SDS PODCAST

EPISODE 363:

INTUITION,

FRAMEWORKS, AND

UNLOCKING THE

POWER OF DATA

Kirill Eremenko: This is episode number 363 with President and CEO at

Aryng Analytics, Piyanka Jain.

Kirill Eremenko: Welcome to the SuperDataScience podcast. My name

is Kirill Eremenko, Data Science Coach and Lifestyle

Entrepreneur. And each week we bring you inspiring

people and ideas to help you build your successful

career in data science. Thanks for being here today

and now, let's make the complex simple.

Kirill Eremenko: Welcome back to SuperDataScience podcast

everybody. Super excited to have you back here on the

show. Are you ready for a rollercoaster of knowledge?

This is going to be a lot of fun. I just got off the phone

with Piyanka Jain, and you will be overloaded with

information about analytics and data science. Literally,

I have so many notes and so much in my head. I

probably need to sit down and process this for quite a

bit of time.

Kirill Eremenko: So Piyanka is the founder, president, and CEO of

Aryng Analytics, an analytics consulting company

where they provide services to enterprises and

businesses on how to be better with data science, data

driven decision making. Also Piyanka is an author of

several books now, of multiple books, best selling

books which you can find on Amazon. We'll talk about

one of the books during the podcast. Piyanka's also a

writer for publications like Forbes, Harvard Business

Review, Inside HR. She has keynoted many

conferences around the world, and also, Piyanka is an

educator. So they have data science courses on

Aryng.com. They have a whole academy of data science

where they provide certifications and help people get

into the space of data science. So as you can tell,

Piyanka is involved in many aspects of data science.

Kirill Eremenko: And what exactly are we going to be talking about in

this episode? There was so much to choose from.

There was so many questions I had, so many topics we

could have gone into. There was virtually, or literally...

Impossible. It was virtually impossible to cover

everything. So what did we cover? Well, we talked

about, among other things, a very important

framework called BADIR, a framework that Piyanka

developed herself. It's B-A-D-I-R. And this is a

framework that allows you to do data science in a very

thought through way. According to Piyanka, with this

framework, you can do lean data science. You can do

data science much quicker than normal. You can

deliver results faster because you're thinking things

through. Not often do you hear about data science

frameworks. I found this one very interesting,

especially how it uses hypothesis based data science.

In this podcast, you'll get an acquaintance with this

framework, and if you'd like to learn more about it,

you can always follow up and check out the book or

other resources.

Kirill Eremenko: In addition to that, we'll talk about putting courses

into context and what that means and how you can do

that for yourself, and why you would need to do that.

SWAT teams in data science and how to know if your

team is a SWAT data science team, how to do lean

data science and what percentage of data science

projects fail and why. You'll actually be very surprised

at the number. In addition to that, we talked about the

four components of data culture and how they come

hand-in-hand, how do they enable each other, and we

discuss the difference between decision science and

data science. So there we go, a podcast full of value.

Can't wait to get started. So without further ado, I

bring to you, president and CEO of Aryng, Piyanka

Jain.

Kirill Eremenko: Welcome back to the SuperDataScience podcast

everybody. Super excited to have you back here on the

show. And today's guest is calling in from California.

Welcome to the show Piyanka Jain. How are you

today?

Piyanka Jane: I'm great and excited to be here.

Kirill Eremenko: Very excited to have you. And this was a first for me

because before the start of the episode, you asked me

a ton of questions about the audience, about how you

can help our listeners better, and all these other

things. I normally don't have that. So very excited. I

can tell right away you have an inquisitive mind, and

that probably serves you quite well in your career in

data science, doesn't it?

Piyanka Jane: It does. A curious mind is a good data science mind.

Kirill Eremenko: For sure. That's a great motto. How are things going in

California these days?

Piyanka Jane: Things are good. We're all sheltered in and it's a good

thing, and hopefully, we are able to contain COVID

soon. But yeah, no. Otherwise things are good. We're

all doing what we need to be doing with social

distancing and so on.

Kirill Eremenko: Yeah. That's right. That's right. Yeah, hopefully it does

go past quite quickly with these new measures. But

I've had a look at your career background, and it's

extremely impressive, from having a published book to

being a CEO of a company that does both consulting

for companies like, as far as I understood, Google,

Box, Apple, General Electric, and many others. Also

you do education in the space of data science. You are

everywhere in the space of data science. Tell us a bit

about yourself. For somebody who hasn't met you

before, how would you describe what you do?

Piyanka Jane: Thank you so much for your kind words. I feel like I'm

just getting started, but for those who are listening in

and want to know a little bit more about me, I am all

about practical data science. I really believe in the

power of data, and for me, data plus intuition, because

we are all intuitive beings, and if we can marry data to

that, we can really optimize our decisions. And that

goes all the way from corporate decisions as a

marketing manager, as a business manager to data

scientists to all the way to our internal as personal

human beings. You want to achieve something. You

want to climb Mount Everest. You have to use data,

and that's how you're going to be able manage and

optimize your progress and your decisions. So I really

believe in that, and I think that's what I evangelize,

and that is what I hope to share... I have to be

infectious about my passion for data science today.

Kirill Eremenko: Love it. Love it. Interesting thing just ran through my

mind when you were saying that. Indeed, if you're

going to climb Mount Everest, you've got to use data. It

might sound strange at the start but when I think

about it, you've got to use data on, okay, I've climbed

this other mountain. Maybe you'll be doing training.

