Biz Tips: Big Data and Data Decentralization

Biz Tips: Big Data and Data Decentralization


Big Data and Data Decentralization

Big Data as the Future of Technology

Do you believe ageing is a code that can be cracked?

Intro to Big Data

Image source: Unsplash

Big data is all the data a business collects. This includes users, customers, or clients demographics, appointment information, which are then stored in either cloud or office-based computer system. After the data has been captured and stored, a predictive algorithm can be applied to the data to find patterns, predict potential changes, and track trends. The sorted data can help a business make precise, faster, and smarter decisions that translate to breakthroughs and brand growth; the relevant data from enormous users help a business or organization reach a decision. This saves time and energy. The extra time and energy can be directed to improve sales pipeline or other important departments of the business that need immediate attention.

There are a lot of data being transmitted on the internet every second — from mobile devices, social media, videos, RFID readers, sensors, etc. Businesses and organizations are grappling to find ways to make use the data to boost their sales or manage their clients efficiently. However, it has become a challenge to find ways to make sense of the data to help these businesses, companies, or organizations achieve that goal. To make sense of the data, they need right and powerful analytical tools. They need smart infrastructure and servers to manage the data to find patterns, predict potential changes, or track trends.

According to Wikibon and IDC:

In 2013, available storage capacity held 33% of the digital universe (4.4 trillion GB). The same research projects that the same capacity will be able to store less than 15% by 2020 (44 trillion GB). Did you know that 2.4 quintillion new bits of data are created every day?

In 2013, less than 20% of all data was destined for cloud storage or processing. By 2020, the share is expected to shoot to 44%.

Digital universe’s data from embedded systems will rise from 2% in 2013 to 10% by 2020.

Big Data’s journey to enterprise value involve:

  1. Capture
  2. Storage
  3. Analysis
  4. Action

How to make sense of the data is the challenge…

Conventional architectures provided by some of the world’s biggest tech corporations such as IBM, Google, Facebook have designed tools to make sense of the enormous data fed to the world every day. For example, IBM’s platform, Cognos Analytics 11.1, Watson Analytic engine and POWER8 servers. The platforms help businesses interpret their data for use in different departments such as marketing, finance, and risk management. The sorted and analyzed data can also be used by businesses to spot new opportunities or inefficiencies.

Revenue from Big Data

According to IDC, a technology research firm:-

Big data vendor revenue was $18.3 billion in 2014. It’s projected to skyrocket to $92.2 billion by 2026.

How’s Big Data Impacting Small Businesses?

Years ago, only corporations like Google and Facebook could collect a huge amount of data to make predictions and smart business decisions. But now even small businesses can use their customers’ data to achieve the same goals as Google or Facebook.

Eden Gardens, Sydney-based plant nursery, lifestyle, and cafe decided to collect data from their customers. For example, they recorded customers’ postcodes. That is a strategic move for them — they can improve customers’ experience using the data. And it just happened that way — after analyzing the data, they found out that their customers travelled from St Ives and Hunters Hill, a distance of 9.46 and 5.46 kilometres away respectively. Guess what they did after finding out that their customers travelled from St Ives and Hunters Hill? Yes, they found ways to improve their customers’ experience. They drilled down into demographics, changed product offerings, and specifically targeted catalogue deliveries in the areas.

However, before they could achieve this success, Michael Tate, the marketing manager had a challenge. When he sat down to analyze the data, he, unfortunately, found out that the most recurring postcode was 999, which didn’t exist. How does that feel? Bad. Tate later realized that the sales team were just randomly punching the postcode without inquiring from the customers.

To solve the problem and have the success they later reported, Eden Gardens, started to use an app called Daily IQ 2.0 from the Commonwealth Bank. Hence they were able to collect more accurate and automated data of where their customers lived.

Robert Claire, Executive General Manager of Business Banking Small to Medium at Commonwealth Bank told HuffPost Australia that “over 50% of small businesses rely on personal experience and ‘gut feeling’ to make decisions. Not hard evidence”. He added that “…small businesses are time poor and are incredibly busy running their businesses so collecting data can overwhelm them”. And therefore, that’s the reason Daily IQ 2.0 has been introduced to these small businesses. The app helps the small business owners by delivering insightful statistics from huge data from Commonwealth Bank’s 1.2 billion daily transactions (in real time).

This is just one case example of the impact of big data on small businesses. Daily IQ is only eligible for use by CommBank business customers. However, as new tools are constantly being discovered, it can be anticipated that more universal (even free) big data tools will be launched to help more small business owners to make sense of their daily data to improve their customers’ experience and boost their sales.

