The Better Business Analysis Institute presence, the Better
Business Analysis podcast. With Kingsman Walsh.
Last week I had the pleasure of talking to Pankaj Zanki who has
over 10 years experience in data and data analytics and we had a
conversation about big data, AI and the insurance industry.
Punkage has worked in many different roles like engineer,
architect, project manager, technical BA for some big
companies in the US like United Health, Home Depot, Progressive
Insurance. Punkage also has a master's
degree in data science and computational analysis from
Georgia Institute of Technology. He's published quite a few
scholarly papers and he currently has some patents
pending, so it was a pleasure to talk to him.
Let's take a listen. Hi, Ben, I'm good and you know,
thank you for inviting me to your podcast.
Not a problem. And sorry about the technical
issues we had this morning. Just read out the bit of a
background about you. You're an interesting character.
Obviously you've come from a technical background, data
background and you've also dabbled in business analysis
and, and, and you give a unique perspective to the world of
specifically big data and AI We're going to talk about today.
My background is rooted in a strong foundation of education
and experience in the data analytics and industry.
I completed my bachelor's into information technology and which
led the groundwork for my technical.
Right now I'm pursuing my Master of Science in data, data Science
analytics from Georgia University from USA, which is
one of the best, top most university in the USA, which
which strengthen my expertise in this rapidly evolving, you know,
the data science industry. Professionally, I have played
multiple roles throughout my decade of experience like data
engineer, data analyst, solution architect, project manager and
you know, I have work on multiple domains like you know,
the retail, insurance, finance, healthcare.
So I do have experience in bringing the insight from the
big data. I have multiple scholarly
articles and white papers are publishing multiple, you know,
the Google Scholar index journals and the conferences,
which is one of the best thing happened for me as well
personally. I also, you know, as you know
that like I also got invited multiple times on multiple
podcasts and for a panel discussion how the big data and
AI is impacting the different industries.
So I'm very excited here to be a part of your podcast.
No. And, and look, we're excited to
have you. So how did you, why was it that
you were captured by the, the world of data and analytics?
What is it about that? What is it about data and
analytic that gets you up in the morning and excited and, and
gets you to go to university again, Do your masters write
white papers? What is it about that particular
area that you are so excited about?
See, data is not about the numbers.
It's a kind of tools, you know, the, you know, which will give
you the, you know, the insights from the messy database.
And that is one of the challenge you have to, you have to be very
curious. Like when you work in the
database industry or data analytics industry, you are, you
should, you should be ready to ask the questions to the
business. This will help the to build that
business strategies to grow your business, to grow your
individual characters as well, right?
And that, that, that is why I'm thinking about like always, data
database or data science industries are going very
faster. If you have a good mindset as
and when I, I work on, you know, the messy data that's, that's
bring the best out of me. How I can we bring the the
insights from this Messi database and Messi data and what
are the different dashboard I can create throughout the
different data source which is available and how I can help my
clients to understand this data. So that is really these are the
different things which has helped me to spark and to work
on the data science. Yeah.
OK. So you've got we talked about
messy data and we'll probably get to that later on as a good
example. Hopefully I don't forget about
that. People talk about structured
data and then people talk about unstructured data.
I guess we're talking about messy data here.
So we'll get to that in a minute because I, I don't think that
the general person in business really understands the
difference there. So we might get to that.
From your perspective, what does big data and AI truly mean to
business? Today we hear the buzzwords.
What do those two words actually mean from your perspective?
See the big big data is nothing but represent the vast amount of
data which is getting generated from the different data sources.
Right now we have social media, different transactions, sensors,
right? Like there are multiple
invoices, billing systems, legacy systems, the audio, audio
data as well as the video data. All these datas are getting
generated from the different data source and the companies
can now collect and analyze this data.
This data when processed effectively offer the deep
insight into the customer behavior, market trends, you
know the what are the different Flyers we should provide or a
promo code we should provide to the customers so that our retail
business can grow more than expected.
Right out out of this big data companies are more workings
toward the predictive mindset and they are started bringing
the insight from the different data sources available.
If you think about the AI right, AI is nothing but the educated
computer system which which is held to automate all this
process, to analyse your different data points, to build
the algorithms and machine learning techniques to to to
interpret your data. AI system can automate the
complex process and also to provide the real time real
insights from your available data sources.
