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Cloud & data - The Engine & Fuel of Business by Deepak Sharma
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Oct 30, 2023
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0:28
And I see me and Harish in you. Reason being, we both are classmates. We did our MBA together
0:38
And while doing the MBA only, we left our job. We started our startup. And today we are
0:45
standing in front of you. I'm working as a chief data scientist and co-founder with FarmFetch
0:50
It's a US-based company. It's set up in St. Petersburg, Florida. And my role is to build
0:57
a data science solution for the company. Or in a nutshell, I can say that I generate information
1:03
and Harish secured that information. Because he's from the security part and I'm the data generation part
1:10
or the information generation part. In terms of experience, total 18 years
1:14
in the area of IT, which include 12 minutes into ytics. I did my specialization from ISB Hyderabad
1:21
in 2014 in data science and then master's from IMDU. know. And I've given a lot of industry sessions to all the top IAMs and IITs. So that's a part of
1:31
my hobby. Okay. So this is what we would like to start with. So today the topic which I'm going to
1:38
talk about is data and cloud. And it is something that is a very, I would say, contemporary topic
1:44
You all must be aware of what is data, what is cloud. I'll not go into detail of what is cloud
1:49
and data, but I will be talking more about where the world is heading. Because once you know that
1:54
where the world is heading, but tomorrow when you pass out from your college, you all will
1:58
be ready for the world. It should not be like what is happening today because by the time
2:03
you will come out, that thing may get obsolete. And that's the reason why these kinds of sessions
2:09
are arranged. And during this session, please feel free to ask your question at any given
2:13
point of time. Don't wait till the end because then the disconnect happens and sometimes we
2:20
forget the question. So whenever you have a question, please feel free to ask. Okay
2:25
Shall we start? So what is data? In a simple term, I would say it's a fact or an information
2:36
Harish said in the starting that he ran two startups and successful exit. That was the
2:42
data for me to start as a co-founder because that's the information which I drive because
2:47
I'm not from a startup background. I was in the job throughout my life. And when I get
2:52
a co-founder who has run the startups and who has experience, success and failure doesn't
2:58
matter. At least he has experience more than me. And that information, I drive out of it
3:02
and that's why we started the company. So that's the power of data which will help you
3:08
to take your decisions and it will help you to understand where your interest lies and
3:14
are you on the right track or not? Okay, that's the purpose why we always talk about data and
3:18
everywhere all the companies they're all talking about data. They say that data is a new oil
3:22
data is a new currency, data is a new fuel and cloud is an engine. Reason being that today even
3:29
money is also converted into data. If you talk about our FinTech, if you talk about and distillation
3:34
everywhere the numbers are converting into some information or some knowledge that we can drive
3:39
out of it. So types of data, so very basic stuff, when we have been talking about type
3:47
of data, there are two types, quantitative and qualitative. You all must be aware of this, so I will not go into detail of it, just a very high level understanding of it
3:53
When I say quantitative, that means I can quantify, I can count, like this number of students sitting here. That's a quantitative. And qualitative means like how many students
4:02
are looking for a startup, how many people are looking for people like working in a startup
4:07
or how many students would like to go for a job or continue with their studies, post-graduations
4:13
So these all are the qualitative, which will categorize the data, which will tell abstract information out of it
4:22
And as a qualitative, you can have two further, nominal and ordinal. Nominal means your class role number, just a placeholder
4:29
I will never say that a person is having role number two is better than a person having role number three
4:34
It's just a placeholder for me to identify that person uniquely. That's the nominal data
4:39
When I talk about ordinal, it means ranking. First rank, second rank, third rank, that is the ordinal data
4:44
We have put the data in the form of ranks in prioritization, but we cannot quantify that the person who's sitting at rank number one
4:51
is twice good as the person sitting at rank number two. That we cannot understand out of it
4:57
So that's ordinal data. When I say about qualitative data, this is something like a discrete and continuous
5:03
Again, which can be counted. Let's say we have 50 members sitting here. The discrete
5:08
one. But what is continuous? Like your age. You cannot calculate your age. Can you calculate
5:16
your age? Anybody can tell your age? Can you calculate that? Because every second your
5:23
age is changing. Every second. Right? So we cannot calculate that age and that's the reason
5:29
why it's called as a continuous order. It keeps on continually changing, like weight
5:33
it keeps on changing. Age, it keeps on increasing as time tickers, right? So that's the reason
5:41
that it's a continuous, it keeps on continuously increasing or decreasing or following some trend
5:45
and following some trajectory. Okay. The important part comes here is a DIKW pyramid
5:53
Now, what is DIKW pyramid? Anybody here who has seen this pyramid earlier
5:57
that will give me a fair understanding of how deep I need to go into it
6:02
Anybody here who has seen this pyramid? No? Okay. Fantastic. So we're all on the same page
6:07
When I say D-I-K-W, I'm talking about data, I'm talking about information
6:11
I'm talking about knowledge, and I'm talking about wisdom. So D for data, I for information
6:18
and then K for knowledge, and W for wisdom. What exactly we're going to achieve out of this data
6:23
is a decision power. A lot of companies are talking about data. Why? Because they want to take decision quicker than
6:31
their competitor. They wanted to understand their customer and try to understand that what my
6:35
customer need so that they can take the right decision at right given point of time. And how
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they'll take decisions depending on the like this DIKW pyramid I'm talking about. When I say data
6:46
data is like anything which is scattered. It could be like a conference is happening in like
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I can have. It's a data which may or may not be having any meaning for you because you
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may not be interested for you or you may be interested for that. So when you connect all
7:04
the data points, it becomes information. So a conference is happening on the upcoming technologies
7:11
in the area of AI and data security and Azure area. So this gives you information out of
