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Now, the role of be either for 
us evolve into not just 

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providing data to essentially 
what I call a identifying the 

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the needle in the haystack, 
right? 

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When you're given a data science
is part of an engineering team 

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or as part of a an end to end 
product, where you are model is 

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embedded. 
You're solving for actions, 

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whereas it is in bi or solid 
footballs gravity, and I also 

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keep encouraging the bi 
developers in my team. 

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They don't call yourself a 
microfiber Jeepers. 

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Don't call yourself a cab. 
Don't call yourself about here. 

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We have our. 
So you are essentially a bi. 

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Hello and welcome to data. 
Shatter the podcast on all 

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things data. 
This podcast is a series of 

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conversations with experts and 
Industry leaders data. 

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And each week. 
We aim to unpack a different 

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compartment of the data 
suitcase. 

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I am your host, Karthik chassis 
that I want blogger newspaper, 

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columnist book, author, and a 
former data Etc conceited. 

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I currently head analytics and 
business intelligence for 

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delivery. 
One of India's largest logistics

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companies. 
You can follow me on Twitter at 

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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. 0e n t hu be a.com. 

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All opinions expressed in this 
podcast, belong to eat and my 

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podcast this and I do not 
reflect the views of any 

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organizations. 
We might be Associated, nothing 

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discussing his podcast, should 
be taken as Financial or legal 

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address. 
I would be lying. 

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If I were to say that business 
intelligence does not have a 

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branding problem. 
What if period of time, the term

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has evolved to mean, a very 
specific kind of software 

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engineering, which involves 
connecting database. 

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Visualization tools. 
However, business intelligence 

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is not necessarily limited to 
that my teammate delivery. 

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For example, does it fair amount
of statistics and over and 

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modeling and machine learning 
and make the odd report? 

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And we are called the business 
intelligence team. 

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And originally if you think 
about it, business intelligence 

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was all about making business 
decisions through the 

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intelligent use of data. 
So my guest today also works in 

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business. 
Intelligence is name is Balaji 

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Cooper Swami. 
And he's director of business, 

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intelligence products and data 
infrastructure at YouTube. 

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He's had a long career in data 
and analytics and decision. 

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Support having worked at Captain
one McKinsey City Mo Rocca of 

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Commerce and lows. 
So you're the director of 

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bi-products and data infested. 
What exactly does it mean? 

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What do you do? 
Yeah. 

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So, one of my core 
responsibilities is to kind of 

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look at in the diverse sources 
of data that there is and 

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connect it to the needs of the 
business and needs for 

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decision-making that various 
stakeholders have beer in terms 

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of partner management of weed. 
In terms of advertisers 

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management. 
My job is to connect the data to

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actions that The they want to 
take based on the based on 

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information that they have to 
ask about. 

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Generic question. 
What exactly do you mean by bi? 

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Because I mean just if you think
about it, all the terms related 

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to data, if you think about it, 
it means different things to 

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different people. 
You say data sends as 10 people 

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about data science. 
Until you did ten different 

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sure. 
Yeah. 

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So what is your definition of 
business intelligence? 

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Yeah, so when I think of 
business intelligence, So there 

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is a saying we call right 
data's, worth pennies and 

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actions are good dollars. 
So, I believe that the role of 

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business intelligence in an 
organization is to convert those

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pennies into Dollars, which 
means that there's a ton of 

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data, especially when you're in 
a business, which is digitally 

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focused. 
There's just so much of user 

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data being created, so much of 
operational data that is being 

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created. 
How do you kind of take that and

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an influence the decisions and 
actions? 

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Since that the that variety of 
leaders, want to take across the

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organization V8 starting from 
say, an analyst where I see to a

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manager. 
We could be a product manager 

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with an icy. 
You're all the way up. 

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We'll see. 
So that I believe the job of the

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bi is to make their lives Easier
by providing insights and 

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actions, that matter, to them 
based on how they can to solve 

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for their. 
Okay. 

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Ours and to solve for their 
results. 

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Okay, and what do you mean, you 
think the market? 

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Things VA does this tool? 
So my I think that doesn't mean 

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bi is relatively old term. 
I maybe there's a probably need 

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to reinvent the terminology of 
what you call BI, but but be I 

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started off and like, older days
where sap and Oracle it 

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basically meant data warehousing
and Reporting, right? 

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00:05:18,300 --> 00:05:21,200
Okay. 
Yeah, and that is what generally

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people think bi is essentially a
job of just collecting all the 

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information and The spitting it 
out. 

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Whereas, whereas I think with 
the with number one, just the 

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volume of data exploding it. 
It would have been fine. 

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If you had like five metrics 
that you want to look at. 

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And that's all you needed to do.
But whether demand for data on 

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the hunger for data increasing 
across across the across the 

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organization, as well as the 
volume of data, also 

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continuously increasing. 
Doing the same thing, 

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essentially is becomes very 
pointless exercise because there

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is too much data to do any 
analysis. 

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So, Now the role of B A. 
Therefore has evolved into not 

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just providing data to 
essentially what I call a 

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identifying the the needle in 
the haystack, right? 

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There's a big case type of data,
so much. 

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That aesthetic that how do you 
identify the needle? 

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That matters to you? 
But how do you identify the most

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important aspects of the data 
sets that matter to you? 

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So that you that can be? 
That can influence your 

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decision-making on a daily 
basis. 

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So that is how I believe it 
started off. 

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As a hey, I'm going to collect 
all of those information from a 

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variety of sources into a data 
warehousing system, which is on 

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opened some reports. 
Yep. 

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To what I believe now is is 
turning that and using using 

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algorithms and statistical 
models to find out what's 

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actually going on in the inside 
the data. 

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And we only surfacing data that 
matters only surfacing insights 

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that matters. 
Okay. 

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Thanks. 
Okay, I think Our definition of 

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bi is broadly sort of aligned 
with my definition of bi and 

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what we in my company also 
believe that he is supposed to 

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do which is like sort of help 
Drive data into actions, help 

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help, help the business. 
With the sort of intelligence 

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that can be derived from the 
data to put it in a very simple 

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manner. 
However, I mean, what I find is 

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that I have a bit of herb 
marketing problem in. 

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This is, like, recently. 
I was hiring, we very quickly 

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agreed on the sell very quickly 
and things like that, but the 

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guy was like, can you I mean 
something other than bi because 

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in the market there is a bit of 
a Down Market, sort of a field 

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to the word GI and this is 
something that I've heard from 

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other people as well. 
Not necessarily people have been

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hiring, but others have also 
mentioned that like, because bi 

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has gotten sort of become 
synonymous with people who, like

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they'll ask, how have you worked
on that Tableau or quickly 

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knowing these tools knowing how 
to build the pipelines and 

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things like that. 
So so I mean I find that The 

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chicken wing ba has got a bit of
a marketing problem. 

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I don't know if you have faced 
that as well. 

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Yes, and yes, and no. 
No, I think it depends about it 

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depends. 
But thinking about how the 

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vision for the, bi is usually 
pitched to. 

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And I think I really thought 
when you thought about bi again,

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right? 
The historical job, that'll be, 

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I professional used to do this. 
Is essentially, just a data 

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collecting Under The Dumping 
job, which may have, which is 

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exactly. 
As you said, right, like a take 

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build all these data pipelines 
and put it in this, which I 

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believe is immensely useful 
guidance cement Foundation, that

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is required to essentially do 
anything with the data you want 

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to add. 
But what from a from. 

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But I think where the the role 
formal rule for how it is 

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evolving, now it is thinking 
about how to stink like a 

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product manager, right? 
So I feel it is more. 

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Or actually a product rule 
versus are versus a, just a bi 

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room, right? 
So go. 

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So I also think that there are 
very specific skill sets. 

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Rather. 
There are parting about three, 

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four layers of skill sets that 
are required in order to be a 

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well-rounded bi to form a 
well-rounded bi team, right? 

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I don't think I can probably 
have a one person who does 

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everything completely. 
But what I think, two things, 

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Fit like a product is what is 
what will Elevate the marketing 

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for BF. 
But I don't think people look at

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it as a product because the 
think it's still a data dump, 

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right? 
And that is historically, it's 

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been associated with that fact, 
but when you start positioning 

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yourself as a data products 
team, hey, where your job is to 

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build scalable Data Solutions. 
I mean, think about it from a 

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standpoint, a lot of software as
a service business that has been

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set up and it really A lot of 
software service which have now 

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a i animal tagged to the hair to
their names. 

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In the end. 
They have a huge component of 

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their work is bi. 
It's actually about collecting 

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data from a variety of 
different. 

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Maybe your client, maybe, 
third-party, consolidating them.

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And then starting to surface 
them in different ways or not in

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one form or the other day doing 
a roll of bi. 

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So I think, I think the 
marketing problem exists, but I 

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have found success in 
positioning, it more as you're 

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building. 
A product and out of the product

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is actually understanding the 
use cases and then connecting 

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the data back to that, right at 
the decision that use cases and 

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connecting the data back to 
that. 

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That is how I have found 
success. 

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And but when you actually really
look at when you split the word 

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business intelligence, right? 
That is why I like it is, it is 

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basically just artificial 
intelligence, but you're calling

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it business intelligence. 
But yeah in the end of the day, 

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that's what it is. 
So so I think there is a stigma 

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associated with the term. 
Be II. 

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00:11:02,500 --> 00:11:07,400
But I think we can use the, I 
think we can use the mahout 

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00:11:07,400 --> 00:11:10,100
where the market is moving with 
respect to actually building 

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data products and thinking from 
a product mindset thinking from 

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a design mindset and then 
bringing all the data to solve 

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for those use cases to 
essentially, to essentially show

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to the candidate or shoe to your
team members, how much of a 

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00:11:26,500 --> 00:11:29,700
broad range of skill sets that 
you can, you need to bring to 

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00:11:29,708 --> 00:11:32,100
the table. 
And also, you can pick up why? 

200
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Does rolled and that is probably
the sets you up into a role in 

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00:11:36,500 --> 00:11:39,200
product manager. 
It sets you up into her role as 

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00:11:39,200 --> 00:11:41,800
a data scientist because you 
will be doing transporting, it 

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00:11:41,800 --> 00:11:44,100
will be doing anomaly detection 
that again as follows. 

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00:11:44,500 --> 00:11:46,800
Yep. 
It sets you up as a role of a 

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00:11:46,800 --> 00:11:51,300
user design because you'll be 
actually it's like understanding

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00:11:51,300 --> 00:11:53,000
decision-making. 
In a business is one of the most

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00:11:53,000 --> 00:11:56,800
important aspects it out spit. 
Also it also gets you up to 

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00:11:56,800 --> 00:12:00,400
speed into the most important 
factors in a business, right? 

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00:12:00,400 --> 00:12:02,000
Like think about it. 
I always say that. 

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00:12:02,100 --> 00:12:04,600
One of my first jobs and I went 
no Capital One was to do 

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00:12:04,600 --> 00:12:06,600
monitoring. 
Okay, and, and within six 

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00:12:06,600 --> 00:12:09,700
months, I knew so much about the
business at not even an expert 

213
00:12:09,700 --> 00:12:12,100
in our particular field, New 
because I used to always sit 

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00:12:12,100 --> 00:12:14,000
with the executors. 
I used to understand how they 

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00:12:14,300 --> 00:12:17,100
look at the business. 
And yeah and solving for that 

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00:12:17,100 --> 00:12:21,000
also gives you and bi also helps
you kind of grow and as a 

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00:12:21,000 --> 00:12:22,500
leader. 
Because are you starting to look

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00:12:22,500 --> 00:12:25,000
at the business from top-down 
also from Bottoms Up, of course,

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00:12:25,000 --> 00:12:27,600
so I think it just, it just 
gives you a broad range of skill

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sets and obviously the tech 
skills associated with staying 

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up-to-date with Streaming data 
that's data and then injesting 

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00:12:35,000 --> 00:12:36,800
that. 
So it has a full gamut of 

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00:12:36,800 --> 00:12:40,300
literally what you need to know 
to be an analytic professional. 

