1. All Rights Reserved 2002, iStrategy Consulting January 16, 2003 Mark Max, Managing Partner Adding Value to the Data Warehouse: Utilizing OLAP Technology and Analytical Applications

2. Mark Max Bio B.S. Accounting & M.S. Business – University of Maryland University of Maryland, Instructor 20 years Consulting, Corporate, Software Vendor Work Experience Started iStrategy Consulting in 1999 –Maryland based consulting firm specializing in Business Intelligence and Data Warehousing –Principals have been working in BI for 15+ years –Experience in BI/DW for higher education –Launching new DW/Analytical Application for Higher Education in Q1 2003 email: mmax@istrategyconsulting.com

3. All Rights Reserved 2002, iStrategy Consulting Discussion Points 1.Information Delivery Challenges 2.Data Warehousing and Business Intelligence Technology 3.Higher Education Analytical Application Framework 4.Demonstration 5.Q&A

4. All Rights Reserved 2002, iStrategy Consulting Shift Towards Information Based Management – High Visibility Areas Recruiting Effectiveness Retention Enrollment Funnel Student Demographics Course Planning Resource Management Outcomes Management Compliance Reporting Early Intervention Key Performance Indicators

5. All Rights Reserved 2002, iStrategy Consulting Emerging Strategies in in Higher Education Strategic Enrollment Management (SEM) “Strategic Enrollment Management is a comprehensive process designed to achieve and maintain the optimum recruitment, retention, and graduation rates of students where ‘optimum’ is defined within the academic context of the institution”. Strategic Planning Engine (SPE) “The heart of the Strategic Planning Engine links strategic decision making with organizational key performance indicators (KPI's).” from Michael G. Dolence & Associates These processes require information!

6. All Rights Reserved 2002, iStrategy Consulting Typical Reporting Challenges No central repository of official information – many non-integrated systems and databases Databases are structured for transaction processing, audit trail and operational needs; they are not organized for ease of reporting! Lack of standardized metrics and information rules (e.g., how is retention % calculated?) Some information needs require data from multiple systems (e.g., Cost per Student) Many informal databases and spreadsheets used by individuals for reporting, analysis, external reporting No standardized tools for reporting and analysis Student Admin Human Resources Housing/ Judicial Alumni Financials Recruiting

7. All Rights Reserved 2002, iStrategy Consulting Application Reporting Complexity Student Administration application database structures are very complex Reporting requires queries for database extracts – need to know SQL language Reporting results are subject to: –a) users understanding of database structure, –b) “interpretation” of query criteria, and –c) proper SQL syntax. Its easy to get the wrong answer! No easy way to combine data across multiple systems and database. Limited number of people who know how to query databases

8. All Rights Reserved 2002, iStrategy Consulting The Impact No ability for self service access to information – users are totally dependent upon others to produce information Time consuming, manually intensive process to produce reports Different people produce reports with the same information but have different results –What is the real answer? –How do you know the information is correct? Have to repeat the same time consuming process each time you want a report No time available for analysis because of the extensive time required to produce information

9. All Rights Reserved 2002, iStrategy Consulting Data Warehousing and Business Intelligence Architecture OLAP Tools Relational Query & Reporting Tools Analytical Applications Data Sources Data/Application Servers Business Intelligence Data Mining Data Mart Data Mart Data Mart Enterprise Data Warehouse Data Mart OLAP ETLETL Departmental Data Marts E T L ETL – Extraction, Transformation and Load OLAP Server Data Warehouse Financials/HR Student Other

10. All Rights Reserved 2002, iStrategy Consulting 2002 Higher Education ERP Survey Source: The Promise and Performance of Enterprise Systems, 2002 ECARS Research Study by Dr. Robert Kvavik (500 Institutions surveyed) 39% of institutions surveyed have implemented or are in the process of implementing a Data Warehouse 37% of institutions surveyed plan to implement a Data Warehouse within the next three years, with almost 1/3 of the projects beginning in 2003

11. All Rights Reserved 2002, iStrategy Consulting Recipe for Failure Start by looking for application data to source a DW Move as much transactional data as possible into a “warehouse database” Purchase a relational reporting or query tool Send users to training -- This approach rarely works! --

12. All Rights Reserved 2002, iStrategy Consulting Big Difference between Data vs. Information vs. Knowledge Data – raw facts that have been collected, processed, stored, but not organized to convey meaning. Information – a collection of data organized in a manner to be meaningful to a recipient. Knowledge – information combined with understanding, experience, accumulated learning, and expertise relevant to a problem, decision, or process. Data Transformation, Derivation and Aggregation are necessary, along with a self service access capability!

