1. University of Nevada, Reno Organizational Data Design Architecture 1 Agenda for Class: 02/06/2014  Recap current status. Explain structure of assignments and how they relate. Co-mingling skills and concepts.  Review 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.  Learn how to make functions and stored procedures in the SQL Server environment. 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 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? Organizational objectives Alignment of objectives with business intelligence Varying Levels of BI Relative usage of BI for decision making

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/ Transactional 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. Three different data models  Transaction (operational) data model: Contains current data required by separate and/or integrated operational systems. Supports the transactional processing of the organization. Is frequently used to support day-to-day decision making. 3 rd normal form.  Reconciled (enterprise data warehouse) data model: Contains detailed, current data intended to be the single, authoritative source for all decision support applications. Usually in 3 rd normal form.  Derived (data mart) data model: Contains data that are selected, formatted and aggregated for end-user decision support applications. Star or snowflake schema. Probably not normalized.

8. 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? 8

9. Transactional (Operational) databases  What is in a transaction database? What is the level of granularity?  Is raw data stored or derived from an existing data store?  What are the key characteristics of the data?

10. Transactional (operational) databases  What are the design goals? 1.Make required data available to support business processes. 2.Protect the integrity of the data. Reduce data redundancy. Prevent data anomalies. 3.Provide for change. Prevent inflexible data structures. Anticipate changes. 10

11. How do we achieve those goals?  Effective systems analysis and design techniques.  Relational DBMS.  Normalization.

12. Enterprise data warehouse (reconciled data)  What is in an enterprise data warehouse? What is the level of granularity?  Is raw data stored or derived from an existing data store?  What are the key characteristics of the data? 12

13. Goals for data warehouse design  Make complete and accurate information easily accessible.  Present information consistently.  Be adaptive and flexible to change.  Provide reasonable and expected performance for information to support decision making.  Protect/secure information.

14. University of Nevada, Reno Organizational Data Design Architecture 14 How do we achieve those goals?  More effective systems analysis and design techniques.  Knowledge of required decision support systems.  Appropriate DBMS.  Appropriate use (or non-use) of normalization.