
is the that's that's as I said, 

679
00:34:11,900 --> 00:34:13,699
I will more and more and more 
problems. 

680
00:34:13,699 --> 00:34:16,699
I mean, this is a great book by 
Professor, Roger ver, well, 

681
00:34:16,800 --> 00:34:19,400
which is our production machines
and he's an economist who 

682
00:34:19,400 --> 00:34:24,100
studies Ai. 
And as this thing is, you make 

683
00:34:24,100 --> 00:34:27,800
it simpler and make it easier. 
You could create more problems 

684
00:34:27,800 --> 00:34:30,500
that you and you make more 
problems only as prediction 

685
00:34:30,500 --> 00:34:31,800
problem. 
And then you kind of hear this 

686
00:34:31,800 --> 00:34:34,300
whole Theory word, Rich 
machines, but that's, that's, 

687
00:34:34,500 --> 00:34:35,300
that's the court. 
Why? 

688
00:34:35,500 --> 00:34:37,400
Because we are making more and 
more problems, prediction 

689
00:34:37,400 --> 00:34:41,100
problems and within that. 
So you kind of incorporate more 

690
00:34:41,100 --> 00:34:43,100
of that stuff. 
Yeah, you'll still need work on 

691
00:34:43,100 --> 00:34:46,000
the stuff but there's a just 
become if you make it easier, if

692
00:34:46,000 --> 00:34:48,500
you bring the cost of production
down, you will create more 

693
00:34:48,500 --> 00:34:49,900
production problems. 
That's fundamentally. 

694
00:34:49,900 --> 00:34:51,600
What's happening. 
Yeah, I think, yeah, I think 

695
00:34:51,600 --> 00:34:54,300
it's, yeah, I think that's later
sort of a virtuous cycle in 

696
00:34:54,300 --> 00:34:57,500
machine learning that because it
everything is open source. 

697
00:34:57,700 --> 00:35:01,100
Everything is open, everything 
is easy to use, like the 

698
00:35:01,100 --> 00:35:03,200
theoretical, three lines of 
scikit-learn code. 

699
00:35:03,200 --> 00:35:07,000
You can do whatever you want. 
So, So it's easy to run which 

700
00:35:07,000 --> 00:35:08,900
means that you have a lot more 
people doing it. 

701
00:35:08,900 --> 00:35:12,000
And so like and in the tooling 
again becomes better and like 

702
00:35:12,000 --> 00:35:16,200
it's sort of just lie leads to 
sort of, sort of much better 

703
00:35:16,200 --> 00:35:18,600
outcomes in better outcomes or 
not. 

704
00:35:18,600 --> 00:35:21,100
It just, you are just, you are 
going to use it a lot more and 

705
00:35:21,100 --> 00:35:22,300
you're going to apply it a lot 
more. 

706
00:35:22,300 --> 00:35:24,500
I need to be incrementally 
better outcome than what you 

707
00:35:24,500 --> 00:35:28,300
started with. 
Yeah, but some of the other 

708
00:35:28,300 --> 00:35:30,800
thing I was trying to ask is 
little, is there. 

709
00:35:30,800 --> 00:35:33,300
Do you think there's some sort 
of an Arbitrage in that their 

710
00:35:33,300 --> 00:35:35,300
data size? 
Because I've long felt that like

711
00:35:35,500 --> 00:35:39,000
The way a lot of people do data 
science, Exeter, like there are 

712
00:35:39,200 --> 00:35:42,200
things that can be done better. 
But take it's difficult to sell 

713
00:35:42,200 --> 00:35:44,900
the lie. 
It's like you, you think there 

714
00:35:44,900 --> 00:35:49,400
is the you think you can sort of
do better as than a lot of 

715
00:35:49,400 --> 00:35:51,600
people are like through. 
Let's say, I don't know, like 

716
00:35:51,600 --> 00:35:53,700
being bilingual or whatever it 
is. 

717
00:35:53,700 --> 00:35:58,000
But like, is there something 
like the, is there some 

718
00:35:58,400 --> 00:35:59,900
Arbitrage for the lack of a 
better word? 

719
00:35:59,900 --> 00:36:01,500
In the way data science is done 
nowadays. 

720
00:36:01,500 --> 00:36:05,000
And is there a way to sort of 
how do you exploit? 