You'll be measuring your pulse. You'll be measuring

how tired you get, how much endurance you have,

how much water you consume, how much oxygen you

breathe, and data will definitely get you there. The

interesting that went through my mind was, some

people might say that if you just use data in everything

from business to personal life to sports, eventually,

you'll be like a robot. You won't have any emotion,

empathy, any kind of random chance that comes with

life that is normal. What would you say to people who

have that opinion?

Piyanka Jane: I have a lot to say about that. One is that when we talk

about data, we don't talk about data driven as in just

believe on data. We always talk about hypothesis

driven, data driven decision making. What does that

mean? What that means is you want to bring your

whole self, your intuition and the intuition of your

colleagues, of your stakeholders to the bearing and

then form what is needed from the data, and then

prove your hypotheses. So for us, data science, or this

aspect of being able to apply, putting data to work is

all about marrying data to intuition that you already

have.

Piyanka Jane: So for example, if you're a marketing manager, you

probably have some really good intuition about your

audience, about what works for them, what products

they like, and so on. Let's use that intuition you have,

the context you have as well as your team members,

as well as your stakeholders, and then form a

hypothesis driven... We teach a framework called

BADIR, that's also there in my book, Behind Every

Good Decision, for those who are interested in

knowing more about it. It's called B-A-D-I-R is the

name of the framework, and we talk about how... For

you to be effective and efficient in data science and

analytics, you basically lay out a hypothesis driven

plan.

Piyanka Jane: So even before you touch data, you lay a hypothesis

driven plan. You think what are the things... If you are

solving a problem, going back to our personal goal,

let's say you were going to go look for treasure in

Pacific Ocean. There two approaches to it, right? One

is, I'm going to be just... just going to jump in because

I want to experience the world so I'm just going to

jump in and start swimming in the ocean, and

hopefully, one day, I will run into a treasure. How

likely is that, Kirill?

Kirill Eremenko: 0%.

Piyanka Jane: Yeah, right? You have to be super lucky. You're relying

on luck and you're relying on... And you're actually

giving up your power because for anybody who sets

sail in Pacific Ocean, you have limited resource. Maybe

you have one-month's supply. So you're basically

saying, "Oh, I have one-month's supply but I'm going

to set sail," you're risking that one-month supply

because you don't even know. Maybe if you had

infinite supply and infinite time, maybe someday you

will run into a treasure. But on the other hand, if

you're like Sherlock Holmes, and you are detective,

and you lay out hypotheses, what does hypotheses

mean? Basically, you have good ideas of where the

treasure could be.

Piyanka Jane: So you look up past shipwrecks, past routes, the

depth of the ocean, all of that, and you figure out, you

narrow down, these are the most likely spots and the

news reports of where debris was collected and so on.

And then you narrow down, these are the five most

important, or 10 most important spots, most likely

spots for treasure. And you go there and then you

send your deep sea divers or your submarines down

there, you're more likely to find that, and at least you

would fail faster as well. You would have looked at

those 10 spots. You would know within 10 days or

whatever else, "Okay. I don't have it. Now what's my

next plan forward?" Versus just kind of going, right?

So that is what we talk about.

Piyanka Jane: When we talk about data driven, we talk about

hypothesis based, data driven. So going back to your

will we become a robot? We are human beings and we

are special beings. We can't quite become robot.

However, you also don't want to just rely on data. And

I have seen people who are so data driven that they

leave their intuition behind, and they come up with

these results sometimes, in the business as well as in

personal, and we look at it, and you're like, "This does

not even make sense." My entire being rejects what

you are concluding, and that's my intuition, right?

Piyanka Jane: So you never want to leave the intuition. Intuition is a

big part of us. Intuition is what keeps us safe.

Intuition is when you're going up the Mount Everest

and you're beginning to feel not so good. You look up

your VOX meter and you say, "Oh, what's my oxygen

level? What I'm absorbing is dropping," right? If you

didn't have intuition, if you're not paying attention,

you won't even know. And before you know, you will

have fainted before you even can look at your data,

right? So you need to bring your whole self to this

game. Data science is not about just data. It's about

bringing your entire self to the table.

Kirill Eremenko: Fantastic. Thank you for the rundown. It was a great

way to see how data science can be combined with

intuition. I think a lot of us would agree that both have

a place. And I actually want to talk a bit more about

your BADIR framework. So I read about it. So the B-A-

D-I-R. What do those letters stand for and what is this

framework all about?

Piyanka Jane: Yeah. So the BADIR is an acronym for these five steps,

and they stand for business question, analysis plan,

data collection, insights, and recommendation. And if

you notice, the part about data collection is step

number three. So many people think, when they think

about data science, they think about, "Oh, let's start

with data." But it doesn't start with data. Good

analytics, good data science project doesn't start with

data. It starts with business question, refining and

flushing out what you really want to find out. What is

it your question... For example, a question could be

why's our sales dropping, or why are our customers

churning, or why is our conversion down, or can we

optimize our conversion? Can we improve our user

acquisition? In what ways can we improve our loss

ratios, and so on? So those can be the questions.

Piyanka Jane: And the point about having a full step for it is

basically, there's an ask that comes in to the

stakeholder, or the first thought if you're a marketing

manager and yourself, a citizen analyst, which means

part of your work, you are doing data science which

almost all of us are right now. In the world these day,

in the business world, the language of business is

data, and so everybody is speaking the language of

data. And if they're not, they're being left behind. So

everybody sort of is sort of having some access to data.

And the first thought that comes to your mind as you

think about, "Oh. I need to do my next campaign, or I

need to figure out whether this feature works or not,"

that's an early question and even if you are a data

scientist, if somebody asks you a question, it's an early

question. You need to define it through the business

question framework to come to the real business

question. And that refinement has many aspects about

what actions somebody's ready to take. If you find the

insights with it, there is the, who are the stakeholders?

