history of data analytics
The Evolution of Data Analytics: A Historical Perspective
history of data analytics
The history of data analytics can be traced back to ancient civilizations, where early methods of data collection and analysis were employed for trade, agriculture, and governance. However, it began to take shape as a formal discipline in the 20th century with the advent of statistics and the development of computers. The 1960s and 1970s saw the emergence of business intelligence tools and data warehousing, enabling organizations to analyze historical data for decision-making. The proliferation of personal computers in the 1980s and the internet in the 1990s further fueled the growth of data analytics, leading to the development of more sophisticated software and algorithms. In the 21st century, the explosion of big data, powered by advancements in machine learning and artificial intelligence, transformed data analytics into a vital field for organizations across industries, enabling real-time insights and predictive capabilities that drive innovation and strategic decision-making.
To Download Our Brochure: https://www.justacademy.co/download-brochure-for-free
Message us for more information: +91 9987184296
1 - Prehistoric Data Gathering (Before 5000 BC): Early humans collected data for survival, ranging from food sources to migratory patterns. This laid the groundwork for understanding data collection and basic record keeping.
2) Early Civilizations (5000 BC 300 AD): Ancient Babylonians and Egyptians began using simple forms of data collection, such as counting cattle and grain storage, leading to early forms of quantitative analysis.
3) The Introduction of Statistics (17th Century): The birth of statistics as a discipline began in the 17th century with mathematicians like John Graunt and Blaise Pascal, who used data to understand population dynamics and gambling odds.
4) Development of Probability Theory (18th Century): Mathematicians like Pierre Simon Laplace and Thomas Bayes formalized probability theory, which became foundational for data analysis and inferential statistics.
5) The Advent of the Statistical Society (19th Century): The establishment of statistical societies and census procedures in countries like the UK and the US promoted systematic data collection and analysis on social issues.
6) Introduction of Computers (20th Century): With the invention of computers in the mid 20th century, data processing and analysis became more efficient, enabling large datasets to be processed quickly.
7) Birth of Data Mining (1960s 1980s): The concept of data mining emerged as advancements in computing allowed for the extraction of patterns and insights from large data sets using algorithms.
8) Development of Software Tools (1980s 1990s): The creation of analytical software, such as SAS and SPSS, facilitated statistical analysis and made it accessible to non specialists, emphasizing the importance of data driven decision making.
9) Business Intelligence Era (1990s): Organizations began utilizing Business Intelligence (BI) tools that enabled data visualization, reporting, and analysis to enhance strategic decision making processes.
10) The Big Data Revolution (2000s): The rise of the internet and digital data led to an explosion of data generation, giving birth to the term “Big Data,” and prompting the need for new technologies to store, process, and analyze massive datasets.
11) Emergence of Data Science (2010s): The term “data science” gained popularity, encompassing various fields such as statistics, computer science, and domain expertise, and focusing on extracting actionable insights from complex data sets.
12) Machine Learning and Artificial Intelligence (2010s Present): Techniques in machine learning and AI have transformed data analytics, allowing for predictive analysis and automation of insights extraction, enhancing the capabilities of data analysts.
13) Data Analytics in Healthcare (2010s Present): The use of data analytics in healthcare has revolutionized patient care, through predictive analytics for disease outbreaks and the optimization of treatment plans based on historical data.
14) Real Time Analytics (2010s Present): The ability to analyze data in real time has become crucial for industries like finance and marketing, allowing organizations to make instant data driven decisions.
15) Ethics and Data Privacy (Emerging Trend): With the growth of data collection, issues surrounding data privacy and ethics have become increasingly important, leading to regulations like GDPR and discussions around responsible data use.
This historical overview provides a framework for understanding the evolution of data analytics, emphasizing key milestones that have shaped the field today. Each point can be elaborated upon during training sessions to provide students with a comprehensive understanding of data analytics' significance and development.
Browse our course links : https://www.justacademy.co/all-courses
To Join our FREE DEMO Session: Click Here
Contact Us for more info:
- Message us on Whatsapp: +91 9987184296
- Email id: info@justacademy.co
Cheapest Online iOS Training in Bhopal
