1. University of Nevada, Reno Organizational Data Design Architecture 1 Organizational Data Architecture (2/19 – 2/21)  Recap current status.  Discuss the issues involved in designing and developing a data architecture for an organization.  Enhance the design exercise from last week to incorporate additional data inputs and requirements. 1

2. 2 Organizational objectives Alignment of objectives with business intelligence Varying Levels of BI Relative usage of BI for decision making Data-Driven Decision Making Origins of Data Internal Data : Generated by the organization ERP databases; Separate application databases; Non database transactions; and Accounting, customer service, inventory, manufacturing, marketing External Data : Not generated by the organization Governmental data such as census, tax, property, climate; Procured data such as financial, marketing, research; Twitter feeds; and Social networking data. Collect Store Maintain Transform Integrate Make Meaningful

3. 3 Organizational objectives Alignment of objectives with business intelligence Varying Levels of BI Relative usage of BI for decision making Data-Driven Decision Making What does the user need to make data driven decisions? What are the characteristics of good quality data? Why isn’t all data inherently of good quality?

4. 4 Origins of Data Internal Data : Generated by the organization ERP databases; Separate application databases; Non database transactions; and Email, Word, Sharepoint External Data : Not generated by the organization Governmental data such as census, tax, property, climate; Procured data such as financial, marketing, research; Twitter feeds; and Social networking data. Where is internal data stored? Who is responsible for data management? How is it usually stored? How is external data obtained? Who is responsible for data management? How is it usually stored?

5. 5 Organizational Data Architecture Data Sources Internal External Data Mart Enterprise Data Warehouse Operational Data Reconciled Data Derived Data

6. Big Questions  Do the layers represent physical databases?  Are all three layers necessary for all organizations?  Are additional layers necessary for some organizations? 6

7. What are the issues for each layer?  Is raw data stored or derived from an existing data store?  What are the key characteristics of the data?  What are the three most important design goals?  What are the biggest challenges during design? 7

8. What is the format of a derived data model?  One spreadsheet-type table?  Fact table and dimension tables.  Multiple tables: Star format Snowflake format 8