They are gone now because the utility bill payment system has been made electronic. There were many in-demand jobs in the twentieth century, and now we just get nostalgic when thinking about them.
For example, switchboard operators (remember the film Telephone Girl and the protagonist’s profession), a job popular until the 1950s, waking people up early in the morning instead of an alarm clock; leech collectors—people gathering leeches for doctors; VCR repairmen (you can count households with VCR on one hand these days), another job lost in the late twentieth century; videostore clerks (well, there are still videostores but most people now prefer online—Netflix and other streaming platforms, and almost no one watches movies on videotape); lamplighters (who lit the streetlamps every night before the streets were illuminated with electricity); milkmen (you can find all kinds of packaged milk in stores now, but about 50 or 60 years ago you could only buy milk brought to you door by a milkman). There are hundreds of jobs and professions that have lost their relevance. However, my purpose in this article is not to look back at the past, but to look to the future and its opportunities and to appreciate them. The 21st century is the age of new, modern technologies, and many jobs of the future will be related to emerging and developing technologies.
In his books Sapiens, Homo Deus and 21 Lessons for the 21st Century, Yuval Noah Harari writes that revolutions have changed and reshaped our lives immensely. For example, the modern type of humans began to form thanks to the Cognitive Revolution 70,000 years ago; the Agricultural Revolution of 10,000 years ago led to the discovery of new areas of economic activity, and the Scientific Revolution 500 years ago (Renaissance) led people to realize the possibility of development by introducing new theories into their lives. Thus, human beings became the masters of their life and of the entire planet. Harari calls the 21st century the “age of data” and claims that super humans in the new era will be more interested in “Dataism” than in such issues as liberalism, democracy and personal autonomy. In other words, he “deifies” the data science. Ultimately, those who control data will control the whole world. So, in order to be more needed and wanted in the future, one will have to become more adaptive and responsive to technological innovations.
It is safe to say that new technologies are based on Big Data technologies. Big Data means that all the data/information in the world is concentrated in one place and made accessible. The meaning of “big” here is relative, meaning that information is not only big in size, but also big in amount, rapidly changing and very diverse. For example, in the early years of the 21st century, 80% of data remained on paper and only 20% was in digital/electronic form. The situation is quite different today. For example, in 2015, 98% of data became digitally accessible. Just like owning gold or oil fields was once an attribute of wealth, today and especially in the future, the more data you have, that is, access to digital data, the richer you are.
In fact, the information (messages, shares, likes, purchases, clicks, etc.) that each of us shares and shows interest in on Facebook, Twitter, Instagram and other social media, e-mail programs, shopping sites and so on creates data. The main thing is that very large volumes (terabytes=1012 bytes, petabytes=1015 bytes, or more) of this data should be aggregated (for comparison, the megabyte we all know equals 106 bytes), updated fast (every minute and faster), be diverse (text, sound, image, and other formats) and analyzed correctly. But where and how is this collected Big Data used?
For example, Mayer-Schönberger and Cukier write in their book Big Data: A Revolution That Will Transform How We Live, Work, and Think (2013) that in 2009 a new H1N1 virus was discovered. Combining certain features of bird and swine flu viruses, this new virus was completely different from them. All public health agencies around the world were worried that a terrible pandemic was under way. Some even claimed that an outbreak on the scale of the 1918 Spanish flu was to be expected, and the scary thing was that the disease did not manifest itself immediately, but within two weeks, and there was no vaccine. The US Center for Disease Control and Prevention (CDC) called on doctors across the country to determine the statistics and areas of spread of the disease, and to notify CDC when patients with the virus arrived. However, this method was not effective in preventing the pandemic and determining the correct statistics, as patients did not go to the hospital immediately, allowing the disease to develop to a certain stage. Interestingly, a few weeks before the H1N1 virus appeared in the headlines, Google presented a serious research article saying that a new wave of the virus was likely to come. The company came to this conclusion by analyzing what Google users were searching the most on the Internet. It did even more research to find out in which regions of the United States the virus had spread the most and to what extent.
Apart from Google, Microsoft CEO Bill Gates has been making predictions about the threat of a pandemic for years (remember his 2015 TED Talk). So, we can take seriously what Gates said in an interview on Today Show (December 2020). He was pessimistic about the next 4-5 months regarding the pandemic, but noted that there would be progress starting spring and our lives would gradually return to normal.
Think about how this early warning system works in a business environment and how useful it is. It’s Big Data analysis that makes it possible.
For example, Oren Etzioni created a startup that analyzes various data and notifies plane ticket buyers when prices will rise or fall. The startup later became a major project called Farecast, which was acquired by Microsoft for $110 million in 2008 and became part of the Bing search engine. Before selling the company, Etzioni thought of applying this method to hotel rooms, concert tickets and the purchase and sale prices of used cars, but the sale of the company to Microsoft put an end to this plan. Farecast is the epitome of a big-data company and an example of where the world is headed.
