Moving away from spreadsheets to come with the single version of the “truth” Dimensional Modeling: foundation for understanding the data and how they all fit together; used to build the data warehouses Data Warehouse: includes data marts and are created based on the models created above. Gather all the data together so that it can be shared by all and stored in a consistent manner. OLAP: create cubes used for reporting the information. Instead of 2 dimensions, can include regions, sales, products, and time. Used for ad hoc reports and to provide decision support interfaces. Data Visualization: presenting the data in an insightful, easy to read format, along with KPIs and other indicators to further the analysis. Data Mining: A discovery process, improve goals by using predictions, forecasting trends, based on the stored data. Our list of courses cover these topics: Dimensional modeling Data warehousing I and II Multi-dimensional analysis I and II Data visualization Data mining Mostly theory with some hands on applications
Here is an Imports cube, which contains two measures, Packages and Last , and three related dimensions, Route, Source, and Time. Last is the last ship date The smaller alphanumeric values around the cube are the members of the dimensions. Example members are ground, Africa, and 1st quarter. The values within the cube represent the two measures, Packages and Last. The Packages measure represents the number of imported packages, and it aggregates by the Sum function. The Last measure represents the date of receipt, and it aggregates by the Max function. The Route dimension represents the means by which the imports reach their destination. The Source dimension represents the locations where the imports are produced. The Time dimension represents the quarters and halves of a single year. Business users of a cube can determine its measures' values for each member of every dimension. This is possible because the members aggregate measure values. For example, the measure values shown in the preceding illustration aggregate within a standard calendar hierarchy in the Time dimension as follows. In addition to aggregating within a single dimension, measures aggregate for all combinations of members from different dimensions. This allows business users to evaluate measures by members in multiple dimensions simultaneously. For example, if an business user wants to analyze quarterly imports that arrived by air from the Eastern Hemisphere and Western Hemisphere, the business user can issue the appropriate query on the cube to retrieve the following dataset.
Mention compressed storage
Textbook: The Data Warehouse ETL Toolkit Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data, Kimball, Caserta, Wiley Technology Publishing, ISBN: 0764567578 Topics: Decision Support Systems: history, give examples of how they work without data warehouse Requirements Gathering: who wants to see what, when, how often, from where. Is the data available, must it be calculated? Data Analysis: see the big picture, various types of source data: flat file, relational db, different hardware, different software, owners of data, data definitions ETL Processes & Deliverables Cleaning & Conforming: what does Good data look like? Dimensional schemas Dimension Tables: characteristics of data Fact Tables: measurements required for reports We do not go into implementation
BI Analyst: Certificate of Accomplishment BI Developer: Certificate of Achievement
Relational DB Analyst: Certificate of Accomplishment Relational DB Developer: Certificate of Achievement
To work with the students who are in the program.
Business Intelligence: Effective Decision Making Bellevue College Linda Rumans IT Instructor, Business Division Bellevue College [email_address]
Current Status Mountains of Data What do I do??? How do I increase sales???? How do I make my product better??? Business Users
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.