And very many, many aspects both on the data science

side as well the decision science side. And so, that's

business question.

Piyanka Jane: Then you lay out a hypothesis driven analysis plan.

You ask yourself and the stakeholders, what is the

solution? So if the question was why are our

customers churning, then many people will have some

good ideas. Your stakeholders may have good ideas

like, "Oh, we have recently increased our price and

that's causing some churn. Our ticketing system is not

working that well or our policies have changed recently

or the customers are churning because we post 90-

days, these things that we do that just not working

well," and so on and so forth. You have lots of good

hypotheses. It's like good spots that you would, going

back to our Pacific Ocean [inaudible 00:15:44], it's

going back to where you're going to look deeper. So

these are good hypotheses. And then you lay out

analysis plan with hypotheses, with your methodology,

with your assumptions, with your data and criteria to

prove this. There's a bunch of stuff.

Piyanka Jane: And from there comes out your data specification

which means if this is the question, and these are my

hypotheses and these are my assumptions, facts, and

my methodology and so on... And by methodology, I

mean your specific approach to data science. So are

you going to use aggregate analysis? Are you going to

go correlation analysis? Are you going to go deeper into

using probably some predictive analytics like

statistical methods? Or are you going to go even

deeper and you'll use machine learning, whatever else?

So you're laying out, is a classification problem? Is it a

regularization problem? You're laying it out right there

and saying how far am I going to go. And that's also a

function of... It's a function of data. It's function of

time you have. It's a function of precision you need

and so on. So there's a lot of things going on. These

are all planning stages.

Piyanka Jane: Remember, you have not yet touched the data, right?

And most people, most data scientists and others,

when they think about data science, they think about,

"Oh. Where's the data? Let me pull that data in Excel,

or let me [inaudible 00:16:54] in Python," whatever

else. But that's not where data science starts from.

Data science starts from question, or flushing out the

question, laying out a hypothesis driven plan. And

when you're playing out hypothesis driven plan, it also

means you are aligning with your stakeholders to say,

"This is what my plan is going to be. This is how much

time it'll take. This is blah, blah, blah. Are we in

alignment?" When you have a handshake, that's when

you go to the most time consuming step of getting data

and then validating it, and triangulating and cleaning

it up. That's all time consuming.

Piyanka Jane: Then start doing your analysis. So the insight step is

also, if you have a recipe for doing insights versus

what many people do which is they set sail in the

ocean of data and they start looking for treasure,

which is a pretty bad idea because it takes you a long

time, and your likelihood of finding treasure is also

really low. So what we recommend is now that you've

done all this work, you have laid out a hypothesis, you

have collected data, now... And collected data meaning

you have collected only the data that you need versus

saying, give me all the data you have. Now because

you have hypothesis, you've used that to know where

exactly you're going to dive deeper. Then the next step

is use recipes to derive insights.

Piyanka Jane: You know if you're going to do correlation analysis,

these are the steps. If you're going to build a linear

regression model, these are the steps. Or if you're

going to go into gradient boosting, these are the steps.

This is what you're going to do, and so on, right? So

you know what the steps are. Follow those steps and

follow the recipe in a structured manner and come to

your insights. At the end of it, share your early

insights with your stakeholder and see if that's making

sense.

Piyanka Jane: And the last step of this BADIR framework is

recommendation. So you make recommendations or

you instrument your model, you productionalize your

model, you instrument your insights, whatever have

you. And that's also very important because I can't tell

you how many good models I've seen sitting in shelf

because people didn't know how to align with

stakeholders, how to communicate your findings to the

right folks in the right way so that you can basically,

inspire them into action. So that in a nutshell, is the

BADIR framework, and for folks who are interested,

they can learn more about that in my book, Behind

Every Good Decision, as well as on our website. If they

want to go and look at aryng.com, they can find a lot

of use cases and case studies on why we believe this

works, and many, many organizations, many Fortune

1000 have already adopted it, this framework as their

common language.

Kirill Eremenko: And I actually wanted to ask you about that. So I'm

seeing on your website that this framework is adopted

by Apple, Google, GE, PayPal, Adobe, SAP, Ebay, and

many, many more companies. How did you get this

framework into these companies?

Piyanka Jane: So not all companies that you spoke about and not

100% of them are adopting it, as you say, but many

organizations are adopting it much widely and some

organizations are adopting it within for example,

customer support group or marketing group and so

on. But the way the... So I mean, one is that we, after

many years of being pestered by our students, I wrote

this book, and basically put then the BADIR

framework and made it open-source. So many data

scientists and business users are picking up the book

and it has a step-by-step guide, so they are picking it

up and they're adopting it. And then as and when they

need further detail, more detailed help, they reach out

to us. So even our non-customers are using it and we

may not be even aware of them, so that's-

Kirill Eremenko: Got you. Got you.

Piyanka Jane: The other thing is, it's a very... I mean, it's a recipe-

based approach. I don't know, Kirill. Do you cook?

Kirill Eremenko: Yes, love to cook.

Piyanka Jane: Loves to cook, okay. So do you know how to make

falafel?

Kirill Eremenko: Falafel? No, I don't know how to make falafel.

Piyanka Jane: Okay. So that's a tricky one. So let's say you are

thinking about, "Oh, I'm going to make falafel." What

would help you the most now that you have to make

falafel?

Kirill Eremenko: A recipe on how to make falafel, I think.

Piyanka Jane: There you go, right? So a recipe. And then the first

time you make, do you think you'll get it perfect?