Right. And so without the big data,
without what we just described, what you just described as big
data, which is bringing out all the different channels of data
you've got sources, whatever it is without that.
Is that a prerequisite for AI to be successful in a business?
Yeah, definitely. Like, you know, if you really
want the AI to successfully in the business, right, we should
have that mindset in our in, in our leadership and adds at the
same time, we also have to train our resources to most and to add
up to the AI, AI technologies. AI.
As I mentioned, AI is nothing but you know, the educated
computer system where you can train them with the available
data to from your traditional database to your cloud base and
your big database. So if you were to explain big
data to your grammar, what why is it different to maybe how
organizations are using their data today?
So I'll give you an example. I'm working for one organization
or one of my clients has a data warehouse, OK, it has part of
their information. They've been on the journey of
creating this data warehouse for years and years and years still
doesn't completely they encapsulate every single data or
the perfect data model for their organization, right?
And they're a large client, they've got a lot of money.
So why is big data different to traditional data?
Yeah, sure. You mentioned about the you know
to to explain the big data to a grandmother, right?
So, so imagine you have a big jar of candies with all kind of
flavors and colors mixed together, right?
Traditional data would be like having a just small jar with
only a few candies. It is easy to count and see what
you have in that small, small jar, right?
But now think about the big data having a giant jar filled with
thousands of candies all mix, you know, all, all all mixed up.
You have candies of many different flavours, colour,
shape and they come from the various places as well.
To to understand what kind of candies are in that jar, right?
You need to sort sort them through very carefully, right?
To use a special tool to figure out things like which flavours
are most common and which colours are most common.
What are the different sizes are available?
What are the different brands are available for that candies?
To divide all these things you need a specific tool.
So that is how the big data come into the picture.
Like you know the big data is different because it is a much,
much bigger and more complex. Instead of just a few few pieces
of information, you have huge amount of data from many
different sources. Just like you know, sorting to
the giant car of candies. Businesses use the special tools
and method to organize and to understand this large amount of
data to make the better decisions.
Yeah. So those same techniques that
work in your small jar of candy are not going to work for your
large jar of candy. You need to start to evolve and
start to work in a different way on a much bigger scale.
What are some of the ways in which big data has helped the
insurance industry? A great example of how the big
data and AI is getting used in the insurance industry is it it
is in the area of, you know, the personalized insurance pricing.
Traditionally, insurance companies are mostly considered
the age, locations and driving history.
But however, with the big data insurance now collect and
analyze a much larger data set that includes your driving
patterns, you're visiting new places, age locations, right?
And all this is happening like you know, for some instance like
some insurance companies are started in installing telematic
device in your car that that helps them to get the real time
real time data from your driving patterns.
And based on that your insurance premiums are getting decided.
So this this approach not only reward the safe drivers
behaviour, but also help the insurance to better better
predict risk and you know, to reduce the losses.
I think some people feel a bit scared around the fact that
we've talked about social media before and the fact that, you
know, to be honest, Mark Zuckerberg better, if they want
to predict what you do next, they can probably do that now
with the data they've got. And we've talked about that and
how scary that is. But the fact that the insurance
company in a real practical way, using this to decide on your
premium levels and risk and what might be a bad driver might what
might be a good driver, what is the benefit for the customer.
One thing is coming to my mind is like the personalized
pricing, insurance companies use the big data to analyse the
detailed customer informations and behaviour for for example,
as I mentioned, the few of the insurance companies started
installing the, you know, telematic, telematic devices
into your car. So the telematic data from the
vehicle insurance can access and drive, you know, to to decide
your premium that that help the customer.
You know, at the same time, big data enabled the insurance to
refine their risk model by, you know, incorporating the wide
range of data sources. Also, you know, the big data
helping them to, to detect the, to detect the fraud into the
claim processing for customers. They, they try to, you know,
submit the fraud claim. So based on the customer
behaviour, their historical pattern, the claims history, the
insurance company can use that data and detect the fraud.
You know the big data also helping the insurance company to
enhance the customer services to take their real time, real time
feedback about the different sources, right?