Data Scientists: Opportunities Knocking at the Door?

The world is changing and a lot of what we’re learning in school will be outdated. As MacArthur Foundation’s recent research proposes, 65% of the current school going children will get employed in jobs that don’t exist yet. Hence, a need to study, learn and master new technologies that might actually take part in creating the future.

Big data is one of those skills that will take part in disrupting the future. For example, back in 2012, Mo Zhou then a fresh graduate from Yale MBA was “grabbed” by IBM to join the tech company’s fast-growing ranks of data consultants. Like any other data analysts and consultants, Ms Mo Zhou and her team at the company helped businesses make sense of data. What’s fascinating about Zhou’s story is how lucky she was to be taken by such as a big company just when she was fresh from grad school.

Truly, this is more than luck; there is something hidden in her success story. Well, it is not hidden because we know what’s the cause of Zhou’s success: she studied a skill which is in high demand in the current market. Or as Malcolm Gladwell stated in his book, Outliersthe ingredients of success at the highest level are not only passion, talent, and hard work.” There are other elements. And for Zhou, one of those other elements might be studying a perfect skill at the perfect time. Currently, Mo Zhou is a data scientist with UberEverything.

According to the US Bureau of Labour Statistics, job growth in the next decade is expected to exceed growth from the past decade (obvious), creating 11.5 million jobs by 2026. Considering the rate at which the world is generating data, the next decade’s success in technological advancement will be defined hugely by how much we make use of the 2.5 quintillion new bytes of data created every day.

Let’s go back to Malcolm Gladwell’s Outliers for some time to understand something unique about Zhou’s success, that will also shed some light to why it’s the best time to learn data science. In the book, Malcolm listed 75 richest people in the human history. The list is diverse and covers people from different origins, their sources of wealth, and years of birth. Some of the people in the list include John D. Rockefeller (#1), Bill Gates (#37), J.P Morgan (#57), John Kluge (#56), and K.P. Singh (#68). After analyzing the list, Malcolm found out that what’s interesting about the list is “Of the seventy-five names, an astonishing fourteen are Americans born within nine years of one another in the mid-nineteenth century”. This means that 18.67% of the wealthiest people in the world came from a single generation in a single country.

Below is the list of those 14 Americans and their years of birth:

  1. John D. Rockefeller (1839)
  2. Andrew Carnegie (1835)
  3. Frederick Weyerhaeuser (1834)
  4. Jay Gould (1836)
  5. Marshall Field (1843)
  6. George F. Baker (1840)
  7. Hetty Green (1834)
  8. James G. Fair (1831)
  9. Henry H. Rogers (1840)
  10. J.P. Morgan (1837)
  11. Oliver H. Payne (1839)
  12. George Pullman (1831)
  13. Peter Arrell Brown Widener (1834)
  14. Philip Danforth Armour (1832)

According to Malcolm, these people didn’t just possess the talent and vision to be successful. They had an extraordinary opportunity. Let’s see. In the history of America, the 1860s and 1870s are when industrial manufacturing intensely emerged. It’s also the period of breaking traditional rules of the economy which had functioned for so long and making new ones. The railroads were built in this era and Wall Street emerged as well in this decade.

Let’s compare the years the above-listed people were born to 1860s and 1870s when America’s economy went through what is supposedly the greatest transformation in its history. In that era, those born in the late 1840s were too young to take advantage of the opportunities that presented themselves as a result of America’s economic transformation. For those born in the 1820s, they were too old and their mindsets were shaped by the pre-Civil War paradigm. Hence, a particular, narrow nine-year window was just the perfect years for seeing and utilizing the opportunities that the transformation in America’s economy brought, according to Gladwell’s research.

The study doesn’t end there. What about Silicon Valley’s tech veterans? The most important date in the history of the revolution of personal computers was January 1975. How did Malcolm come up with that exact period? Well, something special happened in January 1975. Popular Magazine, then a popular magazine that ran from November 1903 to October 1931, ran a cover story of an extraordinary machine, the Altair 8800. The machine “was a do-it-yourself contraption that you could assemble at home.”

