The Economy of Data
- What kind of mark will big data leave on the financial services industry?

This article was written by Adinah Brown from Leverate.
Recently I saw a watchmojo playlist on youtube about people who did not make money from their inventions. Laszlo Biro, who invented the ballpoint pen (aka, the biro). Alexey Pajitnov, who invented Tetris. Mikhail Kalashnikov, who invented the Kalashnikov rifle. Daisuke Inoue, who invented karaoke. Even James Cameron rates a mention.
The London Summit 2017 is coming, get involved!
But guess who is number 1? A gentleman by the name of Tim Berners-Lee, who invented a little thing called the internet. Well, the worldwide web actually. And whilst working at CERN he then developed the web browser. The video asks: "Can you imagine if any of these ideas had been patented?”
Berners-Lee believed that the internet should be free and available to all. In a sense this idea has paved the way for all the next generation activities that we see in our short-term crystal ball as taking over the web. And the one that looms largest over them all is another little thing called data.
Big Data Big Data Big data refers to the collection of data that is too complex and too large for processing by standard database tools. There is no specific quantity of data, which is set as a minimum level to be considered Big data. Image the data collected on global credit card transactions. Many governments used Big data analysis to study the recent pandemic spread. The term Big data was first introduced in 1980 by Charles Tilly.The term Big data was primarily used in computer science, statistics, and econometrics and was made famous in Silicon Valley in the mid-1990s. What Big Data Can Do for YouBig data is the massive amount of data collected over time that are difficult to analyze and handle because the data sets are so enormous. The records are analyzed for marketing trends in business as well as in the fields of manufacturing, medicine, and science. The types of data include business transactions, e-mail messages, photos, surveillance videos, activity logs, and unstructured text from blogs and social media, as well as the vast amounts of data that can be collected from sensors of all varieties. Big data can also refer to the analytical challenge in deriving meaningful information from data in petabyte and exabyte volumes. For example, big data analytics breaks down the data sets into smaller chunks for efficient processing and employs parallel computing to derive intelligence for effective decision-making.Big data is used in a wide range of industries, sectors, or applications. This includes benefits for governments, healthcare, finance, education, media, internet of things (IoT), information technology, and others. Big data refers to the collection of data that is too complex and too large for processing by standard database tools. There is no specific quantity of data, which is set as a minimum level to be considered Big data. Image the data collected on global credit card transactions. Many governments used Big data analysis to study the recent pandemic spread. The term Big data was first introduced in 1980 by Charles Tilly.The term Big data was primarily used in computer science, statistics, and econometrics and was made famous in Silicon Valley in the mid-1990s. What Big Data Can Do for YouBig data is the massive amount of data collected over time that are difficult to analyze and handle because the data sets are so enormous. The records are analyzed for marketing trends in business as well as in the fields of manufacturing, medicine, and science. The types of data include business transactions, e-mail messages, photos, surveillance videos, activity logs, and unstructured text from blogs and social media, as well as the vast amounts of data that can be collected from sensors of all varieties. Big data can also refer to the analytical challenge in deriving meaningful information from data in petabyte and exabyte volumes. For example, big data analytics breaks down the data sets into smaller chunks for efficient processing and employs parallel computing to derive intelligence for effective decision-making.Big data is used in a wide range of industries, sectors, or applications. This includes benefits for governments, healthcare, finance, education, media, internet of things (IoT), information technology, and others. Read this Term
Big data, Analytics Analytics Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making. In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements. How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors. In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions. Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed. Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades. Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability. Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making. In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements. How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors. In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions. Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed. Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades. Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability. Read this Term, AI, IoT, SAAS…. the list of up and coming, take-over-the-financial-world technology in many ways depends on the ability of the technologies to access and use data. It’s data that is the lifeblood of the internet giants, companies like Google, Facebook and Amazon. Google knows what you search for, which helps it continue to improve its algorithms, making searching more accurate and efficient. Facebook sees what you share and tracks your likes to also improve its user experience. Amazon knows what you want to buy, which helps it cater to your potential purchases and offer what you want.
But these also have direct profit impacts – by improving the search accuracy, Google attracts more searchers, who in turn provide more data. Facebook improves its user experience and user numbers, making others more likely to sign up in order to connect. Amazon can identify and scale the products for better purchasing power and better deals, leading to larger margins and more sales.
The increase in users is the profit model that Facebook and Google use to monetize their business. Revenue growth over the last few years for digital advertising has almost exclusively gone to either Google or Facebook. For good reason too, they have the most traffic.
Digital marketing makes its mark
Whilst digital marketing is the obvious manifestation of the use of internet data, since it is a case of the internet data feeding the improvement of internet service, there are other industries that use connectivity to improve their product or service. The automated car industry is almost entirely reliant on the data provided by its cars. Wearables, like Smartwatches and Fitbits, are providing health related data that was a dream only 5 years ago.
Ultimately, this accumulation of knowledge provides us with opportunities beyond simply marketing information and digital advertising. The collection of health data from wearables will give us stronger, real life data about the human body and impact a significant range of medical conditions. Data captured by smart cars will provide real benefits by providing data to help decrease road fatalities. The real revolution that data will bring will be felt in the lives of all of us.
It is this vision that Tim Berners-Lee had the foresight to understand when he decided not to allow the internet to be patented and limited. And with data as the undertow that pushes the waves along, we can hope that his vision will be more than simply how to monetize a user base, but continue to provide improvements that may even justify the intrusion on our privacy.