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You can Branch off into wherever
you want to go after that. 

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00:12:44,100 --> 00:12:44,900
Yep. 
I see. 

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00:12:44,900 --> 00:12:48,600
So I think is a good time to for
me to kind of read out one of my

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00:12:48,600 --> 00:12:50,500
favorite quotes. 
I think I have, I have a 

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00:12:50,500 --> 00:12:53,100
newsletter on data science which
are where I probably use this 

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00:12:53,100 --> 00:12:56,500
code three times already. 
So this one is it's by this guy 

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00:12:56,500 --> 00:12:59,800
called Kay Kyser Fung. 
He is a Kaiser. 

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00:13:00,000 --> 00:13:03,600
Adjunct professor at NYU. 
I like Columbia or NYU. 

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00:13:03,600 --> 00:13:06,800
So on this block he had written 
this a couple of years back. 

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00:13:06,800 --> 00:13:10,700
'Hey, I'm quoting here. 
The data science Community is 

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guilty of talking down on the 
business intelligence function. 

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00:13:14,500 --> 00:13:18,500
There's a misperception that bi 
is for Less skilled people doing

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00:13:18,500 --> 00:13:21,700
boring things. 
The reality is, there is more 

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science in bi than, in so-called
data science, science of, after 

238
00:13:25,700 --> 00:13:29,100
all is about figuring out. 
Why things are as they are 

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Engineers by contrast, use our 
understanding of science. 

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00:13:32,200 --> 00:13:34,700
He's debating touch. 
Yes, and I think that very, very

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relevant to where be, I was 
thought to be a bias is evolving

242
00:13:40,100 --> 00:13:42,200
or where their vision for being 
needs to be exciting. 

243
00:13:42,200 --> 00:13:44,300
It's very relevant. 
Yeah. 

244
00:13:44,900 --> 00:13:47,600
It's like because you need to 
kind of like if you think about 

245
00:13:47,600 --> 00:13:51,200
it, like it's almost like 
somebody will be like you could 

246
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be building a simple sales 
dashboard inside. 

247
00:13:53,700 --> 00:13:56,600
And somebody could be the 
question that will be asked, is 

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why did sales go up this month? 
Let's say, for example, it's a 

249
00:13:59,508 --> 00:14:01,900
good problem to have. 
But why did the sales goal you 

250
00:14:01,900 --> 00:14:03,300
need? 
Need to be able to intelligently

251
00:14:03,300 --> 00:14:07,300
dig in and the better you can 
present your information right 

252
00:14:07,300 --> 00:14:10,100
in the dashboard. 
And so on rather than let's say 

253
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I mean in some cases I've seen 
like especially when you have 

254
00:14:14,600 --> 00:14:18,800
companies that treat bi as a 
function to be staffed by people

255
00:14:18,800 --> 00:14:21,000
less skilled people. 
What, what do you end up 

256
00:14:21,000 --> 00:14:22,600
happening? 
Is there's a constant back and 

257
00:14:22,600 --> 00:14:25,500
forth between the business and 
the bapi saying that be Isis. 

258
00:14:25,600 --> 00:14:29,100
Sales went up business, has why 
did sales go up? 

259
00:14:29,100 --> 00:14:31,500
Then the be Isis or sales. 
Went up in California? 

260
00:14:31,700 --> 00:14:33,200
Why did? 
Is the go up in Calico? 

261
00:14:33,200 --> 00:14:36,200
So this is back and forth. 
So the I think in some sense is 

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00:14:36,200 --> 00:14:39,400
one job, one job of the bi to 
sort of pre-empting solve this 

263
00:14:39,400 --> 00:14:40,400
problem. 
Right, anticipate? 

264
00:14:40,400 --> 00:14:42,900
What questions are going to come
and then like present the data 

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00:14:42,900 --> 00:14:45,100
in a more intelligent Manner. 
And as you said, like, sort of 

266
00:14:45,300 --> 00:14:48,600
pick up the needles from the 
Haystacks in sort of present, 

267
00:14:49,200 --> 00:14:51,800
what is actually required for 
the manager, rather than, like, 

268
00:14:51,800 --> 00:14:54,800
just just dumping the whole 
thing, because that how it was 

269
00:14:55,300 --> 00:14:57,800
meant to be done. 
Yeah, and a lot of times I have 

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00:14:57,800 --> 00:15:00,500
seen demos of dashboards, even 
from vendors. 

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00:15:00,700 --> 00:15:04,000
They come unto me o hero. 
Is a top sales and then they 

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00:15:04,000 --> 00:15:06,900
give you an inability to drill 
down drill down, drill down, 

273
00:15:06,900 --> 00:15:10,000
drill down and I keep asking 
them the question, but why do I 

274
00:15:10,008 --> 00:15:11,100
have to do that? 
Right? 

275
00:15:11,100 --> 00:15:14,800
Yeah, like when when I know that
the data when I know that the 

276
00:15:14,800 --> 00:15:17,700
gold is lying underneath. 
Why don't you can why can't you 

277
00:15:17,700 --> 00:15:19,500
just bring up the gold upfront 
to me? 

278
00:15:19,500 --> 00:15:22,800
And then why do I actually have 
to do so that is really the role

279
00:15:22,800 --> 00:15:25,500
of Pi? 
So what's your what's your view 

280
00:15:25,500 --> 00:15:29,200
on interactive interactive 
dashboards ended? 

281
00:15:31,500 --> 00:15:33,400
So what do you mean by 
interactive do interactive 

282
00:15:33,400 --> 00:15:36,700
dashboards again, like something
like, I mean like where you as a

283
00:15:36,700 --> 00:15:38,100
so. 
So again the way I look at it 

284
00:15:38,100 --> 00:15:41,900
like dashboards can either be 
like static or interactive. 

285
00:15:41,900 --> 00:15:45,600
Static is like I say this is. 
This is how things are, and I 

286
00:15:45,600 --> 00:15:48,800
control the message and you just
consuming interactive verdict. 

287
00:15:49,000 --> 00:15:51,900
Okay, if you take it for 
yourself and like you can do the

288
00:15:51,900 --> 00:15:53,400
cuts, you can do your drill 
Downs, whatever. 

289
00:15:53,400 --> 00:15:55,200
And you figure out what you want
to do. 

290
00:15:55,500 --> 00:15:56,800
So, what's your yes? 
Yeah. 

291
00:15:56,800 --> 00:15:58,000
Yeah. 
Yeah. 

292
00:15:58,000 --> 00:15:59,000
No, no. 
God encourage. 

293
00:15:59,100 --> 00:16:01,400
So. 
So I think the road Of bi is 

294
00:16:01,400 --> 00:16:04,500
very, this is where it is 
different from data science, 

295
00:16:04,600 --> 00:16:06,200
right? 
Where you think about data 

296
00:16:06,200 --> 00:16:10,800
science and embedded data 
science, the the their majority 

297
00:16:10,800 --> 00:16:14,100
of the time the decisions are 
already made by the algorithms 

298
00:16:14,100 --> 00:16:17,300
are made by the models and you 
are essentially living with the 

299
00:16:18,200 --> 00:16:21,600
the cost of miscalculation 
versatile model does, right. 

300
00:16:21,600 --> 00:16:23,800
So that's part of the life. 
As you're automating that 

301
00:16:23,800 --> 00:16:25,600
decision, yet. 
The role of business 

302
00:16:25,600 --> 00:16:27,900
intelligence do is very 
different from the standpoint 

303
00:16:27,900 --> 00:16:30,400
that there is more or less. 
Usually a human layer. 

304
00:16:30,700 --> 00:16:32,800
For the decision is actually 
made figure. 

305
00:16:33,900 --> 00:16:37,600
And, and to understand that 
human layer and understand. 

306
00:16:38,600 --> 00:16:41,500
How do you solve for that this? 
How do you solve for? 

307
00:16:41,500 --> 00:16:43,900
The right decisions? 
Made by the human layer is very,

308
00:16:43,900 --> 00:16:46,900
very important as part of when 
you think about the entire 

309
00:16:46,900 --> 00:16:48,100
bi-product. 
Yep. 

310
00:16:48,200 --> 00:16:51,200
Why I say that, bringing it to 
the context of the two things 

311
00:16:51,200 --> 00:16:55,300
that you mentioned writer. 
Static forces interactive now 

312
00:16:55,300 --> 00:16:58,700
now depending upon the user, 
depending upon the person who is

313
00:16:58,700 --> 00:17:01,200
looking at the dashboard. 
There are variety of You must be

314
00:17:01,200 --> 00:17:05,099
said that they may want to solve
for and one of that could be. 

315
00:17:05,500 --> 00:17:08,300
Hey, I want to know the what 
happened. 

316
00:17:08,400 --> 00:17:11,300
There are a few folks who are 
like, hey were probably 

317
00:17:14,800 --> 00:17:17,200
Understand the models to 
understand the data science is 

318
00:17:17,200 --> 00:17:19,300
going behind a day. 
Say hey, I trust the information

319
00:17:19,300 --> 00:17:21,599
and then I need to move, but 
there are a few folks who will 

320
00:17:21,599 --> 00:17:25,099
be come back and say, hey maybe 
that job, that pnl there is 

321
00:17:25,099 --> 00:17:28,200
their bonuses dependent on it. 
I cannot take a decision that 

322
00:17:28,200 --> 00:17:32,700
your model is putting out me or 
the what you are showing me as a

323
00:17:33,800 --> 00:17:37,300
as a god-sent answer. 
I have to understand why that is

324
00:17:37,600 --> 00:17:42,600
there because at the end of the 
day, it is why my neck is on the

325
00:17:42,600 --> 00:17:44,300
line for the decisions that I'm 
making. 

326
00:17:44,600 --> 00:17:48,400
Yes, I believe that is an art of
showing the recommendations 

327
00:17:48,400 --> 00:17:51,800
first, but then explaining why 
is it such a recommendation 

328
00:17:51,800 --> 00:17:53,400
happened to an interactive 
manner. 

329
00:17:54,000 --> 00:17:56,700
Is what I think our dashboard 
should solve right interest 

330
00:17:56,700 --> 00:17:58,300
rate. 
It should not be a less of. 

331
00:17:58,800 --> 00:18:01,100
I will give you the word and you
can explore it. 

332
00:18:01,100 --> 00:18:02,400
No, that is not what it should 
be. 

333
00:18:02,700 --> 00:18:06,000
You have to neither. 
Here is the recommendation live 

334
00:18:06,000 --> 00:18:07,500
or die by it. 
The second. 

335
00:18:07,500 --> 00:18:10,200
I think the answer is little bit
between which is to say. 

336
00:18:10,200 --> 00:18:13,800
Here are the recommendations and
then you should immediately, and

337
00:18:13,800 --> 00:18:16,700
then ate and ate It and it 
should be interactive enough to 

338
00:18:16,700 --> 00:18:19,400
take you through. 
Why such a recommendation was 

339
00:18:19,400 --> 00:18:23,100
made and what is that slice of 
the data? 

