1 Subject: Business Intelligence Module Number: 3 Module Name: Business Intelligence - Stages 2 Business Intelligence - Stages Business Intelligence - Stages: Introduction, Extract, Transform, and Load (ETL), Data Warehouse, Data Warehouse Architecture, Design of Data Warehouses, Dimensions and Measures, Data Warehouse Implementation Methods: Top-Down Approach, The Bottom-Up Approach, The Federated Approach, The Need for Staged Data, Integrating Data from Multiple Operating Systems, OLAP, Types of OLAP, Multidimensional OLAP (MOLAP), Relational OLAP (ROLAP), Hybrid OLAP (HOLAP), Data Mining, Data Mining and Statistical Analysis, Data-Mining Operations, Data Mining—Data Sources, Data Dredging, Data Management, Data Usage, Enterprise Portal (EP). Syllabus 3 Business Intelligence - Stages The aim of this module is to provide a strong foundation to students to learn the concept of Data warehousing and Business Intelligence. AIM: 4 Business Intelligence - Stages The objectives of this module are to: • Illustrate the concept of ETL. • Understand the Top-Down Approach, The Bottom-Up Approach. • Find out the need for Staged Data. • Discuss the application of Data Warehouse. • Know the various trends towards data warehousing and data mining. • Critically use all the data transformation processes • Analyze data warehouse characteristics and plan warehouse data • Summarize the various Data Mining and Statistical Analysis approaches , Objectives: 5 Business Intelligence - Stages At the end of this module, students will be able to: • Understand the data warehouse concept. • Identify the difference between various data warehouse approaches. • Define the architecture of the data warehouse. • Illustrate the working flow of the dashboard. • Implement data transformation processes • Summarize the concepts of Data Mining and Statistical Analysis approaches • Analyze the data for data warehouse . Outcomes: 6 Business Intelligence - Stages Table of Contents: • Introduction • Extract, Transform, and Load (ETL) • Data Warehouse, Data Warehouse Architecture • Design of Data Warehouses • Dimensions and Measures • Data Warehouse Implementation Methods: Top-Down Approach, The Bottom-Up Approach • The Federated Approach • The Need for Staged Data • Integrating Data from Multiple Operating Systems • OLAP, Types of OLAP, 7 Business Intelligence - Stages Table of Contents: • Data Mining, • Data Mining and Statistical Analysis • Data-Mining Operations, Data Mining—Data Sources, Data Dredging, Data Management, Data Usage, Enterprise Portal (EP). 8 Business Intelligence - Stages Introduction and Definition to Data warehousing • In simple words, a Data warehouse is a type of database that is different from an organisation database. • A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process official language. 9 Business Intelligence - Stages Data extraction process Source:-https://www.informatec.com/sites/default/files/inline-images/etl_2.jpg Extract, Transform and Load 10 Business Intelligence - Stages Extract, Transform and Load 1. Data Extraction: This method can store data from various types of data sources. We need to apply the different strategies for available data source to take data from this. 1. Data Transformation: If data extraction for a data warehouse will post a big challenge, we need to convert it into a standard format, so this is the transformation process. 1. Data Loading: We need to load the standard format in the data warehouse storage system. At eh initial level, we need to move and load the high amount of data. 11 Business Intelligence - Stages Data Warehouse Architecture • A data warehouse architecture is a procedure of describing the data communication process and also showcasing the presentation of the procedure. • Each data warehouse has its own requirement and specialisation, but all have to focus on the same goal • Some applications such as payroll application, payable product and inventory management were created for OLTP. • These applications were created to handle the use requirements. • Applications such as forecasting, reporting, and trend analysis are created. 12 Business Intelligence - Stages Source:-https://static.javatpoint.com/tutorial/datawarehouse/images/data-warehouse-architecture.png 13 Business Intelligence - Stages Data Warehouse Design • A data warehouse is a repository used to store data where the data is coming from multiple data sources. • The purpose of data resource is to merge these applications for (OLAP). • Data warehouse is used to meet the requirements of the business. • Data warehouse design is complex and also lengthy. Business analytical will change from time to time. • Data warehouse and OLAP systems are dynamic, and the design process is continuous. • There are two approaches for data design Top-down and bottom-up approaches. 14 Business Intelligence - Stages Dimension-Modeling Dimensional modelling is a process in which data is represented with a cube operation, making the most suitable logical data with OLAP data. Elements of Dimensional Data Model • Fact • Dimension • Attributes • Fact Table • Dimension Table • Measure Hierarchies 15 Business Intelligence - Stages Elements of Dimensional Data Model Fact Facts are the measurements or evaluations in the form of, or we can say, facts from the business process. Ex. For a Sales business environment, measurement is the quarterly sales number. Dimension • Dimension gives the context a business for a process event. In others word they give who, where ,what, of a fact. In the previous example of sales , for the fact quarterly sales number, dimensions would be that identified are: • Who – Customer Names • Where – Location • What – Product Name 16 Business Intelligence - Stages Elements of Dimensional Data Model Attributes The Attributes are different features of the dimension. • For example, in the Location dimension, the attributes can be • State • Country • Zip-code • Attributes are used to search or filter to classify facts. • State • Country • Zip-code etc. 17 Business Intelligence - Stages Elements of Dimensional Data Model • Attributes are used for to search or filter, we can say to classify facts. Fact Table • A fact table is known as primary table in dimension modelling process. • A Fact Table contains • Measurements/facts • Foreign key to dimension table 18 Business Intelligence - Stages Dimension Table • A dimension table can maintain the record of dimensions of a fact. • They are joined with a fact table with the help of a foreign key. • Dimension tables in the form of de-normalised tables. • The Dimension Attributes represent by columns in a table, i.e. dimension table. • Dimensions describes facts with their attributes. The dimension can also store one or many hierarchical relationships in the table. 19 Business Intelligence - Stages Elements of Dimensional Data Model Measure Hierarchies If your data contains more than one measure (data value), then define a measure hierarchy an • For example, one possible hierarchy in the date dimension is • Year • Quarter • Month • Day 20 Business Intelligence - Stages Fig:-Visualization of dimension modeling Source:-https://learndatamodeling.com/blog/dimensional-data-modeling/