- Business Analytics - Home
- Business Analytics Basics
- Business Analytics - What It Is?
- Business Analytics - History and Evolution
- Business Analytics - Key Concepts and Terminologies
- Business Analytics - Types of Data
- Business Analytics - Data Collection Methods
- Different Tools used for Data Cleaning
- Business Analytics - Data Cleaning Process
- Different Sources of Data for Data Analysis
- Business Analytics - Data Cleaning
- Business Analytics - Data Quality
- Descriptive Analytics
- Descriptive Analytics - Introduction
- How Does Descriptive Analytics Work?
- Descriptive Analytics - Challenges and Future in Data Analysis
- Descriptive Analytics Process
- Descriptive Analytics - Advantages and Disadvantages
- Descriptive Analytics - Applications
- Descriptive Analytics - Tools
- Descriptive Analytics - Data Visualization
- Descriptive Analytics - Importance of Data Visualization
- Descriptive Analytics - Data Visualization Techniques
- Descriptive Analytics - Data Visualization Tools
- Predictive Analytics
- Predictive Analytics - Introduction
- Statistical Methods & Machine Learning Techniques
- Prescriptive Analytics
- Prescriptive Analytics - Introduction
- Prescriptive Analytics - Optimization Techniques
Business Analytics - History and Evolution
Business analytics has evolved significantly, driven by technological advancements, data availability, and analytical approaches.
The history and Evolution of business analytics are summarised as follows −
Business Analytics during 1800
Frederick Taylor, in the 18th century, created the first system of business analytics also known as scientific management in the USA. This concept was used to analyse production processes and labourers' body movements to identify greater efficiencies.
Business Analytics in the early 1900s
Analytics changed the industrial business around the globe.
Business Analytics during the 1950s
In 1956, IBM invented the first hard disk drive to store the data which can be accessed and analysed whenever required. Businesses relied on manual record-keeping and simple statistical methods.
Business Analytics during 1950-1980
The next generation of business intelligence solutions was based on databases and data warehouses to store considerable data. The development of sophisticated computers was able to do complex calculations and data processing. The advent of Management Information Systems (MIS) was used to improve data organization. In the 1970s -1980s, the Decision Support System emerged to help businesses make more informed decisions.
Business Analytics during 1980-2000
During this time Business Intelligence rose and integrated data from various sources to provide comprehensive business insights. Data warehousing emerged at a large scale to store and analyze historical data.
Business Analytics in early 21st Century (2000-2010)
In the twenty-first century, organizations recognized the need for business intelligence solutions. During this period, data generation has risen on the internet, social media, and IoT devices. Companies such as IBM, Microsoft, SAP, and Oracle were at the forefront of providing such solutions to transform the way businesses function. Data mining and predictive analytics were in the highest trend.
The emergence of open-source tools such as Hadoop and R has made access to strong analytics tools, allowing more firms to exploit data.
Business Analytics in Recent Times 2010-Present
Artificial Intelligence and machine learning have become crucial to corporate analytics, enabling improved predictive and prescriptive analytics. The development of real-time analytics enables firms to make decisions based on current data, hence enhancing responsiveness and agility. The widespread adoption of cloud computing has enabled scalable and cost-effective data storage and analytics choices.
Current Evolution of Business Analytics
Business analytics has progressed from data collection and statistical analysis to advanced AI-powered insights. This progression reflects technological advancements, shifts in company needs, and the growing relevance of data in decision-making processes. The future of business analytics promises even better integration with AI and ML, a stronger emphasis on real-time data, and a continuous emphasis on ethical data use and privacy.
The current evolution of business analytics can be traced back to using automation and big data. With the advent of big data, analytics and various data sources were expected to become more scalable and powerful. This contributed to the development of more powerful tools and systems that can handle massive amounts of data. With the advent of cloud technologies, data no longer had to be stored on-site. Because of the vast volume of data that needed to be processed, there was a high need for automated analytical tools at the time.
All of this has prompted businesses to upgrade their old software into more powerful programs that can process enormous datasets quickly and from different sources, such as the cloud and distributed file systems, rather than simply the traditional RDBMS. Business analysts were also equipped with more precise prognostic and forecasting abilities thanks to current business analytics. This is when firms realized the value of data analytics in business. All of this technology already existed, but the industry's expanding need prompted companies of all sizes to begin incorporating data analytics into their daily operations.