7:15
it. And once you get the information, then you will be able to drive a knowledge. And
7:22
knowledge means like, is it good for me? What kind of data, what kind of information they
7:26
don't talk about? That information is something that is going to help you to take your decision
7:31
that whether you should go there or not. And that's why you are sitting here. So in itself
7:35
you are applying this DIKW framework every day, whether in the form of your WhatsApp
7:41
whether in the form of your class notebooks, or wherever you are listening to your YouTube
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videos and all, you are applying this DIKW framework and on this DIKW framework the companies
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are building their strategies. Why they have put up so many effort in collecting the data
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so they can drive information out of it? We have no privilege to speak each and every
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individual one to one And this is where the data which has been put in the area of e in the digitalization part will help us to connect us with the customers or with the target audience And once we have that information with us we can drive our
8:19
knowledge because maybe that information that I've derived may not be useful or may be useful
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so that I can filter out the information out of it and then once we have the knowledge
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it's easy for me to take this in, yes or no. Right? So that's the information which we have
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to capture and a lot of problem happens when we have a corrupt data. There's a very famous
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thing in the area of our AI, garbage in is garbage out. When there's garbage inside the
8:46
machine, you will never expect a very good product out of it. And sometimes the data
8:51
which become very, very biased in nature, maybe because of the lack of knowledge of
8:55
the sampling or lack of knowledge of any other mechanism through which we collect data, if
8:59
that knowledge is not appropriate, we will get a very garbage data and then God will be the savior for that company
9:07
Okay, so garbage in is garbage out and that means that a lot of efforts, a lot of money, a lot of resource spent on collecting this data
9:15
And that's an important part for all of us. Now why data is a fuel in digital economy
9:21
Harish has talked about digitalization, but in the case of digitalization, ultimately
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it's a data which is moving from one source to another source. And why it is a fuel? The
9:31
reason being, good data beats opinion. In my opinion, I would say that the growth of
9:38
the company is 7%. In my opinion. It could be my individual opinion. Maybe I'm not in
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good mood on that day. Maybe I have a very serious fight with my wife. And I'm completely
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like an art of my mind on that day. So on the basis of an individual, a company never
9:53
takes a decision. They always try to set up a process. And whenever they try to set up
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a process, they always look for a generalized mechanism, which means that even if this person
10:04
is not present tomorrow, we would be in a position to take a decision. And that's why
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companies not run on people. They run on processes. People will keep on coming and going out
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That's not a big issue. But generally, what happens, a company forget, and they give preferences to people rather than the processes
10:20
and then the problem starts. And that's why this data is considered as a fuel
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Reason being that it beats the opinion. It will not be depending on the individual
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like in whims and fancies or gut feeling, that this will work or not work
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The data will tell. And our job is not to challenge people who are senior to us
10:38
Always remember this thing, because tomorrow when you go to your companies and start working, you might work in the data science field
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and you will feel that I have my data science knowledge, so I will be the super god here
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No, you are not like that. There is no discount. There is no escape from the learning path
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that the people have taken in the 15, 20 years in that domain. You are supporting their decision with the help of data
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because the genre has changed. The customer has changed. Today, customers don't want to buy a house
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but they want to buy an iPhone. They don't want to buy a car
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They try to offer all an Uber. because the requirements are getting changed
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And when the requirements are getting changed, we are helping our decision makers build the app of data
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where the world is heading, and how we should strategize our requirement
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our expectations, so that we should not be obsolete in the market. Data drives customer experience
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We all understand that. ytics, machine learning, and AI. Now, a lot of time
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I have seen that people got confused with the machine learning and AI, and they try to use that term as a replacement for each another. Always remember that AI
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is a process which will help you to take decisions. And machine learning is a technique through
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which you can apply AI. Okay? Always remember this line, AI is a process. For example, when
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you go from your place to a destination, you use Google map. It gives you an option, there
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are three routes available, which route you want to take. That's an AI because it's giving
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you information to take your decision. It's completely on you. You can take this or you
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depend on the software, which means that we go to Spotify, we go to YouTube, it give a
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recommendation and keep on running that song. We have given the privilege, we have given
