In 1865, Richard Millar Devens presented the phrase “Business Intelligence” (BI) in the “Cyclopædia of Commercial and Business Anecdotes.” He used it to describe how Sir Henry Furnese, a banker, profited from information by gathering and acting on it before his competition. More recently, in 1958, an article was written by an IBM computer scientist named Hans Peter Luhn, describing the potential of gathering business intelligence (BI) through the use of technology.
Business intelligence, as it is understood today, uses technology to gather and analyze data, translate it into useful information, and act on it “before the competition.” Essentially, the modern version of BI focuses on technology as a way to make decisions quickly and efficiently, based on the right information at the right time.
In 1968, only individuals with extremely specialized skills could translate data into usable information. At this time, data from multiple sources was normally stored in silos, and research was typically presented in a fragmented, disjointed report that was open to interpretation. Edgar Codd recognized this as a problem, and published a paper in 1970, altering how people thought about databases. His proposal of developing a “relational database model” gained tremendous popularity and was adopted worldwide.
Decision support systems (DSS) was the first database management system to be developed. Many historians suggest the modern version of business intelligence evolved from the DSS database. The number of BI vendors grew in the 1980s, as business people discovered the value of business intelligence. An assortment of tools was developed during this time, to access and organize data in simpler ways. OLAP, executive information systems, and data warehouses were some of the tools developed to work with DSS.
Online analytical processing (OLAP) is a system that allows users to analyze data from a variety of sources while offering multiple paradigms or perspectives. Databases configured for OLAP use a multidimensional data model, supporting complex analysis and ad hoc queries. The standard applications of OLAP include:
- Business reporting for sales
- Management reporting
- Business process management (BPM)
- Budgeting and forecasting
- Financial reporting and similar areas
- New applications, such as agriculture
OLAP was quite popular because of the variety of ways it offered to assemble and organize information. As a SQL-based program, it lost popularity when NoSQL became popular. (At present, some companies, such as Kyvos Insights and AtScale, have layered OLAP onto a NoSQL base.) OLAP supports three basic operations:
- Slicing and dicing
Consolidation involves combining data that can be stored and processed in multiple ways. For example, all branch auto sales can be totaled by the auto sales manager as a way to anticipate sales trends. On the other hand, the drill-down technique supports navigating through and researching the details. People can view the auto sales by color, style, or gas consumption. Slicing and dicing lets people take out (slice) specific data on the OLAP cube, and view (dice) those slices from different perspectives (sometimes called dimensions, as in “multidimensional”).
Executive Information Systems (EIS)
In the late 1970s, CEOs began using the internet to research business information. This led to the development of software, called executive information systems (EIS), to support upper management in making decisions. An EIS is designed to provide the appropriate and up-to-date information needed to “streamline” the decision-making process. The system emphasizes graphics displays and easy-to-use interfaces in presenting the information. The goal of an EIS was to turn executives into “hands-on” users who can handle their own email, research, appointments, and reading of reports, rather than receiving this information through middlemen/women. EIS gradually lost popularity due to its limitations in being helpful.
Data Warehouses started becoming popular in the 1980s, as businesses began using in-house data analysis solutions regularly. (This was often done after 5 p.m. and on weekends, due to the limitations of computer systems at the time.) Before data warehousing, a significant amount of redundancy was needed to provide different people in the decision-making process with useful information. Data warehousing significantly cut the amount of time needed to access data. Data traditionally stored in a number of locations (often, in the form of departmental silos) could now be stored in a single location.
The use of data warehouses also helped in developing the use of big data. Suddenly, a massive amount of data, in a variety of forms (email, internet, Facebook, Twitter, etc.), could be accessed from a single data store, saving time and money to access previously unavailable business information. The potential of data warehouses for data-driven insights was huge. These insights increased profits, detected fraud, and minimized losses.
Business Intelligence Goes High Tech
Business intelligence (BI), as a technological concept, began shortly after the 1988 international conference Multiway Data Analysis Consortium was held in Rome. The conclusions reached at this conference jump-started efforts for simplifying BI analysis, while making it more user-friendly. Many BI businesses started up in response to the conference’s conclusions, with each new business offering new BI tools. During this period, there were two basic functions of BI: producing data and reports, and organizing and visualizing it in a presentable way.
