It is uncommon knowledge that Business Intelligence (BI) predates the personal computer and other related technological innovations. The fact is that the term Business Intelligence was first used by Richard Devens in the article History of Business Intelligence, published in 1865 in the Cyclopedia of Commercial and Business Anecdotes.
The workbook was a collection of about 3,000 articles around the implications of trade, business, and commerce endeavours. Devens referred to this term to describe how Sir Henry Fumese, a prominent banker leveraged on business intelligence. By examining the political context, and market conditions, he was able to gain a competitive advantage over his competitors.
Development of the Hard Disk Drive (HDD)
When IBM developed the hard disk drive in 1956, it allowed for the storage of immense amounts of information and for digital interpretation of business intelligence.
This innovation was recorded and further analysed in A Business Intelligence System published in 1958 by IBM’s computer expert, Hans Peter Luhn, where he discusses the benefits of collecting business intelligence data through technology. He also predicted the ability of systems learning according to user interest, which became machine learning as we know it.
Today, business intelligence uses technology to collect and study information and allows organizations to make decisions efficiently and act before their competitors.
The Birth of A Relational Database Scheme
Back in 1968, business data collected from various sources would be stored in silos and could only be read and understood by specialists. They would present fragmented and disorganized reports which led to one-dimensional and rigid data interpretations which, however, were unable to cross-talk.
The issue was identified by Edgar Codd who published a piece in 1970 about changing people’s perspective around databases and introduced the concept of a relational database scheme. The model immediately became famous and was adopted by major organizations worldwide.
Development of Decision Support Systems (DSS) & Data Warehousing
Decision Support Systems (DSS) marked the beginning of database management systems and modern Business Intelligence. Various tools such as OLAP (Online Analytical Processing), EIS (Executive Information Systems) and data warehouses appeared to help access and structure data in relation to DSS.
OLAP (Online analytical processing) uses a multidimensional data interpretation model which supports various complex queries and allows for data analysis from a wide range of sources.
Nielsen, a company which produces TV rating reports, was the first organization which applied BI. Through a collaboration with IRI, they developed the first dimensional data report which offered retailers an overview of sales trends and related analytics.
Olap became popular due to the variety of applications it offered including sales and management reporting, marketing, budgeting, forecasting & financial reporting, and BPM (Business Process Management).
In the early 1980s, executives integrated the internet into their business information research processes which led to the production of Executive Information Systems (EIS). EIS would streamline executives’ activities by using graphic interpretations and user-friendly interface to reflect the required information.
EIS would make executives very hands-on by allowing them to handle email, schedules, read reports without the need of any third party.
In the 1980’s, Bill Inmon and Ralph Kimball developed the concept of storing data in so-called data warehouses which allowed for big data to be managed from a single location.
However, the warehouses were extremely technical and could only be read by specialized computer scientists. Also, reports took a very long time to process and could only be produced after business hours and during the weekends because of the scarcity of computer systems back then.
Data warehouses gradually led to the development and use if Big Data. Immense amounts of data and information could be accessed in a wide range of forms including internet, email, and social media platforms and data insights were on the rise. Suddenly organizations’ profits shot up, anti-fraud programs developed, and losses were significantly reduced.
The Link Between Bi & High Tech
Business Intelligence was first interpreted as a technological concept after the Multiway Data Analysis Consortium held in Rome in the late 1980s. The conference aimed at highlighting the need to simplify the process of analysing business intelligence through user-friendly interfaces and systems. I
nstantly, a huge number of start-ups were born around developing the two main objectives of BI, namely production of information and related reports and structuring data in a user-friendly way. Simple tools started to emerge to streamline the decision-making processes by providing easy-to-use and fully integrated functionalities which allowed businesses to work directly with data.
BI & Analytics Intertwined
BI and analytics are both connected to the use of data in making intelligent and efficient business decisions. BI comprises of the technologies which support decision makers.
However, analytics cover the range of tools used for interpreting and reading data and includes data warehousing, business intelligence, enterprise information management and, enterprise performance management.
Types of Analytics
Descriptive analytics explains and targets historical data allowing for a correct and efficient interpretation of past behaviors to predict present outcomes. They can be used to detail the various business patterns and operations of an organization.
Predictive analytics helps in predicting future patterns by using statistical data which offer helpful insights relating to sales trends and customer behaviour. For example, banks use predictive analytics to calculate credit scores which provide insights about a client’s probability of settling instalments.
Prescriptive analytics is a new form of data which offers guidance towards various actions required to reach a solution. Just like consultants, this analytics provides company executives with multiple possible outcomes based on different actions.
They present the what, the why and the how of various decisions. Companies use prescriptive analytics to optimize operations such as streamlining revenues, inventory, marketing processes which lead to immediate business growth.
Benefits of Streaming Analytics
Streaming Analytics or Event Stream Processing is the process of analysing, supervising and interpreting statistical information in-motion data named event streams. These event streams appear because of various actions triggered by an event such as a financial transaction.
Through streaming analytics businesses gain insights about events happening in the market at any time. With the growth of IoT (Internet of Things), the number of events surrounding the decision-making process is rapidly increasing.
Wrapping it Up
What started out as systems and tools available exclusively to experts have become indispensable to everyone in the world of business. BI and analytics have continuously improved since their beginnings to become the foundation of all decision-making processes worldwide. Yet, they both still have a long way to go.