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the access to the machine to take this on our behalf. But sometimes we take this on our
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own depending on what information I get. For example, in the case of healthcare, in the case of education. Okay? So data is something like is a fuel here and the reason why it is
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fuel that it will churn out that information from the system or from the framework. Companies
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view data as a strategic factor rather than as an end product. So today data is not an end product. It is more of like a strategic factor because they decide the strategies on it
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After COVID, a lot of stuff has changed. The data which was 10 years back today is
12:56
completely opposite and I cannot take decision on that data which means there's a shift is happening
13:01
from big data and where the world is heading I'm going to talk about in subsequent slides. Any questions so far? All good
13:12
What is cloud? I just put one slide here. Reason being that today the whole day people
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are going to talk about cloud so I will not like to talk about cloud in detail about their
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models and all. It is only a way to access your resources via internet. That's it. Okay? And you
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pay for the use that you are doing on the platform. That's a simple definition of the cloud. If you
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don't have internet, you cannot apply cloud. You apply internet, access the resource, whichever part
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of the world you are sitting, and you are working seamlessly. That's a cloud environment which we
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talk about. And today in all the sessions, we will take a very deep, detailed level of understanding
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on the area of cloud so let's move forward various form of data on the cloud now what are the various
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forms the data is resting state the data which is accessed data in transit data at arrival and
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backup recovery these are the five stages on which the data changes the transformations and at all
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the stages you require people like hadish because why if the data is stolen at the rest when the data
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is residing in our database, that means data is at rest. So we have to secure the data everywhere
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because sometimes the data is not our own property, sometimes it's customer data. If you're working
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in the case of healthcare, we're talking about the patient data. And when we're talking about patient data, that means we're talking about the PIA, the personal identification information
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And that could be very, very sensitive also. And once this data is being collected
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a lot of responsibilities also comes with us. We cannot simply go and sell that data. You may get
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a lot of calls that are looking for a loan. Sometimes you must have observed that if you have
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a very good amount of money in your bank account you start getting calls of FDs and all. Sometimes
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data may get compromised but because you all are coming to the industry maybe two or three years
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down the line, always remember that your ethics should continue with you and data governance is
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equally important as data collection. Which means that who's going to access the data
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if the person is the right person or not to access the data that is on the honors of you because
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the person has trusted you while sharing this data. Accessing is all about like who can access my data
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and data in transit means like when I requested something the data is moved from source to destination the data is in transit mode and the data arrival means the data has reached
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to a destination and then we take a backup and recovery so that something wrong happened then
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we still in a position to run our business we should have a backup and recovery mechanism so
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So that's a various form of data on the cloud. Now, where the world is moving
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that is very, very important because three years down the line or two years down the line, I don't know at which part of stage
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of your studies you are right now. You might be in the B-Tech, you might be in the MBA
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And by 2025, there's a 70% shift. This is what I'm saying
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It's what Gartner's saying. 75% or 70% shift in the focus area of the organization
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from big data to wide and small. Now, what is big data? You must have talked about the volume, velocity, variety. You must have heard about this. What
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is Big Data? Right? But that volume, velocity, variety was applicable when we have a data
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which is like 10 years 15 years old But after COVID my whole customer has changed The way of accessing the system has completely changed What I will do with that big data That garbage for me now Then the shift starts from your big data to your wide data
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which means I've extracted the variety part out of it and now my focus is more on the variety
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I'm focusing on the customer behavior. So now I'm wanting to check their purchase behavior
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what kind of product the person is buying, from which region, from which country the person is
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buying that and which kind of products this customer is buying. So that is a wide data, which means it is giving me all the varieties of the same kind of a business, which is like sales
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here, which channel the person is accessing, social media, mobile app, desktop, whatever the
