From Data Warehouse to Business Intelligence: The Michigan Journey

Transcription

1 From Data Warehouse to Business Intelligence: The Michigan Journey Presenters: John Gohsman Sean Mallin University of Michigan istrategy Solutions

2 Three campuses Ann Arbor (40,000 students, 23,000 faculty/staff) Dearborn & Flint (12,000 students, 1,800 faculty/staff) Health System Includes: Medical School, 3 hospitals, 30 health centers and 120 outpatient clinics (13,000 total employees) Financial Picture Annual Budget >$4 Billion (10% from State of Michigan) $823M research funding per year (NIH 47%, NSF 9%, DOD 8%) Endowment: $6 billion Michigan values its highly decentralized nature Coordinated autonomy Michigan Facts

3 Founded in 1999 and headquartered in Maryland; 15+ years experience in data warehousing, analytics and performance measurement across various industries In 2003, istrategy released the PeopleSoft edition of HigherEd Analytics TM Student module, the first packaged student analytic application for colleges and universities Focused exclusively on analytics for higher education Value Proposition: Packaged data models and ETL enable 80+ % out-of-the-box Extensibility, plus ongoing product lifecycle of new releases Rapid deployment opportunity Leverage collective knowledge of dozens of institutions Product Modules: Student, Finance, HR, Advancement ERP Vendor Integration: PeopleSoft, Datatel, Banner istrategy Facts

4 Presentation Outline The Data Warehouse Foundation Moving to Business Intelligence Demonstrations What s Next

5 The 80s

6 I N F Source: OLTP O R Exec M A T I O N Decision Support Tools: PL/1, ASI/Inquiry M A T U R I T FIN Management Operational DEVL HR STU PUR Users: Programmers, Power (10 s) Y Report Effectiveness Assessment 1980s

7 The 90s

8 Established Data Administration Strategic Data Planning Policy, Guidelines (Data as an Asset) Governance (Stewards/Managers, not Owners) Data Modeling Services, Naming Conventions Built Data Warehouse Financials, Human Resources, Student, Fundraising Relational approach for flexibility Source: Oracle Data Warehouse Tools: GQL Users: Power (100 s) DA and DW Early 1980s 1990s

9 Strategic Data Plan published Replace all legacy systems Need new technical platform Bought PeopleSoft ERP Commitment to replace each data set Source: Oracle Data Warehouse Tools: GQL Users: Power (~ 1,000) SDP and ERP 1980s 1995

10 1998 Developed DW principles and vision Selected Business Objects Implemented Student Recruiting and Admissions Implemented Financials Financials delivers majority of reports via DW Implemented Rest of Student Administration Implemented Human Resource Management System Source: ODS and Oracle Data Warehouse Tools: PS Query and Business Objects Users: Power and Casual (~2000) ERP/DW Implementation s

11 2000 and Beyond

12 Execs wonder about ERP ROI? Operational efficiency but Are we leveraging data for improved decision-making Units making progress M-Dash and M-Stat in Medical School (Xcelsius) UHR defines and delivers metrics; also pushes key info via /excel Establish Advisors on Information Management Strategy (AIMS) Develop strategy Source: ODS and Oracle Data Warehouse Tools: PS Query and Business Objects Users: Power and Casual (~2000) Return on ERP Investment 1980s 2005

13 Presentation Outline The Data Warehouse Foundation Moving to Business Intelligence Demonstrations What s Next

14 Data Sources Organization Skills, BI Competency Community Users Strategy Applications and Functionality Ad hoc query/reporting, standard/canned reporting, statistical analysis, data mining, predictive modeling, presentation/alert/push technology Infrastructure Data Warehouse, ODS, ETL, Data Quality, Metadata, Data Marts FIN Student HR Devl Process Performance mgt, methodology, education Tools BI end user tools, BI developer tools, XCelsius, OutlookSoft Everest Research Unitspecific What strategy? Lack of campus readiness, skills, or awareness Silo approach Lack of applications Limited tools Complicated data structures Missing infrastructure AIMS Report: Issues

15 Build awareness via BI Community User segmentation; increase market Power (~3000), operational (8000), casual/guided analysis (>10,000) Increase tools portfolio, infrastructure Browser-based solutions for execs, managers, etc. Improve data structures Aggregate, derive: add OLAP dimensional models Incorporated into Administrative Systems Strategic Plan Advisors on Information Management Strategy (AIMS) Training & Methods Subgroup Data Subgroup Business Intelligence Council (BIC) Communications Subgroup BI Community of Experts (BICE) AIMS Report: Recommendation 1980s 2006

16 Operational Guided Analysis using data from multiple sources Power Tools: Business Objects, Proclarity Entry Points Reporting Gateways