Like what are the what are the different products the insurance
company should build and how they can how how the how all
these things can help them in a claim processing faster claim
processing. There are a couple of devices
and in fact, now one of my patent is also got approved,
which is related to a claim processing.
I have the that device patent with like you know it is AVR
device which will collect all your data from your local
locations and that will get tied to your policies and claim and
that will help to to process your claim faster than expected.
AI technologies and the big data technologies is helping
insurance companies to automate the routine task.
So we talked about the fact that insurance is probably, if you
talked about a spectrum, insurance is probably right on
the progressive end, right? That started to use big data for
everything now, right? They've started to leverage it
for everything like you said, not just to manage the risk or
work out of your bad driver. They should pay out.
But also now in terms of what products they should create,
you've just mentioned that you've got a patent for VRT,
which is around collecting that data faster.
So almost, I guess, if you're dealing with an insurance
company who's in this space, it's probably in your best
interest to share information with them.
Would that be your assessments that you're better off allowing
an insurance company deep into your data pocket?
They're going to make predictions about you based on
the data they've got. Yes, that is absolutely true.
This as I mentioned all these new technique data, database
data sources, right, They are helping the insurance company to
decide on the new product developments.
And there are most of the companies are working on the AI
models like how they can build the AI model for from policy
creation to claim creation to underwriting process.
It's quite, it's quite scary. I think for some people.
I think they'll come away and think that's scary, but the
reality is it's happening right now.
It's not something you can ignore.
It's going to be part of your life and other industries are
going to catch on and use that model.
So banking I guess is quite a traditional, but I imagine
banking will start to get into a very similar example where they
can probably predict investments and they can predict which which
products are going to be more attractive to a customer.
Yeah, sure. Now, AI is often misunderstood
and you talked about the the fact that AI is just really, you
know, this engine if you like, but could you break up some of
the components maybe from starting with big data?
So let's just assume we've got our data connected in some way
down to literally maybe a chat B, GB T or a kind of a chat bot.
What are what are the different stages or components that data
moves through in order to get it from the big data set or
unstructured data to useful in terms of AI?
Yeah. So you know, like as I
mentioned, the the big data is like a vast amount of data which
is getting created from the different data source.
And AI is about creating, you know, the computer system that
can perform the task usually require the human intelligence,
right. Think of it is a teaching a
computer to to think and make the decision like like a human
word, right. So the other other factor of it
like the you know, the machine learning, this is a key part of
AI. We are confident learn from the
different data. Just imagine teaching a teaching
a child about the fruits as and when like we we teach the child
about the fruits we show them a lot of a lot of different types
of fruits, different types of picture.
We, we, we put all the data inside his mind.
OK, this is apple. This is banana, right?
With with the real food and real pictures, right?
So that's how he started understanding.
So this is what the machine learning will do with the AI
models. They will help the computer to
learn from the available datas, images, videos, links, right?
The audio files, yes. And there is also part of it
like a natural language processing, right?
Yes. So which is mean by NLP help the
computer understand and you know to interpret with the human
language. For example, you know when you
talk to a Voice Assistant device like Siri or Alexa, right?
NLP is what allow you to understand your words and
response rapidly, right. And there is also a part of
algorithm. So in a simple word, algorithm
is nothing but the recipe. So when you consider when you
are trying to to make the cake right, there is a particular
recipes, you have to follow these steps, right?
Same things happened in the algorithm.
It is a set of instruction that guide the computer on how to
learn from data and to make the to make the correct, correct
decisions. Chart GPT and as well as your
Gemini chart boss. Those are really the next
generations AI, I would say, which is really helping the
customers to build a strong model about the AI, understand
about the AI, how AI can help them in a different direction
like the chart GPT, right? Yes, you can easily get a lot of
answers build the code base model using the ChatGPT.
When we when we think about chat bot right like this is this is
also getting used in the claim processing.
The chat bot is helping all the all the customer to get the to
get the data quickly and to to submit their claims as soon as
fast as they can. So we've got the big data and
then we've got the machine learning, which is almost
protective and pattern matching, things like that.
Then you've got the natural language side, which is
converting what we're asking the AI to an understandable language
in which the computer understands effectively and back
and forth. And then you've got the
algorithm and, and the combination of that as well as,
sorry, the interface, which is the chatbot itself.