Kirill Eremenko: No, of course not. First time always not perfect.

Piyanka Jane: Because you're still understanding, "Hey. I'm going to

salt chickpea. And I'm going to grind it, how fine I'd

grind it. And then what would be the consistency of

that as I drop it into... To fry in oil, as I drop those

balls, how thick they need to be, how viscus or how

liquidy they need to be." So there's a lot of details that

you're going to get. The first time you're going to make,

you're going to get the detail and you'll see the output.

The same way, if you have to learn data science, what

would help you the most? A recipe.

Kirill Eremenko: A course. A book. A guide. A learning path.

Piyanka Jane: Yes, and a recipe. Whatever, a course is about recipe.

A book is about recipe. A recipe. Something that tells

me, do this and then do this and then do this, and

these are the ingredients. And do this and then do

this, right? And the first time I do that, I'll get

somewhat, and I get some understand the second and

the third time. So the way we have structured our

courses, and for your listeners who are interested,

they can go on academy.aryng.com and find these

courses, we are all about how to bake a cake, how to

make a falafel kind of recipe. So we start, we share

this whole BADIR framework and by the time they're

done with even the level one course which is the

business analytics course, they have done this

framework. They have baked the cake, and they have

cooked the falafel at least three times.

Piyanka Jane: And then following that, they work on a project which

means, okay great, you have done this in simulated

data, or you have done that in data which was fairly

clean. Now do this in real world, in your real world. So

for current data scientists who are currently employed,

we tell them, "Okay, pick up a project within your own

work flow." Or for future data scientists who enroll

with us, we give them one of our client projects. And

thereby, they get to practice, again, the same

framework. So they know exactly what they're doing.

As we put them in a client situation or they pick up a

project, they know how to follow the BADIR

framework, and we are there as their mentor at

different points, at the analysis plan stage, at the

insight stage, at the recommendation stage because

they know what they're doing. We know exactly what

they should be following so we can course correct. And

that's the fastest way I have found to learn data

science is using some kind of recipe, some kind of... a

step-by-step method of this is how it works.

Piyanka Jane: Now, as you get advanced in it, you can start using

shortcuts. You can start using iterations and so on

and so forth, right? And so you can think about, you

start with your common, simple vanilla cake, and then

you can start adding some... I'm going today, make

some nuts and raisins, and I'm going today, make

some icing, and I'm going to layer it up. And maybe

one day I'm going to be able to make tiramisu and all

of that, right? So you're going to be able to advance

your skills. And this step-by-step way of learning is

recipe-based, and then step-by-step use case based

approach is what I recommend for people who want to

learn data science.

Kirill Eremenko: Got you. Wow. Thank you for the rundown. So let's

talk a bit more about your courses. So I noticed you

have... For those by the way, for those interested, the

website is Aryng, A-R-Y-N-G. And the course are at

academy.aryng.com. I noticed you have quite a few

interesting courses, and what I wanted to find out is...

These are high ticket items, so over $1,000 per course.

What is your X-factor? So what is it that students can

pick up from this course that will really make it

worthwhile for them?

Kirill Eremenko: Are you subscribed to the Data Science Insider?

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Kirill Eremenko: Our team goes through all this news and finds the top

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SuperDataScience.com/DSI. That's

SuperDataScience.com/DSI and now, let's get it back

to this amazing episode.

Piyanka Jane: Yeah. So there are courses and there are certifications,

and our certifications are... For example, let's pick up

one which is the future data scientist certification. And

what it has is a complete [inaudible 00:27:16] of how

you can transition your career to data science. And so,

it'll have the underlying courses, and it's self-paced so

you come in, and you log in and you... We recommend

one section a week, or if you have more time, one

section a day, and make progress. And then after

you're done with that... And while you're doing that,

we have communities so you're posting questions in

Facebook community. And you also have a monthly

mentoring sessions directly with us live on Zoom, and

thereby, you are able to log in and ask your questions

live, as well as post your questions non-live, 24 by 7

on Facebook community.

Piyanka Jane: So lots of interaction. Students are helping each other.

So there's a community that you have. There's a

learning that you're doing of the fundamental

framework, BADIR, and you're learning it in a context

of marketing of product. If something happens in this

[inaudible 00:28:10] in hospital, how are you going to

do it? If this is happening in winery, how are you going

to think about optimizing and so on? Lots of different

use cases. We are opening their blinders and we are

giving them a toolkit of tools that they can use. Then

the next part of it is-

Kirill Eremenko: Putting it into... Sorry, putting it into context, putting

education into context. I'm just thinking of what can

students take away that they can enact in their own

learning, and it sounds like putting education, data

science into context like you said, in a hospital, in

winery or somewhere else. That helps probably

retention. Also helps understanding the topic better.

Piyanka Jane: Yeah. And then follow that up with a real project. So

they all work, all of these certifications have a project

at the end. So it's all fine and dandy when you learn

something. How many of us have gone and done this

in the corporate world, really? We are taking classes

all the time. You come in. You even do a half-day

offsite for leadership, and you go out there and you

say, "Wow. That was amazing. That's so inspiring."

You come back and it's business as usual, right?

Kirill Eremenko: That's true.

Piyanka Jane: So for the business to be not as usual, for you to

interrupt that way of thinking and to really change

manage, you need to bring it home with you, which

means you need to tie it to a project. And I can't tell

you how many people... I mean, I've seen people just

flower from, "Oh. I'm very, very nervous about data

science," to, "Okay. I've done the course. I'm still not

sure," to, "Now I'm doing a live project with a client

and oh, I get this part. Oh, I can go review. I'm stuck

in this part. I can go review this video," or whatever

else. And then when they're done with the course, they

have delivered a final model, final insights to the client

and the client is really happy. I've seen people go from,

I'm so nervous to all of like, "I get it. I can do it," right?

