Business Intelligence Basics

Social

Business Intelligence basicsBusiness Intelligence Basics – the fundamentals for any Business Intelligence solution can be summarised as:

  • Business Intelligence Data Tools
  • Extract Transform and Load Tools – ETL, which stands for “extract, transform and load,” is the set of functions combined into one tool or solution that enables companies to “extract” data from numerous databases, applications and systems, “transform” it as appropriate, and “load” it into another database, a data mart or a data warehouse for analysis, or send it along to another operational system to support a business process.
  • Source Database and understanding of the basic data schema or data dictionary (e.g. what does the data actually mean in business terms)
  • An understanding of what the Business Intelligence Basics outputs / reports / Management Information  (MI) or dashboards need to look like (A dashboard is a screen that consolidates critical performance metrics all in one place, making it easy for users to stay constantly updated on the information most important to their business.
    Dashboards can be designed to suit a variety of needs, and will therefore take on a variety of forms, from business intelligence dashboards (BI dashboards) to executive dashboards/enterprise dashboards and key performance indicator dashboards (KPI dashboards).
  • Basic server database infrastructure (on which to host the BI database – data warehouse / data mart)
  • Basic server application infrastructure (on which the users will run the reports and view the data) – you can combine the two but often they are separate in order to ensure integrity and maintain performance
  • Clear understanding of the end structure of your data e.g. a structured Data warehouse or Data mart based on a schema / 3rd normal form or an unstructured data set (often used more in Big Data Analytics). A data warehouse is a digital storage centre in which information is compiled, searched, and managed. In most data warehouses, information can be inserted by different parties culling data from many sources. Data in a data warehouse is often modified with compression and hashing systems to expedite searches and transactional processes