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How Business Intelligence Has Evolved Over Time: A Comprehensive Guide (PDF)
Business intelligence (BI) is a broad category of applications, technologies, and processes for gathering, storing, accessing, and analyzing data to help business users make better decisions . BI has been around for decades, but it has undergone significant changes and improvements over time, especially with the advent of the digital age and big data analytics.
Evolution Of Business Intelligence Pdf Download
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In this article, we will provide a comprehensive guide on how BI has evolved over time, from its origins to its current state and future trends. We will also provide a link to download a free ebook (PDF) that covers the history and future of BI in more detail.
The Origins of Business Intelligence
The term business intelligence was coined by Richard Millar Devens in 1865, in his book Cyclopædia of Commercial and Business Anecdotes. He used it to describe how a banker named Sir Henry Furnese gained an advantage over his competitors by collecting and analyzing information from various sources . However, the concept of BI can be traced back even further, to ancient civilizations that used data to plan wars, trade, agriculture, and taxation.
The first modern BI systems emerged in the 1950s and 1960s, with the development of computers and databases. These systems were mainly used for reporting and descriptive analytics, such as summarizing historical data and generating predefined reports. They were also limited by the cost and capacity of hardware and software, as well as the lack of data integration and standardization.
The Evolution of Business Intelligence
BI systems have evolved significantly over time, in terms of technology, functionality, and scope. Here are some of the major milestones and trends that have shaped the evolution of BI:
In the 1970s and 1980s, BI systems became more sophisticated and interactive, with the introduction of online analytical processing (OLAP), decision support systems (DSS), executive information systems (EIS), and data warehousing. These technologies enabled users to perform multidimensional analysis, drill-downs, slice-and-dice, and what-if scenarios on aggregated data from various sources .
In the 1990s and early 2000s, BI systems expanded their capabilities and reach, with the emergence of data mining, data visualization, dashboards, scorecards, key performance indicators (KPIs), business performance management (BPM), and self-service BI. These technologies enabled users to discover hidden patterns, trends, and insights from large volumes of data, as well as to monitor and manage business performance using graphical displays and metrics .
In the late 2000s and 2010s, BI systems embraced the challenges and opportunities of big data analytics, with the adoption of cloud computing, mobile BI, social media analytics, text analytics, sentiment analysis, web analytics, predictive analytics, prescriptive analytics, machine learning, artificial intelligence (AI), and natural language processing (NLP). These technologies enabled users to access and analyze data from various sources and formats, such as structured, unstructured, semi-structured, streaming, geospatial, multimedia, etc., as well as to generate recommendations and actions based on advanced algorithms and models .
The Future of Business Intelligence
BI systems are constantly evolving and improving to meet the changing needs and expectations of business users. Some of the current and emerging trends that will shape the future of BI are:
Augmented analytics: This is the use of AI and machine learning to automate and enhance various aspects of data preparation, analysis, visualization, and storytelling. Augmented analytics aims to make BI more accessible, efficient, accurate,
and actionable for users of all skill levels .
Embedded analytics: This is the integration of BI capabilities into business applications or processes. Embedded analytics aims to provide users with relevant data and insights at the point of decision or action .
Real-time analytics: This is the analysis of data as soon as it is generated or collected. Real-time analytics aims to provide users with timely and actionable information that can help them respond to changing situations or opportunities . c481cea774
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