In the late 1990s and early 2000s, BI services began providing simplified tools, allowing decision-makers to become more self-sufficient. The tools were easier to use, provided the functionality needed, and were very efficient. Business people could now gather data and gain insights by working directly with the data.
Business Intelligence vs. Analytics
Currently, the two terms are used interchangeably. Both describe the general practice of using data in making informed, intelligent business decisions. The term business intelligence has evolved to depend on a range of technologies that provide useful insights. Conversely, analytics represents the tools and processes that can translate raw data into actionable, useful information for decision-making purposes. Different forms of analytics have been developed, including streaming analytics, which works in real time.
Descriptive analytics describes, or summarizes data, and is focused primarily on historical information. This type of analytics describes the past, allowing for an understanding of how previous behaviors affect the present. Descriptive analytics can be used to explain how a company operates and to describe different aspects of the business. In the best-case scenario, descriptive analytics tells a story with a relevant theme and provides useful information.
Predictive analytics is used to predict the future. This type of analytics uses statistical data to supply companies with useful insights about upcoming changes, such as identifying sales trends, purchasing patterns, and forecasting customer behavior. The business uses of predictive analytics normally include anticipating sales growth at the end of the year, what products customers might purchase simultaneously, and forecasting inventory totals. Credit scores offer an example of this type of analytics, with financial services using them to determine a customer’s probability of making payments on time.
Prescriptive analytics is a relatively new field, and is still a little hard to work with. This type of analytics “prescribes” several different possible actions and guides people toward a solution. Prescriptive analytics is designed to provide advice. Essentially, it predicts multiple futures and allows organizations to assess many possible outcomes, based upon their actions. In the best-case scenario, prescriptive analytics will predict what will happen, why it will happen, and provide recommendations. Larger companies have used prescriptive analytics to successfully optimize scheduling, revenue streams, and inventory, in turn, improving the customer experience.
Streaming analytics is the real-time processing of data. It is designed to constantly calculate, monitor, and manage data-based statistical information, and respond immediately. The process deals with recognizing and responding to specific situations, as they happen. Streaming analytics has significantly improved the development and use of business information.
Data for streaming analytics can come from a variety of sources, including mobile phones, the Internet of Things (IoT), market data, transactions, and mobile devices (tablets, laptops). It connects management to external data sources, allowing applications to combine and merge data into an application flow, or update external databases with processed information, quickly and efficiently. Streaming analytics supports:
- Minimizing damage caused by social media meltdowns, security breaches, airplane crashes, manufacturing defects, stock exchange meltdowns, customer churn, etc.
- Analyzing routine business operations in real time
- Finding missed opportunities with big data
- The option to create new business models, revenue streams, and product innovations
Some examples of streaming data are social media feeds, real-time stock trades, up-to-the-minute retail inventory management, or ride-sharing apps. For instance, when a customer calls Lyft, streams of data are joined to create seamless user experiences. The application merges real-time location tracking, pricing, traffic stats, and real-time traffic data to provide the customer with the nearest available driver, pricing, and a time estimate to the destination using both historical and real-time data.
Streaming analytics has become an extremely useful tool for short-term coordination, as well as developing business intelligence over the long term.
Business Intelligence at Present
Business Intelligence requires more than simple performance metrics. It needs weather reports, demographics, and economic and social insights to provide a broad base of information for predicting the future. Real-world events impact business intelligence and the decisions based on it. Some of the current developments providing useful information are:
The Internet of Things (IoT): It is used to receive data from a variety of devices, ranging from manufacturing to mobile phones. Office buildings, communications devices, delivery trucks, office equipment – all stream data as part of the Internet of Things.
Automation supporting business intelligence: Many organizations still rely on manual processes to support their business intelligence. Automated services make fewer mistakes than humans and provide higher-quality data. These services promote better business intelligence.
Analytics has become mainstream: More and more businesses use the three current types of business intelligence – descriptive analytics, predictive analytics, and prescriptive analytics. Descriptive analytics provides the majority of business intelligence, but predictive analytics analyzes historical data as a way to predict the future. Prescriptive analytics attempts to predict future outcomes but also offers recommendations based on its predictions.
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