721
00:36:05,000 --> 00:36:08,200
The Exploit the inefficiencies 
in the system right now because 

722
00:36:08,200 --> 00:36:10,700
right now we have you guys, you 
know, there's a lot of hype 

723
00:36:10,700 --> 00:36:12,300
around data science hyper on 
machine learning. 

724
00:36:12,300 --> 00:36:16,500
So clearly if it's a bubble, 
there is a sort of bitter 

725
00:36:16,500 --> 00:36:19,000
somewhere. 
Yeah, every bubble Arbitrage. 

726
00:36:19,000 --> 00:36:21,700
And then, then you go back to 
the core competency lotion, 

727
00:36:21,800 --> 00:36:24,900
right? 
Of certain things, what I mean, 

728
00:36:25,100 --> 00:36:28,300
this is the question. 
I asked a lot of lot of like 

729
00:36:28,300 --> 00:36:30,300
friends and colleagues and 
clients. 

730
00:36:30,600 --> 00:36:34,100
What is the IP in some of these 
things that we are doing and 

731
00:36:34,100 --> 00:36:37,700
everything Analytics? 
If it's three line of Sky kit 

732
00:36:39,000 --> 00:36:40,800
and then we are going to apply 
algorithm. 

733
00:36:40,800 --> 00:36:43,100
There is completely, definitely 
not the IP right? 

734
00:36:43,100 --> 00:36:45,600
But data access and how you put 
it together and how you 

735
00:36:45,600 --> 00:36:48,100
organized in that I think are 
even the system that IP is 

736
00:36:48,100 --> 00:36:50,800
absolutely there. 
And that I think is important, 

737
00:36:50,800 --> 00:36:53,700
but it is is a little bit, very 
contextual to the problem and 

738
00:36:53,700 --> 00:36:56,900
how you are doing it. 
The very big IP I think in an 

739
00:36:56,900 --> 00:37:01,700
organization is actually how you
incorporate and drive the change

740
00:37:01,700 --> 00:37:05,300
and try to decision-making the 
cultural, change the process. 

741
00:37:05,700 --> 00:37:10,100
That is where a lot of the ipas.
And so if you start thinking 

742
00:37:10,100 --> 00:37:12,700
about from a, the core 
competence model, if this is 

743
00:37:12,700 --> 00:37:15,900
what kind of where my core IP is
I'm going to concentrate on that

744
00:37:16,000 --> 00:37:20,200
one, is it absolutely makes 
sense to take the rest of it and

745
00:37:21,000 --> 00:37:24,100
go with more specialized people 
who just do that, right? 

746
00:37:24,200 --> 00:37:26,700
So that's that's an Arbitrage 
opportunity from an organization

747
00:37:26,700 --> 00:37:28,900
perspective. 
And so it's not better or worse.

748
00:37:28,900 --> 00:37:33,400
It's like thinking about where 
you can do it and it is with it,

749
00:37:33,400 --> 00:37:36,400
which it's where you get scale. 
Scale economics. 

750
00:37:36,400 --> 00:37:39,200
I'm going to go and drive that. 
So that's going to Vector from 

751
00:37:39,200 --> 00:37:40,600
1. 
We can do better for more. 

752
00:37:40,900 --> 00:37:44,100
I think, cost and speed 
perspective and both are very 

753
00:37:44,100 --> 00:37:45,900
important. 
In terms of the quality 

754
00:37:45,900 --> 00:37:47,400
perspective. 
We've already talked about, 

755
00:37:47,400 --> 00:37:50,900
kaggle like better from a better
rabbit runs from a political 

756
00:37:50,900 --> 00:37:56,200
perspective, most problems 90% 
or 85% to 95% won't matter, 

757
00:37:56,200 --> 00:37:57,900
right? 
It will be the last mile of that

758
00:37:57,900 --> 00:37:59,800
cultural change and everything. 
That's where the things are 

759
00:37:59,800 --> 00:38:01,500
going to happen. 
I don't think we novel tries 

760
00:38:01,500 --> 00:38:03,700
that out and a lot of them 
doesn't matter. 

761
00:38:04,600 --> 00:38:07,200
And so in that way, so, yeah, 
that's that's kind. 

