Understanding Business Intelligence and Analytics

This is a course as the title suggests related to generating intelligence for business. And it appears from the title that business can be done with intelligence and analytics and business can be done without intelligence and without analytics as well. Obviously, from the title of the course, it appears that business would benefit from intelligence and analytics. There are two constituents or two components there. So, this being the first class, we will try to develop some essential and fundamental understanding about what is intelligence and what is analytics. So, What is intelligence is a very loaded question because it requires us to understand the philosophy behind intelligence. Many of you must have heard about a measure called intelligence quotient or IQ. And I am sure some of us have taken our IQ test and got some scores. And possibly you are happy. that you have a good IQ score and that is why you are crediting this course. And we all believe that as human beings we are intelligent. And so, here in this course we are applying intelligence not to, apparently not to human beings but to business. And what does business intelligence mean? That is very important as far as this course is concerned. And then it is joined with analytics. So, business intelligence and analytics look like related concepts. And it also appears that since there is a course like this, it also appears that there is some value or business value when business is done with intelligence and analytics as compared to when it is done not with intelligence and not with analytics. So, what is the role of intelligence in business? Business or in general in life and business, we can also think of life at large where you know we function as individuals, we function as groups, we function as organizations for profit and not profit, not for profit. We also function as countries, so our nation states. So, in all this context, there is a role for intelligence. So, we are specifically looking at business intelligence because this course will have several examples related to business intelligence or intelligence from business context and that therefore the title. But being the first class, let us develop a reasonable understanding about these two concepts. What is meant by intelligence? That is the first question. So, There could be different questions if I ask an audience as to what is intelligence? Intelligence means thinking by my own capability, what is right and what is wrong. Somebody says intelligence is about thinking by one's capability or one's innate ability or natural ability. The ability to acquire and interpret that knowledge that they have given. Okay, somebody else says intelligence is the ability to acquire knowledge and interpret what you acquire. All right, okay, these are all good attempts to articulate what is meant by intelligence. So, instead of defining intelligence, let us look at what are some indicators of intelligence. So, intelligence can be defined by what we observe about intelligence. So, that is one way of defining or understanding concepts based on its indicators. So, what do intelligent people do or how do intelligent people function? If you look at intelligent versus non-intelligent people, you can actually look at their behavior, look at their decisions, look at the way they actually ask for information so that they function. in a more intelligent or in a more effective way. So, there is a way intelligent people or intelligent beings in essence take decisions as compared to people or beings who do not have intelligence. You know, I typically take the example of birds. Birds have some intelligence, I am not denying that living beings other than human beings do not have intelligence, but look at them. the birds make their nests the way they have been doing it say 100 years ago or 1000 years ago, they would be making nests, they would be making nests in the same way even today. Maybe we do not know, I do not know exactly, but the difference is very little or it is very negligible. They do not improve or they do not in other words learn from the environment or they do not actually learn and improve. So, therefore, learning is associated with intelligence. All intelligent beings are able to learn. Learn from what? Learn from lessons. Lessons come from where? Lessons come from the environment. The environment or the nature or it is the environment around us or it is the nature around us. that teaches us and what does the nature consist of when we use that word nature. In the context of analytics course, I would say we learn from data. The environment gives us, offers us data, data in diverse forms. We have been experimenting right from the day we were born. We have been growing in our intelligence, our Intellectual capability has been improving thanks to the learning that happens from the data that we receive. Sometimes we generate data through experiments. When we were children, we conducted a lot of experiments to learn. We touched a hot surface and we learned that the hot surface is hot and therefore I should never touch a hot surface. But a child wants to do. all kinds of naughty things, because it is through those disobedience that the child does its experiment and learn what to do, what not to do. So, therefore, we generate a lot of data for our learning. And where does it help us? So, let us come back to the context of business intelligence. Where does intelligence play a role in business? In some, intelligence is important in decision making. Intelligence is a resource or I would say a capability for decision making. An intelligent manager is someone who makes intelligent decisions. And what is an intelligent decision? We say an intelligent decision is an informed decision. Is an informed decision. You can take decisions in informed way and uninformed way. For example, you can join a course like Business Intelligence and Analytics in NPTEL by having no information about the course or you just browse through various course or you like the title, let me just go for the course or just randomly select a course. Suppose you want to do say two courses and suppose there are 50 courses offered in a semester, you just randomly pick to courses. We say this is not an intelligent decision because we did not collect any information to take that decision. So, therefore, that is an uninformed decision. And uninformed decision since you took that decision by random choice, whether you benefit from the course or not is also random or by luck. If you are lucky, you learn well and it benefits you in