340
00:18:23,100 --> 00:18:25,700
Because finally, there is a raw 
data Trend that you have 

341
00:18:25,700 --> 00:18:28,300
captured in the business 
intelligence that is subsurface 

342
00:18:28,300 --> 00:18:30,600
top. 
And also it is great to actually

343
00:18:30,600 --> 00:18:34,400
have a human layer of validation
of that to see whether your 

344
00:18:34,400 --> 00:18:36,700
models are acting picking up, 
the right stuff for Mark. 

345
00:18:37,100 --> 00:18:39,900
So I think they're, I think the 
answer is a little bit both, but

346
00:18:39,900 --> 00:18:45,700
instead of interactive from Top 
down and started from the below.

347
00:18:45,700 --> 00:18:48,600
So I believe there's a, there's 
a, there's a marriage that can 

348
00:18:48,600 --> 00:18:51,200
be made to both of us. 
Good. 

349
00:18:51,600 --> 00:18:53,300
So, okay. 
So now we have grabbed the kind 

350
00:18:53,300 --> 00:18:55,100
of father. 
I think you've got the you mean 

351
00:18:55,100 --> 00:18:57,400
comparing bi to data science. 
You've been sort of talking 

352
00:18:57,400 --> 00:18:59,500
about how it's slightly 
different from data science and 

353
00:18:59,500 --> 00:19:02,300
so on. 
So I mean and I think you and I 

354
00:19:03,500 --> 00:19:06,100
sort of believe that there is 
some amount of data science that

355
00:19:06,100 --> 00:19:09,300
goes into bi. 
But I think the unfortunately, 

356
00:19:09,300 --> 00:19:11,400
the ladder Market income 
statements, that doesn't really 

357
00:19:11,400 --> 00:19:14,000
believe that because it is it's 
about to order the. 

358
00:19:14,000 --> 00:19:17,200
So if fix it, if you are a data 
scientist working in deai, how 

359
00:19:17,200 --> 00:19:20,300
does your job apart from 
accounting for that human 

360
00:19:20,300 --> 00:19:21,700
element? 
The that's there in the 

361
00:19:21,708 --> 00:19:23,900
decision-making. 
How does your job sort of vary 

362
00:19:23,900 --> 00:19:26,600
from being a data scientist on 
the, on the product side? 

363
00:19:26,600 --> 00:19:29,900
Where you're sort of, like, 
where your output is going into 

364
00:19:29,900 --> 00:19:31,500
the tech product? 
And things like that? 

365
00:19:32,300 --> 00:19:33,100
Yeah. 
Yeah. 

366
00:19:33,300 --> 00:19:38,200
Now this is very different. 
I think the very different from 

367
00:19:38,200 --> 00:19:41,100
standpoint that when you are, 
when you are given a data 

368
00:19:41,100 --> 00:19:45,500
science as part of an engine. 
Team or as part of a, an end to 

369
00:19:45,500 --> 00:19:47,400
end product, where your model is
embedded. 

370
00:19:47,800 --> 00:19:51,800
You're solving for actions. 
Whereas it bi or solvent for 

371
00:19:51,800 --> 00:19:54,100
causality, right? 
So there are two different kind 

372
00:19:54,100 --> 00:19:58,500
of problems that you're solving.
And there is really I mean, in 

373
00:19:58,500 --> 00:20:01,500
fact if you look at it as you 
said, but of the the big motor 

374
00:20:01,500 --> 00:20:03,800
design, I'm going back. 
What the professor said is 

375
00:20:03,800 --> 00:20:05,700
really understanding causality, 
right? 

376
00:20:05,700 --> 00:20:11,600
I think for us as a business 
intelligence when I think about 

377
00:20:11,600 --> 00:20:13,400
it and if you were Ready. 
Dissenters, right? 

378
00:20:13,400 --> 00:20:15,200
I think there are. 
I see there are three layers, 

379
00:20:15,200 --> 00:20:16,700
right? 
One is getting the raw data. 

380
00:20:16,700 --> 00:20:19,500
Is generally done by your date. 
Somebody who's talented as a 

381
00:20:19,508 --> 00:20:21,600
data engineer. 
Alexander is a sec. 

382
00:20:21,700 --> 00:20:24,600
The second layer is what I call 
the cap. 

383
00:20:24,600 --> 00:20:26,300
The metrics. 
Calculated metal clear. 

384
00:20:26,300 --> 00:20:29,500
This is basically you're 
aligning on cleaning up the 

385
00:20:29,508 --> 00:20:32,600
information and making sure that
the data and the metrics for 

386
00:20:32,600 --> 00:20:35,500
calculating sales, you know that
your poxy optimism for the right

387
00:20:35,500 --> 00:20:37,000
sales number and so on. 
Right? 

388
00:20:37,200 --> 00:20:39,600
Yeah, and then the last next 
layer is actually, I think is a 

389
00:20:39,600 --> 00:20:42,200
model modeling layer, the 
modeling layer there. 

390
00:20:42,400 --> 00:20:45,600
Is is about Trend detection, 
which means that you've got 

391
00:20:45,600 --> 00:20:48,700
understand? 
How to detect Trends, right? 

392
00:20:48,700 --> 00:20:51,100
Because you need to have a 
benchmark need to understand 

393
00:20:51,100 --> 00:20:53,300
where you're comparing that. 
Well, how are you? 

394
00:20:55,200 --> 00:20:57,600
What you're comparing it again? 
So now there could be a variety 

395
00:20:57,600 --> 00:21:00,100
of different benchmarks, right? 
And especially in an environment

396
00:21:00,100 --> 00:21:02,600
that we are in. 
You cannot use your ear over 

397
00:21:02,600 --> 00:21:05,900
here as a as a benchmark. 
Just live with it right out yet.

398
00:21:05,900 --> 00:21:09,300
So you have to think about ways 
in which, you can be smarter 

399
00:21:09,600 --> 00:21:11,900
ways in which you can actually 
use machine learning to 

400
00:21:11,900 --> 00:21:13,700
identify. 
What, what should have been 

401
00:21:13,700 --> 00:21:18,100
expected and what is the trend? 
And, and whether or not The 

402
00:21:18,100 --> 00:21:21,900
actuals are along those Trends 
and and there are also ways in 

403
00:21:21,900 --> 00:21:24,800
which you can triangulate 
through through rules based 

404
00:21:25,000 --> 00:21:26,300
algorithms as well. 
Right? 

405
00:21:26,300 --> 00:21:29,000
Yeah, in terms of triangulating 
through year-over-year, 

406
00:21:29,000 --> 00:21:32,900
quarter-over-quarter benchmarks,
percentile, benchmarks, and 

407
00:21:32,900 --> 00:21:34,800
there are so many ways in which 
you can calculate Trend. 

408
00:21:34,800 --> 00:21:37,100
Don't thinking about, how do we 
do that? 

409
00:21:37,200 --> 00:21:40,300
And calculate the trend itself 
is one of the big on blocks that

410
00:21:40,300 --> 00:21:42,900
you can give to the business 
that that they don't have to do 

411
00:21:42,900 --> 00:21:45,600
it manually. 
Yeah, then there is an aspect of

412
00:21:45,600 --> 00:21:48,700
as you said, right figure. 
An hour depending upon the use 

413
00:21:48,700 --> 00:21:50,400
case that there could be a 
normal use that you want to 

414
00:21:50,408 --> 00:21:53,000
solve for because there is 
always something that is broken,

415
00:21:55,000 --> 00:21:58,600
be it in terms of in terms of 
credits. 

416
00:21:58,600 --> 00:22:01,900
It would be it would be a 
default rates. 

417
00:22:02,300 --> 00:22:06,000
There's usually some kind of a 
classification model angle that 

418
00:22:06,000 --> 00:22:09,100
comes into it in almost every 
aspect of business where you 

419
00:22:09,100 --> 00:22:12,900
have to think not only from from
a linear Trend standpoint where 

420
00:22:12,900 --> 00:22:17,000
you have to catch those catch. 
Those escalations or cats. 

421
00:22:17,200 --> 00:22:20,800
Something that is broken or in 
the retail World, especially the

422
00:22:20,800 --> 00:22:23,300
pandemic. 
We all know that the supply 

423
00:22:23,300 --> 00:22:28,100
chain went through a huge Crunch
and and orders were not being 

424
00:22:28,100 --> 00:22:30,300
able to fulfill and they were 
getting cancelled. 

425
00:22:30,500 --> 00:22:32,600
So solving for cancellation. 
There are different ways in 

426
00:22:32,600 --> 00:22:39,700
which you can apply the models 
to say, how can I prevent 

427
00:22:39,700 --> 00:22:42,800
something from happening one? 
So, let me take a step back. 

428
00:22:42,800 --> 00:22:44,400
Right? 
So one is to say, Hey you cute 

429
00:22:44,400 --> 00:22:45,800
is what happened? 
Yep. 

430
00:22:45,800 --> 00:22:50,600
Second is the cutest. 
What happened, and what? 

431
00:22:50,700 --> 00:22:54,100
I say words of what behind a 
wall, which is yes, a third 

432
00:22:54,100 --> 00:22:57,300
needle, the segment, the most 
important segments, that move 

433
00:22:57,300 --> 00:23:01,300
that method really is your just 
one second. 

434
00:23:01,700 --> 00:23:07,700
Yeah. 
Sorry, it's my alarm going off. 

435
00:23:09,200 --> 00:23:12,900
So just going back and saying, 
hey, there are three aspects of 

436
00:23:12,900 --> 00:23:15,700
it to four aspects of it. 
First is understanding. 

437
00:23:15,700 --> 00:23:18,700
What, what is the data? 
The second is understanding the 

438
00:23:18,700 --> 00:23:21,800
segment of the data that really 
matters that you need to look 

439
00:23:21,800 --> 00:23:24,600
at. 
Yeah, and then the third is what

440
00:23:24,600 --> 00:23:26,800
are the cause of drivers for 
that segment. 

441
00:23:26,800 --> 00:23:29,800
So that is when you think about 
actually good actually thinking 

442
00:23:29,800 --> 00:23:34,200
about not just going drilling 
down what correlating that with 

443
00:23:34,200 --> 00:23:37,200
factors that that you can move. 
Needles that you can move, 

444
00:23:37,200 --> 00:23:39,700
right? 
And then the fourth is, okay. 

445
00:23:39,700 --> 00:23:42,400
How can we get ahead of the 
problem, right? 

446
00:23:42,400 --> 00:23:45,200
Rather than now, even till now 
we have been detected. 

447
00:23:45,200 --> 00:23:47,300
No problem. 
Yeah, it's job is to how do we 

448
00:23:47,300 --> 00:23:50,400
get ahead of the problem and 
solve the problem before it 

449
00:23:50,400 --> 00:23:53,100
actually happens. 
So, so when you think about 

450
00:23:53,100 --> 00:23:58,400
this, Literally, after solving 
for the data, almost everything 

451
00:23:58,400 --> 00:24:01,000
that I explained is generally a 
data scientist job. 

452
00:24:01,100 --> 00:24:03,900
And especially when you're 
thinking at scale, it is not a 

453
00:24:03,900 --> 00:24:06,400
it is not a hundred line axle 
that you're working on. 

454
00:24:06,400 --> 00:24:08,800
You're probably working on 
gigabytes and terabytes of data 

455
00:24:08,800 --> 00:24:12,100
with probably thousands into 
thousands of Collins to look at.