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mechanism they have. Okay. They have our mobile app installed or not. So these all are various
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varieties from which a person is collecting the data. And that's why the shift is now moving to
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the wider data, which means that I'm more focusing on the variety rather than the volume
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because the time has gone when a company takes two years or three years to build an AI system
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Today they need an AI system within three months because after six months nobody knows another
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COVID wave comes up. We cannot afford to have a solution which will be coming two years, three years
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down the line and it becomes opposite before it got launched. Okay so that's a focus where companies
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like in making and that's the reason why I put this slide here so that you all should be very
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very much clear that every variety of the data is important for you and the shift is
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happening from big data to wide data. Now what is small data here? Small data means
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I'm talking about a particular domain, a particular set, like for example, the transactions. That
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is small data for me. A transition of let's say Chroma store for let's say Janakpuri area
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Okay, that is small data for me, which will tell me about the customer base, the kind
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of transition they are doing in the Chroma store in the Janakpuri area. So this is what
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you call the small data, which will completely focus on one particular use case and try to
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solve that use case. Is that clear? The shift between the big data, wide data and small
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data and the way the world is heading? Thanks. Now, we say that cloud is working as an engine
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Now how cloud is working as an engine? Anybody can recognize this model here? Anybody has
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seen that model anywhere? You have seen. Okay. So what is this model all about? And what
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term we give it to this model? Okay, no problem. So this is where my role starts. Okay. So
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this is an engine, and we have deployed that engine on the cloud. Reason being that when
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I put a fuel in my car, I need an engine to burn that fuel so that energy can be generated
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and I can make a best value out of it. That's the purpose of why we look for an engine
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and why we look for a cloud, because the engine is ready. It's just assembling is required
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If I need to do an ETL job on the AWS, I might be looking for a glue as a tool
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If I'm looking for a data storage, I might be looking for an S3 bucket. So these all are the components of the engine
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I just need to assemble it and run it. And that's the reason that why within three months
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companies are expecting a result to come because they do not want a time should be spent in building that engine. That engine
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is available. Only the knowledge is required that which component work at what stage, and
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if you are aware of that component, then it will be a very easy job for us to assemble
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that engine and collect the data and start taking value out of it. And this particular
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model which I'm showing you right now, this is called data fabric. Now what is data fabric
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Fabric is like I've connected all the sources of the data in such a seamless manner that
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whenever I need one particular type of data, it will be accessible for me. You can simply
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think of fabric as something like the shirt and the clothes you wear. It's a fabric, right
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It has connected all the points and it has given a shape to that particular stuff. In
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our case, it's data. If data is not taking a shape, then it's no use for me, right? And
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model is all talking about the data fabric here. I'm talking about data lake, I'm talking about here
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data warehouse, application and cloud storage. The data is collected there, get churned and we apply
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AI model on it. I'm not only talking about the machine learning, I'm talking about the
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natural language processing, I'm talking about the deep learning, I'm talking about the artificial
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networks, I'm talking about the transfer learning. There are multiple ways through which we can
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achieve AI. I'm talking about robotics also because ultimately AI is what the process of taking a
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decision and the term which I just talked about machine learning, deep learning, neural networks
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these all are the techniques through which we can achieve AI okay and the whole techniques are
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working in the area of this globe which is I've shown it here in the center because all the
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intelligence is happening here the data is on the left hand side and on the right hand side is the
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results which can be exposed to my customer which is like reports. I can use the reports
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and today the reports are not like just a static report. Today the reports are also dynamic
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You must have heard about Tableau as a tool, right? You can generate dashboards dynamically
