1. All Rights Reserved 2003, iStrategy Consulting March 17, 2003 Mark Max, Managing Partner Institutional Reporting and Analysis Utilizing OLAP Technology and Analytical Applications This work is the intellectual property of the author. Permission is granted for this material to be shared for non-commercial, educational purposes, provided that this copyright statement appears on the reproduced materials and notice is given that the copying is by permission of the author. To disseminate otherwise or to republish requires written permission from the author.

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 Founded iStrategy Consulting in 1999 –Consulting and software firm specializing in Business Intelligence and Data Warehousing –Principals have been working in BI for 15+ years –Experience in BI/DW for higher education –Launched new DW/Analytical Application for Higher Education in 2003 email: mmax@istrategyconsulting.com

3. All Rights Reserved 2003, iStrategy Consulting Shift in Higher Education Towards “Fact Based” Management High Focus Areas Recruiting Effectiveness Enrollment Funnel/Admissions Yield Student Demographics Retention Course Optimization Outcomes Management Early Intervention Key Performance Indicators Resource Management

4. All Rights Reserved 2003, iStrategy Consulting Higher Education Lifecycle Recruiting Admission s Enrollment Retention Program Complet ion Fund Raising Information Chain Fact-based Decisions/Improved Results Recruiting Admissions Registrar Institutional Research Financial Aid Office of Planning Alumni/ Fund Raising Provost Deans Academic Affairs Institutional Information Users

5. All Rights Reserved 2003, iStrategy Consulting Typical Reporting Challenges Student Admin Human Resources Housing/ Judicial Alumni Financials Recruiting Transactional systems don’t provide sufficient reporting capabilities 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 if reported 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

6. All Rights Reserved 2003, iStrategy Consulting Transaction System 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

7. All Rights Reserved 2003, 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

8. All Rights Reserved 2003, iStrategy Consulting DW Success Rates Research shows that many DW projects do not achieve targeted results! Typical approach that is prone to fail: –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

9. All Rights Reserved 2003, iStrategy Consulting Information vs. Data 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.  Big Difference between Data and Information

10. All Rights Reserved 2003, iStrategy Consulting DW Casual User vs. Power User Different audiences with different:Different audiences with different: –Information needs –Analytical capabilities –Technical aptitudes and skills –Level of understanding of application data –Time constraints 80% – 90% of information consumers are casual users  Need to consider both in technology decisions

11. All Rights Reserved 2003, iStrategy Consulting OLAP Tools Analytical Applications Data Mining Data Mart Data Mart Data Mart Data Mart OLAP Departmental Data Marts E T L OLAP Server Data Warehousing and Business Intelligence Architecture Data Sources Reporting & Analysis Enterprise Data Warehouse ETLETL ETL – Extraction, Transformation and Load Relational Query & Reporting Tools Data Warehouse Financials/HR Student Other

12. All Rights Reserved 2003, iStrategy Consulting Critical Success Factors in Delivering Self Service Information & Analytics 1.Address both Power Users and Casual Users 2.Focus on Information Incorporate a Dimensional Data Model Recognize the need for Data Transformation and Derivation 3.Utilize a combination of relational and OLAP technology (relational technology alone will not meet the needs) 4.Target a quick high value success, instead of trying to provide every bit of data that someone could possibly request (80/20 rule) 5.Implement an Analytical Application instead of deploying query tools

13. All Rights Reserved 2003, iStrategy Consulting Confusing BI Product Space 20 to 40 vendors; many overlapping products that may appear similar but are fundamentally different in capabilities and architecture Reporting vs. Analytics – there’s a big difference! Relational vs. OLAP Technology –Multidimensional Presentation vs. OLAP engine –MOLAP vs. ROLAP vs. HOLAP 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!  Need to select BI technology based on user community, analytical reporting needs and objectives

14. All Rights Reserved 2003, iStrategy Consulting Research Findings by The Data Warehouse Institute “...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 users need.” Wayne Eckerson, Director of Education and Research for The Data Warehousing Institute (TDWI)

15. All Rights Reserved 2003, iStrategy Consulting Why an Analytical Application? (vs. Reporting Tools) Definition – domain-specific solution that enables users to access, analyze, and act upon information in the context of business or management processes [by TDWI] Casual Users – majority of information users (80 – 90 %) are casual users who will have difficulty mastering a reporting tool. An Analytical Application will be more effective 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 embedded 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 Custom Analytical Functionality – enables customized application functionality to be integrated with reporting and analysis (e.g., Student Peer Group Analysis)

16. All Rights Reserved 2003, 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 institutional objectives: recruiting effectiveness, retention, student achievement, course curriculum, etc. 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)

17. All Rights Reserved 2003, iStrategy Consulting Build vs. Buy Analytical Applications market will grow to $6 Billion by 2005 - IDC Value Proposition: –Reduced Implementation Time –Reduced Cost –Reduced Risk –Expectations validated before implementation Would you consider building a student registration system or a general ledger? If a packaged Data Warehouse and Analytical Application for Higher Education was available, would it make sense to consider buying instead of building?

18. All Rights Reserved 2003, iStrategy Consulting iStrategy’s HigherEd Analyzer TM 1 st Analytical Application for HigherEd iStrategy’s HigherEd Analyzer TM... 1 st Analytical Application for HigherEd 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 Data Warehouse Information Delivery EngineInformation Consumers Guided AnalysisAnalyticalModules DownloadExtracts Key Perf. Indicators ComplianceReportsStandardReports PersonalDashboard Ad Hoc AnalysisPersonalReports Analytical Application

19. All Rights Reserved 2003, iStrategy Consulting HigherEd Analyzer Product Positioning A packaged analytical application for Higher Education –Open technology platform –80% to 95% out-of-the-box –Framework to enhance data model and content for specific needs –Pre-built integration with leading ERP vendors (SCT and Datatel by Q3) –API to integrate with Legacy apps or existing DW Accelerated implementation process –Install and configure –Validate data conversion and recurring data feed –Workshop for content customization –Train and deploy HigherEd Analyzer Data Warehouse Existing DW ETL Modules HigherEd Analyzer Portal Personalized Dashboard Guided Analysis Interactive Reports HigherEd Analyzer Data Warehouse ERP: Peoplesoft SCT Datatel Legacy ETL Modules

20. All Rights Reserved 2003, iStrategy Consulting Application Demonstration

21. All Rights Reserved 2003, 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

22. All Rights Reserved 2003, iStrategy Consulting Technology Architecture Microsoft SQL Server Windows 2000 Server Microsoft Analysis Services ProClarity Analytical Server Microsoft IIS Web Server

23. All Rights Reserved 2003, 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

24. All Rights Reserved 2003, 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