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.