13. All Rights Reserved 2002, iStrategy Consulting DW Casual User vs. Power User Different audiences with different:Different audiences with different: –Information needs –Analytical capabilities –Technical aptitudes –Level of insight into application data –Time constraints 80% – 90% of information consumers are casual users  Need to consider both in technology decisions

14. All Rights Reserved 2002, iStrategy Consulting Confusing BI Product Space 25 to 50 legitimate vendors; many overlapping products that may appear similar but are fundamentally different Reporting vs. Analytics – there’s a big difference! Relational vs. OLAP Technology –MOLAP vs. ROLAP vs. HOLAP –Multidimensional Presentation vs. OLAP engine Products/Vendors: Front-end only vs. Back-end only vs. Both Open vs. Proprietary platforms Web vs. Client Server –HTML vs. Rich web client (JAVA, Active-X) Open component architecture vs. self contained products –Portal integrationConclusions  There’s no magic product that does it all!  Understand your user base, information needs and objectives before selecting BI technology

15. All Rights Reserved 2002, iStrategy Consulting Why OLAP Technology? Multi-dimensional presentation is the natural orientation for business information and analysis –Intuitive and easy to use –Hides user from underlying relational data model OLAP Technology is very fast –Most reports run within 1-3 seconds –Speed advantage substantial in highly aggregated reports such as multi-year trends –Without OLAP, the burden is on the developer to build the aggregation Enables calculations that are impractical using relational technology –e.g., moving averages, prior period % change Produces consistent information –Pre-calculated results –Not subject to unexpected SQL query behavior

16. All Rights Reserved 2002, iStrategy Consulting Aggregation Management: Relational Summary Tables Scenario Fact table with four dimensions Each dimension has four levels in its hierarchy (e.g., Time: Section, Course, Subject, All) How many summary fact tables are required to support every combination of dimension level? 255 If you don’t build 255, how many should you build and which ones? What if you have a 20 dimensional Student Term Fact Table? OLAP Technology makes aggregation management very easy!

17. All Rights Reserved 2002, iStrategy Consulting Why an Analytical Application? (vs. Reporting Tools) Casual Users – majority of information users (80 – 90 %) are casual users who will have difficulty mastering a reporting tool. An Analytical Application will be much easier to use and be more highly utilized Hide Database Complexity – most reporting tools require the user to understand the reporting database content and relationships. An analytical application enables casual users to get information without understanding the underlying database and functionality of reporting tools Guided Analysis – an application framework provides the opportunity to guide users through an analytical process and better leverage the metrics and analytical capabilities inherent in the solution Personalization – provide users with the ability to personalize their content and interface Embed Customized Analytical Functionality – enables customized application functionality to be integrated with reporting (e.g., Student Peer Group Analysis)

18. All Rights Reserved 2002, iStrategy Consulting What the experts are saying! “...most decision support software is gathering dust on office bookshelves” “Whether you build and/or buy, the key is to … deliver a robust analytic application that delivers the information and analysis that business users need.” Wayne Eckerson, Director of Education and Research for The Data Warehousing Institute (TDWI)

19. All Rights Reserved 2002, iStrategy Consulting Analytical Application for Higher Education Information Scope –Serve a broad audience: institutional research, management reporting, compliance reporting, operational analysis –Span complete student lifecycle: admissions, enrollment, course activity, graduation –Address key objectives: recruiting effectiveness, retention, student achievement, course curriculum and schedule Provide self service access to information: –Intuitive and easy to use (the basics are simple) –Minimal training required –Easy to deploy Functionality: –Interactive standard reports and charts, –Guided Analysis, –Key Performance Indicators (KPIs), –Personalized Dashboard (KPIs and Charts) –Ad hoc analysis, –“Actionable” analytical tools (e.g., support early intervention through student risk analysis, student peer group analysis)