So that's what it-

Kirill Eremenko: Gotcha... So in the courses, they would have actual

live projects with clients. Is that the case?

Piyanka Jane: In the certifications. People have options of just taking

courses a la carte and they can learn on their own

time and do courses. What we recommend is the

certifications which has projects at the end with us as

live mentors and with live clients, again, all working

remotely. We have students logging in from Nigeria to

Australia to of course, big percentage of them are in

US, as well as all throughout Europe. And so, they're

working remotely but with live client and with us live

in the mentoring session. And once they're done with

that, they have the confidence, "Hey. I understand the

fundamentals of data science and I can apply it to

solve problem and I've seen the end-to-end of at least

one project all by myself or with someone from my

team."

Piyanka Jane: And then, for people who are looking to transition, we

have mentoring sessions of step number one, do your

targeting of your job. All the things that you've

learned, now let's apply it to job search. Targeting of

your job. Making your resume to your target profile.

Because a lot of times people think, "Oh. Now I have

done the certification. Let me add this one line item to

my resume, and now I'm an analyst." If you're looking

to transition your career, your resume needs to

transition as well. It needs to now tell a story of you as

an analyst, you as a data scientist. So that's the

second mentoring session we have. And the third one

is how do you interview and how do you ace that,

right? So we have follow on, end-to-end process where

we're holding hand and making sure that the people

cross over to the other side. And that basically

increases the success rate. So for people who are

looking to transition, that's a huge success rate.

Piyanka Jane: We also have a similar certification for current data

scientists. Again, with the project and the with this

kind of learning and hand-holding, they get the

confidence that they can do it and then they are able

to do it, and then they see the stakeholder alignment.

They see what happens to people once they deliver the

kind of project the way we are talking about. And we

have gotten so many letters, I can't tell you, like,

"Piyanka, you won't believe. I got invited to this

meeting where I would never be invited after this

project." And yes, if you're going to align with

stakeholders, if you're going to use this framework,

and make sure that you're doing the decision science

part, you start to appear as a partner versus as a

downstream somebody who takes order. So it changes

the world altogether when you start doing things in the

way that can engage people in the right way. And same

for [inaudible 00:32:21] analysts.

Piyanka Jane: So our approach is sort of end-to-end. I'm all about

results. So for me, when any algorithm, any math, any

statistics is useless until it gets me results. And so for

me, again, as I guided thousands of students with this

transition... I also have another book. Sorry, I'm

bombarding folks with another book, Acing Your

Analytics Career Transition, which is right now

because of COVID being made free. It's on Amazon. It's

called Acing Your Analytics Career Transition. And it's

a very quick read on Kindle. So it's like a 40-page read

or something. And it lays out these steps, step-by-step,

and whichever program they choose to go, whatever

else, you need to follow a step-by-step method of really

transitioning. You can't follow a haphazard path and

expect that your career would be of that of a data

scientist by just taking courses here, courses there. I

mean, take courses but in the context of identifying

what your target is. Back calculate. Look at your own

resume. Figure out the gaps. So all of that is there in

the book, and hopefully, that will be a good guide for

some of your listeners.

Kirill Eremenko: Amazing. Thank you. Very cool that you made it free.

That is very admirable and maybe, probably will help

lots of people.

Piyanka Jane: Yes, hopefully.

Kirill Eremenko: So talking about these courses. Very interesting. So

the certification is something that you are actually

organizing internally. I haven't seen that before, so

that is very cool. Tell me a little bit about your SWAT

data science. So SWAT, I know the SWOT framework

as W-O-T. Strengths, weaknesses, opportunities,

threats for business, but you have another framework

in addition to your BADIR framework called S-W-A-T.

What does that stand for and how does it work?

Piyanka Jane: Sorry. So that's not a framework. So this is going back

to my days in PayPal. I was heading up business

analytics there for North America, and before that, I

was part of leading product analytics for merchant

consumer on the product side at PayPal. And at that

time, it was some series of projects that I did and the

credibility I won. I and my team became like a SWAT

team. You know the SWAT team who come in when

things are not going... When things are complex

situation, a SWAT team is parachuted into that

situation and they can control it and they can get stuff

done. So we came to be known as a SWAT team, and it

was a pretty small team that I lead. It was here as well

as international. And yet, we came to be known as a

SWAT team, and the reason was... And what that

meant was, even if we were part of product analytics, if

there's a problem in Omaha in customer support, I

would be called in. And we'd be saying, "Leave

everything. Drop everything. This is urgent. Come into

this meeting and take this over, and for the next one

month, this is what you're focusing and I need results

by Monday, April 22nd," right? So that was how it

used to be.

Piyanka Jane: And I recognized and I used to wonder, what is it that

made us a SWAT team? What is it that we got... I

mean, there were lots of data scientist team at that

point. And what was it that got us that much

credibility that got us... We didn't have any extra or

special tool. We used the same tool that most other

data scientists had. And we recognized, the power was

us, our hypothesis driven method. This BADIR

framework that after I left PayPal, I sort of formalized,

was what I was doing internally in my head, and my

team was doing it because I was teaching them. As I

onboarded my data scientists, I would teach them this

method, not in this framework the way it is, but I

would inherently teach them this framework. And

what this does is, it gets results quicker.

