1.  First two parts of class ◦ Part 1: What is business intelligence and why should organizations consider incorporating more technology-related intelligence in decision making? ◦ Part 2: How is a database designed to facilitate business intelligence?  Part 3 of class ◦ Highlight the key implementation issues in the development of a data warehouse:  Getting data into a data warehouse.  Getting information back out of a data warehouse within a reasonable time period formatted effectively for decision making.

2.  Design a database to support decision making. ◦ Practice: implement a pre-defined database to see how it is used for decision making.  Describe the components of a data warehouse. ◦ Practice: create a data store; use ETL to populate the data warehouse; implement OLAP; use visualization tools.  Understand the issues that affect the success and failure of implementing data warehousing and BI applications. ◦ Practice: Experience the complexity of a “simple” but comprehensive BI tool.  Port of Subs: implement a small data warehouse.

3.  Data warehouse ◦ Structured, unstructured, internal, external, transaction-level, and derived data. ◦ Data storage repository.  Extract, transform and load methods ◦ Methods of loading accurate and consistent data into the data warehouse. ◦ Methods of integrating data from disparate sources.  Metadata repository ◦ Data definitions and meanings. ◦ Business rules and process decisions.  Analytical tools ◦ OLAP: Online Analytical Processing ◦ Statistical analysis. ◦ Data Mining.  Data Visualization ◦ Dashboards. ◦ Graphical, tables, pictures.

4. Integration Services (SSIS) SQL Server database (SSDB) Analysis Services(SSAS) Reporting Services (SSRS)

5.  Relational database management system. ◦ Aligns with rules of a relational DBMS. ◦ Transact-SQL.  Includes metadata repository.  SQL Server Management Studio. ◦ Accessible from UNR COB labs through remote desktop; a college resource rather than a university resource.

6.  Extract, transform, load package.  Create ETL processes without having to perform custom programming. ◦ Imports from a variety of differing data formats, exports to SQL Server or to other data formats. ◦ Drag and drop “programming”. ◦ Has standard processes (examples: data transformation, reformatting, aggregation).  Bulk load data with standardized procedures.  Accessible from the Business Intelligence Development Studio.

7.  Server-based reporting platform.  Reports can be delivered via a browser, a Windows application, or a SharePoint site.  Used by both IS professionals and power users.  Accessible from the Business Intelligence Development Studio.

8.  Creates a pre-defined, pre-calculated “cube” for analysis.  A method of “avoiding” creating a data mart; create a “cube” through analysis services, and deliver that cube to end users for further analysis.  Termed the “unified dimensional model” by Microsoft.  Accessible from the Business Intelligence Development Studio.

9. Let’s learn SQL Server Business Intelligence Tools through a practice tutorial Maximum Miniatures (MaxMin, Inc.)

10.  Number of accepted and rejected products by batch, by product, by machine, by day.  Elapsed time for molding and hardening by product, by machine, by day.  Elapsed time for painting and curing by paint type, by product, by machine, by day.  Product rolls up into product subtype, which rolls up into product type.  Day rolls into month, which rolls into quarter, which rolls into year.  The information should be able to be filtered by machine manufacturer and purchase date of the machine.

11. MaxMin and SQL Server BI (pg. 21)

13.  Build the database through Management Studio.  Populate the database through SSIS.  Create a data mart “cube” with SSAS.  Look at the “cube” with SSRS.  Look at the “cube” with a pivot table in Excel.

14.  Can use SQL CREATE statements or follow the wizard instructions in the book.  Issues to be aware of: ◦ No constraints other than primary keys. ◦ Referential integrity is not maintained. ◦ I provide SQL CREATE statements if the wizards prove problematic.

15.  Performed through batch process – extract, transform and load.  Usually automated.  Will create two batch processes  The first process populates most of the dimension tables.  The second process populates the last dimension table and both of the fact tables.  Will demonstrate control flows and data flows

16.  Data population occurs at time intervals relevant to the business.  Individuals should NOT populate tables online; everything should populate through the batch process.  Batch process should be fail safe.

17.  SQL SELECT statements ◦ Can access the data warehouse like any other database. ◦ Even medium-size databases may be slow, especially if there is much aggregation required. ◦ Indexing is critical. The exercises do not demonstrate indexing, we will discuss it after you have a chance to do the exercises.  OLAP (online analytical processing) ◦ Pre-defined aggregation. ◦ Build another data structure to sit on top of the relational database, or serve as a replacement for the relational database. ◦ Goal is to radically decrease access time.

22.  Will use Analysis Services in SQL Server BI.  What will you do? ◦ Define the structure of the cube. ◦ Define the data sources of the cube. ◦ Define the hierarchical structures to use for aggregation. ◦ Define the aggregations. ◦ Build the cube. ◦ Deploy the cube.

23.  Look at it. ◦ Use whatever visualization method is available to look at the cube.  Excel pivot table.  Report generator.  Management Studio.  Use it for additional processing. ◦ Can run specialized queries in whatever language is available. In SQL Server, that language is MDX ◦ Can access it via other programming languages and MDX ◦ Use it as a source for data mining (can also use database for data mining).

24.  Remember: Purpose of a data warehousing system is to get information to support decision making.  What visualization tools will we use? ◦ Excel ◦ SQL Server Reporting Services  Reporting Services can produce: ◦ Standard paper-type reports. ◦ Web-based reports. ◦ Charts (either paper or web-based).

27. Online Analytical Processing (OLAP) Data Mining  Provides multi-dimensional data analysis techniques.  Works primarily with data aggregation.  Provides advanced statistical analysis.  Provides advanced graphical output.  Supports access to very large databases.  Provides enhanced query optimization algorithms.  Analyze the data; uncover patterns hidden in the data; form computer models based on the findings; and use the models to predict business behavior.  Proactive tools, used for prediction and discovery of behavior.  Some are based on standard statistical tools of correlation and regression  Most are based on artificial intelligence software such as decision trees, neural networks, fuzzy logic systems, inductive nets and classification networking.

28. Online Analytical Processing (OLAP) Data Mining  Which customers spent the most with us in the past year?  How much did the bank lose from loan defaulters within the past two years?  What were the highest selling fashion items in our San Diego stores?  Which store/location made the highest sales in the past year?  Which types of customers are likely to spend the most with us in the coming year?  What are the characteristics of the customers most likely to default on their loans before the year is over?  What additional products are most likely to be sold to customers who buy shorts?  In which area should we open a new store next year?

29.  Do the book exercises to learn how to use SQL Server BI tools.  Additional instructions provided and available on the class web site.  Logins and databases have been created for you on SQL Server.  You have access to Management Studio and Business Intelligence Development Studio. ◦ You have access via remote desktop. ◦ Name of the computer is STS.COBA.UNR.EDU ◦ Name of our instance of SQL Server is BSQL\Students

30.  Discuss the conceptual issues surrounding the BI applications: ◦ ETL ◦ Pre-processing aggregations and other data. ◦ Data mining ◦ Data visualization  Answer questions about the book exercises.