456
00:24:12,200 --> 00:24:14,000
Yep. 
So from that standpoint, it 

457
00:24:14,000 --> 00:24:18,100
cannot be done by with pivot 
tables or anything else. 

458
00:24:18,100 --> 00:24:20,500
It's got to be solid at scale. 
It's got to be solved through 

459
00:24:21,400 --> 00:24:23,000
through Machinery. 
Yeah. 

460
00:24:23,600 --> 00:24:24,900
Okay. 
So coming to machinery. 

461
00:24:25,100 --> 00:24:27,200
So, I mean let's just go a 
little deeper here, right? 

462
00:24:27,200 --> 00:24:29,700
Like so when you are building a 
machine that, again, the 

463
00:24:29,700 --> 00:24:32,400
question is similar in terms of 
like, if you're solving the data

464
00:24:32,400 --> 00:24:35,800
science problem that the, you're
solving for Action, which goes 

465
00:24:35,800 --> 00:24:39,300
into a product versus if you are
solving for a sort of a bi 

466
00:24:39,300 --> 00:24:43,100
problem, where the where you're 
solving for to find out 

467
00:24:43,100 --> 00:24:46,400
causality and things like that. 
How do you, how do they ml 

468
00:24:46,400 --> 00:24:49,000
models themselves change? 
Is there a certain kind of 

469
00:24:49,000 --> 00:24:52,300
models that that are favored for
the data science kind of a thing

470
00:24:52,300 --> 00:24:56,500
and certain kind that you would 
favor for the I mean in terms of

471
00:24:56,500 --> 00:25:01,200
large classes and so on like 
that you favor for the bi K Pi 

472
00:25:01,200 --> 00:25:04,000
world and so on. 
Yeah, I mean it's a very 

473
00:25:04,000 --> 00:25:06,100
interesting thing to think 
about, right? 

474
00:25:06,100 --> 00:25:11,700
Because when you are very when 
you have an and this is how I 

475
00:25:11,700 --> 00:25:14,200
think about it, right? 
And again I go back goes back to

476
00:25:14,200 --> 00:25:17,300
why are we building the model 
for each of those use cases, 

477
00:25:17,300 --> 00:25:17,600
right? 
Yeah. 

478
00:25:17,600 --> 00:25:20,600
Building a model in a product to
actually make a decision. 

479
00:25:20,600 --> 00:25:24,900
So so not to to actually 
implement the decision that the 

480
00:25:25,100 --> 00:25:28,300
Already been, right. 
So so from that standpoint you 

481
00:25:28,300 --> 00:25:33,600
have to, you really have to 
optimize for, you have to 

482
00:25:33,600 --> 00:25:35,600
optimize for the right decision.
Right? 

483
00:25:35,600 --> 00:25:39,600
Which is BS that verses explain 
ability. 

484
00:25:39,600 --> 00:25:42,900
When I think about how much you 
have to explain a model versus 

485
00:25:42,900 --> 00:25:46,200
how right a decision has to be. 
Yeah, you solve for the decision

486
00:25:46,200 --> 00:25:49,700
being right versus experiment in
bi while. 

487
00:25:49,700 --> 00:25:53,300
Many times. 
I have seen that we end up, we 

488
00:25:53,300 --> 00:25:55,500
end up doing classification. 
Models. 

489
00:25:55,500 --> 00:25:57,800
And we end up trying to figure 
this piece out. 

490
00:25:57,800 --> 00:26:03,400
And we and really a NN model 
comes out as a, as a as the best

491
00:26:03,400 --> 00:26:05,400
model yet. 
What we end up going with the 

492
00:26:05,408 --> 00:26:07,600
logistic regression though. 
It is less slightly less 

493
00:26:07,600 --> 00:26:09,400
performant. 
I can actually easily explain 

494
00:26:09,400 --> 00:26:12,800
the factors that is going behind
those logistic regression to the

495
00:26:12,800 --> 00:26:17,600
use to the users of the tool and
and therefore can explain to 

496
00:26:17,600 --> 00:26:21,600
them what happened and that 
enables The role of bi, which is

497
00:26:21,600 --> 00:26:25,400
to actually power and decision 
versus actually just implement, 

498
00:26:25,400 --> 00:26:26,900
or just do the decision. 
Right? 

499
00:26:26,900 --> 00:26:30,600
So, I think that is where I see,
as a big class of difference 

500
00:26:30,600 --> 00:26:34,000
with respect to how a model, how
you should think about a model 

501
00:26:34,100 --> 00:26:36,900
in a product setting, versus how
you should think about a model 

502
00:26:36,900 --> 00:26:39,600
from a, from a cause of a risk, 
especially aspect, you know, 

503
00:26:39,600 --> 00:26:42,300
trying to explain setting. 
That is 1. 

504
00:26:43,100 --> 00:26:47,200
Then the second piece also is 
that when you again, just 

505
00:26:47,200 --> 00:26:49,400
because of the fact that there 
is a human error associated with

506
00:26:49,400 --> 00:26:51,900
that, you got to be very careful
with respect to the input 

507
00:26:51,900 --> 00:26:53,600
drivers that are going in as 
well. 

508
00:26:53,700 --> 00:26:58,500
Right? 
Yeah, not be in a several times 

509
00:26:58,500 --> 00:27:01,900
in a product said embedded model
has seen like people just dump a

510
00:27:01,900 --> 00:27:04,300
huge set of feature set. 
And then then the model. 

511
00:27:04,300 --> 00:27:08,500
Do its job right here. 
You've got a be in a situation 

512
00:27:08,500 --> 00:27:10,700
where you actually have to be 
very careful in thinking about 

513
00:27:10,700 --> 00:27:12,200
what are the factors that 
opinion? 

514
00:27:12,600 --> 00:27:14,700
Because in the end of the day 
you're gonna surface that you 

515
00:27:14,700 --> 00:27:16,300
have to see whether it makes 
business sense. 

516
00:27:16,300 --> 00:27:20,100
So I think there is a, there's 
a, there's a you go to A little 

517
00:27:20,100 --> 00:27:25,500
bit more from a standpoint of 
actually put yourself more and 

518
00:27:25,500 --> 00:27:29,200
empathize with the business user
in a bi V than you have to in a 

519
00:27:29,200 --> 00:27:31,200
world where you're actually 
building a data science model 

520
00:27:31,200 --> 00:27:34,900
for in a product s. 
So the oak is likely cycling 

521
00:27:34,900 --> 00:27:36,700
back sake. 
So you seem to have a bit of an 

522
00:27:36,700 --> 00:27:39,500
unusual profile for somebody 
who's working in bi and so on. 

523
00:27:39,500 --> 00:27:41,100
So, how did you get it to be? 
I ache. 

524
00:27:41,100 --> 00:27:42,700
What, what you? 
What was your journey? 

525
00:27:42,700 --> 00:27:45,100
Like, I know, you were in 
Capital One and Mackenzie and so

526
00:27:45,100 --> 00:27:47,300
on but what is your journey 
like? 

527
00:27:47,800 --> 00:27:51,000
Ya know, I think the interest 
Eating crediting curve, as I 

528
00:27:51,000 --> 00:27:55,500
said, initially started off at 
Capital One, a thing as I told 

529
00:27:55,500 --> 00:27:59,000
you one of my first jobs was to 
essentially own portfolio 

530
00:27:59,000 --> 00:27:59,800
monitoring. 
Right? 

531
00:27:59,800 --> 00:28:03,300
And I and I and I saw the power 
that it had in terms of just 

532
00:28:03,300 --> 00:28:09,000
teaching your business and and 
and almost always I are a 

533
00:28:09,008 --> 00:28:13,500
realized that the whenever I 
could went into new business and

534
00:28:13,500 --> 00:28:16,000
build up seeking a role like 
that because it immediately 

535
00:28:16,000 --> 00:28:17,700
taught me the business very 
quickly, right? 

536
00:28:17,700 --> 00:28:23,500
So so I understood the power of 
And I went into good and then 

537
00:28:24,200 --> 00:28:28,200
and even abstract adults were on
the time and when I went into 

538
00:28:28,200 --> 00:28:33,300
Mackenzie as well, I started 
this, that's when you remember 

539
00:28:33,300 --> 00:28:36,000
Tableau and all of those things 
are actually coming out because 

540
00:28:36,000 --> 00:28:42,400
all the monitoring I did before 
was pulling in from from 

541
00:28:42,400 --> 00:28:45,500
teradata and then, and then 
writing me be scripts to 

542
00:28:45,500 --> 00:28:49,100
automate it in SF in Excel and 
all of that stuff, right? 

543
00:28:49,100 --> 00:28:51,200
And yeah. 
And all that's what I used to do

544
00:28:51,200 --> 00:28:53,700
in the past. 
But then you started seeing the 

545
00:28:53,700 --> 00:28:57,300
tools that will coming in like 
Tableau and other solutions that

546
00:28:57,300 --> 00:29:01,800
were actually the surfacing of. 
And I built a lot of in fact, 

547
00:29:01,800 --> 00:29:03,900
even in the neck. 
And they did a lot of projects 

548
00:29:03,900 --> 00:29:10,800
where, where where I essentially
help think about understanding 

549
00:29:10,800 --> 00:29:13,600
say, Call Center Performance and
things like that through these 

550
00:29:14,000 --> 00:29:15,700
trees through these realization 
to. 

551
00:29:15,700 --> 00:29:21,700
So while believe in never Left 
the concept of doing the bi 

552
00:29:21,700 --> 00:29:24,500
work, but at the same time, it 
was about how do we. 

553
00:29:27,300 --> 00:29:30,800
But I, but it in this 
inner-city, the first phase of 

554
00:29:30,800 --> 00:29:32,400
my life. 
It was about just learning the 

555
00:29:32,400 --> 00:29:33,800
technical skills. 
Improving. 

556
00:29:34,000 --> 00:29:36,800
Hey, getting the data together 
dumping it and figuring out the 

557
00:29:36,800 --> 00:29:39,300
trends and so on. 
Yeah, the second phase of it 

558
00:29:39,400 --> 00:29:42,900
mackynzie was about usually I 
was is the work I did was part 

559
00:29:42,900 --> 00:29:45,400
of her strategy product. 
So it is about how do I how does

560
00:29:45,400 --> 00:29:47,500
that connect with strategy right
corner? 

561
00:29:47,600 --> 00:29:50,200
Center was an example. 
Other one was an example where 

562
00:29:50,500 --> 00:29:56,000
there was literally, no, the 
client did not even have a 

563
00:29:56,000 --> 00:29:58,600
mechanism in which they could 
store an information. 

564
00:29:58,600 --> 00:30:01,800
So we literally had people track
an order and write it down 

565
00:30:01,800 --> 00:30:04,200
manually saying, what's actually
going on and then we fed the 

566
00:30:04,200 --> 00:30:06,800
data it would Excel and then we 
built out a dashboard to 

567
00:30:06,800 --> 00:30:09,000
identify bottlenecks in their 
processes, right? 

568
00:30:09,200 --> 00:30:13,700
So varieties of different ways 
in which we solve for b, i from 

569
00:30:13,700 --> 00:30:17,000
a standpoint, but we realized it
was a banker secret strategy. 