Piyanka Jane: So for example, today, for our clients, we can get a

really high end, very good accuracy, highly functioning

machine learning model in about eight to nine weeks,

and no other consulting companies can. And that's

very lean like consulting team of two to three people,

we can produce machine learning models so quickly,

same for AI or deep learning models. And the reason is

because we are hypothesis driven, and we do a fair bit

of the same BADIR framework. We do a fair bit of work

upfront aligning stakeholders. So not only does our...

We produce work fast, but the percentage of time our

work gets used is also really high. And that was the

same thing for me. When I was at PayPal, almost every

model or every project we worked on went on to

produce millions of dollars of impact, and had a

amazing shelf life, meaning amazing... Some of the

models were operational after three years or four

years, and the reason was that-

Kirill Eremenko: You mean still operational after three or four-

Piyanka Jane: They were operational and they still form the

foundation of many things because we did a lot of

work on the decision science aspect, the stakeholder

alignment, really understood the question, and then

we were hypothesis driven. So all this brain work that

we followed, this gave us... One is it gave us

acceleration. Second is because we were so success

metric driven because that is inbuilt into our

framework, we were almost always... The stakeholders

could not wait to act on our insights instead of us

having to influence and go after them and say, "This is

what we need to do." They could not wait. It's like a

relay race. They were jumping up and down, ready to

take the baton from us which rarely happens. And the

third thing was, because we did a whole lot of work...

because we were hypothesis driven, we were really,

really fast.

Piyanka Jane: So that same analogy then I took over when I went to...

Eight and a half years ago when I started Aryng, I took

that same analogy and I basically framed the team,

our whole philosophy is similar. It's all about rapid

ROI and also practical data science. We're not about

pie in the sky, fairytale data science. Give us all your

data. We are going to help you monetize it and it will

take over months and months and we'll keep trickling

some insights to you. For us, it's all about practical

data science. What data do you have right now? What

are the decisions you're looking to make? And how can

we get you the fastest go-to-market with that?

Kirill Eremenko: Got you. Well, Piyanka, this is one of the most

saturated podcasts. I can't keep up with you. You have

so many ideas, so many things. I'm just going to jump

to the next question I had for you. So in one of your

videos you talked about analytics projects, and this

ties in quite well with what we just discussed that

having a hypothesis at the start of your analysis,

before you even start your analysis by asking those

business questions and doing analysis plan according

to this BADIR framework, come out with the

hypothesis and then only moving to data collection

deriving insights and recommendations. So doing

those first two steps, coming up with hypothesis helps

your analytics projects be more relevant and creates

success. In one of your videos you said that a huge

percentage of data science and analytics projects

actually fail. I could not believe how low the percentage

of successful project was. Could you walk you us

through that again, please?

Piyanka Jane: Yeah. And it's dismal. Gartner published a report I

think two years ago in 2017, or three years ago, which

then they later corrected to even... It basically said

that 85% of big data or data science projects... And by

the way, this whole space is about $200 trillion

investment that goes in, and of that-

Kirill Eremenko: Trillion. 200 trillion.

Piyanka Jane: Trillion dollar.

Kirill Eremenko: Into analytics?

Piyanka Jane: Yeah. Meaning I'm talking about the big space of data

science and big data. All the infrastructure investment

and so on. Let me correct. There's $200 billion-plus

investment that goes in overall, world over, globally.

And of that, Gartner predicted less than 85% of them

actually drive an impact. So 85%-plus projects

actually fail meaning they get instrumented or they sit

on a shelf somewhere. Nobody uses them, or they get

abandoned halfway through, whatever else. They just

fail.

Kirill Eremenko: They could even look like a success. We derive the

insights but nobody's using those insights.

Piyanka Jane: Nobody's using it because you build the best of the

model. And this by the way, is the biggest pet peeve. I

keynote at many conferences and one of the

conferences I was keynote at is Predictive Analytics

World and some of their top data scientists come to

these conferences. And their biggest... And when I ask,

I often start with my keynote a thing, "Oh, how many

of you have work on data science project and it didn't

go anywhere?" And almost all hands go up.

Piyanka Jane: It's one of the biggest pet peeves of data scientists. I

built the most amazing, lowest misclassification, high

accuracy model, but my stakeholders are not listening.

They have moved onto something else, or whatever

reason. So as for Gartner, it was 85%, right? And then

some other experts came in and they did the...

CIO.com for example, published some other reports

which said of the ones which even get finished and get

out, you would deem successful, less than 15% of

them actually drive any significant impact. So by the

time you do all this math, it's looking like of the 200

billion-plus investment, we're talking about 2%.

Kirill Eremenko: 2%?

Piyanka Jane: 2% is actually driving any impact. It's abysmal. This is

horrible. But it is real, and I have seen this live and

that again, goes back to my world when I used to go

back... going back to my PayPal world and now also in

my role at Aryng and our client work. I mean, clients

pay for data science work so you would think our

probability of success would be higher, but I can't tell

you how many times we are coming to project halfway

through or somewhere, even end where it's going

nowhere from some other consulting companies or

whatever else, and the companies come and said, "This

is going nowhere." They've said, "We have already

attempted it. It has failed. Can you correct it now?"

And often, one project that has failed has taken

months to fail also. So it's not like you fail fast, and

the stakeholders have found out you've failed. It's like,

"Oh, we were looking at using NLP. We were looking at

improving our refund or return rates. This is

automotive part. And we did this large scale analysis.

It took us six to eight months, and we realized that

because of this and this and this and this and this

reason, we can't get any lift and the model which was

built is not operational. We asked them to recollect

that." All of that. There's issues galore. And it took

them eight months.

Piyanka Jane: So there's so much failure and so much money getting

wasted, so much time getting wasted, all because...