your career. If you are not lucky, it does not benefit you. So, you are increasing or you are depending on chance than on information when it is available to take your decisions. Therefore, random choice in situations like this when information is available is an unintelligent decision or an uninformed decision. So, therefore, I am arguing that Business intelligence is a capability that business could develop to take good decisions or informed decisions. And in this context, what is an informed decision? What is an informed decision in the choice of a course? And the reason why we post a lot more information about the course, like what is the course content? What are the materials that will be used in the course? Who will be teaching the course? and what is the profile of the instructor and when it will be offered and how to get the course credited, how to get a certificate. There is lot of information that we add to the title of the course. Why is that information added? It is for a decision maker to read and analyze and see if this course matches with the interest of an individual. So, therefore, in decision, there is some sort of matching of information on one side with a goal on the other side as to what you want to achieve. So, in short, I would say, business intelligence and analytics is for business decisions. It is for decision making. Decision making is the most important aspect of business management. Business managers, what do they do? At different levels of management, from operational level to strategic level, they take a lot of decisions which has short-term, medium-term and long-term impact or implications. And we advise that business decisions should be based on information. Information is the key to making successful decisions. So, it is in that context that we talk about business intelligence and analytics. So, BI and A as it is called in short is a fairly new phenomenon and the world of business did benefit from data-driven decisions for the last several decades and that has assumed several names or several labels in the past. decades. I will give you an overview of that as to how it began and how it extended to contain diverse type of data and techniques and where it stands today. And that is an integral part of this course. And very soon, I will also explain to you what are the contents of this course and how you can effectively participate and learn in this course. But, before we get into those details, let me also dwell on some of the drivers for BI and A. As I said, data driven decisions has been part of business world, or it is a sort of support that information systems provided to business in addition to automation. But today, I would say for the last one decade, these terms BA and analytics have gained some traction in the industry. And there is a context to it. And that is what we are going to see in the first class today. All right. So, let me show you some pictures and figures and graphs in the subsequent slides, which will help you understand. what is going on in the environment. So, this is the cover page of the newspaper, The Economist, which I have been subscribing. So, sometime back, they had a very interesting cover page like this. And I looked at it for some time, and I found that this is very insightful. And if you closely look at this cover page, the magazine or the newspaper as they call it is depicting the activity of certain businesses. We are talking about business intelligence and analytics. So, these are all companies or organizations and interestingly they are all American companies and there is some bias there, but they are all American technology companies. They are all American technology companies. companies starting from Amazon, Uber, Microsoft, Google, Facebook and Tesla. And they are all placed in the sea, in offshore. They are not on the shore, but they are offshore and they are depicted as drilling. They seem to be in the activity of offshore drilling and apparently drilling oil from beneath the sea, just like oil companies, where they actually have offshore stations for drilling oil. And of course, you can see from the tails they are emitting zeros and ones. So, it appears that they are all engaged in what you call drilling and mining, we can call it oil mines. So, they are doing oil mining. Well, that is a very interesting picture. So, what would this be indicative of? Are there some suggestions? The oil is showing like a data and oil is actually fueling the oil companies. That means data is fueling all the companies. So, there is an answer that oil is here shown as data, maybe the 01 actually gives us suggest that it is data and oil, sorry, data is feeding these companies that is one answer. Well, but it looks like that they are drilling oil, it is an oil mine. So, but In reality, we know that these are not oil companies, Amazon is not an oil company, or Google is not an oil company and why this distortion in what they do. And this is not something new, most of us are familiar with this usage today that oil is the, sorry, data is the new oil. Data is the new oil and if data is the new oil and oil is something that is required for business. To facilitate the business companies. So, business is driven not by oil, but by data. That is why this picture is shown. So, oil as the new, sorry, data as the new oil. And that is a very interesting metaphor. So, what does data do to these organizations? That is interesting to think. So, What is Google's business, which is a central company that is shown? It looks like they are very much central in the business of data. What does Google do with data? So, we all are users of Google. I am sure all without any exception should have used Google at one time or the other. Children in school classes today, Google, of course, Google. appears to be giving way to ChatGPT and many AI tools that is coming up today, which help you in your examinations. But let us be a bit old generation users of Google. So, Google is here. This is Google. The white space they gave with a bar for you to place your search keys, your search keys. So, you can see that a lot of us here, we are all users of Google. we all connect with Google to run our search. So, the input as a user that we give to Google is a search key. We do not have anything other than search keys. What we search for? We are searching for a MOOC course. We are searching for a master's program. We are searching for a computer or a smartphone. There are a lot of things that we search for. We are searching for a partner. we are searching for a place, we are searching for music, we are searching for books. So, the