Again, if 1975 was the golden year for personal computers who would be in the best possible position to take advantage of or enjoy the opportunities that personal computers brought? Let’s find out. Don’t talk about people who were born in 1975 because they were too old to exploit the new opportunities brought by personal computers. Or they had already worked at IBM and had no interest in making the transition to deal with “the little pathetic computers”. In addition, they had made a pretty nice living working for IBM. People born in 1952 who had graduated from college a few years, probably had married, and expected a child in a few months, if they didn’t already have a toddler. So they couldn’t easily abandon their first jobs for Altair 8800. Hence those born in 1952 or before were not in the best position to take advantage of the new personal computer paradigm. Those born in 1958 were probably still in high school and therefore were too young to take advantage of the new potential advantages that personal computer would bring. Malcolm suggests, “ideally, you want to be twenty or twenty-one, which is to say, born in 1954 or 1955”. And the theory is right in a way…

  • Bill Gates was born on October 28, 1955
  • Paul Allen was born on January 21, 1953
  • Bill Joy was born on November 8, 1954
  • Steve Ballmer was born on March 24, 1956
  • Steve Jobs was born on February 24, 1955
  • Eric Schmidt was born on April 27, 1955

However, Malcolm isn’t of course, suggesting that every successful software tycoon in Silicon Valley was or must be born in either 1953, 1954, 1955, or 1956. Also, not every business magnate in the US was born in the mid-1830s. Malcolm has just brought into perspective a clear pattern that helps us to realize and acknowledge that success isn’t exclusively an individual affair. The listed people had a special opportunity to work hard and make good out of the lucrative opportunity that came at the right time for them.

Learning data science in this era is like being born in January 1955. Or being born in the mid-1830s.

In his new book “21 Lessons”, Yuval Noah Harari, the bestselling author of “Sapiens” has written that “even if you don’t know how to cash in on data today, it is worth having it because it might hold the key to controlling and shaping life in the future.”

The rich won’t die

Do you believe ageing is a code that can be cracked?

Well, tech giants are already investing billions of dollars in anti-ageing companies. These are people who believe that ageing is a code that can as well be cracked. As a technophile, I also believe that the code can be cracked. And this is the decade to crack the code — with the abundant data and rising new tools to make sense of the enormous data being generated on a daily basis, it can be argued that longevity can be increased.

Larry Ellison, through The Ellison Medication Foundation, has invested hundreds of millions of dollars in anti-ageing research. As indicated on the website, 1.2 billion people will be 60 years or more by 2025, and “significant breakthroughs in understanding the basic biological processes that underlie ageing and age-related diseases are the best hope we have for achieving genuine prevention or amelioration of age-related debilitation and disease.”

Larry Page, the co-founder of Google has pumped in $1 billion to Calico, a company that’s tackling ageing — one of life’s greatest mysteries. As indicated on the website, “the company’s mission is to harness advanced technologies to increase our understanding of the biology that controls lifespan.” What an interesting project!

Peter Thiel has also shown interest in parabiosis, a process which involves blood transfusion from a younger person to older person. The technique works in the principle that young people’s blood has stronger, more vibrant, and active cells. The process will help older people obtain younger and stronger cells which will increase their heartbeats and eventually delay their ageing.

Research has already found out that the average lifespan of those who’ll be been born in 2050 will be 80–90. This is due to vital leaps and progress in research, which is aiming to find new insights on how to curb killer-diseases. Furthermore, these researchers and scientists are not contented by that; they believe that there is no reason why people can’t make it to three or four-digits in age. The above-mentioned software billionaires are the “crazy people” at the moment but, “what can be acquired by one person”, as Jessica Powell wrote on Medium “is often coveted by many; what is coveted by many is often a big market opportunity.” Thanks to Jessica for her well researched and deep article on the tech billionaires’ longevity increment efforts.

Change in leadership and authority?

Google defines liberty as “the state of being free within society from oppressive restrictions imposed by authority on one’s way of life, behaviour, or political views.” The horrible failure of fascism, imperialism, communism, and capitalism has left the world with one option — liberty. You might argue that communism or capitalism hasn’t failed. But they’re not widely used to unify or solve a large percentage of the world’s problems — they’re still viewed as either hindrance from growth or a tool to exploit the middle class and low-class societies.

As Yuval Noah Harari has written in 21 Lessons, “…humanity won’t abandon the liberal story, because it doesn’t have any alternative…” However, liberalism is finding it extremely hard to provide answers to the biggest problems the world is currently facing — for example, it can’t answer the problems of technological disruptions or climate change. The liberal story has always believed that all authority stems from the free will of individual humans and is expressed in either their feelings, desires, or choices. Some of the liberal principles include: voter knows best, the customer is always right, and follow your heart. But as it has been proven, these principles are not always right. The customer is not always right; sometimes they come back and complain about a product or service that they picked by themselves (their feelings, desires, and choices).

Wait, does that mean that we need to take some time to listen to algorithms? Probably. It’s time for authority to shift from humans to algorithms. We are living in perfect time; when two important revolutions are happening. Well, this generation is making the revolution to happen. Congratulations to Silicon Valley’s hard-working engineers and software developers! The first revolution is the mystery deciphering of the human brain and feelings by biologists. The second revolution is the hard work put by computer scientists in making sense of the enormous data being generated on a daily basis.

