Business Intelligence
Process to get information about business from available data is known as Business Intelligence.Business intelligence can be defined as having the right access to the right data or information needed to make the right business decisions at the right time. The data might be raw or might have been analyzed in some way. Having access to such information enables management of the business by fact instead of by primarily relying on intuition.
We’re here to help you understand basic BI concepts. In this Beginner’s Guide to Business Intelligence, we’ll introduce three foundational BI components, explain why and when you would need to use them, and share examples of vendors that offer these capabilities. The three components we’ll cover are:
- Data Warehouses
- Extract, Transform and Load (ETL)
- Online Analytical Processing (OLAP)
What is a Data Warehouse?
A data warehouse stores… data. But its technology is much more sophisticated than its name. A data warehouse allows you to consolidate data from several sources (i.e. other software systems) and then perform queries and analyses.
“A data warehouse is a subject-oriented, integrated, time-variant, nonvolatile collection of data in support of management’s decision-making process.” Bill Inmon (1992).
Subject-Oriented: A data warehouse can be used to analyze a particular subject area. For example, "sales" can be a particular subject.
Integrated: A data warehouse integrates data from multiple data sources. For example, source A and source B may have different ways of identifying a product, but in a data warehouse, there will be only a single way of identifying a product.
Time-Variant: Historical data is kept in a data warehouse. For example, one can retrieve data from 3 months, 6 months, 12 months, or even older data from a data warehouse. This contrasts with a transactions system, where often only the most recent data is kept. For example, a transaction system may hold the most recent address of a customer, where a data warehouse can hold all addresses associated with a customer.
Non-volatile: Once data is in the data warehouse, it will not change. So, historical data in a data warehouse should never be altered.
A data warehouse is a database designed for querying, reporting, and analysis.
Data is raw material & unorganized facts that need to be processed
When data are processed, organized, structured or presented in a given context so as to make them useful, they are called Information.
The ETL concept sounds easy, but the execution is complex. We’re not talking about simple copy and paste stuff here. Each step in the process has its challenges. For example, during the extract step, data may come from different source systems (e.g. Oracle, SAP, Microsoft) and different file formats such as XML, flat files with delimiters (e.g. CSV), or the worst – old legacy systems that store data in arcane formats no one else uses anymore.
OLAP (online analytical processing) is computer processing that enables a user to easily and selectively extract and view data from different points of view.
For example, a user can request that data be analyzed to display a spreadsheet showing all of a company's beach ball products sold in Florida in the month of July, compare revenue figures with those for the same products in September, and then see a comparison of other product sales in Florida in the same time period.
On-line retrieval and analysis of data to reveal or expose business trends and statistics not directly visible in the data directly retrieved from a data warehouse. Also known as multidimensional analysis.
Online analytical processing (OLAP) is another key process and technology found in almost all data warehouse environments and business intelligence systems. But while the data warehouse and ETL tools support “back end” processes, OLAP tools support the presentation layer or “front end” processes such as querying, analysis and reporting. OLAP tools provide the impressive tables, charts and visualizations that make BI exciting!
What is Extract, Transform and Load (ETL)?
ETL tools perform three functions to move data from one place to another:- Extract data from sources such as ERP or CRM applications;
- Transform that data into a common format that fits with other data in the warehouse; and,
- Load the data into the data warehouse for analysis.
The ETL concept sounds easy, but the execution is complex. We’re not talking about simple copy and paste stuff here. Each step in the process has its challenges. For example, during the extract step, data may come from different source systems (e.g. Oracle, SAP, Microsoft) and different file formats such as XML, flat files with delimiters (e.g. CSV), or the worst – old legacy systems that store data in arcane formats no one else uses anymore.
What is Online Analytical Processing (OLAP)?
OLAP (online analytical processing) is computer processing that enables a user to easily and selectively extract and view data from different points of view.
For example, a user can request that data be analyzed to display a spreadsheet showing all of a company's beach ball products sold in Florida in the month of July, compare revenue figures with those for the same products in September, and then see a comparison of other product sales in Florida in the same time period.
On-line retrieval and analysis of data to reveal or expose business trends and statistics not directly visible in the data directly retrieved from a data warehouse. Also known as multidimensional analysis.
Online analytical processing (OLAP) is another key process and technology found in almost all data warehouse environments and business intelligence systems. But while the data warehouse and ETL tools support “back end” processes, OLAP tools support the presentation layer or “front end” processes such as querying, analysis and reporting. OLAP tools provide the impressive tables, charts and visualizations that make BI exciting!