first place that usually we place our search is the Google. Then of course, we can go to other places where Google guides us to. Google simply tells us where all you can get information about the keywords that you placed. And that is where Google algorithm helps us because Google returns answers based on certain rank, okay, and that is known as page ranking algorithm. Some of you must have heard about it, okay. That is a key invention that the founders of Google made in Stanford, that how you can actually return results to a user in the order of importance of those pages, okay. So, to give an important score to the resulting pages is Google's contribution, okay, or the value that Google adds and returns. those search results. And we oftentimes benefit from those results. We go to Google because we get a value. The results, there may be thousands of sites where those keywords are used, but Google tells us where you should go first. A few sites that are listed on the top. And that is the value as users we receive. And haven't we wondered as to in providing the service to us, Google must be investing a lot of money. money, because they have to run the search algorithm, they have to actually make the search results available to you. So, there are costs involved in servicing users around the world, which may be millions of users around the world using Google at the same time. It is a lot of costs for Google and they do not ask us for money. Google does not ask us for money. So, therefore, Google's business model, as we say, Google's business model is not a straight business model where you give something, you know, you ask for something, you get it and then you pay. After getting the search results, we do not pay Google. That is very interesting. They give us something valuable, but none of us pay Google. Then who is paying Google? Google, we, as we know that Google has another market, we call it two-sided markets. Google has another market which is connected to the Google platform, we call it a platform. Google platform has another side, it is called two-sided network. So, it is a platform business model and as we know, most of us know these are advertisers. And when you ask for an NPTEL course or when you ask for a laptop computer, Okay, Google actually tells you where you can actually find laptop computers, of course, some advertisements also come you know that, and it also lists where you should buy the laptop computer from. And that is where Google has made a very unique connection between the advertisers and the users. And that is a valuable very target, we call it targeted advertising, some of you must be aware of this. The advertisers know who is looking for a laptop. Instead of advertising that we are selling laptops all around the world, Google tells them who is looking for a laptop. And they make that unique connection. So, this matchmaking algorithm, you know that Google's job is to create algorithms that would actually get you the best results that also make the right match between users and advertisers. These are algorithms or algorithms which uses data, which uses the search keys and your profiles if you have actually allowed Google to profile you. All that data is used by Google in suggesting advertisements to you from advertisers. Advertisers get value because whatever money they spend is going to be more targeted than doing non-targeted advertising. Now, as you know, in this type of business model, when you do not pay Google as a user, what you give Google is your data. And what is your data? You know that Google collects data about you through multiple services. One is the search keys. Then they also encourage you to sign into Google. They encourage you to sign into Google, create a Google account, say for Gmail. So, One sign-in is enough for Google to profile you with the data that actually you provide continually to Google. For example, what are the keywords you have been using? So, that is very useful for Google in understanding your needs or what you call in profiling you. That is the resource. We say data is the new oil. When you say data is the new oil, the you see that is working in Google because continually what is driving Google s algorithm is the data. What is training Google s algorithm is the data that you provide. If for example, there is an international law where it says no social media or online business should use or store users data. if there is a complete ban on the use of data, you can see that in no time or in a short time, many of these giants, the giants that you see here will have to shut down their business. Because it is like running a manufacturing company without having the oil. So, you know that in manufacturing companies, you need oil. Why you need oil? Because you need the pumps to run, the drivers to run, the motors to run, where does the energy come from oftentimes? There will be captive power plants. How do the power plants run? Most of them based on oil or coal in our country. Of course, we are moving to other alternate sources of fuel, but fuel is essential for running any industry that has moving equipment because somewhere somebody has to drive it. So, the drive or the energy has to come from fuels. And here you can see that technology companies like Amazon, Uber, Microsoft, Google, Facebook, Tesla are all using data to drive their business. Data to take decisions, data to actually make matches, data to provide targeted advertising, data to do customer segmentation, loyalty programs. And to look at a company like Uber, oftentimes we are faced with this question, what is the business that Uber does? Uber does, well, there is some confusion as soon as you face this question, well, Uber is into transportation, you may answer. Well, how many cars or automobiles do Uber own? Answer will be, maybe very few, their executives may have automobiles. but they do not have any automobiles or taxi cars owned by them. They do not hire any drivers. The drivers are not their employees, automobiles are not their assets. And what are their assets? Their assets are of two kinds. One is data, data given by users or what we call user generated data. User generated data, you give the data to Google, you give the data to Uber or Amazon in the form of your buying or your feedback, etc. Who gives data to Facebook? What is Facebook's oil? You are feeding oil for Facebook, the users are actually providing data to Facebook and Facebook uses that data for advertising. So, it is all user generated data. Most of us are used Users of a platform known as Wikipedia, best example of user generated data. Do