Transcript Meeting Presentation

UBC IT
Integrated Reporting Working Committee
December 1, 2011
Agenda
 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 )
Follow Up from last Meeting
 Terms of Reference
 Membership
 Meeting Date and Time
Program Status
The program team has been focused on the following activities:
 Finance Discovery
 Student DW Pilot
 Student DW Analysis
Program Status
2011
Apr
May
Jun
Jul
Aug
2012
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
2013
Aug
Sep
Oct
Nov
Dec
Jan
Program Organization
Program Charter
Program Governance
Finance Discovery
Mobilization
Financial Reporting Foundation
Student Revenue
Reconcilliation
IR Roadmap
Student Revenue Analytics
Finance & Procurement Data Warehouse
Solution Architecture
Campus Solutions Fit/Gap
Solution Blueprint
Student DW Foundation
Student Records Analytics
ISI Dashboard Pilot
Student Data Warehouse
PAIR Datamart
PAIR Dashboard
PAIR Datamart
DEV
VERF
UAT/PERF
Environment Design & Strategy
PROD
Environment Support
Technical Architecture
Test Approach
Test Planning & Execution
Program Test Strategy
OBIEE
BI App / Campus
Solutions
Train-the-Trainer
Preparation
Training & Change Management
Today
End User Training
Feb
Mar
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?
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
…
Existing Situation at UBC
Possible Future at UBC
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.)
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.
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
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’
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
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
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
Pilot Scope
Governance
Strategy/Vision
Demand Planning
Metadata
Technical
Extract, Transform,
Load
Skills/Training
Data Access
Reporting /
Analysis
Master
Data
Reports
Profiling
Research
Dashboards
Operational
Data Store
Business Views
Cleansing
Atomic
Data Warehouse
Data Enrichment
Land &
Building
•
•
•
•
Harmonization
Demographic
Lineage
Etc.
Mobile
Unstructured
Information
Data Mining
Publish /
Alerts
Enterprise Data Warehouse
Performance
Management
External
Data Security
Operations
Data Privacy
Capacity
Data Retention
Authentication
Schedule
Authorization
Audit
Single Sign-on
Error-Handling
ID & Access Mgmt
Backup/Archive
Performance
Enterprise Portal
Ad-hoc
Analysis
Subject Specific Data Marts
Data Quality
HR
Methodology
Business
Data Warehouse
Source Data Sets
Batch
Academic
Standards
Data Management
Near Real-time
Student
Release Management
Metadata
Data Integration
Data Sources
Finance
Capability Definition
Pilot Scope
Governance
Strategy/Vision
Demand Planning
Metadata
Technical
Extract, Transform,
Load
Skills/Training
Data Access
Reporting /
Analysis
Master
Data
Reports
Profiling
Research
Dashboards
Operational
Data Store
Business Views
Cleansing
Atomic
Data Warehouse
Data Enrichment
Land &
Building
•
•
•
•
Harmonization
Demographic
Lineage
Etc.
Mobile
Unstructured
Information
Data Mining
Publish /
Alerts
Enterprise Data Warehouse
Performance
Management
External
Data Security
Operations
Data Privacy
Capacity
Data Retention
Authentication
Schedule
Authorization
Audit
Single Sign-on
Error-Handling
ID & Access Mgmt
Backup/Archive
Performance
Enterprise Portal
Ad-hoc
Analysis
Subject Specific Data Marts
Data Quality
HR
Methodology
Business
Data Warehouse
Source Data Sets
Batch
Academic
Standards
Data Management
Near Real-time
Student
Release Management
Metadata
Data Integration
Data Sources
Finance
Capability Definition
Pilot Scope
Governance
Strategy/Vision
Demand Planning
Metadata
Technical
Extract, Transform,
Load
Near Real-time
Student
Standards
Skills/Training
Data Access
Reporting /
Analysis
Data Warehouse
Source Data Sets
Master
Data
Reports
Profiling
Research
Dashboards
Operational
Data Store
Business Views
Cleansing
Atomic
Data Warehouse
Data Enrichment
Land &
Building
•
•
•
•
Harmonization
Demographic
Lineage
Etc.
Mobile
Unstructured
Information
Data Mining
Publish /
Alerts
Enterprise Data Warehouse
Performance
Management
External
Data Security
Operations
Data Privacy
Capacity
Data Retention
Authentication
Schedule
Authorization
Audit
Single Sign-on
Error-Handling
ID & Access Mgmt
Backup/Archive
Performance
Enterprise Portal
Ad-hoc
Analysis
Subject Specific Data Marts
Data Quality
HR
Methodology
Business
Data Management
Batch
Academic
Release Management
Metadata
Data Integration
Data Sources
Finance
Capability Definition
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
Key Ingredients
2. A Business Problem
 Understand applicant funnel
 # students
 by campus
 by faculty/program
 by applicant type
 applying from region
 by 1st choice, top choice
 etc
 Point in time comparisons
 ……
Data + Business Problem = Pilot Data Model
Demonstration
 You will see a sampling of:




Oracle environment (OBIEE) and tools
Business Model
Sample dashboards and analyses
Self-service reporting
Questions?