570
00:30:17,300 --> 00:30:21,800
Then the Phase of my life was at
Boomerang Commerce, where it 

571
00:30:21,800 --> 00:30:25,300
became where I realize the 
importance of scale because I 

572
00:30:25,300 --> 00:30:28,400
that point I had to think about 
pricing decision for the entire 

573
00:30:28,400 --> 00:30:31,200
world of retailers, right? 
How do we think about solving 

574
00:30:31,200 --> 00:30:35,000
the problem at scale? 
So so going from Bonnet, going 

575
00:30:35,000 --> 00:30:39,600
from understanding the data 
science of the bi world, to 

576
00:30:39,600 --> 00:30:42,300
understanding the Strategic 
importance of the bi word at 

577
00:30:42,300 --> 00:30:44,800
McKinsey, to understand in the 
technique importance of 

578
00:30:44,800 --> 00:30:47,500
technology and platforms of the 
mechanic at boomerang. 

579
00:30:47,600 --> 00:30:50,700
Commerce is kind of what join 
all the pieces of the puzzle 

580
00:30:50,700 --> 00:30:56,400
together in terms of me thinking
about continuing in the Journey 

581
00:30:56,400 --> 00:30:59,800
of a be in continuing the 
journey in the sphere of Pi. 

582
00:31:00,200 --> 00:31:04,400
Yeah, then lows. 
And right now at Google I'm kind

583
00:31:04,400 --> 00:31:07,300
of putting together the lessons 
that I have learned throughout 

584
00:31:07,300 --> 00:31:10,600
my career and kind of joining 
the technology, the data signs 

585
00:31:10,600 --> 00:31:14,400
as well as the business decision
making to to essentially bring 

586
00:31:14,400 --> 00:31:19,000
the insights and actions forward
versus say, just just Bir 

587
00:31:19,000 --> 00:31:20,900
technology can do by themselves,
right? 

588
00:31:20,900 --> 00:31:23,500
It's kind of putting them 
together and getting a hold of 

589
00:31:23,500 --> 00:31:26,200
getting a sum that is greater 
than the sum, of course. 

590
00:31:26,600 --> 00:31:29,200
So I guess you're sort of some 
sort of. 

591
00:31:29,600 --> 00:31:31,600
You've done some data science, 
work in the past and you've done

592
00:31:31,600 --> 00:31:33,600
some strategy work in the past. 
You've done some dashboard work 

593
00:31:33,600 --> 00:31:35,300
in the past. 
So it's almost like all of this 

594
00:31:35,300 --> 00:31:37,300
has sort of come together for 
you to kind of. 

595
00:31:37,600 --> 00:31:42,500
Now, sort of do your bi work to 
put it. 

596
00:31:42,500 --> 00:31:43,600
Yes. 
Okay. 

597
00:31:43,600 --> 00:31:44,200
Awesome. 
Awesome. 

598
00:31:44,200 --> 00:31:44,800
Awesome. 
Awesome. 

599
00:31:45,200 --> 00:31:50,700
So, I think in some sense, in 
that sense, It's all about bi in

600
00:31:50,700 --> 00:31:52,900
play. 
Yeah, I think you described it. 

601
00:31:52,900 --> 00:31:53,800
Right? 
Like it's up. 

602
00:31:53,900 --> 00:31:58,100
It's a sort of a product product
manager, kind of a thing gets 

603
00:31:58,100 --> 00:32:00,800
on. 
So now, like, I mean, slightly 

604
00:32:00,800 --> 00:32:03,900
switching tracks, like look for 
a lot of people when they think 

605
00:32:03,900 --> 00:32:05,900
of bi. 
They think about it in terms of 

606
00:32:05,900 --> 00:32:08,100
tools. 
So, especially in the market as 

607
00:32:08,100 --> 00:32:10,400
it stands right now. 
If you look at the bi job market

608
00:32:10,400 --> 00:32:14,200
and the other day, I had joined 
the radicand EI and Reddit 

609
00:32:14,200 --> 00:32:16,600
subreddit on pi. 
And there, it was all about 

610
00:32:16,800 --> 00:32:20,700
people were discussing about It 
is Tableau better than power bi 

611
00:32:20,700 --> 00:32:22,700
better than it is all about 
tools. 

612
00:32:22,700 --> 00:32:25,400
And it was about the knowledge 
of to send someone to question 

613
00:32:25,400 --> 00:32:26,600
for. 
You, is like your Dubai. 

614
00:32:26,600 --> 00:32:29,000
I mean, I know you work for 
Google now, so you kind of might

615
00:32:29,000 --> 00:32:32,000
be, you might be considered to 
use mostly Google's tools. 

616
00:32:32,000 --> 00:32:34,600
But like, are you a sort of, 
like, have you been sort of 

617
00:32:34,600 --> 00:32:36,900
constrained by to sorry. 
Do you have a favorite tool? 

618
00:32:36,900 --> 00:32:39,700
Or like, what is your or in 
terms of? 

619
00:32:39,708 --> 00:32:42,900
How do you how do you look at 
this entire bi tools universe? 

620
00:32:46,800 --> 00:32:51,300
It's a, it's an interesting 
question because I think it can 

621
00:32:51,300 --> 00:32:53,700
its, there's no, there's no 
straightforward answer to that, 

622
00:32:53,700 --> 00:32:57,100
right? 
When you're solving for, when 

623
00:32:57,100 --> 00:33:00,400
you're solving for a success 
inside an organization, right? 

624
00:33:01,200 --> 00:33:05,700
You've got to use the tool. 
So thinking about and I have 

625
00:33:05,700 --> 00:33:07,800
done this test at variety of 
different places. 

626
00:33:08,400 --> 00:33:10,500
Bringing a new to bringing a 
favorite tool. 

627
00:33:10,500 --> 00:33:12,600
M. 
Versus. 

628
00:33:13,800 --> 00:33:16,600
Working with a tool that may not
have, all the features that you 

629
00:33:16,600 --> 00:33:19,600
have but has the ecosystem that 
supports it. 

630
00:33:19,800 --> 00:33:21,500
Yep. 
Right. 

631
00:33:21,600 --> 00:33:25,500
And I have found always that the
tool where the equal there is an

632
00:33:25,500 --> 00:33:27,700
already, an established 
ecosystem that supports it 

633
00:33:28,600 --> 00:33:34,000
almost far out performs in terms
of the, in terms of the impact, 

634
00:33:34,000 --> 00:33:37,300
overall value created by the 
thing, don't do it may not be 

635
00:33:37,300 --> 00:33:40,300
the best visual tool, though. 
It may not give as much, you 

636
00:33:40,300 --> 00:33:43,000
expects ability. 
But but the fact is that, if 

637
00:33:43,000 --> 00:33:46,800
there is not Of a platform or a 
set of ecosystem that is 

638
00:33:46,800 --> 00:33:54,100
supporting the, a particular 
tool you will end up becoming 

639
00:33:54,200 --> 00:33:58,400
you'll end up basically having 
something that could shine. 

640
00:33:58,400 --> 00:34:02,000
But there is no, there's no 
Breeze kind of making it Shine, 

641
00:34:02,000 --> 00:34:03,400
right? 
Whereas if somebody even if 

642
00:34:03,400 --> 00:34:06,300
something that may not be a 
shiny, but if there's an entire 

643
00:34:06,300 --> 00:34:08,900
team that is lifting at all. 
I believe that drives Home 

644
00:34:08,900 --> 00:34:13,400
Project having said that, having
said that I have, I have work. 

645
00:34:13,500 --> 00:34:17,400
In technically, I've had now had
the opportunity to work and all 

646
00:34:17,400 --> 00:34:22,100
three different areas right at 
the start of that. 

647
00:34:22,107 --> 00:34:25,300
I work for, we were an aw shop, 
right? 

648
00:34:25,300 --> 00:34:28,900
And then, so we've had an 
opportunity to work with work 

649
00:34:28,900 --> 00:34:30,300
with red shift, and then 
Tableau. 

650
00:34:30,300 --> 00:34:36,900
And then in between we did had 
have a snowflake as well as a 

651
00:34:36,900 --> 00:34:41,100
computational platform. 
So, I think that really really 

652
00:34:41,100 --> 00:34:47,300
worked well with a We have low 
steam was embracing and open 

653
00:34:47,300 --> 00:34:52,199
source, open source Technologies
as much as possible. 

654
00:34:52,500 --> 00:34:55,199
So, that is an area where I got 
an opportunity to work with a 

655
00:34:55,199 --> 00:34:59,200
variety of different open 
source, tools, like like Druid 

656
00:34:59,200 --> 00:35:04,900
and, and Kyle, and so on. 
Then, then the next piece is 

657
00:35:04,900 --> 00:35:08,000
now, now in Google. 
Now, I am in a Google shop. 

658
00:35:08,000 --> 00:35:10,700
Make, oh, yeah. 
So from here, I'm getting, I'm 

659
00:35:10,700 --> 00:35:13,400
getting a sense to work with. 
Not only tools. 

660
00:35:13,500 --> 00:35:15,800
That are available in the 
market, but also a lot of in 

661
00:35:15,800 --> 00:35:19,400
build tools that are available 
for for googlers. 

662
00:35:19,400 --> 00:35:25,400
And yep, and I'm able to get to 
see a lot of the wide variety of

663
00:35:25,400 --> 00:35:27,600
it. 
And honestly, this is where I 

664
00:35:27,600 --> 00:35:30,600
write my opinions are far from 
the standpoint that there is 

665
00:35:30,600 --> 00:35:34,800
actually no best to it's about 
tools that the tools that that 

666
00:35:34,800 --> 00:35:39,200
you are. 
And I also keep encouraging the 

667
00:35:39,200 --> 00:35:41,900
bi developers and my team to say
hey don't call yourself a 

668
00:35:41,900 --> 00:35:43,400
microfiber is a person. 
Don't call yourself. 

669
00:35:43,500 --> 00:35:46,500
The photographer said, don't 
call yourself a power bi person 

670
00:35:46,800 --> 00:35:49,100
you have our. 
So you are essentially a bi 

671
00:35:49,200 --> 00:35:52,800
person that you've got to be in 
a position like similar to a 

672
00:35:53,200 --> 00:35:55,800
software engineer. 
They don't go out and stay. 

673
00:35:55,800 --> 00:36:00,000
Okay, if a C++, I men if it's 
not C++ I'm out and you cannot 

674
00:36:00,000 --> 00:36:04,600
be in the same word as as that. 
So you have to you have to 

675
00:36:04,600 --> 00:36:07,700
essentially think as a bi 
professional and then tools are 

676
00:36:07,700 --> 00:36:09,900
just tools are just the tools, 
right? 

677
00:36:09,900 --> 00:36:11,800
But you are the one who create 
in the value. 

678
00:36:12,900 --> 00:36:15,900
So a Again, tying it, behave in.
I think you are in my definition

679
00:36:15,900 --> 00:36:17,600
of bi is that there's a lot of 
intelligence. 

680
00:36:17,600 --> 00:36:20,300
There's a lot of data science 
that goes into the into the 

681
00:36:20,300 --> 00:36:23,200
computations before let's say 
the final dashboard or equal to 

682
00:36:23,200 --> 00:36:27,800
whatever is developed and so on.
So in general how compatible are

683
00:36:27,800 --> 00:36:33,100
the sort of the tools out there 
in the market to sort of 

684
00:36:33,300 --> 00:36:34,500
building in this kind of 
intelligence. 

685
00:36:34,500 --> 00:36:37,400
I mean, I have a confession that
I have really despite having 

686
00:36:37,400 --> 00:36:40,000
been in sort of broadly, this 
business for about 10 years. 

687
00:36:40,000 --> 00:36:42,300
Now. 
I have not really taken to any 

688
00:36:42,300 --> 00:36:45,800
of these tools and I sort of Do 
everything from first principles

689
00:36:45,800 --> 00:36:47,400
using our or something like 
that? 