This article was written by Adinah Brown from Leverate.
Recently I saw a watchmojo playlist on youtube about people who did not make money from their inventions. Laszlo Biro, who invented the ballpoint pen (aka, the biro). Alexey Pajitnov, who invented Tetris. Mikhail Kalashnikov, who invented the Kalashnikov rifle. Daisuke Inoue, who invented karaoke. Even James Cameron rates a mention.
The London Summit 2017 is coming, get involved!
But guess who is number 1? A gentleman by the name of Tim Berners-Lee, who invented a little thing called the internet. Well, the worldwide web actually. And whilst working at CERN he then developed the web browser. The video asks: "Can you imagine if any of these ideas had been patented?”
Berners-Lee believed that the internet should be free and available to all. In a sense this idea has paved the way for all the next generation activities that we see in our short-term crystal ball as taking over the web. And the one that looms largest over them all is another little thing called data.
Big Data Big Data Big data refers to the collection of data that is too complex and too large for processing by standard database tools. There is no specific quantity of data, which is set as a minimum level to be considered Big data. Image the data collected on global credit card transactions. Many governments used Big data analysis to study the recent pandemic spread. The term Big data was first introduced in 1980 by Charles Tilly.The term Big data was primarily used in computer science, statistics, and econometrics and was made famous in Silicon Valley in the mid-1990s. What Big Data Can Do for YouBig data is the massive amount of data collected over time that are difficult to analyze and handle because the data sets are so enormous. The records are analyzed for marketing trends in business as well as in the fields of manufacturing, medicine, and science. The types of data include business transactions, e-mail messages, photos, surveillance videos, activity logs, and unstructured text from blogs and social media, as well as the vast amounts of data that can be collected from sensors of all varieties. Big data can also refer to the analytical challenge in deriving meaningful information from data in petabyte and exabyte volumes. For example, big data analytics breaks down the data sets into smaller chunks for efficient processing and employs parallel computing to derive intelligence for effective decision-making.Big data is used in a wide range of industries, sectors, or applications. This includes benefits for governments, healthcare, finance, education, media, internet of things (IoT), information technology, and others. Big data refers to the collection of data that is too complex and too large for processing by standard database tools. There is no specific quantity of data, which is set as a minimum level to be considered Big data. Image the data collected on global credit card transactions. Many governments used Big data analysis to study the recent pandemic spread. The term Big data was first introduced in 1980 by Charles Tilly.The term Big data was primarily used in computer science, statistics, and econometrics and was made famous in Silicon Valley in the mid-1990s. What Big Data Can Do for YouBig data is the massive amount of data collected over time that are difficult to analyze and handle because the data sets are so enormous. The records are analyzed for marketing trends in business as well as in the fields of manufacturing, medicine, and science. The types of data include business transactions, e-mail messages, photos, surveillance videos, activity logs, and unstructured text from blogs and social media, as well as the vast amounts of data that can be collected from sensors of all varieties. Big data can also refer to the analytical challenge in deriving meaningful information from data in petabyte and exabyte volumes. For example, big data analytics breaks down the data sets into smaller chunks for efficient processing and employs parallel computing to derive intelligence for effective decision-making.Big data is used in a wide range of industries, sectors, or applications. This includes benefits for governments, healthcare, finance, education, media, internet of things (IoT), information technology, and others. Read this Term
Big data, Analytics Analytics Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making. In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements. How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors. In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions. Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed. Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades. Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability. Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making. In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements. How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors. In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions. Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed. Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades. Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability. Read this Term, AI, IoT, SAAS…. the list of up and coming, take-over-the-financial-world technology in many ways depends on the ability of the technologies to access and use data. It’s data that is the lifeblood of the internet giants, companies like Google, Facebook and Amazon. Google knows what you search for, which helps it continue to improve its algorithms, making searching more accurate and efficient. Facebook sees what you share and tracks your likes to also improve its user experience. Amazon knows what you want to buy, which helps it cater to your potential purchases and offer what you want.
But these also have direct profit impacts – by improving the search accuracy, Google attracts more searchers, who in turn provide more data. Facebook improves its user experience and user numbers, making others more likely to sign up in order to connect. Amazon can identify and scale the products for better purchasing power and better deals, leading to larger margins and more sales.
The increase in users is the profit model that Facebook and Google use to monetize their business. Revenue growth over the last few years for digital advertising has almost exclusively gone to either Google or Facebook. For good reason too, they have the most traffic.
Digital marketing makes its mark
Whilst digital marketing is the obvious manifestation of the use of internet data, since it is a case of the internet data feeding the improvement of internet service, there are other industries that use connectivity to improve their product or service. The automated car industry is almost entirely reliant on the data provided by its cars. Wearables, like Smartwatches and Fitbits, are providing health related data that was a dream only 5 years ago.
Ultimately, this accumulation of knowledge provides us with opportunities beyond simply marketing information and digital advertising. The collection of health data from wearables will give us stronger, real life data about the human body and impact a significant range of medical conditions. Data captured by smart cars will provide real benefits by providing data to help decrease road fatalities. The real revolution that data will bring will be felt in the lives of all of us.
It is this vision that Tim Berners-Lee had the foresight to understand when he decided not to allow the internet to be patented and limited. And with data as the undertow that pushes the waves along, we can hope that his vision will be more than simply how to monetize a user base, but continue to provide improvements that may even justify the intrusion on our privacy.