Business Intelligence introducation.pptx

Business Intelligence 17-2.pptx

1. Business Intelligence Data Warehouse Ibrahim Hesham Prof. Manal abdel qader

2. Data warehouse DSS/ESS/EIS Data/ Text Mining Business Intelligence Predictive analytics & Descriptive analytics OLAP Dashboards BI various tools & techniques

3. Architecture of BI Data Warehousing Business Analytics Business performance Management is an emerging portfolio of applications and methodology that contains evolving BI architecture and tools in its core User Interface Dashboards provide a comprehensive visual view of corporate performance measures, trends, & exceptions The data warehouse and its variants are the cornerstone of any medium-to-large BI system Working on data retrived from data warehouse in two category: 1.Reports & query, data-text-web mining

4. Database Organized collection of structured data (rows & columns) Data warehouse It is a repository of current and historical data of potential interest to managers Data Lake Is a centralized repository that allows for the storage of vast amounts of different data Data Mart DM is a subset of datawarehouse usually smaller and focuses on a particular subject or department. Data Resources

5. Data warehouse Data warehouse data warehouse (DW) is a pool of data produced to support decision making Data are usually structured to be available in a form ready for analytical processing activities Is a subject-oriented, integrated, time-variant, nonvolatile collection of data in support of management’s decision-making process

6. Data warehouse framework 01 Get data from data sources 02 Apply ETL tools and techniques 03 Distribute data to data marts 04 Apply analytical processes

7. Extract Transform Load Extract I s getting data from data storage units such as databses Transform (preprocessing) A pplying different methods on retrieved data Load R eturning data to data storages such as data warehouses

8. ETL ETL was introduced as a process for integrating and loading data for computation and analysis ETL provides the foundation for data analytics and machine learning workstreams ETL cleanses and organizes data in a way which addresses specific business intelligence Integration, Analysis, Reporting

9. Data Warehouse External Data from outer sources ERP D ata from enterprise resource planning TPS D ata from transaction processing systems WEB Data from web scrabbing

10. Transformation D e-duplicating Remove duplicated data Filtiring Filters data to get the insights Audits Checking the data is good Statistics D o descriptive statisitcs Feature Selection It’s the third planet from the Sun Changing data structure C hange the data type, change split columns

11. References BUSINESS INTELLIGENCE, ANALYTICS, AND DATA SCIENCE: A Managerial Perspective, Fourth edition DECISION SUPPORT AND BUSINESS INTELLIGENCE SYSTEMS, ninth edition Fundamentals of database, sixth edition (PDF) A comparative study of various ETL process and their testing techniques in data warehouse (researchgate.net)

12. Thank You