690
00:36:47,600 --> 00:36:52,200
But so how how compatible are 
there tools to connect? 

691
00:36:52,300 --> 00:36:54,800
Because my impression of some of
them is that they are just. 

692
00:36:54,800 --> 00:36:56,800
You just need to write the 
pipelines to go from the 

693
00:36:57,100 --> 00:36:59,400
database to the thing. 
I'd like, putting in the 

694
00:36:59,400 --> 00:37:03,100
intelligence is, is, I mean, 
it's very nearly impossible, in 

695
00:37:03,100 --> 00:37:05,400
my opinion to put in 
intelligence into an SQL query, 

696
00:37:05,500 --> 00:37:07,300
some sense. 
So, yeah. 

697
00:37:07,400 --> 00:37:13,100
No, I think there are several, 
there are several ways in which,

698
00:37:13,400 --> 00:37:17,900
I have seen to use these tools 
and insert intelligence. 

699
00:37:19,100 --> 00:37:22,200
So one obviously is to have your
pre calculated data sets. 

700
00:37:22,500 --> 00:37:24,500
We are feeding into your 
Tableau, right, which means 

701
00:37:24,500 --> 00:37:27,200
you've already done, your 
models, your scores have been 

702
00:37:27,200 --> 00:37:31,100
calculated and if that is the 
data that was feeding, right? 

703
00:37:31,100 --> 00:37:34,800
So so if you can create a 
separation of data set and 

704
00:37:34,800 --> 00:37:38,600
secondly and I believe there are
rules are coming up and I think 

705
00:37:38,600 --> 00:37:41,100
Booker and Tableau they're 
creating ways in which you can 

706
00:37:41,100 --> 00:37:44,100
actually do detection prediction
and a Addiction and even 

707
00:37:44,100 --> 00:37:48,700
simulation scenarios inside the 
inside, the pool itself so that 

708
00:37:48,700 --> 00:37:57,600
I think is a so, so the the the 
software's are essentially 

709
00:37:57,600 --> 00:38:02,100
making it easier day by day to 
do to use the tools that they 

710
00:38:02,100 --> 00:38:04,700
have. 
But again, I think about it for 

711
00:38:04,700 --> 00:38:06,400
somebody who wants to start from
first principles. 

712
00:38:06,400 --> 00:38:08,900
It may not give you as much 
flexibility in terms of the 

713
00:38:08,900 --> 00:38:10,400
modeling techniques that you may
want to use. 

714
00:38:10,400 --> 00:38:15,300
It may not give you the 
flexibility in terms of in terms

715
00:38:15,300 --> 00:38:20,100
of precisely the choosing your 
thresholds and so on as you may 

716
00:38:20,100 --> 00:38:24,500
want, but the tools right now 
are actually capable of giving 

717
00:38:24,500 --> 00:38:28,300
you very lightweight ml, in 
terms of just inserting a 

718
00:38:28,300 --> 00:38:30,900
particular algorithm and just 
finding out an auto a score. 

719
00:38:30,900 --> 00:38:34,100
Right? 
There are the there are tools 

720
00:38:34,100 --> 00:38:36,800
that are helping you do that. 
Even if that Tableau even inside

721
00:38:36,800 --> 00:38:40,300
looker and and I'm assuming 
property is well, but 

722
00:38:40,900 --> 00:38:43,900
irrespective of that. 
I think the best mechanism Best 

723
00:38:43,900 --> 00:38:47,100
way to optimize it is actually 
pre-calculated very similar to 

724
00:38:47,100 --> 00:38:50,600
what you would do, like use our 
or Python and run. 

725
00:38:50,600 --> 00:38:53,700
All your models in in your 
computational platform. 

726
00:38:53,800 --> 00:38:58,200
And then use the visualization 
tool mostly as a user Discovery.

727
00:39:00,700 --> 00:39:05,900
You user user experience problem
versus the computational 

728
00:39:05,900 --> 00:39:08,300
platform. 
But if somebody who's not as 

729
00:39:08,300 --> 00:39:11,400
experienced as say, you are an 
experienced data, scientists are

730
00:39:11,400 --> 00:39:14,800
in terms of building that I 
believe there are options. 

731
00:39:14,800 --> 00:39:18,000
There is options for them to do 
something like, wait, inside the

732
00:39:18,000 --> 00:39:19,300
tourists, were somewhere in the 
beginning. 

733
00:39:19,300 --> 00:39:22,100
I think you had mentioned that 
like, he look at bi as a product

734
00:39:22,100 --> 00:39:24,700
where the you have people with 
different sort of skills coming 

735
00:39:24,700 --> 00:39:27,400
together and so on. 
So let's say somebody is looking

736
00:39:27,400 --> 00:39:31,900
to develop is looking to build a
new bi team in some 

737
00:39:31,900 --> 00:39:34,600
organizations. 
Let's assume that for take my 

738
00:39:34,600 --> 00:39:37,600
situation where I I joined six 
months back in until then we 

739
00:39:37,800 --> 00:39:40,800
didn't really have a bi function
and I have been setting it up 

740
00:39:40,800 --> 00:39:43,300
and so on. 
What's your view on integers? 

741
00:39:43,500 --> 00:39:45,900
How do you build a deep? 
What is sort of skill sets to 

742
00:39:45,900 --> 00:39:47,900
look for among the different 
people. 

743
00:39:47,900 --> 00:39:50,500
I am assuming that you need to 
bring in people from different 

744
00:39:50,500 --> 00:39:53,500
sort of backgrounds here because
it's not it's not like you can't

745
00:39:53,500 --> 00:39:57,000
bring in let's say 10 DIY 
professionals to put a team and 

746
00:39:57,000 --> 00:39:58,600
so on. 
I guess you need different skill

747
00:39:58,600 --> 00:40:00,100
set. 
So what are the skill sets that 

748
00:40:00,100 --> 00:40:02,300
you would sort of look for when 
building a team player. 

749
00:40:03,400 --> 00:40:09,100
That's a great question. 
So so I think again just going 

750
00:40:09,100 --> 00:40:12,800
back to their appeal when you 
think about the layers from 

751
00:40:12,800 --> 00:40:14,200
data. 
A Shinto. 

752
00:40:15,400 --> 00:40:18,600
Action generation or inside 
generation, right? 

753
00:40:18,900 --> 00:40:24,100
So I think there is one one team
that is required to own the data

754
00:40:24,200 --> 00:40:26,900
and the logic, okay? 
As 11 basic. 

755
00:40:26,900 --> 00:40:29,400
I think there are four layers 
spheres of ownership. 

756
00:40:29,400 --> 00:40:33,300
One team owns data across or or 
one skill, set the bones data 

757
00:40:33,300 --> 00:40:35,000
and of it is not dripping or 
call it a team. 

758
00:40:35,600 --> 00:40:39,700
There's one skill set that bones
insights at scale which means 

759
00:40:39,700 --> 00:40:44,500
that they are responsible for 
understanding the power inside. 

760
00:40:44,600 --> 00:40:47,500
Generated in the business and 
also producing them at scale 

761
00:40:47,500 --> 00:40:51,400
using data science. 
There is one team that owns 

762
00:40:51,400 --> 00:40:54,400
consumption and user Journey 
rap. 

763
00:40:54,400 --> 00:40:56,500
So say. 
Hey, now this data is there. 

764
00:40:56,600 --> 00:41:01,500
How do you kind of translate all
of that into a into a user 

765
00:41:01,500 --> 00:41:03,900
journey in terms of how whether 
they want to consume it as a 

766
00:41:03,908 --> 00:41:05,900
dashboard that they want to 
consume it as an email? 

767
00:41:05,900 --> 00:41:07,300
There. 
Is it an alert? 

768
00:41:07,300 --> 00:41:10,600
Or is it like just a written 
summary of your insights? 

769
00:41:10,600 --> 00:41:12,200
Whatever it is? 
That how do you want to consume 

770
00:41:12,200 --> 00:41:14,500
it mean? 
So that is for third? 

771
00:41:15,300 --> 00:41:17,400
And then the foreplay, which is 
the canvas that is spreading 

772
00:41:17,400 --> 00:41:19,600
across as what I call the 
product management new. 

773
00:41:20,100 --> 00:41:22,100
Yep. 
They open both the users success

774
00:41:22,200 --> 00:41:25,800
as well as team success. 
Yeah, so so I think these are 

775
00:41:25,800 --> 00:41:31,800
the four buckets of talent pool.
The needs to be brought in to 

776
00:41:31,900 --> 00:41:35,000
solve for a b are. 
Now if you're starting from 

777
00:41:35,000 --> 00:41:39,800
scratch, what is p? 
0 data is P0 that stakeholder 

778
00:41:39,800 --> 00:41:42,500
management is p 0, which is hey,
somebody who kind of understands

779
00:41:42,500 --> 00:41:44,300
the use case, understand solves 
a problem. 

780
00:41:44,600 --> 00:41:47,100
I think that becomes P0 so 
you've got to start with that, 

781
00:41:47,300 --> 00:41:50,600
right? 
Which probably, Many of the 

782
00:41:50,600 --> 00:41:54,000
companies already have invested 
a lot of time and effort and at 

783
00:41:54,000 --> 00:41:56,200
least with the data over the 
course of the tower over course 

784
00:41:56,200 --> 00:41:59,500
of the years, but but I think if
you're starting from scratch, 

785
00:41:59,500 --> 00:42:00,800
that would be the first thing 
you start. 

786
00:42:01,300 --> 00:42:04,700
Then you, then you would think 
about adding in bringing in the 

787
00:42:04,700 --> 00:42:08,600
data scientists to as part of 
your scrum teams to think about.

788
00:42:08,600 --> 00:42:11,700
Hey, how do you now look at now?
The data is being used. 

789
00:42:12,000 --> 00:42:14,000
How do I get one? 
How are they looking? 

790
00:42:14,000 --> 00:42:15,700
However, the business 
stakeholders looking at, at 

791
00:42:15,700 --> 00:42:17,900
what? 
What, what kind of insights are 

792
00:42:17,900 --> 00:42:21,300
they looking for? 
How are the Actions influence. 

793
00:42:21,500 --> 00:42:23,800
How are these houses data 
influence into action, so that 

794
00:42:23,800 --> 00:42:26,900
you can now stream line, the 
process and favorite inside the 

795
00:42:26,900 --> 00:42:31,700
scale, and the third, and I user
experience is very important. 

796
00:42:31,700 --> 00:42:35,300
Again, if it is an external 
facing tool, that would probably

797
00:42:35,300 --> 00:42:38,900
also become as p 0. 
But if you have an internal bi 

798
00:42:38,900 --> 00:42:42,300
setting us up for doing an 
internal decision support, we 

799
00:42:42,300 --> 00:42:47,100
can take the luxury of giving 
them a not perfect you X so that

800
00:42:47,100 --> 00:42:49,100
so that we can continue to build
on work on it. 

801
00:42:49,500 --> 00:42:52,900
As a P2 and if you are thinking 
from a standpoint as a bi 

802
00:42:52,900 --> 00:42:56,000
leader, but this is what we, 
this is how I would think of P0 

803
00:42:56,000 --> 00:42:59,500
P1 and P2, in terms of how I 
would set up the set of the 

804
00:42:59,500 --> 00:43:02,100
organization and bit of the 
organization while also creating

805
00:43:02,100 --> 00:43:05,700
value in the same same page. 
Yeah, and what kind of skills 

806
00:43:05,700 --> 00:43:08,100
would you look for any time in 
the, for the data? 