20. All Rights Reserved 2002, iStrategy Consulting Higher Education Analytical Application Institutional Research Academic Affairs Admissions Office Strategic Planning Deans/ Assoc. Deans Department Chairs Registrar’s Office Compliance Reporting Financial Aid Administrative Departments Student Term Class Offering Student Class Enr. Admissions Graduation Faculty Term Guided AnalysisAnalyticalModules DownloadExtracts Key Perf Indicators ComplianceReportsStandardReports PersonalDashboard Ad Hoc AnalysisPersonalReports Analytical Application Data Warehouse Information Delivery EngineInformation Consumers

21. All Rights Reserved 2002, iStrategy Consulting Demonstration Background Information

22. All Rights Reserved 2002, iStrategy Consulting Student Administration Information Categories 1.Admissions 2.Student Demographics 3.Enrollment Trends 4.Retention 5.Class Offering and Utilization 6.Student Class Enrollment 7.Student Performance 8.Student Risk Analysis 9.Student Peer Group Analysis 10.Graduation 11.Faculty Information

23. All Rights Reserved 2002, iStrategy Consulting Student Administration Dimensional Data Model Class Offering Student Term Student Class Enrollment Admissions Graduation Faculty Term Fact Areas Admissions: Application Method Applicant Home State Prior Applicant Ind. Applicant Fin Aid Interest Applicant Housing Interest Recruiting Category Applicant Status Admit Category Cohort Faculty Attributes: Faculty Faculty Ethnicity Faculty Gender Faculty Rank Tenure Status Graduation: Graduated Indicator Degree Years to Graduate Institutional: Term School/Major Academic Department Student Term: Academic Level Academic Standing Student Term Status FT/PT Indicator Class/Grade: Subject/Class Course Level Class Type Grade GPA Band Student Attributes: Student Student Citizenship Student Ethnicity Student Gender Student Home State Dimensions

24. All Rights Reserved 2002, iStrategy Consulting User Interface Terminology Grid/Chart Presentation Orientation: Rows, Columns, Pages Dimension/Measures Hierarchy Drill Down Page Selection Rotate Dimension Filtering –Top/Bottom Ranking –Exception based selection Drill to Detail

25. All Rights Reserved 2002, iStrategy Consulting Application Demonstration

26. All Rights Reserved 2002, iStrategy Consulting Technology Architecture Microsoft SQL Server Windows 2000 Server Microsoft Analysis Services ProClarity Analytical Server Microsoft IIS Web Server

27. All Rights Reserved 2002, iStrategy Consulting Operational Databases Flat Files Relational Warehouse Dimensions/AttributesStar Schema Fact Tables Data Transformation Services (DTS) Staging Tables Bulk Load Process Data Transformation Services (DTS) Edit & Transformation Data Transformation Services (DTS) Microsoft Analysis Server (OLAP) Cubes Microsoft SQL Server Data Warehouse 1 2 3 Data Warehouse Architecture Student Admin Application DW Build Process 1.Bulk load data from transaction system into temporary staging tables (most recent n terms) 2.Perform edit, data derivation and relational DW build transformations 3.Build aggregate OLAP cubes

28. All Rights Reserved 2002, iStrategy Consulting Underlying Technology: Key Components Microsoft SQL Server –Analysis Services OLAP engine at no additional cost –Data Transformation Services (DTS) at no additional cost –Will scale sufficiently (relatively low transaction volume) –Cost effective –Large install base –Easy to manage Proclarity –Established product with awards for best end user BI product (from Microsoft and DM Review) –Strong blend of end user functionality and ease of development –Single solution that addresses both casual and power user –Leverages strengths of Analysis Services (drill to detail, actions) –Easy to deploy and manage; extensible –Web and client server deployment options –Reasonably priced Active Server Pages (ASP) for Dashboard

29. All Rights Reserved 2002, iStrategy Consulting Keys to Success Set reasonable expectations –It’s impossible to address every imaginable information need –It’s better to successfully deliver 80% - 90% of the requirements than to deliver nothing –Continue to expand scope based on needs Target a quick success story Ensure that the casual users have an application interface that is: –Simple to use –Fast –Supports analytics as the user skills develop Design must incorporate transformation of data to a dimensional data model Provide a good support infrastructure