1. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Business Intelligence Tools and Techniques Robert Monroe March 18, 2008

2. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Agenda Quick survey Overview of Business Intelligence Tools and Techniques Course structure, grading, and expectations Data management fundamentals

3. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Survey Please complete and hand back the survey Survey helps me to: –Understand your goals and expectations for the course –Evaluate your previous IT knowledge and experience –… adjust the class accordingly

4. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Introducing Business Intelligence Tools and Techniques

5. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Corporations Are Drowning in Data … but thirsty for actionable knowledge Our ability to collect and store data seems to have surpassed our ability to make sense of it! Important trends: –Storage capacity continues to rise rapidly –Cost of storage continues to drop

6. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Business Intelligence Core question: How can an organization manage and leverage large data sets to make better business decisions? Business Intelligence (BI) –A broad category of applications and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. (Wikipedia) Two common uses for BI tools –Measuring where you are / how your business is performing –Identifying problems and opportunities

7. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Business Intelligence Systems Improve Decision Making Source: O’Brien, Management Information Systems, 6 th ed.

8. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques In-Class Exercise Take out a piece of paper and pencil Select a company that you are familiar with and a managerial role in that company Write down five pieces of quantitative information that you would most want to have to manage your business (or your part of the business) effectively

9. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques A Business Intelligence Roadmap

10. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Module 1: Course Intro, Data Management Fundamentals What is Business Intelligence? –How can it help me make better business decisions? –What kinds of questions can BI tools help me answer? What is the relationship between data, information, & knowledge? What does it mean to ‘Compete on Analytics’ –Why would I want to do so? –How might I do so effectively? Data Info Knowledge

11. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Module 2: Data Warehousing What is a Data Warehouse? –How about a Data Mart? –How is a Data Warehouse different from a ‘regular’ database? Why do we need another database that just duplicates data that we already have? How can fill a data warehouse with comprehensive, timely, and high-quality data?

12. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Module 3: Reporting and OLAP How do I convert the data in my data warehouse into actionable information or knowledge? What tools are available to help non-programmers analyze warehouse data? What is dimensional modeling? Why is it powerful? What kinds of questions are OLAP tools designed to answer?

13. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Module 4: Info Viz and Data Mining What tools are available to help me visualize very large data sets? Why would (or wouldn’t) I want to use visual tools to explore my data? What do data mining tools do? What different kinds of data mining tools and techniques are available? How do I tell which tools are appropriate for which situations?

14. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Module 5: Dashboards What is an executive dashboard? –Are they only for executives? –Why are they useful? –What are their drawbacks? How can I implement dashboards effectively in my organization?

15. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Module 6: ‘Real-Time’ Business Intelligence How can we move from historical analysis to ‘real- time’ analysis? Why is this hard to do in practice? What tools and techniques are available to support real- time analysis?

16. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Module 7: Implementing BI, Ethical use of BI What does my organization need to do to implement a successful BI program? What ethical issues arise with BI capabilities? How can we insure that our BI capabilities are used ethically? –What does it mean to do so?

17. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Dashboard: Expected Effort First two weeks focus on BI foundation –Eat your vegetables, exercise more Middle classes focus on using various BI tools effectively –Use the tools, Luke Final classes combine fundamentals, tools, people, processes, and ethics –Pull it all together → →

18. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Course Structure, Grading, and Expectations

19. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Course Goals Understand how to apply various Business Intelligence (BI) tools and techniques to analyze and evaluate large data sets to make better business decisions Understand the benefits, drawbacks, and applicability of various approaches to BI Improve awareness of a variety of challenges and ‘gotchas’ that arise when implementing BI systems –… and how to avoid them

20. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Course Philosophy Focus on applying BI technology to solve business problems, not building BI systems You will develop new skills by doing and participating –You will need to use the BI tools –When in doubt try something, experiment –Most work done in teams – learn from/with your peers –Casual interactive class – your participation is important Many of the technologies we will look at are relatively new –Not everything will work perfectly the first time… –Flexibility, patience, and a willingness to explore will help a lot Let’s have some fun – life’s too short to do otherwise

21. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Expectations, Etiquette, and Academic Integrity Waitlist Office hours, 3:30 – 4:30 MWF Expectations and etiquette Academic integrity Teaching Assistant –Bao-Jun Jiang, baojunj@andrew.cmu.edu

22. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Pass/Fail I allow students to take the course pass/fail provided that they agree to: –Attend class regularly –Prepare for class as if they were taking it for a grade –Complete all of the assignments –Take the final exam at its regular time and place –Complete all of the necessary administrative paperwork

23. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Blackboard And The Course Wiki Blackboard is used only for archival postings Most information is posted on the class wiki –http://cmu-bitt.wikispaces.com –Read permissions open to everybody, need to register to get write permissions –Contact Bao-Jun if you have not received an invitation by this evening Wiki participation is strongly encouraged –Participation on wiki counts towards course participation grade –Add interesting and useful things that you find to the wiki –Wiki will remain available as a resource after course ends –Please don’t mess with things like assignments, due dates, etc.

24. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Grading Grades will be computed as follows: –Homework exercises (3)45% –Final exam 30% –Class attendance, preparation, 25% and participation  Late assignments policy: 25% deduction each day late  I curve final grades, not individual assignments  Please see regrade request policy in syllabus document

25. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Assignments Three homework assignments –Groups of 2-4 people Assignment #1: Data warehousing –Analyze data warehousing scenario and make business, technology, and process recommendations based on your analysis (management option) –Create a simple data warehouse and ETL process to load it (tech option) Assignment #2: Reporting and OLAP tools –Use Microsoft’s Reporting and/or OLAP tools to retrieve, analyze, and present useful information from a data warehouse and OLAP cubes Assignment #3: Case analysis, dashboards or visualizations –Case analysis – Continental or SYSCO cases (management option) –Analyze scenario/case and design dashboard(s) and/or data visualizations to meet business needs (tech option)

26. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Computing Resources There are many good BI platforms We will primarily use Microsoft’s SQL Server 2005 –Client tools –Reporting Services –Analysis Services –Integration Services (ETL tool – optional) We will also experiment with a variety of other BI tools You must provide a laptop that can run SQL Server 2005 client –At least client tools, servers are optional –600Mhz proc, 512MB of RAM, 0.5–2.0GB of disk space –Install instructions are available on Blackboard –Please try to install SQL Server 2005 client tools before Monday’s class

27. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Data Management Fundamentals

28. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Definitions What is the difference between data, information, and knowledge? Data is a collection of raw value elements or facts used for calculating, reasoning, or measuring. Data may be collected, stored, or processed but not put into a context from which any meaning can be inferred. [Los03] Information is the result of collecting and organizing data in a way that establishes relationships between data items, which thereby provides context and meaning. [Los03] Knowledge is information to which experience, interpretation, and reflection are added by individuals so that it becomes a high value form of information –The OR Society http://www.orsoc.org.uk/about/topic/projects/kmwebfiles/knowledge.htm

29. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Exercise 3/21/05$27.743/22/05 $27.01 3/21/05$19.783/22/05 $19.72 3/21/05$21.413/22/05 $21.50 3/21/05$83.813/22/05 $84.24 MSFT INTC CSCO IBM 3/21/05 3/22/05 3/22/05 3/21/05 3/22/05 3/22/05 3/21/05 3/21/05 $27.74 $19.78 $21.41 $83.81 $27.01 $19.72 $21.50 $84.24 CSCO MSFT INTC IBM Closing Stock Prices

30. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Goal: Convert Data to (Actionable) Knowledge Data Info Knowledge Increasing Value Why is this so hard to do in practice?

31. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Challenge: What To Capture and Store? The amount of data that can be captured is enormous –Storing data is relatively cheap (  free @ the margin) –Structuring and retrieving data is relatively expensive –Converting large data sets to actionable knowledge tends to be relatively challenging and expensive Rules of thumb for deciding what to capture and store –Start with what you want to get out and work backwards –Evaluate what is already available –Insure that you capture high-quality data –Analyze fundamental data requirements for the enterprise, independent of the specific project at hand

32. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Exercise: What To Capture And Store Scenario 1: You are a marketing VP for a large chain food retailer. You need to figure out how to properly price and promote a specific brand of snack chips over the next year What questions do you need to ask? What analyses would you like to do to answer them? What data will you need to do these analyses? Where will you get that data? –Is your organization likely to already have all the data that you need? –Are there other data sources that you should try to take advantage of and incorporate into your analyses?

33. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Exercise: What To Capture And Store Scenario 2: You are an executive at Ferrari who needs to decide how to allocate the latest and greatest sports car your company is introducing in six months to maximize your company’s profits long-term What questions do you need to ask? What analyses would you like to do to answer them? What data will you need to do these analyses? Where will you get that data? –Is your organization likely to already have all the data that you need? –Are there other data sources that you should try to take advantage of and incorporate into your analyses?

34. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Exercise: What To Capture And Store Scenario 3: You are a HR executive responsible for recruiting salespeople. Your bonus each year is directly tied to how well the salespeople you bring in do in their first three years at your company. You’ve read Moneyball and Competing on Analytics, and you want to take a more analytic approach to your job What questions do you need to ask? What analyses would you like to do to answer them? What data will you need to do these analyses? Where will you get that data? –Is your organization likely to already have all the data that you need? –Are there other data sources that you should try to take advantage of and incorporate into your analyses?

35. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques The Relational Data Model The Relational Model has become the de-facto standard for managing operational business data Core concepts in a relational model: –Tables (relations) –Records (rows) –Data fields (columns) –Primary keys –Foreign keys Products Product IDDescriptionColorSizeQty Available 52Shoes (pair)Blue1025 64Socks (pair)WhiteLarge200 145BlouseGreen714 158PantsBlue32/340

36. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Data, Information, Database Example Purchases Order IDCustomer NameProduct IDQuantityDate 5623Jimmy Hwang52312/15/2004 5624Sue Smith64512/16/2004 5625Jane Chen145112/16/2004 Products Product IDDescriptionColorSizeQty Available 52Shoes (pair)Blue1025 64Socks (pair)WhiteLarge200 145BlouseGreen714 158PantsBlue32/340 Jimmy Hwang purchased 3 pairs of size 10 shoes on 12/15/2004 What other information can we derive from these data tables? Data in Database Tables Information

37. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Relational Data, Tables, Records, and Metadata Example Purchases Order IDCustomer NameProduct IDQuantityDate 5623Jimmy Hwang52312/15/2004 5624Sue Smith64512/16/2004 5625Jane Chen145112/16/2004 Products Product IDDescriptionColorSizeQty Available 52Shoes (pair)Blue1025 64Socks (pair)WhiteLarge200 145BlouseGreen714 158PantsBlue32/340 Table Name: Products ProductID Int (pkey) Description Text(50) Color Text(50) SizeText(20) QtyAvailableInt Table Name: Purchases OrderIDInt (pkey) CustomerNameText(75) ProductIDInt (fkey) QuantityDecimal DateDateTime Data (Records) in Database Tables Metadata

38. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Normalization And Denormalization Data normalization is the process of decomposing relations with anomalies to produce smaller, well-structured relations –Basic idea: each table only holds data about one ‘thing’ Goals of normalization include: –Minimize data redundancy –Simplifying the enforcement of referential integrity constraints –Simplify data maintenance (inserts, updates, deletes) –Improve representation model to match “the real world” Normalization sometimes hurts query performance

39. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Example: Denormalized Table Insertion anomaly: when an employee takes a new class we need to add duplicate data (Name, Dept_Name, and Salary) Deletion anomaly: If we remove employee 140, we lose information about the existence of a Tax Acc class Modification anomaly: Employee 100 salary increase forces update of multiple records These anomalies exist because there are two themes (entity types) into one relation – course and employee, resulting in duplication, and an unnecessary dependency between the entities Employee Emp_IDNameDept_NameSalaryCourse_TitleDate_Completed 100Margaret SimpsonMarketing48000SPSS6/19/2005 100Margaret SimpsonMarketing48000Surveys10/7/2004 140Alan BeetonAccounting52000Tax Acc12/8/2004 110Chris LuceroInfo Systems43000SPSS1/12/2004 110Chris LuceroInfo Systems43000C++4/22/2003 190Lorenzo DavisFinance55000 150Susan MartinMarketing42000Java8/12/2002 150Susan MartinMarketing42000SPSS6/19/2005 Example Derived from Hoffer, Prescott, McFadden, Modern Database Management, 7th ed.

40. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Normalizing Previous Employee/Class Table Course_Completion Emp_IDCourse_IDDate_Completed 10016/19/2005 100210/7/2004 140312/8/2004 11011/12/2004 11044/22/2003 15016/19/2005 15058/12/2002 Employee Emp_IDNameDept_NameSalary 100Margaret SimpsonMarketing48000 140Alan BeetonAccounting52000 110Chris Lucero43000 190Lorenzo DavisFinance55000 150Susan MartinMarketing42000 Course Course_IDCourse_Title 1SPSS 2Surveys 3Tax Acc 4C++ 5Java This seems more complicated Why might this approach be superior to the previous one?

41. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Indexing An index is a table or other data structure used to determine the location of rows in a file that satisfy some condition Indices reduce the time needed to retrieve records … but increase the time and cost to insert, update, or delete Indexing is critical for high performance in large, complex db’s, –Especially data warehouses and data marts Products Product IDDescriptionColorSize 52Shoes (pair)Blue10 145Socks (pair)WhiteLarge 62BlouseGreen7 12PantsBlue32/34 532SkirtGreen7 ………… Product_Index Product IDRow 124 521 623 1452 5325 ……

42. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Alternative Data Models The relational data model is the current de-facto standard for storing and managing corporate data There are other data storage models, usually associated with legacy systems –The data you need for your analysis may be stored in them! Four common alternative data models –Flat file –Hierarchical –Network –Object

43. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Structured Query Language (SQL) SQL provides a standard language for describing, manipulating, and querying data from relational databases SQL allows applications to interact with databases without requiring a tight binding between the application and the underlying DBMS All of the major relational database vendors implement some form of SQL in their database products Example Query: SELECT ProductName, ProductPrice FROM Products WHERE SupplierName=‘Acme’ ORDER BY ProductsPrice DESC;

44. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Query Example English: Find the 10 most expensive products that we stock SQL: SELECT TOP 10 Products.ProductName AS TenMostExpensiveProducts, Products.UnitPrice FROM Products ORDER BY Products.UnitPrice DESC; Query Results:

45. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Transactional and Analytical Systems Transactional systems: System that are used to run a business in real time, based on current data. Also called “systems of record” Analytical systems: Systems designed to support decision making based on historical point-in-time and prediction data for complex queries or data mining applications BI systems are generally analytical systems

46. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Examples of Transactional and Analytical Systems Transactional System Examples Supermarket checkout system ATM machines Purchase order processing Student course registration Warehouse/inventory tracker Airline ticketing system E-Z Pass Analytical System Examples Data warehouses Data marts Enterprise spend analysis –Where do we spend our $$$ Sales force productivity analysis –By sales person, region, or product line Product-line profitability analysis –Which products are most profitable? –Which do we lose money on?

47. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Why Not Use Operational Data Stores For BI? It is good practice to separate operational and analytical systems and data Why? –To improve system performance –To improve database managability and maintainability –Optimize each type of system for it’s primary purpose

48. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Wrap Up

49. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques For Thursday We will be discussing part 1 of Competing on Analytics –Reading assignment is available on the wiki Come prepared to apply the concepts in part 1 of the book in class discussions to analyze how some well- known organizations might be able to improve their business by aggressively pursuing the principles of analytic excellence described in the book –Feel free to suggest organizations to discuss prior to class: I’ll be taking requests as I spin your favorite on-the-fly cases –Post suggestions for organizations to discuss in class, along with a brief description of why they would be an interesting to discuss, to the course wiki by Wednesday evening.