1. UBC IT Integrated Reporting Working Committee December 1, 2011

2.  Follow Up (20 min, Monique Van Vliet)  Terms of Reference  Committee membership  Program Status (15 min, David Truong)  Finance Discovery  Student DW Pilot  Student DW Analysis  Program Business Case (15 min, Lyla Crighton)  Student Data Warehouse Pilot Demo (30 min, Heather Epstein/Tony Gill)  Q & A (10 min ) Agenda

3. Follow Up from last Meeting  Terms of Reference  Membership  Meeting Date and Time

4. Program Status The program team has been focused on the following activities:  Finance Discovery  Student DW Pilot  Student DW Analysis

5. Program Status

6. Drivers for BI at UBC Critical stakeholders need integrated information and query/reporting access Accountability Are we using the funds we are given effectively and in the manner intended? Informed decisions What is the cost of a bum in a particular seat? Is it the right bum in the seat? are we giving them our best?

7. Existing Issues End users don’t have tools they need to extract, analyse and present information. Reporting out of the big systems is an issue Combining data from different systems is even worse It is highly manual, incomplete, and of uncertain quality Data governance is distributed (How do YOU define an FTE?) A variety of toolsets and projects working to integrate data in uncoordinated fashion …

8. Existing Situation at UBC

10. Possible Future at UBC

12. Objectives Two reporting objectives: Release the data accessible - self service – accurate – intuitive and user friendly Bring it together across systems, rapid – meeting changing business needs e.g. revenue by faculty – drill down to various level - down to student Create coordinated data governance – (e.g.: agreed and documented definitions; designated owners of data, etc.). Primarily focus on Financials and Student initially (operational reporting and integration) Required deliverable to provide integrated revenue/student data! Validation of funding (faith)! Will stop and review success and expand (HR, Procurement, Research, etc.)

13. Industry Intelligences Best Practices now involve storing as granular level of data as possible, up through aggregation (if necessary) in the data warehouse Bottom up foundation Not one off KPIs 50% of sites expect to redeploy new data warehouses over 5 yrs (early custom attempts being re-architected) New trends include visualization, cloud BI – mostly industries with high impact to revenue (gaming) - single purposed - not comprehensive Open Source starting to receive recognition from industry press – still not enough references for Gartner to evaluate.

14. Peer Situation (McGill, U of T, U of C, Harvard, Stanford, Florida State). All have some form of BI/DW started from 10 to 3 yrs ago and Universities continue to invest in BI Pioneers started with custom built reporting warehouses Some pioneers are reassessing architectures and newer sites are proceeding with commercial toolsets All report increased demand

15. Goals Ability to obtain KPIs not for today, but the ability to obtain the unknown KPI’s of the future Solid Foundation Unleash the data to those that can do something with it Not kill our selves… provide self service...’Teach people to fish’ Don’t build a ‘house of cards’

16. Requires Help and Understanding from the working group – keep us on track! Good foundation Transactional data – first Not to get distracted – baby steps – keep the vision in mind Pilot, learning opportunity and sharing opportunities Focus on accuracy and adoption initially Patience & support – this is a multi year program not a one shot deal…we need to get the foundation right help to manage expectations on campus

18. Student Data Warehouse Pilot Project Pilot Goals:  Assess Oracle BI (OBIEE) reporting tools  For power users, end users, developers  For business and IT  From the learning curve  Using data that is familiar  Try out the self-service reporting model  Apply and assess our methodology  NOT a data warehousing pilot  used existing data, as is

19. Our Methodology  Based on UBC-IT PMO Methodology …… with a BI twist  A work in progress  Pilot is a place to apply methodology  Findings can be applied to the methodology

20. Pilot Scope Governance Metadata Methodology Technical Business Strategy/Vision Demand Planning Capability Definition Release Management StandardsSkills/Training Metadata Data Security Data Privacy Authentication Authorization Operations AuditError-Handling Data Retention Single Sign-on ID & Access Mgmt Capacity Schedule Backup/ArchivePerformance Data Access Reporting / Analysis Data Mining Ad-hoc Analysis Reports Enterprise Portal Dashboards Publish / Alerts Mobile Performance Management Data Management Data Warehouse Atomic Data Warehouse Subject Specific Data Marts Source Data Sets Unstructured Information Master Data Operational Data Store Business Views Enterprise Data Warehouse Data Integration Data Sources Data Quality Extract, Transform, Load Data Enrichment Finance Batch Near Real-time Student HR External Research Land & Building Cleansing Profiling Harmonization Demographic Lineage Etc. Academic

21. Pilot Scope Governance Metadata Methodology Technical Business Strategy/Vision Demand Planning Capability Definition Release Management StandardsSkills/Training Metadata Data Security Data Privacy Authentication Authorization Operations AuditError-Handling Data Retention Single Sign-on ID & Access Mgmt Capacity Schedule Backup/ArchivePerformance Data Access Reporting / Analysis Data Mining Ad-hoc Analysis Reports Enterprise Portal Dashboards Publish / Alerts Mobile Performance Management Data Management Data Warehouse Atomic Data Warehouse Subject Specific Data Marts Source Data Sets Unstructured Information Master Data Operational Data Store Business Views Enterprise Data Warehouse Data Integration Data Sources Data Quality Extract, Transform, Load Data Enrichment Finance Batch Near Real-time Student HR External Research Land & Building Cleansing Profiling Harmonization Demographic Lineage Etc. Academic

22. Pilot Scope Governance Metadata Methodology Technical Business Strategy/Vision Demand Planning Capability Definition Release Management StandardsSkills/Training Metadata Data Security Data Privacy Authentication Authorization Operations AuditError-Handling Data Retention Single Sign-on ID & Access Mgmt Capacity Schedule Backup/ArchivePerformance Data Access Reporting / Analysis Data Mining Ad-hoc Analysis Reports Enterprise Portal Dashboards Publish / Alerts Mobile Performance Management Data Management Data Warehouse Atomic Data Warehouse Subject Specific Data Marts Source Data Sets Unstructured Information Master Data Operational Data Store Business Views Enterprise Data Warehouse Data Integration Data Sources Data Quality Extract, Transform, Load Data Enrichment Finance Batch Near Real-time Student HR External Research Land & Building Cleansing Profiling Harmonization Demographic Lineage Etc. Academic

23. Key Ingredients 1. Willing Participant: Carleton Ng Willing to :  share his needs  follow our methodology  articulate his requirements  watch and wait as we build  try self-service reporting  learn the tools  build his own reports  help assess the pilot

24. Key Ingredients 2.A Business Problem  Understand applicant funnel  # students  by campus  by faculty/program  by applicant type  applying from region  by 1 st choice, top choice  etc  Point in time comparisons  ……

25. Data + Business Problem = Pilot Data Model

26. Demonstration  You will see a sampling of:  Oracle environment (OBIEE) and tools  Business Model  Sample dashboards and analyses  Self-service reporting

27. Questions?