| Short Explaination of BI | |
Business Intelligence is a term introduced by Howard Dresner of Gartner Group in 1989. He described Business Intelligence as a set of concepts and methodologies to improve decision making in business through use of facts and fact based systems.
Over time as use of Business Intelligence has become mainstream more definitions of Business Intelligence have emerged.
Business intelligence (BI) is a broad category of application programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. BI applications include the activities of decision support, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining.
(Definition source: www.sauder.ubc.ca/cgs/itm/itm_glossary.html)
TDWI (www.tdwi.org) defines Business Intelligence as The processes, technologies and tools needed to turn data into information, information into knowledge and knowledge into plans that drive profitable business actions. Business intelligence encompasses data warehousing, business analytic tools and content knowledge management.
Customer data integration (CDI) hubs continue to become a strategic driver for organizations who need to gain unified views of their customers across their sales and distribution channels for multiple product lines.
Businesses seeking to achieve a sustainable advantage over their competition are turning to information management systems for detailed data analysis. These systems and methods have evolved to what is now known by the broader term “business intelligence” or “BI”.
Metadata is literally data about data. the need to distil the useful information from the mass of information available, manually creating your metadata, can add value, but also adds tremendous cost. it is possible to leverage companies’ existing metadata too much or too little data is not useful.
Customer data marts store information that can be analyzed with data mining tools to build customer behaviour models. Data marts are also used to create targeted advertising campaigns to increase customer return rates.
· Goal Alignment queries: determine the short and medium-term purposes of the programme. What strategic goal(s) of the organization will the programme address? What organizational mission/vision does it relate to? A crafted hypothesis needs to detail how this initiative will eventually improve results / performance (i.e. a strategy map).
· Baseline queries: Current information-gathering competency needs assessing. Does the organization have the capability of monitoring important sources of information? What data does the organization collect and how does it store that data? What are the statistical parameters of this data, e.g. how much random variation does it contain? Does the organization measure this?
· As BI has moved to the Web and to enterprise wide deployments, many BI tools today have a service oriented architecture (SOA). An SOA allows different BI services to perform specialized tasks and, when necessary, to be distributed across multiple servers.
· The BIToolz provides a query governor that lets you control the number of concurrent query processes, number of rows returned per query, and time for a query to run. These restrictions are customizable at various levels such as per server, groups of users, role, or individual user.
· BI tools can be customized or embedded within other applications
· support access to many databases through ODBC. However, to leverage SQL extensions for specific databases often requires native support for a given database.
A cube is a matrix of virtual table structures providing a multidimensional view of data, rather than the more limited 2-D view provided by relational database structures.
Star Schema is a relational database schema for representing multimensional data. It is the simplest form of data warehouse schema that contains one or more dimensions and fact tables. It is called a star schema because the entity-relationship diagram between dimensions and fact tables resembles a star where one fact table is connected to multiple dimensions. The center of the star schema consists of a large fact table and it points towards the dimension tables. The advantage of star schema are slicing down, performance increase and easy understanding of data.
A snowflake schema is very appealing to database administrators, because it normalizes the dimensional relationship. A snowflake design breaks a dimension’s hierarchical levels into separate tables, with foreign key relationships between them. The cleanest designs use surrogate keys for all dimension level tables. The foreign keys ensure referential integrity, guaranteeing that a Product rolls up into one and only one Product Group. Users hate snowflake schemas because they are difficult to navigate, and because query performance in the relational database is degraded relative to the corresponding star schema.
Crate a Data Warehouse
The data warehouse gathers all the information from the various legacy systems. Specialized data marts are then created with a subset of the information in the data warehouse. These data marts are easier to use because they only have the particular information the specific user group needs. The use of several data marts also allows the querying load to be spread among several different computers.
Create a Data Mart
The data mart is the prototype or the first step of a data warehousing process. An enterprise picks the division or group that would most benefit from data-based knowledge. A data mart is built with that group's data. Additional types of information are added to the data mart as time goes on until it is turned into a data warehouse.
Data Mining: The process of finding hidden patterns and relationships in the data.
Analyzing data involves the recognition of significant patterns. Human analysts can see patterns in small data sets. Specialized data mining tools are able to find patterns in large amounts of data. These tools are also able to analyze significant relationships that exist only when several dimensions are viewed at the same time.
Users can ask data questions using standard queries when they know what they're looking for. Queries can be written for questions like this: "Which of our out-of-town customers have given us the most business in the last year?"
Data mining is needed when the user's questions are more vague or general in nature. Data mining questions would include: "What attributes characterize the customers that gave us the most business in the past year?"