807
00:43:08,100 --> 00:43:11,200
And logic? 
You'd is it like data Engineers 

808
00:43:11,200 --> 00:43:13,100
or what kind of skills. 
Are you looking for? 

809
00:43:13,500 --> 00:43:15,100
Each a buckets? 
Yeah. 

810
00:43:15,100 --> 00:43:17,100
So let's go into the data piece 
right now. 

811
00:43:17,100 --> 00:43:19,100
Now, as I told you a lot of it 
is dependent. 

812
00:43:19,200 --> 00:43:21,900
Pending upon the platform on 
which you are sitting on, right?

813
00:43:21,900 --> 00:43:25,500
But so so several times. 
I have seen, you actually need 

814
00:43:25,600 --> 00:43:29,700
hardcore data Engineers or if if
that entire engineering combine 

815
00:43:29,700 --> 00:43:32,900
is made, it is become very easy 
through some kind of a platform 

816
00:43:32,900 --> 00:43:34,900
initiative. 
You could go with. 

817
00:43:35,000 --> 00:43:39,500
I mean, I've seen technical 
analyst also grip, be pretty 

818
00:43:39,500 --> 00:43:41,600
good at managing a chronic 
datasets, right? 

819
00:43:42,300 --> 00:43:45,100
Because there are right now we 
have tools which are which we 

820
00:43:45,100 --> 00:43:49,100
just help you subscribe to 
streaming batches of data at 

821
00:43:49,300 --> 00:43:52,100
Easily. 
It helps you build your own QA 

822
00:43:52,100 --> 00:43:54,400
alert. 
It helps you were on your own 

823
00:43:54,400 --> 00:43:57,200
car, bomb data, consistency 
checks, without doing anything, 

824
00:43:57,200 --> 00:44:00,700
all all by, just all with SQL. 
You don't have to do anything. 

825
00:44:00,700 --> 00:44:03,000
You don't need to know any other
language in order to all of this

826
00:44:03,000 --> 00:44:03,800
stuff. 
Yeah. 

827
00:44:03,800 --> 00:44:06,300
So so with that said, there is a
team. 

828
00:44:06,300 --> 00:44:09,400
Now if the tools are not 
available, you will end up 

829
00:44:09,400 --> 00:44:13,800
needing somebody who is who is 
much more well versed with 

830
00:44:14,500 --> 00:44:17,300
working with data platforms 
versus a somewhere. 

831
00:44:17,300 --> 00:44:19,100
All of that is made easy through
the platform. 

832
00:44:19,200 --> 00:44:23,300
The second piece is as you said 
statistical analysis. 

833
00:44:23,300 --> 00:44:29,200
So, this is where I mean, this 
would be your traditional data 

834
00:44:29,200 --> 00:44:34,300
scientist data scientist. 
Rules folks, who have again. 

835
00:44:34,300 --> 00:44:36,800
I would look again, my thing 
here, as you said, right? 

836
00:44:36,800 --> 00:44:39,200
It's different from maybe a 
product data, scientist role, 

837
00:44:39,200 --> 00:44:41,300
who may be well versus an 
engineer. 

838
00:44:41,300 --> 00:44:47,000
The, the team, the person here 
the has to have empathy for the 

839
00:44:47,000 --> 00:44:49,100
business, right? 
And from that standpoint. 

840
00:44:49,500 --> 00:44:55,800
So I again, I see a lot of 
analysts who have behaved in the

841
00:44:55,800 --> 00:44:59,100
past who have experienced in our
who have actually probably got 

842
00:44:59,100 --> 00:45:02,500
under a master's in data science
or analytics seem to be very 

843
00:45:02,500 --> 00:45:05,800
good candidates for this 
particular road because they're 

844
00:45:05,800 --> 00:45:08,400
able to do business analysis. 
Very good. 

845
00:45:08,400 --> 00:45:12,700
Well, and also able to nothing, 
how do I solve that business 

846
00:45:12,700 --> 00:45:18,800
analysis at scale using using 
algorithms efficiently, so that 

847
00:45:18,800 --> 00:45:21,500
would be The second please. 
So user research. 

848
00:45:22,500 --> 00:45:26,000
This I will admit is one of my 
areas where I'm still continuing

849
00:45:26,000 --> 00:45:28,300
to develop and forming my 
thoughts and opinions on. 

850
00:45:28,300 --> 00:45:32,000
But I think you did read a 
statistic that most of them are 

851
00:45:32,000 --> 00:45:36,500
companies between or in 2020. 
The indeed. 

852
00:45:36,500 --> 00:45:39,000
There's they're engineered for 
ux design. 

853
00:45:39,000 --> 00:45:42,500
Ratio was about 20 is to 1, 
whereas now it's about eight is 

854
00:45:42,500 --> 00:45:46,000
21. 
So so, so the focus on user 

855
00:45:46,000 --> 00:45:48,700
journey is obviously a user. 
Ux design will obviously 

856
00:45:48,700 --> 00:45:49,800
increase. 
Every day. 

857
00:45:50,400 --> 00:45:53,500
And, and I am constantly 
thinking about how how do I 

858
00:45:53,500 --> 00:45:55,800
build in that? 
How do I bring in the right? 

859
00:45:56,400 --> 00:45:58,700
Right. 
And I think the talent is 

860
00:45:58,700 --> 00:46:01,800
actually baked is this missing, 
especially in the bi were, 

861
00:46:01,900 --> 00:46:05,300
because again, you have somebody
who had who understands the 

862
00:46:05,300 --> 00:46:08,600
data, as well as the business to
kind of Be Sedated in the 

863
00:46:08,600 --> 00:46:12,100
center. 
So from that perspective, and I 

864
00:46:12,100 --> 00:46:16,400
think somebody who's interested 
in solving for this has a, is, 

865
00:46:16,600 --> 00:46:21,300
will probably be in high demand 
and And, and I'm really looking 

866
00:46:21,300 --> 00:46:23,900
for folks to work with folks who
are in that area. 

867
00:46:24,400 --> 00:46:28,700
And then the last piece is your 
product managers and 

868
00:46:29,000 --> 00:46:33,200
inexperience analytical leaders 
do really well in this because 

869
00:46:33,400 --> 00:46:36,300
they also read because you need 
to kind of span all the way from

870
00:46:36,300 --> 00:46:40,100
data engineering, understanding 
the metrics of the logic, to 

871
00:46:40,100 --> 00:46:42,600
begin sighs, working with the 
stakeholders understanding 

872
00:46:42,700 --> 00:46:46,000
understanding, how the CEO makes
a decision, which is how a 

873
00:46:46,000 --> 00:46:50,200
director makes a decision and 
kind of Geling the all the dots 

874
00:46:50,200 --> 00:46:53,500
together. 
So I have seen several senior 

875
00:46:53,500 --> 00:46:58,500
managers of analytics, or 
managers of analytics with with 

876
00:46:58,500 --> 00:47:03,400
good, with good, high ownership 
of high ownership and 

877
00:47:03,400 --> 00:47:04,200
accountability. 
Right. 

878
00:47:04,200 --> 00:47:07,900
But they really become good 
product managers because they 

879
00:47:07,900 --> 00:47:10,800
also know how to lead some time 
weight and your lead without 

880
00:47:10,800 --> 00:47:16,700
Authority as a product manager. 
And and also they have probably 

881
00:47:16,700 --> 00:47:19,700
done many of these jobs that In 
one form or the other. 

882
00:47:19,700 --> 00:47:22,100
And I think. 
But I like, the clear weakness. 

883
00:47:22,100 --> 00:47:26,100
I see in this entire funnel in 
the talent pool that I see 

884
00:47:26,100 --> 00:47:28,300
outside is at the ux. 
Yep. 

885
00:47:28,500 --> 00:47:31,300
Yep, because I think ux you need
to connect you to have an 

886
00:47:31,300 --> 00:47:34,200
appreciation for data as well. 
They did, in this case, itís, 

887
00:47:34,200 --> 00:47:36,400
not a general. 
You've exigent aux engineer. 

888
00:47:36,400 --> 00:47:39,100
You need to actually have 
somebody who who can also do 

889
00:47:39,100 --> 00:47:40,700
analysis. 
So it's like, if there are be as

890
00:47:40,700 --> 00:47:43,900
water, bu X, that would be 
amazing, but you don't see many 

891
00:47:43,900 --> 00:47:45,500
people switching from vs. 
USA. 

892
00:47:45,500 --> 00:47:47,800
One of them be to data science 
of great engineering. 

893
00:47:48,100 --> 00:47:50,300
Yep. 
We are actually I sort of got 

894
00:47:50,300 --> 00:47:53,700
lucky that I got a former 
front-end engineer who wanted to

895
00:47:53,707 --> 00:47:56,900
move to analytics. 
Exactly right as you. 

896
00:47:56,900 --> 00:47:59,900
But why don't sort of? 
So one other comment that you 

897
00:47:59,900 --> 00:48:02,700
sort of made here and there, 
just like you kept talking about

898
00:48:02,700 --> 00:48:04,900
scrum teams when it when it came
to be. 

899
00:48:04,900 --> 00:48:07,200
I so scrum. 
I mean, I don't exactly know 

900
00:48:07,200 --> 00:48:09,300
what it means, but I know that 
it comes from the agile, 

901
00:48:09,300 --> 00:48:10,900
methodology and things like 
that. 

902
00:48:11,200 --> 00:48:15,000
So what's your take on the usage
of agile in bi? 

903
00:48:15,000 --> 00:48:18,300
And like, I'm assuming you have 
some experience in that Lake? 

904
00:48:18,500 --> 00:48:28,300
The pros and cons of that. 
Yeah. so, So, I firmly believe 

905
00:48:28,300 --> 00:48:33,600
that. 
The benefit that I get some 

906
00:48:33,600 --> 00:48:35,600
modules, predict abilities, 
okay. 

907
00:48:35,600 --> 00:48:38,300
All right, because everybody 
knows what's happening every 

908
00:48:38,300 --> 00:48:40,400
week, this predictability and 
transparency. 

909
00:48:40,700 --> 00:48:43,200
Yeah, right? 
And I think, yeah, I mean you, 

910
00:48:43,900 --> 00:48:47,200
so from a standpoint, everybody 
knows. 

911
00:48:48,100 --> 00:48:51,100
So if you think about what is 
the big detractors of success 

912
00:48:51,100 --> 00:48:54,400
for a bi team one? 
As you keep getting Pink by all 

913
00:48:54,400 --> 00:48:56,000
the users saying. 
When is this going to come at us

914
00:48:56,000 --> 00:48:57,600
that cannot calm? 
Well, I need this. 

915
00:48:57,600 --> 00:48:59,700
I need that, right. 
So there is that whole list of 

916
00:48:59,707 --> 00:49:03,500
priority issue. 
The second the second issue 

917
00:49:03,500 --> 00:49:08,300
comes where, when all of this 
happens you have to, you 

918
00:49:08,300 --> 00:49:10,600
actually have to disturb the 
team who are actually building 

919
00:49:10,600 --> 00:49:13,300
the solution in order to tell 
them. 

920
00:49:13,300 --> 00:49:15,200
Hey Venice is going to come - 
are going to write. 

921
00:49:15,200 --> 00:49:18,100
So that is one. 
This is the third and then and 

922
00:49:18,100 --> 00:49:20,100
may never have seen when you not
had an agile is. 

923
00:49:20,100 --> 00:49:22,100
There's no clear roles and 
responsibilities. 