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you can do storytellings, and you give access to your customer to play with the report. It's not
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like whatever the standard quarterly or bimonthly or monthly reports are no longer, let's say
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valid here. Today, every customer needs to be empowered. They feel that if I'm collaborating
21:57
with an IT company, I should feel empowered rather than dependent on it. The shift is also happening
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and today, let me tell you, the customer is also doing their homework. It's not like that. There was
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a time when whatever the IT company says, customer says yes. Today, customer also do the studies
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join courses to all the great institutions and they prepare because in the business
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everybody has a role to play. Okay? Now, this is the engine which will help you to generate the best
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value out of it. And what we call this engine? We call it a data fabric, I just mentioned
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And what is it? It's a composable which means you can compose. Whatever requirement you have
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you can make that requirement out of it. It's flexible also, and it is scalable also
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Now what is scalable? Scalable means when the demand comes up, it will help me to meet that
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demand and when the demand goes down, I'm still in a position to come to my original stage. So
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scalable and the elasticity. The elasticity word is missing here. Elasticity means it's a kind of
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rubber band. I apply a force on it, it stretches, I release my force, it comes back to the original
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position. And that's the reason that we need a composable, flexible and scalable way to maximize
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the value of my data because if I'm not able to maximize the value, then I will not be making
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justice with my customers and with my business. And this is an upcoming framework which is
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happening in the world of AI. The whole country, sorry, the whole companies in the area of whether
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it's in IT or non-IT, they all are moving to Data Fabric. And by 2025, everywhere you will see that
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this is going to apply in all the big organizations and even the small and the medium organizations
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It's an emerging design concept that gives a framework to think about how to stack existing
23:40
tools. It is not saying the new tools, existing tools. Okay? And then the resources and the
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processes, how to bring all this stuff together in a framework so that I can get the best
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value out of it. This is what the data fabric is. This is not a fixed architecture. I'm
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not saying it's a waterfall model. I'm not saying it's an agile methodology, nothing like that. I'm saying that what a requirement, let me have a flexibility into it. Okay? When
24:05
When you join a team as a data fabric you become a tailor
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You take the dimension of the person and you prepare a cloth or a shirt or a like a trouser
24:17
for that person. Now why we do that? As a data scientist we become a what we become a tailor
24:28
So we become a tailor, we understand the requirement of our customer and accordingly we design
24:32
that framework because earlier what was happening the whole data was collected centrally at one location and that was creating a problem of latency that was creating a problem of replications also We are preparing multiple copies of that data
24:46
and once you have that multiple copies then helping companies to take out that multiple
24:53
copies we have to make the data integrated also that all copies are on the same track
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Okay, there should not be any change in the versions of the data. So that's the biggest
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problem and this is what the data fabric has sold. The key pillars of it, like we have
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the data catalog which will connect all the metadata to make a dynamic kind of environment
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so that we all can connect with the data whenever we require it. And then we have the AI ML
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algorithm running on top of it and we set up orchestration. Now what is orchestration
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Which is something like, have you seen the orchestra and the music? A person who like
25:28
can give some waves and like in some musicians so like people they start running like they had the
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guitars and they had the drums and according they follow the orchestra and together they
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follow the musician and they like and prepare a very good sound so data operation means like we
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set up a pipeline pipeline something like same thing about daily metro it's a pipeline and we
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as a human being as data so that pipeline is moving the data from let's say telegbisuk not
25:54
place whenever the requirement comes in. So that's a data pipeline and we as a data and the whole
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orchestration which is happening at the control tower of Delhi metro is something is really going
26:04
to happening in the company where people will start harnessing the value out of it. Okay, so this
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data fabric I will not go much more detail into it. Reason being that it's a very very high topic
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like in heavy topic and half of that is not sufficient for it. Benefits we all understand
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that it's an efficient democratization, scalability, integration, control and agility. So these are the
26:23
basic benefits. It has multiple ways to apply and it can depend on how much knowledge we
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have to apply this into our D2D system. So this is all from my side. Anybody has any
26:35
question on the topic? Yes. Sir, how can you point out the future of cloud in India
26:48