they buy books or do they buy research papers to provide us the information we ask for as an encyclopedia, online encyclopedia? No, they do not buy any resources. Who bring the data? It is the users. It is all user generated content. But platforms have the job of running certain algorithms or certain processes known as today known as curation. They are all curators. Just like in museums, you have curators for antics. They are curators. They ensure that the results provided are reliable, accurate, credible, and users can sort of use that output generated by these platforms. So, they do curation. So, you look at Wikipedia, they do a lot of curation of the content using algorithms first of all, and also using content editors. So, essentially, we are seeing that data is feeding these businesses and that data comes from users. And therefore, users do not pay these companies in currency units, but it is what we call as the conventional bartering. You gave your data. and they give you information that you are asking for and that data is converted to information through algorithms and therefore that information is useful for users to take decisions. So, that is where the value of this company is coming. So, therefore, you could see here that for intelligence, data is the source, data is the raw material and data needs to be converted into information to support decisions. So, and that particular business of converting data to business, to intelligence is business intelligence, business intelligence. And a related term is analytics, which is as we see as we go forward that analytics is more advanced and it not just structures the data or it does not just provide you certain useful data, but it also analyzes the data using advanced algorithms and help you implement solutions. So, we will discuss that as we go. So, These companies are most successful companies of the world today, most known companies of the world today and many of them in fortune 500 and these companies use data as their raw material. So, this is to sort of motivate you for crediting this course. So, in this slide you see acquisitions in the digital world which I should confess is a bit dated again taken from the economist. So, at some point, they looked at the major acquisitions that happened in business and who acquired whom as shown in the form of a table with icons. And you can see certain interesting acquisitions in the digital world or the certain interesting acquisitions done by so-called technology companies. in recent times, not very recent. So, you can see Facebook acquired two companies, Instagram in 2012, but that value of the deal was just 1 billion. But when Facebook acquired WhatsApp in 2014, what was the valuation of the company? What was the valuation of WhatsApp? That is a very interesting figure, 22 billion. WhatsApp acquired, sorry, Facebook, today Meta, acquired WhatsApp in 2014 at 22 billion dollars. That's a very, very high price. That is, you can see that a company's worth was 22 billion and that was acquired by Facebook or Meta today. So, just for a moment, think about what is the what is that value of WhatsApp that a large company like Facebook acquired it? Another related question is what was the revenues of WhatsApp when it was acquired? In 2014, what was the revenues generated by WhatsApp? If I had to put that, 0 dollars or 0 rupees. It was not making any money. It did not start making any money. And that is very interesting, isn't it? A company being which is not making any revenues or profit means it is only cost and that being acquired at 22 billion. So, it is of course for certain potential, certain reasons, certain strategic reasons that Facebook acquired WhatsApp. And by now, It is clearly known that WhatsApp was growing at a phenomenal rate in 2014 or user base of WhatsApp was growing at phenomenal rate. And Facebook saw the growth of a platform or a competing platform at a very high rate. And Facebook realized at that point that this company could be a future rival. So, in strategy, we call it. pre-empting a future competition, pre-empting a future competitor. It acquired so that it does not become a competitor for Facebook in future. That is one strategic reason. But why this high price? That is not answered. The high price is thanks to the business model of WhatsApp. In other words, I would say it is owing to the data and analytics capability. So, WhatsApp was growing in user base means what? In terms of number of users and their data as to who is connected to whom, that is the most important data or the network is the most important data that is captured by instant messaging platforms like WhatsApp, which is very useful for platforms like Facebook, which is very similar in their advertising model. So, advertising is going to be the revenue for WhatsApp too and Google, sorry, what Facebook would realize that they get a lot of data and information from WhatsApp. Look at another acquisition, which I wanted to highlight, okay, which is Microsoft's acquisition of LinkedIn in 2016, okay, of course, some years ago. LinkedIn is a Microsoft company. And what is LinkedIn's business? Many of you may have your account with LinkedIn, okay, you create your professional profile in LinkedIn, Facebook is for social networking, LinkedIn is for professional networking, that's the way it began. And of course, later on, it started, you know, it started having features similar to social networking sites like chat and post, etc. to increase user engagement. But LinkedIn was again acquired at a very high price. And what explains this high valuation of LinkedIn? Again, it is data and algorithms. Data about whom? Data about professionals all over the world. What do they do? What are their qualifications? Where did they move in their career? And a lot more information that is useful for the business world. is captured dynamically in the database of LinkedIn. In other words, I would say these companies were valued at these high values or prices because of their databases. Because database contains the data or database in whatever form it could be, it could be relational databases or some other form. But these companies, when they got acquired had data that their acquiring companies found to be very valuable. So, why we talk about it? Data has become a very important source or a very highly valued source for business today. And therefore, you can see the value of this acquisitions is also related to the value of the data and the value of analytics. And analytics driven companies today have huge evaluations thanks to the capability based on data and data analytics.