So those elements, there are actually a number of elements,
right? I think people just think AI is,
there's all of those things. And my understanding is that
each one of these areas have evolved over the years and now
the combination of them are starting to come come out as
what we see as a product like ChatGPT or, or or or copilot or
like you said, Gemini, how do you see AI and human analysts?
This is going to be a big question for BAS.
Human analysts like BAS working together to drive bitter
insights. According to me, AI is helping a
big time for human analyst or a business analyst.
The AI and human analyst working together can really boost how we
can use the data. AI is, you know, great at
shifting to the huge amount of data quickly and, you know,
spotting the pattern and the human analyst, they can, you
know, make the sense out of it. Like, according to me, we should
let the AI to, to do the heavy lifting to bring the data from
the different data source, to clean it and to bring the, to
bring the specific pattern, to identify the pattern between the
available data and, you know, to build, to build the dashboard.
And at the, at the same time, human analyst or business
analyst, they should work on the business requirements.
What, what are the different things we should come out of
this dashboard? Like what story we should have
to build out of this dashboard? And what are the different, you
know, patterns we are able to see or how how to make it a?
Sensible patterns, right? These are the different stories
the human analyst can build out of this big data which is
available. Yeah, it's quite interesting.
So because people say it as a replacement of some of the
activities in ABA, whereas ABA should really be focused on the
business and the analysts. Like you said, it's the heavy
listing. Yeah, the heavy listing side can
be done by AI. It's about knowing the right
questions to ask. Also, like you said, what what
are the requirements, what what are you trying to achieve from
the data that's correct. From your perspective, what do
you think the key the ingredients are for building a
successful data-driven culture? As and when we try to build a
successful data-driven culture within an organization,
according to me, it should start with the leadership support.
All right, without your leadership support, your
employees cannot change their mindset into a data-driven
culture with the set of tones for importance for a data in a
decision making. It is it is really essential to
have a clear vision and specific goal for how data will be used
to drive an organization organizational level strategies.
So according to me, our organizations and the
leadership, they should arrange some training, training
activities for their resources. They should train them, they
should provide the available materials and they should also
explain what is the, what is our mission and objectives as a part
of this data-driven process. And at the at the same time,
they, they should start some recognition, right, like giving
some awards to the employees as and when they bring some kind of
big, big time insights from from the available data.
So that will that will definitely motivate all the
other resources as well. Yeah.
So that's providing incentives effectively to learn this new
pattern. Yeah, I think that's a really
good idea. So I'm going to ask a side
question here because I have been involved in a couple of
executive teams. We are the board and this is
probably happening all around the world right now.
There's probably board members meeting or an executive team
meeting going. We need to get into AOI like
every our competitors are getting into AOI.
They're not sure what AOI is. They're like, we must embrace
AI. And then that may be the just
that's the only directive they've given their
organization. What do you think that's a
mistake? And, and you talked about before
having a very specific mission vision.
Do you think it's enough to just say get into AI, or do you think
you need to be really clear about what you want to achieve
like any other project? Yeah.
I think as I mentioned before, there should be a very specific
objectives why we are moving to the AI, What are our end goal?
Is it our end goal to make our product more, you know, the
client specific or should we have to, you know, the bring
more clients to our organization as and when like you know, also
there are a couple of companies they started building the AI
considering the future, like if they build the AI then they will
have more clients. And there are there are other
types of companies like they have already, you know, some
requests from the clients like to automate this process using
the AI technique. So that's how some, some of the
companies moving toward the AI, but but all the businesses and
companies as well as their leadership, they should have
very clear vision and mission why they want to start using the
big data or AI and what are the different factors how we can
train our resources, what might be our roadblocks.
Because AI and big data, if you, if you are moving toward that
side, then initially, initially there will be some, some XXX
amount of, you know, expenses you have to do to train your
resources to, to buy some new, maybe new tools, new
technologies. Yeah, 100%.
And I guess that's some of the biggest obstacles is probably
that initial investment that you're then paying off along the
way. What in terms of the future of
big data and AI, what are you most excited about?
What do you think the new emerging technologies will look
like? I'm I'm very excited about the
explainable AI, which is called XAI.
As the AI system become more complex, there is a growing need
of transparency in the how they make the decision.