So his question is future of cloud in India
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First of all, do we understand what is cloud? And do we understand how big India is
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What kind of businesses we run? The population here? Do we have a privilege to set up a system
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where I do not know what will demand of my product in the future
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and can I predict that I might be requiring 100 or 1,000 servers in the future
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Very difficult for us, for any business. This is where the cloud comes in
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and that's the reason why cloud is playing a very, very important role, because as I said, it's an engine
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and everybody requires an engine to take the maximum value of the data
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And if I talk about the future of cloud in our Indian ecosystem
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everything is moving to cloud these days. Even after this COVID-19, even small industries are planning to move in the cloud
27:53
They all are brushing up the skills. They all are telling their resources to start learning about cloud
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because there's a very important point I would like to mention here
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whether it's an AWS or Microsoft or let's say GCP. They give you an environment to run your business, but they will never enter into a data trajectory
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They will always expect that you have a cloud team which will take care of your data because that privacy is something all companies are afraid of
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That my data may get shared with some other person and they wanted to bring that misconception out of the mind of the people
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And at the same time, they have so much secure environment that any Tom Dick and Harry cannot go and start hacking it
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hacking it. And the kind of services they are offering, they are using that same service
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for their day-to-day operation. So that's the reason why we have a very good future
28:44
of the cloud. Okay? Yes? Can you talk about FarmFetch and what you guys are doing in that
28:49
The FarmFetch? Thank you. Thank you for giving me a chance to talk about FarmFetch. So we
28:54
in the area of FarmFetch, we are selling fresh fruits and vegetables. In US, there's a very
29:00
big problem of frozen foods. So what they do, they have like an, let's say Walmart, they
29:06
have Target, they have I would say many other retail stores. They have defreezers where
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they keep all the stuff there and people do not have an opportunity to eat fresh fruit
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and vegetables. What we do, we take an order, we extract that stuff from the farms and within
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24 hours, we deliver that product, the fruits and vegetables, so that the freshness is maintained
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there and the taste should not get compromised. So this is what we are doing
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Have I said sorry? No, we are just definitely awesome because we are a startup
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We cannot do all the work on our own. Otherwise we'll keep on fixing those issues
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If we have an expert with us, we'll definitely offload that work to the expert so that we can focus on our business
30:02
We are not a transportation company. So why should we have our own transportation system? Because transportation has their own challenge supply chain
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and the wastage is not. So Harish would also like to add something on here because we both are the co-founder of that company
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So let's hear from him also. Since the question is for Farm Fetch, so yes
30:19
So the idea is not having our own cold storage or running our own supply chain
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The idea is not having cold storage at all. The idea is that once it is harvested
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it should reach within 24 hours on your plate. You understand? I'll talk about data now
30:38
30% of the total produce in US goes into the trash. And out of that, 47% of that goes into the trash
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when it is at home. So the consumer is the biggest, you know
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waster, I mean, who is wasting the food, right? Why? You know, because if a fruit or vegetable is harvested
31:05
its lifespan, you know, depending on various types of fruits and vegetables, lifespan spans from, you know, two weeks to a month, okay
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Now, if something is grown in California, is being shipped to Florida
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the whole process itself takes at around one and a half week of time
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Then it goes to the retail store, it goes to the shelf, and these retail stores optimize it in such a way
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they know that after two days this produce will go waste. They will sell you
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And you put it in your refrigerator, in two days, it's gone
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And that's why we have the biggest wastage happening at the consumer end. And that's what we are trying to solve. We are actually creating a hyper local economy, wherein the produce will be produced within that hyper local environment and it will be directly sent to the customer without actually running through any cold storage
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that will increase the freshness, increase the local economy and will also decrease the carbon footprint
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because we are not guzzling a lot of fuel to transport the fruits and vegetables across states or across countries
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So that's the core idea. Thank you. I think he has a follow-up question
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Yes, we are. You can go and check farmfish.com. You might not find any fruits and vegetables here
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but we are operating in 33701 zip code. You can put your position as 33701 and you can check
32:42
Thank you. Thank you. Thank you very much. So I hope that you got some insight today
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