762
00:38:07,200 --> 00:38:09,800
That's kind of where ever you. 
There's a Professional Services.

763
00:38:09,800 --> 00:38:13,700
Were you see a lot of the value,
a 1, and the other other aspect 

764
00:38:13,700 --> 00:38:14,800
of it. 
In product companies. 

765
00:38:14,800 --> 00:38:18,100
They start thinking about what 
is what will be in platform and 

766
00:38:18,100 --> 00:38:21,000
what will be like the ecosystem 
around my platform. 

767
00:38:21,100 --> 00:38:23,200
And that's how they make 
decisions in my mind, which is 

768
00:38:23,200 --> 00:38:25,500
like, okay, in platform. 
I'm going to do it internally 

769
00:38:25,500 --> 00:38:27,200
and around the platform. 
There's a lot of other things 

770
00:38:27,200 --> 00:38:29,100
that will happen. 
Lll kind of created some sort of

771
00:38:29,100 --> 00:38:31,600
a partner ecosystem. 
I'll open a lot of things you 

772
00:38:31,600 --> 00:38:35,000
can do. 
So, I think there's a no The 

773
00:38:35,000 --> 00:38:36,900
rather than thinking about 
Arbitrage, I think of our core 

774
00:38:36,900 --> 00:38:39,600
competency and where the value 
or what is the, what is the, 

775
00:38:39,600 --> 00:38:41,900
what is the IP that cannot be 
replicated or no one else is 

776
00:38:41,900 --> 00:38:45,200
going to build you, how you want
to deploy in your organization, 

777
00:38:45,200 --> 00:38:48,400
within your own cultural nuances
and everything, you're operating

778
00:38:48,400 --> 00:38:50,700
in 15 different countries. 
And you dip, this in that one 

779
00:38:50,700 --> 00:38:52,500
else is going to bother about 
creating that you have to figure

780
00:38:52,500 --> 00:38:53,500
out. 
Figure that out yourself. 

781
00:38:53,700 --> 00:38:55,200
Everyone else. 
Someone else probably has done, 

782
00:38:55,200 --> 00:38:57,200
it came five times, 15 times, 30
times. 

783
00:38:58,200 --> 00:39:00,900
You can probably leverage some 
of it, if it's not kind of core 

784
00:39:00,900 --> 00:39:02,400
part of your core product in 
some ways. 

785
00:39:02,400 --> 00:39:05,100
That's, that's, I think of all 
the ways to think about it, as 

786
00:39:05,100 --> 00:39:07,100
well, as IP. 
Where am I? 

787
00:39:07,100 --> 00:39:10,200
Not so much Arbitrage from a 
cost in business perspective, 

788
00:39:10,300 --> 00:39:12,900
but it comes up speed and choir 
and decision, quality 

789
00:39:12,900 --> 00:39:14,100
perspective and somewhere over 
there. 

790
00:39:14,100 --> 00:39:16,200
I think you mentioned about 
like, how do I deploy it in 

791
00:39:16,200 --> 00:39:19,700
different countries and so on. 
So I think since you run a very 

792
00:39:19,700 --> 00:39:22,600
large Global organization, I 
think you would have worked with

793
00:39:22,700 --> 00:39:24,800
organizations pretty much 
everywhere. 

794
00:39:25,100 --> 00:39:27,800
So how was the start of the 
uptake of Analytics? 

795
00:39:27,900 --> 00:39:30,700
It assigns sort of varied across
geography. 

796
00:39:30,700 --> 00:39:33,900
So the last 10 years and how was
it in Clayton 2010. 

797
00:39:33,900 --> 00:39:36,200
How is it now? 
Like, how does Europe differ 

798
00:39:36,200 --> 00:39:37,500
from us? 
And so on? 

799
00:39:38,200 --> 00:39:41,100
I mean, it's actually really 
interesting larger companies. 

800
00:39:42,500 --> 00:39:45,100
The correlation is a very 
simple, one who has invested. 