And there's lot of main reasons. The main reasons for

failure that I have seen is lack of data maturity, which

means people are not even believing the data, or

getting the data out is itself a... It's like putting your

hand in a lion's mouth and getting something out of

that. It's really, really hard. Data science rigor is often

low. People often have data scientists who start from

the step D and do part of I, and they call it done,

right? So they don't do the end-to-end. Often

organizations don't have an engagement model

between business and data science. Sometimes they

don't even have... Well, this is more common. I mean,

lack of enterprise data literacy is a big one where the

data science team is good. They are producing

insights. But the organization, the marketing, the

product, they don't understand data science. They are

wary of data. And so, you give them a machine

learning model, they're not believing it. And so-

Kirill Eremenko: Could I just jump in here? This is an interesting topic

on data literacy because according to you, data culture

consists of three things. Data literacy is one of them.

What are the two others? I was just curious.

Piyanka Jane: So there are four Ds of data culture.

Kirill Eremenko: Oh, four actually.

Piyanka Jane: Four Ds, yeah. So data literacy is one of them. But the

most important or the foundation on which data

culture sits is data maturity, right? The data maturity

being do I have easy and appropriate access to single

source of truth for all, right? So our data scientist

needs a different access. A marketing manager needs a

different access. But do I have appropriate access to

single source of truth, or an easy access? Or does it

take me forever like, "Oh, I click this button. I wait 10

minutes." Do you think your marketing manager is

going to wait 10 minutes to get that report? No.

They're going to start finding shortcuts. You know this

excel report that comes in from the other system?

Maybe I'm going to look at that, whatever else. So data

maturity is the critical. It's the foundation of if you're

looking to establish a culture of data, you need to get

some degree of data maturity. And on a scale of 0 to

10, at least 7 and above and then you'll be functional.

Piyanka Jane: Second part is data literacy. Now that you have access

to data, do the people know what to do with data?

You've given access to the marketing managers, the

product managers, the operations people, the

customer support people. They have access to data.

When the customer calls in, they have access to data,

not only about what this customer's history is, how

much they have spent and all of that. Now, do they

know what to do with it? Do they even know what...

The customer support call center agent, they see their

own call performance data, their average hold time,

average speed of answers, whatever else. Do they know

what to do with it? Do the supervisors know what to

do with it? So that's data literacy.

Piyanka Jane: When appropriate level have appropriate... When

people have appropriate level of data literacy at the

right level for them, and they're able to use the data

effectively to make decision at their level to be able to

use that in discussions to drive conclusion, then your

organization has appropriate level of data literacy. And

currently, data literacy's really low in organizations.

We have gone into organizations where data literacy...

Less than 2%, less than 3% of people have the right

level of data literacy. And in the best case scenario,

maybe 10%, 15% of the people are going to be at the

right level of data literacy. Still 80% don't have the

right skills at their level.

Kirill Eremenko: Wow. Wow. That's crazy. What are the other two Ds?

Piyanka Jane: The other two Ds are data driven leadership. So if the

leadership does not have a vision for a data driven

organization, they don't or they're not holding their

team accountable to use data to drive decision, they're

not using something like zero-based budgeting, you

give me the money, that's when you get the money and

so on, then that organization cannot have a data

driven culture because the leadership itself is not

embodying it, and they don't necessarily see it as an

asset.

Piyanka Jane: And the last one which ties all of this together is data

driven decision making process. So if you don't have

data in a structured decision making process, if data

is not part of the decision making process, then you

can do data all you like. You can build models all you

like. But the decisions are getting made in a parallel

track almost independent of data. And of course, your

organization will not be able to leverage the data, and

will not be data driven. So these are the four Ds. I'm

going to summarize it. Data maturity, data literacy,

data driven leadership, and decision making process.

Kirill Eremenko: Wow. Very, very interesting. I know we're going to have

to wrap up soon, but I have one more question for you.

Piyanka Jane: Sure.

Kirill Eremenko: You mentioned decision science. But what is the

difference between decision science and data science?

Piyanka Jane: That's a great question. Again, I'm going back to the

power of BADIR framework or power of why a SWAT

team works wherever they work, is analytics or putting

data to work has two components. There's data

science and decision science. Data science is all the

algorithmic aspects of the things you need to do to... or

technical aspects to do the technical analysis,

collection of data, identification of the data type you

need, setting up your null hypothesis and all of that.

That is all data science.

Piyanka Jane: Decision science is all the things that you need to do

to make sure that those insights that you produce

goes towards impacts, which means the business

considerations, the stakeholder constraints and

communications, timelines, the realities of the

business, that is all decision science. So the science

that addresses all of those and incorporates that into

data science is decision science. And when you marry

the data science with decision science, you get the

power of data.

Kirill Eremenko: Fantastic. Love it. All right, we'll end on that. I think

this was an amazing excurse into the world of data

science. I have a lot to process after this. Before we go,

Piyanka, could you please help our listeners, where

can they find you, follow you, get to know more about

Aryng, if they'd like to explore this space further?

Piyanka Jane: Sure. So they can connect with me on LinkedIn, or

follow me on LinkedIn. And then my name, if they look

up Piyanka Jain and Aryng. Aryng is the company

name, A-R-Y-N-G. They can find us either through

LinkedIn or Aryng. Or they can also follow me on

Twitter. My hashtag is analyticsqueen.

Kirill Eremenko: Fantastic. And of course, pick up the book. Sounds

very exciting. Behind Every Good Decision. Great

reviews on Amazon. Love it. What inspired you to write

the book?