So, What is intelligence is a very loaded question because it requires us to understand the philosophy behind intelligence. Many of you must have heard about a measure called intelligence quotient or IQ. And I am sure some of us have taken our IQ test and got some scores. And possibly you are happy.

that you have a good IQ score and that is why you are crediting this course. And we all believe that as human beings we are intelligent. And so, here in this course we are applying intelligence not to, apparently not to human beings but to business. And what does business intelligence mean?

That is very important as far as this course is concerned. And then it is joined with analytics. So, business intelligence and analytics look like related concepts.

And it also appears that since there is a course like this, it also appears that there is some value or business value when business is done with intelligence and analytics as compared to when it is done not with intelligence and not with analytics. So, what is the role of intelligence in business? Business or in general in life and business, we can also think of life at large where you know we function as individuals, we function as groups, we function as organizations for profit and not profit, not for profit. We also function as countries, so our nation states.

So, in all this context, there is a role for intelligence. So, we are specifically looking at business intelligence because this course will have several examples related to business intelligence or intelligence from business context and that therefore the title. But being the first class, let us develop a reasonable understanding about these two concepts. What is meant by intelligence? That is the first question.

So, There could be different questions if I ask an audience as to what is intelligence? Intelligence means thinking by my own capability, what is right and what is wrong. Somebody says intelligence is about thinking by one's capability or one's innate ability or natural ability.

The ability to acquire and interpret that knowledge that they have given. Okay, somebody else says intelligence is the ability to acquire knowledge and interpret what you acquire. All right, okay, these are all good attempts to articulate what is meant by intelligence. So, instead of defining intelligence, let us look at what are some indicators of intelligence. So, intelligence can be defined by what we observe about intelligence.

So, that is one way of defining or understanding concepts based on its indicators. So, what do intelligent people do or how do intelligent people function? If you look at intelligent versus non-intelligent people, you can actually look at their behavior, look at their decisions, look at the way they actually ask for information so that they function. in a more intelligent or in a more effective way. So, there is a way intelligent people or intelligent beings in essence take decisions as compared to people or beings who do not have intelligence.

You know, I typically take the example of birds. Birds have some intelligence, I am not denying that living beings other than human beings do not have intelligence, but look at them. the birds make their nests the way they have been doing it say 100 years ago or 1000 years ago, they would be making nests, they would be making nests in the same way even today.

Maybe we do not know, I do not know exactly, but the difference is very little or it is very negligible. They do not improve or they do not in other words learn from the environment or they do not actually learn and improve. So, therefore, learning is associated with intelligence. All intelligent beings are able to learn. Learn from what?

Learn from lessons. Lessons come from where? Lessons come from the environment. The environment or the nature or it is the environment around us or it is the nature around us.

that teaches us and what does the nature consist of when we use that word nature. In the context of analytics course, I would say we learn from data. The environment gives us, offers us data, data in diverse forms. We have been experimenting right from the day we were born.

We have been growing in our intelligence, our Intellectual capability has been improving thanks to the learning that happens from the data that we receive. Sometimes we generate data through experiments. When we were children, we conducted a lot of experiments to learn. We touched a hot surface and we learned that the hot surface is hot and therefore I should never touch a hot surface.

But a child wants to do. all kinds of naughty things, because it is through those disobedience that the child does its experiment and learn what to do, what not to do. So, therefore, we generate a lot of data for our learning.

And where does it help us? So, let us come back to the context of business intelligence. Where does intelligence play a role in business?

In some, intelligence is important in decision making. Intelligence is a resource or I would say a capability for decision making. An intelligent manager is someone who makes intelligent decisions. And what is an intelligent decision? We say an intelligent decision is an informed decision.

Is an informed decision. You can take decisions in informed way and uninformed way. For example, you can join a course like Business Intelligence and Analytics in NPTEL by having no information about the course or you just browse through various course or you like the title, let me just go for the course or just randomly select a course. Suppose you want to do say two courses and suppose there are 50 courses offered in a semester, you just randomly pick to courses. We say this is not an intelligent decision because we did not collect any information to take that decision.

So, therefore, that is an uninformed decision. And uninformed decision since you took that decision by random choice, whether you benefit from the course or not is also random or by luck. If you are lucky, you learn well and it benefits you in your career. If you are not lucky, it does not benefit you.

So, you are increasing or you are depending on chance than on information when it is available to take your decisions. Therefore, random choice in situations like this when information is available is an unintelligent decision or an uninformed decision. So, therefore, I am arguing that Business intelligence is a capability that business could develop to take good decisions or informed decisions. And in this context, what is an informed decision?