801
00:39:45,200 --> 00:39:47,700
Whereas the Erp investment 
Gohan, and in which order 

802
00:39:47,700 --> 00:39:50,800
attempt on. 
Because once you put the process

803
00:39:50,800 --> 00:39:54,100
part of the data together, 
right, then you need to figure 

804
00:39:54,100 --> 00:39:56,200
out how to make the Caribbean, 
your view figure out the 

805
00:39:56,200 --> 00:39:57,300
process. 
Now you make my processing 

806
00:39:57,300 --> 00:39:58,900
Wireless. 
Watch that all. 

807
00:39:58,900 --> 00:40:01,300
If you want to make a broader 
decision, making intelligent 

808
00:40:01,300 --> 00:40:03,900
what I will kind of wherever you
need to have some of the basic 

809
00:40:03,900 --> 00:40:08,300
stuff in Plumbing in place. 
So, which is why you see us, as 

810
00:40:08,300 --> 00:40:10,600
a massive Market, your app as, 
like, you were senior brother 

811
00:40:10,600 --> 00:40:13,300
Master market and everything. 
So I so that's kind of like 

812
00:40:13,500 --> 00:40:16,000
that's very easy. 
High level things to look at, 

813
00:40:16,000 --> 00:40:20,100
but what I see in emerging 
economies and emerging economies

814
00:40:20,100 --> 00:40:23,400
and especially if you do working
Gates, it's completely crazy. 

815
00:40:23,400 --> 00:40:27,300
I mean you have been I was at a 
conference in Hong Kong a few 

816
00:40:27,300 --> 00:40:29,100
years ago. 
Insurance companies in Hong Kong

817
00:40:29,200 --> 00:40:30,200
to Mainland China. 
Point out of here. 

818
00:40:30,200 --> 00:40:32,200
With that example. 
We had some Mainland Chinese 

819
00:40:32,200 --> 00:40:35,600
companies presenting and then we
had companies from like 

820
00:40:35,600 --> 00:40:37,800
Australia presenting. 
Right? 

821
00:40:37,800 --> 00:40:40,200
So like the whole Japan 
Australia, you can see like 

822
00:40:40,200 --> 00:40:43,900
entire ecosystem of that, that's
fascinating is the use cases 

823
00:40:43,900 --> 00:40:49,300
were like like Star Trek, kind 
of like, it's like Matrix and 

824
00:40:49,300 --> 00:40:52,300
it's like 1970s. 
That's, I mean, it was that 

825
00:40:52,300 --> 00:40:54,400
stock and of some of those 
things that they were. 

826
00:40:54,400 --> 00:40:55,800
We're talking about. 
We are talking about an 

827
00:40:55,800 --> 00:41:02,000
insurance company that does. 
They recruit 100,000 agents into

828
00:41:02,000 --> 00:41:05,400
the network in an automated way 
through video chat through IAI, 

829
00:41:06,200 --> 00:41:09,700
right? 
I'm doing that right now. 

830
00:41:10,400 --> 00:41:12,700
These guys are talking about all
of these kind of things and the 

831
00:41:12,700 --> 00:41:15,000
next guy is talking about 
adjourn waterfall. 

832
00:41:15,100 --> 00:41:17,300
I like the children. 
Go to school to learn what all 

833
00:41:17,300 --> 00:41:20,900
the developers schoolwork. 
I like, for preventing whatever.

834
00:41:21,000 --> 00:41:22,400
Like, some customers from 
leaving. 

835
00:41:22,800 --> 00:41:23,300
I will I. 
Okay. 

836
00:41:23,300 --> 00:41:26,700
There's a this 1980. 
Someone attempted it in some 

837
00:41:26,700 --> 00:41:29,200
ways. 
And 2010, everyone has some 

838
00:41:29,200 --> 00:41:31,800
version of it but these guys are
doing something else altogether.

839
00:41:32,000 --> 00:41:36,000
So so and there is a little bit 
of the cultural thing about a, I

840
00:41:36,000 --> 00:41:38,800
think in China, which is, I 
think people appreciate it. 

841
00:41:38,800 --> 00:41:41,400
But if you until you see it and 
this is the first time I saw it,

842
00:41:41,500 --> 00:41:43,200
it's like, okay, they are in the
operating in a completely 

843
00:41:43,200 --> 00:41:45,300
different world altogether there
right now. 

844
00:41:45,300 --> 00:41:47,600
So they have kind of Leap Frog. 
The point I'm trying to make is 

845
00:41:47,800 --> 00:41:49,500
there are there are jogger, 
fees, which I'm going to just 

846
00:41:49,500 --> 00:41:51,200
Leap Frog. 
I think India. 

