Spanish multinational bank BBVA has appointed one of its veterans, David Puente, to the newly created role of Global Head of Data, a highly touted position at the group aimed at advancing its 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 agenda, according to a company statement.
The promotion of Mr. Puente represents his first role in this area of focus, having previously worked at BBVA as a project manager. Banks over the past few years have been diverting resources and personnel into such avenues as big data research and other digitized agendas in a bid to take advantage of shifting demographics and a move away from traditional banking.
Mr. Puente’s appointment will see him focus on big data and other software initiatives at BBVA, which will build on its existing efforts. Currently, BBVA boasts a functional data and 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 center, though the shakeup in its management structure represents a continued stroke in fostering its strategic use of data across all the areas and businesses of the group.
Mr. Puente has been with BBVA since 2004 – per the appointment, he will be reporting directly to CEO Carlos Torres Vila. His official mandate will be overseeing, developing, and implementing BBVA’s global data strategy, with a focus on the aforementioned channels.
According to Mr. Torres Vila, in a statement on the appointment and agenda of the group: "Maximizing the potential of data is essential to create opportunities for our clients and customers and this new organization will allow us to accelerate our plans."
Spanish multinational bank BBVA has appointed one of its veterans, David Puente, to the newly created role of Global Head of Data, a highly touted position at the group aimed at advancing its 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 agenda, according to a company statement.
The promotion of Mr. Puente represents his first role in this area of focus, having previously worked at BBVA as a project manager. Banks over the past few years have been diverting resources and personnel into such avenues as big data research and other digitized agendas in a bid to take advantage of shifting demographics and a move away from traditional banking.
Mr. Puente’s appointment will see him focus on big data and other software initiatives at BBVA, which will build on its existing efforts. Currently, BBVA boasts a functional data and 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 center, though the shakeup in its management structure represents a continued stroke in fostering its strategic use of data across all the areas and businesses of the group.
Mr. Puente has been with BBVA since 2004 – per the appointment, he will be reporting directly to CEO Carlos Torres Vila. His official mandate will be overseeing, developing, and implementing BBVA’s global data strategy, with a focus on the aforementioned channels.
According to Mr. Torres Vila, in a statement on the appointment and agenda of the group: "Maximizing the potential of data is essential to create opportunities for our clients and customers and this new organization will allow us to accelerate our plans."