What is an informed decision in the choice of a course? And the reason why we post a lot more information about the course, like what is the course content? What are the materials that will be used in the course? Who will be teaching the course?

and what is the profile of the instructor and when it will be offered and how to get the course credited, how to get a certificate. There is lot of information that we add to the title of the course. Why is that information added? It is for a decision maker to read and analyze and see if this course matches with the interest of an individual.

So, therefore, in decision, there is some sort of matching of information on one side with a goal on the other side as to what you want to achieve. So, in short, I would say, business intelligence and analytics is for business decisions. It is for decision making.

Decision making is the most important aspect of business management. Business managers, what do they do? At different levels of management, from operational level to strategic level, they take a lot of decisions which has short-term, medium-term and long-term impact or implications.

And we advise that business decisions should be based on information. Information is the key to making successful decisions. So, it is in that context that we talk about business intelligence and analytics. So, BI and A as it is called in short is a fairly new phenomenon and the world of business did benefit from data-driven decisions for the last several decades and that has assumed several names or several labels in the past.

decades. I will give you an overview of that as to how it began and how it extended to contain diverse type of data and techniques and where it stands today. And that is an integral part of this course.

And very soon, I will also explain to you what are the contents of this course and how you can effectively participate and learn in this course. But, before we get into those details, let me also dwell on some of the drivers for BI and A. As I said, data driven decisions has been part of business world, or it is a sort of support that information systems provided to business in addition to automation. But today, I would say for the last one decade, these terms BA and analytics have gained some traction in the industry.

And there is a context to it. And that is what we are going to see in the first class today. All right.

So, let me show you some pictures and figures and graphs in the subsequent slides, which will help you understand. what is going on in the environment. So, this is the cover page of the newspaper, The Economist, which I have been subscribing.

So, sometime back, they had a very interesting cover page like this. And I looked at it for some time, and I found that this is very insightful. And if you closely look at this cover page, the magazine or the newspaper as they call it is depicting the activity of certain businesses.

We are talking about business intelligence and analytics. So, these are all companies or organizations and interestingly they are all American companies and there is some bias there, but they are all American technology companies. They are all American technology companies. companies starting from Amazon, Uber, Microsoft, Google, Facebook and Tesla.

And they are all placed in the sea, in offshore. They are not on the shore, but they are offshore and they are depicted as drilling. They seem to be in the activity of offshore drilling and apparently drilling oil from beneath the sea, just like oil companies, where they actually have offshore stations for drilling oil.

And of course, you can see from the tails they are emitting zeros and ones. So, it appears that they are all engaged in what you call drilling and mining, we can call it oil mines. So, they are doing oil mining. Well, that is a very interesting picture.

So, what would this be indicative of? Are there some suggestions? The oil is showing like a data and oil is actually fueling the oil companies.

That means data is fueling all the companies. So, there is an answer that oil is here shown as data, maybe the 01 actually gives us suggest that it is data and oil, sorry, data is feeding these companies that is one answer. Well, but it looks like that they are drilling oil, it is an oil mine.

So, but In reality, we know that these are not oil companies, Amazon is not an oil company, or Google is not an oil company and why this distortion in what they do. And this is not something new, most of us are familiar with this usage today that oil is the, sorry, data is the new oil. Data is the new oil and if data is the new oil and oil is something that is required for business.

To facilitate the business companies. So, business is driven not by oil, but by data. That is why this picture is shown.

So, oil as the new, sorry, data as the new oil. And that is a very interesting metaphor. So, what does data do to these organizations? That is interesting to think.

So, What is Google's business, which is a central company that is shown? It looks like they are very much central in the business of data. What does Google do with data?

So, we all are users of Google. I am sure all without any exception should have used Google at one time or the other. Children in school classes today, Google, of course, Google. appears to be giving way to ChatGPT and many AI tools that is coming up today, which help you in your examinations. But let us be a bit old generation users of Google.

So, Google is here. This is Google. The white space they gave with a bar for you to place your search keys, your search keys.

So, you can see that a lot of us here, we are all users of Google. we all connect with Google to run our search. So, the input as a user that we give to Google is a search key. We do not have anything other than search keys. What we search for?

We are searching for a MOOC course. We are searching for a master's program. We are searching for a computer or a smartphone.

There are a lot of things that we search for. We are searching for a partner. we are searching for a place, we are searching for music, we are searching for books.

So, the first place that usually we place our search is the Google. Then of course, we can go to other places where Google guides us to. Google simply tells us where all you can get information about the keywords that you placed.

And that is where Google algorithm helps us because Google returns answers based on certain rank, okay, and that is known as page ranking algorithm. Some of you must have heard about it, okay. That is a key invention that the founders of Google made in Stanford, that how you can actually return results to a user in the order of importance of those pages, okay. So, to give an important score to the resulting pages is Google's contribution, okay, or the value that Google adds and returns. those search results.