In the past, before money was used in trade, the parties traded with each other by exchanging goods. Today, companies can pay another company to get the information they need or offer their services in exchang. For example, we all at least once received a call from a company’s call center offering some product. We all wondered where they got our number, sometimes even our name or address. Some companies can even go a step further and provide access to more confidential information. For example, they can get the information about the latest use of the credit card received by the customer from any bank or about the bank-customer relationship (whether the loans are repaid on time, what purchases are made, etc.). They may even surprise us a little and gather information about our correspondence and live broadcasts. They can get it for money via third-party companies, as well as by placing certain cookies and web beacons on their websites. The customer is unaware that they are being tracked. The simplest option is to obtain information (data) by sending direct requests to customers. Thus, the customer voluntarily shares the information the company wants. However, the above two options may be more useful for big data analysis by big companies. For example, have you ever received a notification about a discount campaign on your phone when you pass by a store? Or do you go to the movies and receive information about discounts in some store in the building where the theater is located? Beacon technology is what makes it possible. In other words, thanks to the beacon technology, which enters information into the system about the location of customers at a particular time, the company, which receives information about their proximity, can draw attention to a particular product or service by sending information to the customer. This, in turn, serves to increase sales and company revenue.
So, we have learned Big Data can be used in social, everyday and business life, and what benefits its application can bring. But this is not all. In the 21st century, China has taken a step that surprised the whole world, using Big Data to build a total government control system. Since January 1, 2021, this system has become fully operational. It’s called the Social Credit System. Sounds nice, doesn’t it? The first thought that comes to mind is that they are giving out loans. And there is some truth to that, but not in the way you think—only “good” citizens are eligible. The Chinese government decided to use Big Data to build a “world” (within its own country) of people who are highly useful to society. Here, citizens are grouped into “bad”, “average” and “good” and can benefit from the luxuries of social life, based on their credit score that depends on which group they belong to and whether their behavior is “good”. For example, no plane or train tickets will be sold to a citizen who behaves “poorly”, and those who behave “well” will be able to get loans from the bank at the lowest interest rates, buy products on an installment plan without down payment, or take advantage of discounts. Watch this video for more information.
Thus, citizens’ messages, with whom and how they communicate, their contacts, shopping information, where they live and where they go, and so on—all will be under state’s control. In fact, today we are all under covert surveillance. This is not a conspiracy, but a business development strategy presented by modern technologies and Big Data. Only China, once again far ahead of the whole world, has made it open and authoritarian. The benefits of this are the prevention of fraud and crime.
Even major companies engaged in Big Data analysis pursue two main goals in this work: one is to identify strategies that will increase their profits, and the other is to prevent possible fraud and crime through this system. Therefore, in order to survive in the business environment in the 21st century, to keep up with the times, a company will need to have more backbone technologies and meet the requirements of the time. Whoever meets these requirements faster and in a more comprehensive and diverse form wins. These are the jobs that will be in high demand until the end of the 21st century: Big Data admin, data analyst, data engineer, data architect, etc. Companies that want to grow rapidly will be looking for these experts working with data. Apart from that, cutting-edge technologies, such as Google’s MapReduce (which became obsolete in 2014 and was replaced with Apache Mahout) and Yahoo’s Hadoop (Apache Hadoop), are also widely used for Big Data analysis.
About 10 years after the advent of computers, SQL was used to create and work with databases. Today, the volume of data has expanded so much that there is a need for new types of databases and new types of tools to work with them in addition to the SQL databases. In the meantime, NoSQL came to the fore in the 2000s, and Hadoop in 2010. All three—SQL, NoSQL and Hadoop—have the same goal, but different features. For example, one may prefer to use a car to travel from Baku to Sumgayit or a plane to Istanbul, depending on distance and time. However, one can board either a plane or a ship to go to Aktau. Du Dayong in his book Apache Hive Essentials (2013) likens SQL to a car, Hadoop to a plane, and NoSQL database to a ship.
For example, after the Sloan Digital Sky Survey astronomy project started in 2000, its telescope in New Mexico collected more data in a single week than in the entire history of astronomy. In 2010, the volume of material in this study amounted to 140 terabytes. This work was then continued with the Large Synoptic Survey Telescope in Chile. The required amount of data in the new project could be obtained in just five days. The same is true for business, especially in the financial environment. For example, Google processes 24 petabytes of data per day. In the US stock market, $7 billion worth of shares are traded every day. Many of these transactions take place on the basis of the estimates obtained through machine software. Thus, bigger data requires more and more talented data analysts. Given that the world is moving forward with each passing day, we can say that those who can find a place for themselves in this field can achieve greater development.