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Enterprise Data Warehousing (EDW) By: Jordan Olp.

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Presentation on theme: "Enterprise Data Warehousing (EDW) By: Jordan Olp."— Presentation transcript:

1 Enterprise Data Warehousing (EDW) By: Jordan Olp

2 Overview  Definition  Brief History  Data Setup/Structure  Benefits/Downfalls  Future  Conclusion  Questions

3 Definition  EDW is a central data repository containing data from multiple usually separated areas that store current and historical data that is used for data analysis and reporting.

4 Brief History  Traces back to the 1960’s  Became prominent in computing industries in 1980-90’s  William Inmon  Considered to be the “Father of Data Warehousing”  Wrote numerous front running books on the topic including “Building the Data Warehouse”  Is a leading voice for the Top-Down methodology for Data Warehousing design

5 Brief History cont.  William Inmon cont.  Founded Prism Solutions, provided one of the first industry Data Warehousing tools in the early 1990s  Ralph Kimball  Also wrote numerous books on the topic including “The Data Warehouse Toolkit”  Provided solid practical modeling information from industry honed examples on modeling and setup  Is a leading voice for the Bottom-Up methodology for Data Warehousing design

6 Data Setup/Structure  Where the data originates  How the data is processed  Design methodologies on how data is stored

7 Pre-Warehouse Data Allocation  Data is never sent directly from origin into Data Warehouse  Always stored in separate specific locations usually based on department

8 Data Allocation cont.  Sales  Inventory  Accounting  Human Resources  Customer Relationship Management  Marketing  Information Technology  Customer Services  Research & Development  …

9 Extract, Transform, Load (ETL)  Cleansing, Reformatting, Modeling  This is the process of copying the data from its origin, making it more useful, and then loading it into the Data Warehouse  One or multiple tools can be used to complete this process, sometimes even another tool than provided by the Data Warehouse itself

10 Extract  Data is gathered from the original sources into a staging area where the data is Transformed before entering the Data Warehouse  Metadata – “data about data”  Is created after the Extract process and is also moved into the Data Warehouse with the rest of the data  Cleansed/error checked, sometimes dada is corrupted from older systems and is removed from the valid data – Metadata helps the process

11 Transform  Here the data is reformatted to make it more usable by the database  Rules/Function/Metrics are applied to meet technical needs of the Data Warehouse  Encoded, derived, joined, sorted…

12 Load  Simply moves the Extracted, Transformed data from the staging area into the Data Warehouse  Load times vary on the business and their needs


14 Design Methodologies  Facts  Single piece of data used as a value or measurement  Normalized approach  Facts are stored in tables, that are then grouped by data subject  Dimensional approach  Fact tables store data together, and dimensional tables reference fact tables

15 Design Methodologies Top-Down Design  Relies heavily on data normalization  Reduces data redundancy  Allows for precise analytics for this design  Data is closely knit together at atomic levels  Data corresponds very closely to the real world events they related to

16 Design Methodologies Bottom-Up Design  First need a Top-Down understanding of the business and processes you are trying to achieve  Relies on separated access layers within the Data Warehouse – commonly called Data Marts  Because of dimensional capabilities can achieve faster results – however it allows for data redundancy

17 Benefits  Data bundling  Application specific databases  Business Intelligence - Reporting analytics  Easily accommodates legacy and historical data  Usually improves data quality  Usefulness increases as data is accumulated

18 Downfalls  Hefty price tags – hardware and software  Usually require its own team to manage  ETL process is staggering  Adding new data sources or changing Warehouse scheme is difficult

19 Future Usefulness  More data means better analytics  Better predictability  Well established Data Warehouses give companies a competitive edge  Benefits of reporting far outweigh costs  Data Warehousing…Evolved -> Data Activation  Textual Information

20 Conclusion  High cost – High reward  Allows predictions of trends  Can bring together nearly unrelated data and make amazing use of it  More accurate and relevant data = better analytics = better reporting information = better business responses

21 Questions/Comments

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