And we oftentimes benefit from those results. We go to Google because we get a value. The results, there may be thousands of sites where those keywords are used, but Google tells us where you should go first.

A few sites that are listed on the top. And that is the value as users we receive. And haven't we wondered as to in providing the service to us, Google must be investing a lot of money. money, because they have to run the search algorithm, they have to actually make the search results available to you. So, there are costs involved in servicing users around the world, which may be millions of users around the world using Google at the same time.

It is a lot of costs for Google and they do not ask us for money. Google does not ask us for money. So, therefore, Google's business model, as we say, Google's business model is not a straight business model where you give something, you know, you ask for something, you get it and then you pay. After getting the search results, we do not pay Google. That is very interesting.

They give us something valuable, but none of us pay Google. Then who is paying Google? Google, we, as we know that Google has another market, we call it two-sided markets. Google has another market which is connected to the Google platform, we call it a platform. Google platform has another side, it is called two-sided network.

So, it is a platform business model and as we know, most of us know these are advertisers. And when you ask for an NPTEL course or when you ask for a laptop computer, Okay, Google actually tells you where you can actually find laptop computers, of course, some advertisements also come you know that, and it also lists where you should buy the laptop computer from. And that is where Google has made a very unique connection between the advertisers and the users.

And that is a valuable very target, we call it targeted advertising, some of you must be aware of this. The advertisers know who is looking for a laptop. Instead of advertising that we are selling laptops all around the world, Google tells them who is looking for a laptop.

And they make that unique connection. So, this matchmaking algorithm, you know that Google's job is to create algorithms that would actually get you the best results that also make the right match between users and advertisers. These are algorithms or algorithms which uses data, which uses the search keys and your profiles if you have actually allowed Google to profile you. All that data is used by Google in suggesting advertisements to you from advertisers.

Advertisers get value because whatever money they spend is going to be more targeted than doing non-targeted advertising. Now, as you know, in this type of business model, when you do not pay Google as a user, what you give Google is your data. And what is your data?

You know that Google collects data about you through multiple services. One is the search keys. Then they also encourage you to sign into Google. They encourage you to sign into Google, create a Google account, say for Gmail.

So, One sign-in is enough for Google to profile you with the data that actually you provide continually to Google. For example, what are the keywords you have been using? So, that is very useful for Google in understanding your needs or what you call in profiling you.

That is the resource. We say data is the new oil. When you say data is the new oil, the you see that is working in Google because continually what is driving Google s algorithm is the data.

What is training Google s algorithm is the data that you provide. If for example, there is an international law where it says no social media or online business should use or store users data. if there is a complete ban on the use of data, you can see that in no time or in a short time, many of these giants, the giants that you see here will have to shut down their business.

Because it is like running a manufacturing company without having the oil. So, you know that in manufacturing companies, you need oil. Why you need oil? Because you need the pumps to run, the drivers to run, the motors to run, where does the energy come from oftentimes?

There will be captive power plants. How do the power plants run? Most of them based on oil or coal in our country.

Of course, we are moving to other alternate sources of fuel, but fuel is essential for running any industry that has moving equipment because somewhere somebody has to drive it. So, the drive or the energy has to come from fuels. And here you can see that technology companies like Amazon, Uber, Microsoft, Google, Facebook, Tesla are all using data to drive their business.

Data to take decisions, data to actually make matches, data to provide targeted advertising, data to do customer segmentation, loyalty programs. And to look at a company like Uber, oftentimes we are faced with this question, what is the business that Uber does? Uber does, well, there is some confusion as soon as you face this question, well, Uber is into transportation, you may answer.

Well, how many cars or automobiles do Uber own? Answer will be, maybe very few, their executives may have automobiles. but they do not have any automobiles or taxi cars owned by them. They do not hire any drivers. The drivers are not their employees, automobiles are not their assets.

And what are their assets? Their assets are of two kinds. One is data, data given by users or what we call user generated data.

User generated data, you give the data to Google, you give the data to Uber or Amazon in the form of your buying or your feedback, etc. Who gives data to Facebook? What is Facebook's oil? You are feeding oil for Facebook, the users are actually providing data to Facebook and Facebook uses that data for advertising.

So, it is all user generated data. Most of us are used Users of a platform known as Wikipedia, best example of user generated data. Do they buy books or do they buy research papers to provide us the information we ask for as an encyclopedia, online encyclopedia?

No, they do not buy any resources. Who bring the data? It is the users.

It is all user generated content. But platforms have the job of running certain algorithms or certain processes known as today known as curation. They are all curators. Just like in museums, you have curators for antics.