The convergence of these two revolutions (biotechnology and infotechnology) produces powerful algorithms which are able to surveil and interpret human feelings with higher accuracy than humans themselves. When there is a device that can surveil my feelings with higher accuracy and interpret it, why shouldn’t I just instruct the device to deal with my problems or negative feelings as it sees fit (for my own benefit)? That means that I can highly trust my biometric sensor than myself and that’s how the authority is swiftly shifted to computer algorithms. So human dictatorship might also reduce? We’ll see.

He who owns the data…

It’s good news that humanity is getting liberated from human dictatorship and authoritarianism. However, there is a problem still. Who can access these huge amounts of data that aids in the making of powerful algorithms which can understand us better? It’s corporations like Facebook, Google, Amazon, Tencent, Baidu, among others. And apparently, there is a problem with these corporations owning the massive data. Well, the problem is not them owning the data, but rather how they use the data.

As artificial intelligence eliminate economic value and political power off humans — which tries to bridge the gap of inequality in the world, the direction changes to biological inequality. As we’ve seen, the tech billionaires are already pumping in millions of dollars to research startups and medical centres which are trying to crack the code of ageing. In other words, the tech billionaires don’t want to die; they want to live forever. The rise of AI has given them enough reasons to spend their stupendous wealth on these research startups and laboratories.

Analyzed deeply, the AI and bioengineering revolution “might result in the separation of humankind into a small class of superhumans and a massive underclass of useless Homo Sapiens”, as Yuval Noah Harari has written in “21 Lessons.” Harari has also suggested that “if we want to prevent concentration of all wealth and power in the hands of a small elite, the key is to regulate the ownership of data.”

If land was the most precious asset in the 1800s and 1900s, data is the most precious asset in the current generation. And the race to obtain and make use of the data that is generated and transmitted around the world at a very first speed is on. The tech giants are in a race to obtain the massive data and their business model, as Harari has called it in his book, is “attention merchant.”

The tech giant’s supposedly short term business model of capturing users’ attention by providing free information, services, and entertainment and in turn selling the users’ attention to advertisers has perfectly worked. However, the giants might just be aiming at a better business model or rather aiming to capture users’ attention in a smarter way. Harari has argued that “[these tech giants’] true business model isn’t to sell advertisements at all. Rather, by capturing [users’] attention they manage to accumulate immense amounts of data about [the users], which is worth more than any advertising revenue.” In other words, users are not these tech giants’ customers but rather their product.

Facebook’s Crisis

In a leaked exchange to Silicon Alley Insider between Mark Zuckerberg and a friend back in 2004 when he was still an undergraduate at Harvard Business School and working on The Facebook.

A friend asked Zuck how he had managed to obtain 4,000 emails, photos, and other personal information from other Harvard students. Zuck answered: “people just submitted it. I don’t know why. They ‘trust me.’ Dumb fucks.” Later on, in an interview, Breyer, who is part of Facebook’s board of directors, said that Zuck told him that he absolutely regretted making those disdainful remarks. Zuck added to say that “I have grown and learnt a lot.” Zuck is absolutely right and he who didn’t forgive Zuck for those remarks might be having a problem because he was still such a young person and didn’t know that Facebook might reach where it is currently at.

People evolve and you should not be defined by some mistakes you made five, ten years ago. Just as Zuck said, people grow up and learn from their past mistakes. It’s better to make mistakes and learn from them than to play the safe game.

Fourteen years later, Zuck found himself in probably the toughest test of his lifetime. Facebook was faced with a data privacy crisis. Irascible Facebook users deleted their accounts, panicked investors sold stock, and intense pressure from lawmakers and worldwide regulators were at the neck of Facebook’s data privacy case.

Zuck isn’t 20 years this time and probably has learnt and gained a lot in relation to data privacy. U.K’s Cambridge Analytica, a political data analytics firm got access to 50 million Facebook users’ data, which the firm later used to influence US presidential election in 2018. In addition, the access to the data happened without Facebook’s knowledge or permission.

Mark Zuckerberg appearing before the Congress

The irascible Facebook users and victims couldn’t allow Mark Zuckerberg to just apologize to the public. Apologize? Not enough. What is needed of Mark Zuckerberg and Facebook is a new commitment to restoring the users’ trust. The company can keep on apologizing for any mess they make on the way, but the truth is they’re making billions of dollars from the users’ data that they collect each and every day.

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