847
00:41:51,200 --> 00:41:53,300
India probably also isn't the 
kind of the same situation, 

848
00:41:53,300 --> 00:41:57,600
scalable whole notion around of 
a national Focus around it. 

849
00:41:58,200 --> 00:42:01,200
Like you kind of take, do you 
like the Erp World goes to 

850
00:42:01,200 --> 00:42:04,700
properly a cloud Erp World, any 
kind of, so very quickly, you 

851
00:42:04,800 --> 00:42:07,800
all the Legacy things don't, 
even matter, your regulations in

852
00:42:07,800 --> 00:42:10,300
your perspectives and data 
privacy, probably has something 

853
00:42:10,300 --> 00:42:14,000
to do with it and just the focus
around like an entire ecosystem.

854
00:42:14,300 --> 00:42:16,900
I think, for a few geographies, 
where a bunch of these things 

855
00:42:16,900 --> 00:42:19,100
will fall in place and like in 
Mobile phones. 

856
00:42:19,100 --> 00:42:22,700
Are you think about when tax and
mpesa and Kenya versus risk? 

857
00:42:22,800 --> 00:42:25,800
We are still figuring out a lot 
of the advanced economies, how 

858
00:42:25,800 --> 00:42:27,500
to kind of make some of the 
context work. 

859
00:42:27,900 --> 00:42:30,000
It's kind of the same thing. 
They'll be a leapfrog effect on 

860
00:42:30,000 --> 00:42:32,100
analytics and AI in certain by 
inserting. 

861
00:42:32,100 --> 00:42:33,900
They got emerging economies 
which looks very very 

862
00:42:33,900 --> 00:42:35,600
fascinating to see. 
I mean Indian education. 

863
00:42:35,600 --> 00:42:39,200
I think I can see that so some 
of that stuff will happen, but 

864
00:42:39,200 --> 00:42:41,400
the Western world has been 
essentially followed a very 

865
00:42:41,400 --> 00:42:42,900
simple thing about where they 
are. 

866
00:42:42,900 --> 00:42:45,600
We dollars have been spent and 
if I've spend those dollars, 

867
00:42:45,700 --> 00:42:47,800
they are latex, will probably 
follow and that's what we have 

868
00:42:47,800 --> 00:42:51,100
been fundamentally a lot of the 
companies have followed. 

869
00:42:51,900 --> 00:42:52,700
Okay. 
I'm rich. 

870
00:42:52,700 --> 00:42:54,300
This has been a fascinating 
conversation. 

871
00:42:54,300 --> 00:42:57,700
I mean, I love our conversation 
over the last 45 minutes. 

872
00:42:57,800 --> 00:43:00,500
So in closing, what do you think
about David? 

873
00:43:00,500 --> 00:43:03,100
Do you have any sort of final 
things to say about what we see 

874
00:43:03,100 --> 00:43:05,700
as the hype and data science 
that's going on right now? 

875
00:43:05,700 --> 00:43:10,200
How it's going to play out. 
I mean because if cycles for a 

876
00:43:10,200 --> 00:43:15,200
reason, right? 
So for folks, who've grown up 

877
00:43:15,300 --> 00:43:18,600
like, you started their careers 
in like late 90s, like me. 

878
00:43:18,600 --> 00:43:20,800
I mean, we saw the internet hype
cycle in some ways, right. 

879
00:43:20,800 --> 00:43:22,800
I mean, there was like a pasta 
and I kind of started just 

880
00:43:22,800 --> 00:43:27,700
before the last of the.com and 
there was a little bit of ago, 

881
00:43:27,900 --> 00:43:29,900
Wide area. 
But because we had invested so 

882
00:43:29,900 --> 00:43:32,200
much and we are done so much 
great things at that point in 

883
00:43:32,200 --> 00:43:34,400
time. 
You just suddenly saw all kinds 

884
00:43:34,400 --> 00:43:36,200
of productivity all kinds of 
cool things happening. 