924
00:49:22,100 --> 00:49:24,700
Like you sometimes have the D 
data engine, somebody who's 

925
00:49:24,700 --> 00:49:26,800
killed, wants to be a data 
engineer. 

926
00:49:27,000 --> 00:49:29,700
Do we have you work? 
Whereas somebody who wants to do

927
00:49:29,700 --> 00:49:34,200
statistical analysis? 
Is is doing is just bending. 

928
00:49:34,400 --> 00:49:39,200
I just had like some reports. 
Yeah, so so so now that you have

929
00:49:39,800 --> 00:49:43,300
secure lab clear roles and 
responsibilities means that you 

930
00:49:43,300 --> 00:49:46,300
are able to actually put the 
right people in the right, get 

931
00:49:46,300 --> 00:49:49,800
the right talent and and fit 
them in so that they feel happy 

932
00:49:49,800 --> 00:49:51,800
about their job and to get job 
satisfaction. 

933
00:49:52,300 --> 00:49:56,600
Yeah. 
Less disturbance in a, in a 

934
00:49:56,600 --> 00:49:58,600
standpoint, right? 
If you're not releasing for two 

935
00:49:58,600 --> 00:50:00,400
bonds, people will always 
disturb you but if you're 

936
00:50:00,400 --> 00:50:03,500
releasing every Two weeks people
know exactly what's coming in at

937
00:50:03,500 --> 00:50:04,900
there. 
Is that level of transparency 

938
00:50:04,900 --> 00:50:07,100
that comes in? 
So given that people don't get 

939
00:50:07,100 --> 00:50:09,400
Disturbed, there is a little bit
of a cost to that which is that 

940
00:50:09,400 --> 00:50:12,800
there are scrum team scrum 
meeting that happen every day 

941
00:50:12,800 --> 00:50:15,300
and we have several terminals 
that needs to do that. 

942
00:50:15,300 --> 00:50:17,800
We need to do there's a cost of 
it but it's a predictable 

943
00:50:17,800 --> 00:50:20,000
coughs, it's not an 
unpredictable cost, right? 

944
00:50:20,000 --> 00:50:23,000
Some suddenly. 
There is a CEO asking question. 

945
00:50:23,000 --> 00:50:25,100
Then you got to drop everything 
and then you've got to solve for

946
00:50:25,100 --> 00:50:27,300
that and then everything goes 
Haywire, but here it's a 

947
00:50:27,300 --> 00:50:29,500
predictable cost. 
You know, exactly the product 

948
00:50:29,500 --> 00:50:32,900
manager is able to handle. 
All of that, chaos that can 

949
00:50:32,900 --> 00:50:36,300
protect us from Team from, from,
from the, from just focusing on 

950
00:50:36,300 --> 00:50:37,300
the work. 
Yep. 

951
00:50:38,400 --> 00:50:42,400
Then the last the last piece 
under the last pieces, I 

952
00:50:42,400 --> 00:50:45,100
Traditions obviously, right? 
I think in a world where we want

953
00:50:45,100 --> 00:50:50,500
to and I'll seem too good. 
Pros and cons of I trading fast 

954
00:50:50,500 --> 00:50:54,300
versus making the product usable
really invisible before you 

955
00:50:54,500 --> 00:50:57,900
deliver it, and I think, I think
that is very fascinating ideas 

956
00:50:57,900 --> 00:51:02,000
on both side. 
And I think it depends upon it 

957
00:51:02,000 --> 00:51:04,700
depends upon organization of the
culture of the users, as to, 

958
00:51:04,700 --> 00:51:07,000
which the way you want to lean 
on, you want to hydrate very 

959
00:51:07,000 --> 00:51:10,000
fast and really something very 
Scrappy, or do you want to 

960
00:51:10,100 --> 00:51:13,000
really make the product usable 
before your release? 

961
00:51:13,300 --> 00:51:18,400
So, so, so, that is so that is 
also an area where Deep house 

962
00:51:18,400 --> 00:51:20,500
because you're able to get 
feedback that you can go back 

963
00:51:20,500 --> 00:51:22,700
to, you don't have to wait, four
months or two months to actually

964
00:51:22,700 --> 00:51:24,200
get. 
So, so yeah, in order to release

965
00:51:24,200 --> 00:51:26,500
every two weeks, you need to 
actually have, you need to 

966
00:51:26,508 --> 00:51:28,800
actually have operate like a 
factory, the cons of it. 

967
00:51:28,800 --> 00:51:31,000
As I said, one. 
As you said, there is a cost 

968
00:51:31,000 --> 00:51:35,600
associated with it and, and 
sometimes and I know Engineers 

969
00:51:35,600 --> 00:51:37,900
hate attending meetings. 
So there is regular meeting that

970
00:51:37,900 --> 00:51:39,700
was scheduled to people. 
Keep saying, hey, why do I need 

971
00:51:39,700 --> 00:51:42,700
to attend this? 
Because the sea is calm, most of

972
00:51:42,707 --> 00:51:47,100
the time, there is usually a 
stop there is sometimes a storm,

973
00:51:47,200 --> 00:51:50,800
right? 
You're paying a cost during the 

974
00:51:51,000 --> 00:51:53,900
when the sea is calm in order to
prevent stop. 

975
00:51:53,900 --> 00:51:55,900
So Gap. 
So when you actually don't, when

976
00:51:55,900 --> 00:51:58,000
you don't see the star, people 
always ask the question as to, 

977
00:51:58,008 --> 00:52:01,100
why am I in the meeting yet? 
When you take away those 

978
00:52:01,100 --> 00:52:03,300
meetings and the storm starts 
hitting then we like people get 

979
00:52:03,300 --> 00:52:06,500
let's get that right? 
So yes, I think that is the so 

980
00:52:06,500 --> 00:52:09,300
usually I have seen pushback 
from engineering to obviously 

981
00:52:10,700 --> 00:52:15,100
the second thing is that It also
creates a lot of impact with 

982
00:52:15,100 --> 00:52:19,500
respect to all the other teams 
are extremely chronograph occur.

983
00:52:19,500 --> 00:52:20,200
Right? 
Exactly. 

984
00:52:20,200 --> 00:52:22,600
After the most common eating 
with all your engineers and all 

985
00:52:22,600 --> 00:52:25,500
your product managers being in a
room and solving for all of that

986
00:52:26,000 --> 00:52:28,300
wood style puts pressure in 
terms of K. 

987
00:52:28,300 --> 00:52:31,100
Maybe somebody taking a night 
call or somebody's taking D 

988
00:52:31,100 --> 00:52:32,200
call. 
But if you actually have, you 

989
00:52:32,200 --> 00:52:36,400
would be it. 
It qisas that pain. 

990
00:52:36,700 --> 00:52:39,200
But apart from that, I have seen
generally have seen the 

991
00:52:39,200 --> 00:52:43,300
benefits. 
Outweigh the The costs 

992
00:52:43,300 --> 00:52:46,000
associated with that and 
especially even as a leader. 

993
00:52:46,000 --> 00:52:49,100
I get I get a level of 
transparency of how the scrum 

994
00:52:49,100 --> 00:52:52,000
team is progressing, and whether
they're working for towards ago,

995
00:52:52,000 --> 00:52:53,500
he has. 
And if there is a, if there's a 

996
00:52:53,500 --> 00:52:57,100
need that I need to enter me 
intervene to, to course-correct 

997
00:52:57,700 --> 00:53:01,300
unable to do so before, like, 
Things are Slip too far, right? 

998
00:53:01,300 --> 00:53:04,200
So I think that's, that's really
the help that I get from a shell

999
00:53:04,200 --> 00:53:06,600
as well and whatever the data 
science part of it. 

1000
00:53:06,600 --> 00:53:09,100
Like, I mean, if you're in model
building and things like that, 

1001
00:53:09,100 --> 00:53:12,600
the what I would using my old 
favorite favor, What I would 

1002
00:53:12,600 --> 00:53:15,500
classify as they start to work 
where like, you need to sort of 

1003
00:53:15,500 --> 00:53:19,200
like where it's the development,
doesn't happen in a linear 

1004
00:53:19,200 --> 00:53:21,100
fashion. 
But like example, is that you 

1005
00:53:21,100 --> 00:53:24,500
end up getting your insight into
only how conducive is that to a 

1006
00:53:24,500 --> 00:53:26,700
child? 
Yeah, and I think there are when

1007
00:53:26,700 --> 00:53:29,500
you have data science in the 
team, then there are essentially

1008
00:53:30,400 --> 00:53:31,900
again, there are always two 
parts to it. 

1009
00:53:31,900 --> 00:53:33,300
Right? 
As you said right, there is one 

1010
00:53:33,600 --> 00:53:39,000
gotta go to definitely invest, 
time into doing invest time into

1011
00:53:39,000 --> 00:53:43,600
solving for the big one. 
This is generally during data 

1012
00:53:43,600 --> 00:53:46,000
science and also any region, a 
free ride where you are solving 

1013
00:53:46,000 --> 00:53:47,900
for a big technical Observer 
ready, right? 

1014
00:53:47,900 --> 00:53:50,400
Like that. 
You gotta solve for our you got,

1015
00:53:50,400 --> 00:53:55,100
you have to invest time in your 
in your Sprint's to do that, 

1016
00:53:55,300 --> 00:53:57,200
right? 
While at the same time, you got 

1017
00:53:57,200 --> 00:53:59,500
also think about what a quick 
and dirty way in which I can 

1018
00:53:59,500 --> 00:54:03,200
move the ball forward and not 
wait for the end of the world 

1019
00:54:03,200 --> 00:54:05,400
before we actually crafted best 
model. 

1020
00:54:05,700 --> 00:54:08,100
Yeah, if it means that you want 
to solve because many of the 

1021
00:54:08,100 --> 00:54:10,800
Tanners, you said, right? 
How can you make the research 

1022
00:54:10,800 --> 00:54:13,800
usable on a date? 
Mrs. Slater, that's really, 

1023
00:54:13,900 --> 00:54:15,600
that's really the role. 
Where the product manager, the 

1024
00:54:15,600 --> 00:54:18,000
data scientist needs kind of sit
together and say, hey, if you 

1025
00:54:18,000 --> 00:54:20,500
guys are doing because many 
times you end up with a lot of 

1026
00:54:20,500 --> 00:54:22,700
bivariate to multivariate 
analysis that seeing, if there's

1027
00:54:22,700 --> 00:54:25,300
the rule, rule space way in, 
which I can start getting some 

1028
00:54:25,300 --> 00:54:27,500
value out of it. 
Yeah, for before you make, 

1029
00:54:27,500 --> 00:54:31,100
except the different ways in 
which we can try to try to use 

1030
00:54:31,100 --> 00:54:34,100
the research in and monetize 
that we searched every 

1031
00:54:34,500 --> 00:54:37,600
throughout, the Sprint's versus 
just wait for the final result. 

1032
00:54:59,000 --> 00:55:01,000
Thank you for listening to data 
shatter. 

1033
00:55:01,600 --> 00:55:05,100
If you like this show, please 
leave a comment, share and 

1034
00:55:05,100 --> 00:55:08,400
subscribe to the podcast. 
You can find this podcast on 

1035
00:55:08,400 --> 00:55:12,200
Apple podcasts Spotify or 
wherever else you go to get. 

1036
00:55:12,300 --> 00:55:15,300
Podcasts. 
Once again, this is Karthik 

1037
00:55:15,300 --> 00:55:16,800
signing off. 
Thank you.