Business intelligence software is a type of application software designed to retrieve, analyze, transform and report data for business intelligence. The applications generally read data that have been previously stored, often, though not necessarily, in a data warehouse or data mart.
The development of business intelligence software can be traced back to 1865. This was the year when Professor Richard Miller Devens coined the term 'business intelligence' referring to common reminiscent between bankers' decision making in his book 'Cyclopaedia of Commercial and Business Anecdotes'. The same term is used nowadays for all corporate data-related analytic processes.
It took more than 150 years for business intelligence to become a separate scientific process embraced by entrepreneurs and develop the methods it offers nowadays. In its initial form, this analytic concept was laid down by IBM researcher Hans Peter Luhn in his 1958 IBM Journal article titled 'A Business Intelligence System'. Luhn is also known as the inventor of Key Word in Context (KWIC) indexing, whose work marked the efforts to make business statistics more understandable for non-expert users.
The first comprehensive business intelligence systems were developed by IBM and Siebel (currently acquired by Oracle) in the period between 1970 and 1990. At the same time, small developer teams were emerging with attractive ideas, and pushing out some of the products companies still use nowadays.
In 1988, specialists and vendors organized a Multiway Data Analysis Consortium in Rome, where they considered making data management and analytics more efficient, and foremost available to smaller and financially restricted businesses. By 2000, there were many professional reporting systems and analytic programs, some owned by top performing software producers in the United States of America.
In the years after 2000, business intelligence software producers became interested in producing universally applicable BI systems which don't require expensive installation, and could hence be considered by smaller and midmarket businesses which could not afford on premise maintenance. These aspirations emerged in parallel with the cloud hosting trend, which is how most vendors came to develop independent systems with unrestricted access to information.
From 2006 onwards, the positive effects of cloud-stored information and data management transformed itself to a completely mobile-affectioned one, mostly to the benefit of decentralized and remote teams looking to tweak data or gain full visibility over it out of office. As a response to the large success of fully optimized uni-browser versions, vendors have recently begun releasing mobile-specific product applications for both Android and iOS users. Cloud-hosted data analytics made it possible for companies to categorize and process large volumes of data, which is how we can currently speak of unlimited visualization, and intelligent decision making.
In 2010, Sisense introduced the benefits of its In Chip data analytic method, which, according to the company, creates scalable columnar databases and processes data 10 times faster than conventional in-memory technologies.  Simiarly to other column based storages, it is able to returns answers to basic questions in much lower latency compared to row based storage systems.
In the beginning of 2016, the BI market noted record profits of approximately $9 billion, as modern suites respond to greater productivity demands than plain data analytics. The apps of today are expected to solve marketing problems, carry out detailed business health diagnoses, and most of all to operate in all business environments and corporate ecosystems. Another recognizable feature is customization, which allows companies to make every BI system work in accordance with their operational rules.
The key general categories of business intelligence applications are:
Except for spreadsheets, these tools are provided as standalone applications, suites of applications, components of ERP systems, or as components of software targeted to a specific industry. The tools are sometimes packaged into data warehouse appliances.
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