Piyanka Jane: I wish it was an inspiration but it was more of a

forcing factor. At that point, we were doing lots of

public workshops. And every workshop that we would

do or we would conclude, people would be by the time

the... The day four, day five people, our students

would be pestering us that, "Do you have a book? Do

you have a book?" And I said, "We don't have a book."

And I of course, for one reason, I always thought,

"Well, who has time to write a book?" I mean, I

wouldn't even know where to start. And I'm a natural

speaker but writing is not that easy for me. So I said,

"Well, I don't think... I'm not sure." But then that kept

repeating over and over, and somebody planted a seed

and it starts...

Piyanka Jane: And then, right around that time, Wiley have called us.

Wiley reached out, out of the blue. And also [inaudible

00:52:12]. And they said, "Oh, we're thinking of

publishing a book. Would you like to write something

along this line?" And I was like... It was all coming

together. So I said, "Okay. Well, I don't know what it

takes but we can attempt it." And by golly, I mean...

Because I'm all about practical, it took us a while to

get to the level that I wanted-

Kirill Eremenko: It's a big process, right, writing a book. It's a big job.

Piyanka Jane: It was a big process because... And I had a team. I had

my co-author, Puneet Sharma, as my colleague at

PayPal, whose now at Google. Great guy. We

collaborated together. And he and I are very much in

alignment with how we see the power of data, so that

was great. But then, none of us are writers and so we

had to find some really good editor who could edit

out... really put content in perspective for users to

understand because we were saying a lot of things but

if we are technical, somebody has to call us out on it

like, "Hey. It's not making sense."

Piyanka Jane: And so my dear friend, Laxmi, came about on a hike,

one of the hikes we were doing up PG&E here. She

started talking to me and by vocation, she is not a

writer so I had never thought of her. But as we hiked

that steep four-mile up which... It's a very tricky hike

because every turn, you think, "Okay. I'm almost to

the top." But it takes you about [inaudible 00:53:37]

pretty steep hike. And she got the entire gist of what

we were trying to do, and this one chapter... I was

basically kind of whining to her that, "Hey, I hired this

editor and they are correcting our English but they

keep taking the content out. It's not working well."

Piyanka Jane: And since she started talking to me, and then the way

she sort of reframed what I was saying, I was like, "Oh.

Do you have time to work on this project on with us?"

And she thankfully did. And that time, I was pregnant,

and also, she was pregnant and it was so funny. And

then Puneet was struck in between two pregnant

ladies who were like... Our hormones are high and

we're trying to collaborate on this project over phone,

over live. And then we hired a graphic design team

because we are both very-

Kirill Eremenko: I love the images in your book. They're so good.

Piyanka Jane: Isn't it?

Kirill Eremenko: So the one with the sharks, hello data science. That is

so funny. You got some really cool illustrations.

Piyanka Jane: Yeah, thank you. And we hired one of the best teams

because I am already visual. And I said, "I don't want

to write a dry book. I want to make it fun." And so we

got this team together, and finally what came out, I

was happy with and then it got published. So I know

you asked me a short question and I gave a long

answer.

Kirill Eremenko: No, no. Love it. Love it. I highly recommend. I'm a big

believer in this. My own book is also about helping

people to get into data science. This sounds like it's a

different perspective. You introduce the BADIR

framework there. I think it's a fantastic book for people

to pick up. Definitely check it out. It's available on

Amazon. Yeah, looks like a great book.

Piyanka Jane: Thank you. Thank you so much, Kirill. It was a

pleasure talking to you, and it was such a joy having

this conversation.

Kirill Eremenko: Thank you, Piyanka. Yeah, lots to process. I think we

might need to do a second podcast sometime down the

line.

Piyanka Jane: Absolutely. Would love to.

Kirill Eremenko: So there you have it ladies and gentlemen. That was

Piyanka Jain, president and CEO of Aryng. I hope you

enjoyed this podcast and got a lot of value out of it. I

know it probably felt like drinking out of a fire hose.

Piyanka has so much knowledge, so much information

on the space of analytics. That's why I said at the end

that probably, we need to do a second episode to dive

deep into specific topics here. I had so many

interesting favorite parts here. I loved the discussion

about what data culture is, the four components, the

difference between data science and decision science,

always an interesting topic. Probably my biggest

favorite out of all of them was the hypothesis based

approached to data science. I think that is a very

refreshing approach rather than just diving in and

trying to solve everything, trying to boil the ocean. We

all know that you need to ask the right questions, but

this hypothesis based data science actually takes it to

a whole new level. So if you're interested in learning

more, check out the BADIR framework.

Kirill Eremenko: As usual, you'll find the show notes at

SuperDataScience.com/363. That's

SuperDataScience.com/363. There you'll find any

links and materials we mentioned on the episode,

including Piyanka's book, or books I should say, one

which you can purchase on Amazon. I think one she

said is free. Then you can find Piyanka's courses there.

You can find Piyanka's company for if you want to do

any consulting projects with her, and of course

LinkedIn, Twitter, everywhere else where you can

follow Piyanka. Piyanka does quite a bit of keynotes

around the world, probably also in virtual events. So

make sure to follow her and maybe you can attend on

of the upcoming events with her as well.

Kirill Eremenko: And on that note, if you know anybody who would

benefit from this podcast, make sure to send them the

link, SuperDataScience.com/363. Very easy to share,

and maybe you can help somebody become an even

better data scientist by applying some of the methods

that we spoke about today. Thank you so much for

being here today. Really appreciate you spending this

hour with us and taking the time to tune into the

SuperDataScience podcast. Hope we delivered on

bringing you an amazing guest once again, and I will

see you back here next time. Until then, happy

analyzing.