So XAI is more focused on making the AI model more understandable
and more transparent. This technology will help to
bridge the gap between the AI and powerful capabilities and
the need for human to oversight the accountability.
At the same time, I'm also excited about the quantum
computing, which is also on the horizon as the game changer for
a big data in AI. I think with the with the
quantum computing right and it is ability to process the worst
amount of data at unpredicted speed that will that will help
all the businesses right to get the real time feedback faster
than expected. I think the future of big data
and AI is moving towards more intelligence, transparent and
efficient system where this XAI technique right which I have
mentioned explainable AI which will help to bring the gap,
which will help to reduce the gap between the trust between
the customer and the companies. Because I think there's a lot of
concern around ethics in AI and like you said, transparency.
So that sounds like an exciting thing for us to watch out for.
Do you, in terms of young professionals and BA's data
analysts, are there like some top five skills that they should
be learning when it comes to AI and big data?
I would start with the very basic, which is their domain
knowledge, right? So as and when they start with
any, any data project, they should have the domain knowledge
about that. Like you know how the insurance
data looks like, how the retail data looks like, what are the
different factors for banking data, right?
So to gain the to that, that kind of knowledge you will get
when as and when you more study about that particular domain.
There are lot of data literacies, right?
Like what kind of datas comes out of, you know, the, the, the
insurance claim for auto as well As for your personal claim,
right? There are different types of
claims, right? So we have to understand all
those claims. Also at the same time I would
suggest that they should stick to one programming language,
maybe Python or R which is commonly used throughout the all
big data and AI technologies. They should have one
visualization tool, Power BI or Tablou or any kind of
visualization tool. Or maybe Excel is also fine, but
they should have the curiosity and to learn more about the data
to bring the inside of the data. And they also understand like
how we can move the data from one source to other source.
So they should understand the ETL process, right?
What are the different? What are the different ETL
processes are there? Yeah, extract, transform and
load, right? I also I will also suggest them
to focus on the communication skill because you are going to
build your dashboard right out of messy data.
But at the at the end you have to explain that dashboard to
your leadership team. You are C level executive so you
should have that communication skill to be at your top like to
to convince your leadership. OK, this is my dashboard and
this is the pattern I understand and you should go with this
route. Because you're focused on domain
knowledge, which is business, you're focused on explaining to
the executive. That's business.
You've talked about some tools that you can use to talk to the
data, but one of the things you didn't talk about, which I found
interesting, was actually, you know, nitty gritty kind of the
technical side. What you've actually focused on
more is more the business side than the nitty gritty technical
side, which I like because I think that's really important
and that's where BA's generally play.
But the value seems to be using that information to make
business decisions. Why I'm more putting a more
weightage on the business side or a domain knowledge because
you know the technology will come and go.
There will be today there will be a Python or tomorrow maybe C#
or Java, right? Some of the companies, some of
the companies is more comfortable with the Java, some
of the companies are more comfortable with Python today.
Some companies use Power BI, some companies use the Tableau.
So these are the different tools and technologies that are
available in the market. But at the end, we should have
our analytic basics, very strong data reading techniques should
be very strong to understand the data.
That should be very strong. Even though if you don't know
about the Python or R right, you can easily use the SQL or PL.
SQL or Oracle and you can bring your data to your visualization
tool. Thank you so much for your time.
I wanna before you go Pankaj, what is the most important thing
you would like our listeners to take away from our conversation
about big data and AI? To all the listeners, I would
like to say the AI and the big data, these are the technologies
which will be there for a longer period of time.
So get go get used to it this learn more or be curious.
Ask the questions to your leadership, ask the question to
your you know the mentors and make more connections.
There are a lot of organizations or meet ups are meet ups are
happening right? So they should register.
They should volunteers their time and to learn from the
industry leaders and experts. Like how the big data and the
AIS are getting used into a different industries.
At the same time they should also to work on their basic
skills, maybe one programming language, domain knowledge,
visualization tools. So be curious, keep keep
learning, keep studying. Do a networking, learn from your
leaders and on from your networks.
Thank you so much, Pankaj. We'll catch up soon and we'll
follow your journey as you write more around AI and big data.
We hope to speak to you soon. Thank you.
Thank you, Ben. It was a pleasure, you know, to
speak to with you today.