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Wikipedia describes 'Business Intelligence' :

Business intelligence (BI) refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business information and sometimes to the information itself. The purpose of business intelligence-a term that dates at least to 1958-is to support better business decision making. Thus, BI is also described as a decision support system (DSS)

BI is sometimes used interchangeably with briefing books, report and query tools and executive information systems. In general, business intelligence systems are data-driven DSS.

BI systems provide historical, current, and predictive views of business operations, most often using data that has been gathered into a data warehouse or a data mart and occasionally working from operational data. Software elements support the use of this information by assisting in the extraction, analysis, and reporting of information. Applications tackle sales, production, financial, and many other sources of business data for purposes that include, notably, business performance management. Information may be gathered on comparable companies to produce benchmarks.

Reference: http://en.wikipedia.org/wiki/Business_intelligence

As we can see the BI market, if described in the loosest terms, is a set of commonly used terminology, tools, and technologies that are used to solve a set of commonly related problems.

The terminology, tools, and technologies include (with links to the relevant Wikipedia entries):

  • Reporting: standard reports, parameterized reports, ad-hoc reports, desktop reporting, report bursting, enterprise reporting
  • Data transformations: Extract-Transform-Load (ETL), Extract-Transform-Load (ELT)
  • Dashboards,
  • Analysis: slice-and-dice, pivot tables, on-line analytical processing (OLAP), cubes, dimensions and hierarchies, drill-downs
    *Storage Models: data marts, data warehouses, star schemas, snowflake schemas, fact tables, de-normalization,
  • Predictive Analytics: data mining, predictivea analytics, machine learning
  • Analytical applications: balanced scorecards
  • Flavors: operational BI, real-time BI

Describe (briefly):

  •     Reporting, analysis, dashboards, ad-hoc, OLAP
  •     Historical / trending vs live / operational reporting
  •     Transactional vs operation data store vs data-mart / data-warehouses.
  •     ETL
  •     Dimensions, attributes, measures, and keys
  •     Typical architectures

 
Include references to external sources.

BI projects are often complex and expensive undertakings that have several problems.

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