885
00:43:36,400 --> 00:43:41,000
So so 11 that yeah, it will 
probably kind of there'll be a 

886
00:43:41,000 --> 00:43:43,700
little bit of a disillusionment 
at some point and then it kind 

887
00:43:43,700 --> 00:43:47,300
of scales up or you can you can 
argue this has been going on and

888
00:43:47,300 --> 00:43:49,100
last eight years, nine years 
were a little bit of her 

889
00:43:49,100 --> 00:43:51,800
disillusionment and now the 
poor, the productivity 

890
00:43:51,800 --> 00:43:54,400
productivity platter will start 
start in a very different way. 

891
00:43:54,700 --> 00:43:57,700
So I don't know what the right 
answer is. 

892
00:43:57,800 --> 00:44:01,400
Going to be, but one thing is 
there, the high definitely leads

893
00:44:01,400 --> 00:44:03,400
somewhere at some of the high 
points of the, some of the 

894
00:44:03,400 --> 00:44:06,400
things we've been talking about 
leads to more General 

895
00:44:06,400 --> 00:44:08,800
acceptance. 
And that I think is a given, 

896
00:44:08,900 --> 00:44:12,100
whether it will be, and I think 
it would be hugely it and I 

897
00:44:12,100 --> 00:44:13,900
said, I think Arthur Clarke to 
see. 

898
00:44:13,900 --> 00:44:16,600
Clark said, I think, right. 
I mean you are, you normally are

899
00:44:16,600 --> 00:44:20,000
wrong about the future in the, 
you overestimate it in the short

900
00:44:20,000 --> 00:44:21,800
term and underestimated in 
long-term. 

901
00:44:22,200 --> 00:44:24,800
This is one of those. 
This is one of those friends. 

902
00:44:24,800 --> 00:44:27,000
And one of those things where I 
think they're absolutely 

903
00:44:27,000 --> 00:44:29,400
underestimated. 
Long term and the hype and 

904
00:44:29,400 --> 00:44:31,400
everything is because we are 
trying to overestimate in the in

905
00:44:31,400 --> 00:44:33,400
the in the short term. 
I think that's bull of that 

906
00:44:33,400 --> 00:44:35,700
stuff is happening at the same 
time, which is fascinating to 

907
00:44:35,700 --> 00:44:38,600
see. 
And and and part of the hike 

908
00:44:38,600 --> 00:44:42,600
will go away because this is the
business analyst of future is 

909
00:44:42,600 --> 00:44:45,100
the data science of today. 
So everyone is going to be a 

910
00:44:45,100 --> 00:44:48,100
data scientist or a business 
analyst of some sort, which is 

911
00:44:48,100 --> 00:44:50,300
over you call it when they dig 
become their, you call them data

912
00:44:50,300 --> 00:44:52,300
scientist or you call the 
business analyst to the new 

913
00:44:52,300 --> 00:44:55,900
business, that is 2.0. 
So, yes, it will be a lot more 

914
00:44:55,900 --> 00:44:58,000
pervasive, and once it to learn 
more, Whatever. 

915
00:44:58,000 --> 00:45:00,500
It is just fear of a matter of 
fact that it will happen. 

916
00:45:00,500 --> 00:45:02,100
So that that one is definitely 
coming. 

917
00:45:02,100 --> 00:45:06,500
And I mean, in some ways it is, 
I feel I'm like, less than and 

918
00:45:06,600 --> 00:45:09,800
super excited to be just see how
everything unfolds for someone 

919
00:45:09,800 --> 00:45:12,100
like Avenue. 
And I were trying aware, just in

920
00:45:12,100 --> 00:45:13,500
some ways. 
We have kind of given a careers 

921
00:45:13,500 --> 00:45:15,600
to this whole thing and it's 
been evolving in front of us. 

922
00:45:37,500 --> 00:45:39,500
Thank you for listening to data.
Shut. 

923
00:45:40,100 --> 00:45:43,600
If you like this show, please 
leave a comment, share and 

924
00:45:43,600 --> 00:45:46,800
subscribe to the podcast. 
You can find this podcast on 

925
00:45:46,800 --> 00:45:50,500
Apple podcast Spotify or 
wherever else you go to. 

926
00:45:50,700 --> 00:45:53,800
Your podcasts. 
Once again, this is Karthik 

927
00:45:53,800 --> 00:45:55,300
signing deaf. 
Thank you.