Generally, Business Intelligence is made up of an increasing number of components, these are:
reporting, analytics and dashboards.
In 1989, Howard Dresner (later a Gartner Group analyst) proposed "business intelligence" as an umbrella term to describe "concepts and methods to improve business decision making by using fact-based support systems. It was not until the late 1990s that this usage was widespread.
Applications in an enterprise
- Measurement – program that creates a hierarchy of performance metrics (see also Metrics Reference Model) and benchmarking that informs business leaders about progress towards business goals (business process management).
- Analytics – program that builds quantitative processes for a business to arrive at optimal decisions and to perform business knowledge discovery. Frequently involves: data mining, process mining, statistical analysis, predictive analytics, predictive modeling, business process modeling, complex event processing and prescriptive analytics.
- Reporting/enterprise reporting – program that builds infrastructure for strategic reporting to serve the strategic management of a business, not operational reporting. Frequently involves data visualization, executive information system and OLAP.
- Collaboration/collaboration platform – program that gets different areas (both inside and outside the business) to work together through data sharing and electronic data interchange.
- Knowledge management – program to make the company data driven through strategies and practices to identify, create, represent, distribute, and enable adoption of insights and experiences that are true business knowledge. Knowledge management leads to learning management and regulatory compliance.
Success factors of implementation
Before implementing a BI solution, it is worth taking different factors into consideration before proceeding. According to Kimball et al., these are the three critical areas that you need to assess within your organization before getting ready to do a BI project:
- The level of commitment and sponsorship of the project from senior management
- The level of business need for creating a BI implementation
- The amount and quality of business data available.