They are curators. They ensure that the results provided are reliable, accurate, credible, and users can sort of use that output generated by these platforms. So, they do curation. So, you look at Wikipedia, they do a lot of curation of the content using algorithms first of all, and also using content editors.

So, essentially, we are seeing that data is feeding these businesses and that data comes from users. And therefore, users do not pay these companies in currency units, but it is what we call as the conventional bartering. You gave your data.

and they give you information that you are asking for and that data is converted to information through algorithms and therefore that information is useful for users to take decisions. So, that is where the value of this company is coming. So, therefore, you could see here that for intelligence, data is the source, data is the raw material and data needs to be converted into information to support decisions. So, and that particular business of converting data to business, to intelligence is business intelligence, business intelligence.

And a related term is analytics, which is as we see as we go forward that analytics is more advanced and it not just structures the data or it does not just provide you certain useful data, but it also analyzes the data using advanced algorithms and help you implement solutions. So, we will discuss that as we go. So, These companies are most successful companies of the world today, most known companies of the world today and many of them in fortune 500 and these companies use data as their raw material.

So, this is to sort of motivate you for crediting this course. So, in this slide you see acquisitions in the digital world which I should confess is a bit dated again taken from the economist. So, at some point, they looked at the major acquisitions that happened in business and who acquired whom as shown in the form of a table with icons. And you can see certain interesting acquisitions in the digital world or the certain interesting acquisitions done by so-called technology companies.

in recent times, not very recent. So, you can see Facebook acquired two companies, Instagram in 2012, but that value of the deal was just 1 billion. But when Facebook acquired WhatsApp in 2014, what was the valuation of the company?

What was the valuation of WhatsApp? That is a very interesting figure, 22 billion. WhatsApp acquired, sorry, Facebook, today Meta, acquired WhatsApp in 2014 at 22 billion dollars. That's a very, very high price.

That is, you can see that a company's worth was 22 billion and that was acquired by Facebook or Meta today. So, just for a moment, think about what is the what is that value of WhatsApp that a large company like Facebook acquired it? Another related question is what was the revenues of WhatsApp when it was acquired?

In 2014, what was the revenues generated by WhatsApp? If I had to put that, 0 dollars or 0 rupees. It was not making any money.

It did not start making any money. And that is very interesting, isn't it? A company being which is not making any revenues or profit means it is only cost and that being acquired at 22 billion.

So, it is of course for certain potential, certain reasons, certain strategic reasons that Facebook acquired WhatsApp. And by now, It is clearly known that WhatsApp was growing at a phenomenal rate in 2014 or user base of WhatsApp was growing at phenomenal rate. And Facebook saw the growth of a platform or a competing platform at a very high rate.

And Facebook realized at that point that this company could be a future rival. So, in strategy, we call it. pre-empting a future competition, pre-empting a future competitor. It acquired so that it does not become a competitor for Facebook in future.

That is one strategic reason. But why this high price? That is not answered. The high price is thanks to the business model of WhatsApp.

In other words, I would say it is owing to the data and analytics capability. So, WhatsApp was growing in user base means what? In terms of number of users and their data as to who is connected to whom, that is the most important data or the network is the most important data that is captured by instant messaging platforms like WhatsApp, which is very useful for platforms like Facebook, which is very similar in their advertising model.

So, advertising is going to be the revenue for WhatsApp too and Google, sorry, what Facebook would realize that they get a lot of data and information from WhatsApp. Look at another acquisition, which I wanted to highlight, okay, which is Microsoft's acquisition of LinkedIn in 2016, okay, of course, some years ago. LinkedIn is a Microsoft company.

And what is LinkedIn's business? Many of you may have your account with LinkedIn, okay, you create your professional profile in LinkedIn, Facebook is for social networking, LinkedIn is for professional networking, that's the way it began. And of course, later on, it started, you know, it started having features similar to social networking sites like chat and post, etc. to increase user engagement.

But LinkedIn was again acquired at a very high price. And what explains this high valuation of LinkedIn? Again, it is data and algorithms.

Data about whom? Data about professionals all over the world. What do they do? What are their qualifications?

Where did they move in their career? And a lot more information that is useful for the business world. is captured dynamically in the database of LinkedIn.

In other words, I would say these companies were valued at these high values or prices because of their databases. Because database contains the data or database in whatever form it could be, it could be relational databases or some other form. But these companies, when they got acquired had data that their acquiring companies found to be very valuable. So, why we talk about it? Data has become a very important source or a very highly valued source for business today.

And therefore, you can see the value of this acquisitions is also related to the value of the data and the value of analytics. And analytics driven companies today have huge evaluations thanks to the capability based on data and data analytics.

Transcript for:Understanding Business Intelligence and Analytics

Transcript for:
Understanding Business Intelligence and Analytics