Data... The modern digital fuel
Published: 07:05 PM,May 13,2024 | EDITED : 11:05 PM,May 13,2024
Data will replace fossil fuels to become the new oil.
Entering the modern digital era, which is accompanied by changes in our cognitive tools, takes us to a knowledge revolution that forms a developed economic system. With the comprehensive spread of this digital revolution, fossil fuel (oil) is no longer the main standard for measuring the wealth and economy of nations. Instead, data will replace fossil fuels to become the new oil, which countries compete for in the economic race. Big Data is now the focal point of the economic and investment giants due to its importance in the modern economic phase. Data fuels the economic system across all sectors, including industrial, health, and military.
In its general content, data represents information extracted from various sources. There is data related to the industrial sector, such as machine operation data and breakdowns, which can be monitored, recorded, stored, and utilized to determine optimal methods of operation and maintenance.
Health sector data also comes in various forms, including patient records, illnesses, treatments, drug reactions, X-rays, and medical analyses, which, when analyzed, help improve the healthcare system and enhance its performance. There is also general data that can be collected from the streets, such as traffic data and violations (offenses and accidents).
When analyzed, this data helps identify traffic impediments like congestion and violations, increasing the chances of addressing and reducing these issues. Personal consumer data can also be collected from real-world and online markets in various ways to determine consumer preferences and attract them to products that suit their choices. These are just simple examples of the types of data available in our world today.
The importance of data lies in its ability to reflect the real world. Data can be used to measure current levels and determine the best future directions. Data or the modern fuel of the economy has become a separate field that almost integrates with all other sciences without exception, including humanities and natural sciences. It also includes artificial intelligence (AI), which would not exist in our lives without data, as data is its fuel and source of superiority.
Data drives AI to the maximum levels of development, and the more data there is (in quantity and quality) to reach what we know as Big Data, the greater AI's abilities and speed of development. As data increases and its sources diversify, our ability to obtain useful information also increases. For instance, data provides an accurate analysis of the economic future based on previous and current economic and financial practices. Analyzing this data using AI helps solve the problem of long-term analytical processes and data complexity, contributing to achieving the required accuracy.
There are three main elements by which data levels can be measured: Volume, which expresses the size of the data; Velocity, which represents the speed of data flow and analysis; and Variety, which refers to the types, formats, and sources of data.
Data comes in three branches according to its usefulness and use: Structured Data, which is organised in a way consistent with the type of required data, making it easy to use and process with traditional analysis tools; Semi-Structured Data, which is less organised and coordinated in type than the first type and requires more professional methods to process; and Unstructured Data, which is unorganised and has randomness and non-selectivity of data by type. The best ways to deal with this type of data are through AI models, and the closest example of this type of data is that which feeds generative AI models like Chat GPT and its counterparts.
Big Data is usually attributed to high-capacity computer memory units like 'Zettabyte' which means the storage capacity is enormous, making these storage capabilities expensive. They need precise processing and refinement to reach the highest levels of utilisation. Therefore, one of the challenges that hinder the ability of countries and their institutions to acquire modern oil (Big Data) is the enormous storage capabilities and providing the highest levels of cybersecurity to prevent any attempts to steal this data for harmful interests.
There are many ways to collect data, such as sensors and electronic devices mostly connected to the Internet of Things (IoT), where these electronic and sensor systems are interconnected through the Internet to increase data transfer speed and quality. These systems (sensors) and their uses are many, including in the industrial and health sectors and on the streets, as mentioned in previous paragraphs. Data can also be collected through the information users (consumers) share on social media, emails, and website visits. This data can come in various formats, including texts, images, videos, browsing locations, and views. Major tech companies that own and operate these digital platforms can access this personal data, raising many security and ethical issues.
In its general content, data represents information extracted from various sources. There is data related to the industrial sector, such as machine operation data and breakdowns, which can be monitored, recorded, stored, and utilized to determine optimal methods of operation and maintenance.
Health sector data also comes in various forms, including patient records, illnesses, treatments, drug reactions, X-rays, and medical analyses, which, when analyzed, help improve the healthcare system and enhance its performance. There is also general data that can be collected from the streets, such as traffic data and violations (offenses and accidents).
When analyzed, this data helps identify traffic impediments like congestion and violations, increasing the chances of addressing and reducing these issues. Personal consumer data can also be collected from real-world and online markets in various ways to determine consumer preferences and attract them to products that suit their choices. These are just simple examples of the types of data available in our world today.
The importance of data lies in its ability to reflect the real world. Data can be used to measure current levels and determine the best future directions. Data or the modern fuel of the economy has become a separate field that almost integrates with all other sciences without exception, including humanities and natural sciences. It also includes artificial intelligence (AI), which would not exist in our lives without data, as data is its fuel and source of superiority.
Data drives AI to the maximum levels of development, and the more data there is (in quantity and quality) to reach what we know as Big Data, the greater AI's abilities and speed of development. As data increases and its sources diversify, our ability to obtain useful information also increases. For instance, data provides an accurate analysis of the economic future based on previous and current economic and financial practices. Analyzing this data using AI helps solve the problem of long-term analytical processes and data complexity, contributing to achieving the required accuracy.
There are three main elements by which data levels can be measured: Volume, which expresses the size of the data; Velocity, which represents the speed of data flow and analysis; and Variety, which refers to the types, formats, and sources of data.
Data comes in three branches according to its usefulness and use: Structured Data, which is organised in a way consistent with the type of required data, making it easy to use and process with traditional analysis tools; Semi-Structured Data, which is less organised and coordinated in type than the first type and requires more professional methods to process; and Unstructured Data, which is unorganised and has randomness and non-selectivity of data by type. The best ways to deal with this type of data are through AI models, and the closest example of this type of data is that which feeds generative AI models like Chat GPT and its counterparts.
Big Data is usually attributed to high-capacity computer memory units like 'Zettabyte' which means the storage capacity is enormous, making these storage capabilities expensive. They need precise processing and refinement to reach the highest levels of utilisation. Therefore, one of the challenges that hinder the ability of countries and their institutions to acquire modern oil (Big Data) is the enormous storage capabilities and providing the highest levels of cybersecurity to prevent any attempts to steal this data for harmful interests.
There are many ways to collect data, such as sensors and electronic devices mostly connected to the Internet of Things (IoT), where these electronic and sensor systems are interconnected through the Internet to increase data transfer speed and quality. These systems (sensors) and their uses are many, including in the industrial and health sectors and on the streets, as mentioned in previous paragraphs. Data can also be collected through the information users (consumers) share on social media, emails, and website visits. This data can come in various formats, including texts, images, videos, browsing locations, and views. Major tech companies that own and operate these digital platforms can access this personal data, raising many security and ethical issues.