Introduction to Business Intelligence definition and types
- 2. Meaning Business Intelligence is a set of processes, architectures, and technologies that convert raw data into meaningful information that drives profitable business actions. Business Intelligence (BI) refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business information. The purpose of Business Intelligence is to support better business decision making. Essentially, Business Intelligence systems are data-driven Decision Support Systems (DSS).
- 3. Why Business Intelligence Increased competition Increased competition is a major reason why businesses need to use business intelligence. Change in business environment The business environment is constantly changing, and businesses need to be able to adapt to these changes in order to stay ahead of the competition. Bombardment of Information Businesses are bombarded with information from a variety of sources, and it can be difficult to know what information is important and how to use it effectively.
- 4. Why Business Intelligence The question now is what to do with this data and how to use it in the best possible way. Difference between Data Analytics and Business Intelligence • BI is the process of studying data in order to draw conclusions about the information it contains. • BI- Focuses on historical data and present performance to inform future decisions. • Data Analytics: Has a broader scope, encompassing past, present, and potentially future data. • BI is all about presenting data for informed decision-making, while data analytics is more about uncovering hidden insights from data. • In practice, businesses often use both BI and data analytics together to get a complete picture. BI involves the strategic decision-making based on that data.
- 5. History of Business Intelligence • The first use of the term “business intelligence” was by Mr. Richard Miller Devens, an American author in his book Cyclopaedia of Commercial and Business Anecdotes, first published in 1865. • He used it to describe how Sir Henry Furnese, a successful banker, profited from information by actively gathering and acting on it before his competition. • This pointed out the fact that it was more reliable to use data and empirical evidence, rather than gut instinct, to develop a business strategy. • The idea was further enhanced by others who saw value in information.
- 6. History of Business Intelligence (cont'd) During the last decade of the 1800s, that is 1890, Frederick Taylor introduced scientific management with time studies that analyzed production techniques and laborers’ body movements to find greater efficiencies that boosted industrial production. Taylor became a consultant to Henry Ford, who in the early 1900s started measuring the time each component of his Ford Model T took to complete on his assembly line. His work and his success revolutionized the manufacturing industry worldwide.
- 7. BI powered by Computers • Electronic computers were not developed till 1930s but were quickly developed during World War II, as part of the effort by the allies to crack German codes. • Up until the 1950s, computers relied mostly on punchcards to store data. These were huge piles of cards with tiny holes in them, which would store the information to be processed by the computers. In 1956, however, IBM invented the first hard disk drive, making it possible to store large amounts of information with greater flexibility of access.
- 8. BI powered by Computers (cont'd) • In 1958, IBM researcher Hans Peter Luhn published a historical paper called A Business Intelligence System. He wrote about the potential of a system for “selective dissemination” of documents to “action points” based on “interest profiles.” His work has remarkable significance even to this day since he predicted several business intelligence trends which are cutting-edge nowadays, as the ability for information systems to learn and predict based on user interests. Today we call it machine learning. Luhn is popularly recognized as the father of business intelligence. • Even though the concept proposed by Luhn caught the attention of several interested parties, the idea was considered too expensive at the time to have any practical use. More technological progress was needed to make it an economically viable solution.
- 9. BI powered by Computers (cont'd) In the next decade, computer use exploded. Each computer was a gigantic machine which occupied the entire floor of a building and had to be managed by several high-skilled engineers to function properly. Experts again tackled the idea of using computers to extract conclusions from the data, but the main problem was that there was no centralized method available to bring together all the data in one place. Data, by itself, could not generate any insights. To solve this challenge, the first database management systems were designed. Later, they would simply be called databases. This new tool provided its value, being used to finally make conclusions from the available data.
- 10. 1970 onwards.... Big players enter.... IBM, SAP, Siebel • Lower prices for storage space and better databases allowed for the next generation of business intelligence solutions. • Data warehouses are databases designed to aggregate lots of data from other sources of data (mostly other databases), allowing a much deeper analysis with the ability to cross- reference these different sources. It was still, however, too technical and expensive. Reports needed to be run and maintained by a host of expensive IT technical staff. • In the 90s, data warehouse costs declined as more competitors entered the market and more IT professionals got acquainted with the technology. This was the period of “Business Intelligence 1.0.”
- 11. ETL and OLAP tools ETL (extract, transform, and load) a set of tools, similar to a programming language, that made it easier to design the flow of data within a data warehouse. OLAP (online analytical processing) helped to create different visualization options for the queried data, empowering the analysts to extract better conclusions from the information at hand.