17 Increase Awareness; Educate Created BI Council and subgroups Created annual BI Awards MAIS makes parallel progress while campus readiness improves Decision to upgrade Business Objects and acquire site license Decision to build web reporting solution for guided analysis (internal controls, PI reports) Decision to purchase istrategy Financial module, and partner with istrategy to develop the HR Metrics dimensional model Research archive/purge, ETL/CDC tools Source: ODS, Oracle DW, SQL Server Tools: PS Query, Business Objects, Proclarity,.Net Users: Power and Casual (~2500) 1980s Internal & External Progress

18 Presentation Outline The Data Warehouse Foundation Moving to Business Intelligence Demonstrations What s Next

19 SQL Server Data Warehouse BI / Information Access MS Analysis Services (OLAP) Cubes M-Reports 3 rd Party Talent Mgmt. Software + 3 Build Cubes (SSIS) Relational Warehouse (Star Schema Design) Performance Point (ProClarity) MS Excel/ MS Access 2 Edit & Transformation (SP) Source Staging Tables Open Access Reporting Tools (e.g., Crystal Reports, Business Objects, Cognos, Hyperion, Reporting Services) 1 Bulk Load Process (SSIS) Data Warehouse/BI Architecture

20 Staff Profiles - Average Years of service - Average Age - Gender profile - Minority profile Staff Mobility - Hiring - Movements -Terminations WorkForce Management WorkForce Value and Funding Source Cost Financial FTE Financial Measures - Average FTE Revenue - Average FTE Expense - Average FTE Comp Expense Talent Acquisition Job Requisitions - Average Open Requisitions - Average days open - Average Days to fill All Rights Reserved 2007 HR Metrics Sample Content

21 M-Reports will deliver business intelligence to users in a customizable user interface Alerts, metrics and personalized reports based on user profile, preferences and role based security Guided analysis through data Data sourced from multiple underlying databases (OLTP; ODS; Data Warehouse: relational data sets and OLAP; unit data) MAIS and University units can develop and publish content M-Reports Vision

22 Presentation Outline The Data Warehouse Foundation Moving to Business Intelligence Demonstrations What s Next

23 Increase size of team: 18 to 27 or more Expand mission, increase functions Add Analytical skills Increase Application Development Enhance BI Community Increase Consulting and Training Enhance Data Administration Improve Data Set Development Increase Tools Support Creating a BI Organization

24 More OLAP dimensional models Broad content in M-Reports Dashboards (KPIs, personalized thresholds) Push (reports, alerts) Predictive Analytics Business Performance Management Deliver More Solutions

25 Build a solid foundation Policies, skills, DW Make progress at all levels of framework Engage campus Executive leadership Networks Community awareness and understanding Deliver solutions to all audiences Different data structures Portfolio of tools Source: ODS, Oracle DW, SQL Server Tools: PS Query, Business Objects, Proclarity,.Net Users: Power and Casual (15,000-18,000) Summary 1980s

26 For More Information Visit: Or contact:

27 U-M Business Intelligence Overview Enterprise Data Warehouse BI Tools HE (PeopleSoft) Fin Oracle HR BusinessObjects FIN (PeopleSoft) ETL GL Stu Development Devl PR M-Reports eresearch ETL Microsoft Reporting Copy (PeopleSoft) istrategy HR istrategy FIN Internal Cubes Proclarity

28 Relational Data Warehouse Environment M-Pathways Oracle 10g HE PeopleSoft (Ver. 9) Data Warehouse Oracle 10g BI Tools BusinessObjects XIR2 WebI and Infoview FIN PeopleSoft (Ver. 8.8) Extract/Transform using SQR Load Legacy data sets M-Pathways data sets Business Objects Predefined Reports Ad Hoc Queries

29 U-M Star Schemas/Cubes: M-Reports Internal Controls Sources Single Purpose Star Schemas Staging BI Tools istrategy Sources Multi Purpose Star Schemas Staging BI Tools

30 M-Reports Design Schema (Temp Pay Example) M- Pathways Security Layer Web Services Business Logic Layer Data Access Layer Data Bases M-Reports Portal External Customers Non M-Reports UI Using MS Reporting Services for Grids and Graphs. Need to decide about additional sw. Future Security Component (written in-house) Future Assumption: No direct access to LCC from outside an application (including UI) Temp Pay Get Temp Pay Get Temp Pay By Funding Dept Web services are very simplistic M-Reports BLL Get Distinct Campus Temp Pay BLL Get Temp Pay Get Temp Pay By Funding Dept BLL Components contain all business logic M-Reports DAL Build General Parms Select Distinct Campus Temp Pay DAL Select Temp Pay DAL Components build and pass SQL/MDX Commands Cubes vs. Relational