Transcript Slide 1

Building Data Warehouse at Rensselaer
Ora Fish
Rensselaer Polytechnic Institute
Best Practices in Higher Education Student Data Warehousing Forum
Northwestern University
October 20-21, 2003
Agenda
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Background
Development Methodology
Rollout Strategy
Summary Status – where are we now
Benefits
Lessons Learned
Demonstrations of the Financial Analysis
Data Mart – Executive Information Systems
Q&A
Facts about Rensselaer
(RPI)
Founded in 1824 by Stephen Van Rensselaer
“We are the first degree granting
technological university in the Englishspeaking world”
Research University
Total Students 9,145
 Graduates – 4,006
 Undergraduates – 5,139
Faculty - 450
Facts about Presenter
Bachelor in Math and Computer Science
from Tel-Aviv University, Israel
MBA from RPI
23 years in system implementation and
software development in variety of
technical and management positions
Over 8 years in RPI
Involved with the Data Warehouse for the
past five – six years
Fundamental Problem
Operational systems are not designed for
information retrieval and analytical
processing
First attempt to fund DW
project Fails
Reasons for failing funding in Fall of 1997 :
Timing
Expectation
Lucking business sponsor
Analytical culture
What does it really means??
Second Attempt to Fund DW
Project is Successful
Spring 2000 - The following changes had occurred:
Timing – Banner does not addresses reporting;
Views are too slow to be used
Organizational changes (New President, CIO)
Performance Planning
We have build a Prototype
Buy-in Process
Demonstrate to those who need this
information desperately
The word is out
From the CIO to the committees to the
cabinet
Buy-in Process
We are prepared to address:
Budgets
Timelines
We are ready with the white paper to
communicate the key components
(iterative development under overall
planning, business users involvement,
meta data, approaches)
The Fundamental Goal
The fundamental goal of the Rensselaer
Data Warehouse Project is to integrate
administrative data into a consistent
information resource that supports
planning, forecasting, and decision-making
processes at Rensselaer.
Development methodology
Phase I – Building Foundation
Phase II – Iterative Process of Building
Subject Oriented Data Marts
On going Operations: Support and
Training; Maintenance and Growth
Rolling Implementation
FY02
Infrastructure Planning/Staffing
Software
Database/Hardware
Production Platform
Policy
Data Policy
Datamarts
Finance/Research
Position Cntrl/Labor
Human Resources
Enrollment
Grad Financial Aid
Undergrad Fin Aid
Contracts & Grants
Admissions Pipeline
Operations Support
Software Upgrade
Database Upgrade
Hardware Growth
Req
FY03
Dev & Test
FY04
Rollout
FY05
Phase I – Building Foundation
Organizational Structure
Project framework and high level
plan
Building Technical Infrastructure
Develop Data Policies and
Procedures
Hiring
Project Organizational Structure
Sponsorship Group
Progress report
Steering Committee
Forming Implementation groups; Defining scope and deliverables
Implementation issues
Implementation groups
Data Warehouse Group
Bus ine s s Inte llige nce Se le ction Com m itte e
Financial Analys is Im ple m e ntation Group
Financial Analys is Re vie w e r Group
Data Policy Gr oup
Project Framework
High Level Analysis
Prioritization process
Hire and train staff, Choose
consultant
Establish communication
channels (web site, newsletters, kickoff
event…)
High Level Analysis and
Prioritization process
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Enro
ll me
nt M
gm t
Stud
e nt L
ife
Ins ti
tute
Ad v.
Prov
o st O
ffice
Rese
arch
Fin a
nce
Gov
' t Re
la tio
ns
Pres
i den
ts St
aff
HR
BUSINESS PROCESSES
Enrollment Analysis
Student Pipeline Analysis
Faculty Workload Assessment
Financial Analysis
Contract and Grants Analysis
Proposal Pipeline Analysis
Financial Analysis - Research
Graduate Financial Aid
Alumni Demographics and Tracking
Alumni Contact Management
Human Resources
Facilities Management
Sch o
ols/ D
e ans
Dep'
t Ch
ai rs
Regi
strar
CONSTITUENCIES
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Prioritization Process
High
PP
AC
FR
FA
SP
CG
HR
Value to
Rennselaer
Low
FW
EA
AD
GF
Feasibility
FM
High
Building Technical Architecture
DATA SOURCES
transactional
systems
banner reporting
instance (clone)
AIX
Banner
Reporting
Instance
DATA ACQUISITION
• extraction
• transformation
• modeling
• loading
DATA WAREHOUSE
• central repository
• subject-based data marts
• metadata
• conformed dimensions
production data warehouse machine
AIX 4-CPU 4GB RAM
Oracle 9i
DWDB
(targets)
Informatica
PowerCenter
ETL Server
DATA DELIVERY
• user-facing applications
• report generation
• subject-based data cubes
• data mining
Brio Portal
AIX
Brio
ODS/JF Node
DATA CONSUMPTION
• business intelligence
• decision-support
• OLAP
• querying
• reporting
end-user machines
Microsoft
Excel
analysts
Oracle 8i
[DWDB]
Brio Portal
Informatica
Repository
Brio
Insight
metadata
analysts
Banner
Oracle 9i
[PROD]
banner production
AIX
Brio
Shared
Metadata
Brio
WebClient
webserver
AIX
Dash Boards
content
Viewers
Technical Architecture Inventory
ERP – Banner from SCT
ETL – Power Center from Informatica
Data Base – Oracle 8i
Models – Star schemas with conformed
dimensions
Web Front end tools – Brio, Dash Boards
Desktop Front End tools – Brio, Excel
Data Security, Privacy and Access Policy
Security
& Privacy
Access
& Use
Can be defined as striking the “right” balance between
data security/privacy and data access
Value of data is increased through widespread access
and appropriate use, however, value is severely
compromised by misinterpretation, misuse, or abuse
This policy considers security and privacy paramount
Key oversight principle:
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Cabinet members, as individuals, are responsible for overseeing
establishment of data management policies, procedures, and
accountability for data governed within their portfolio(s), subject
to cabinet review and CIO approval
Phase II
Components of Building Subject
Oriented Data Marts
Defining Scope and Timelines
Modeling
Development
Record Metadata
Local Testing
Core Administration Testing
Design and Develop Security
Core Administration live in Production
Front-End development for the campus
Campus Rollout
JAD & RAD
Approach
Defining Scope
Identify Constituency
Detailed Requirements Definitions
Analyze Data Sources / raise issues
Define Scope
Acceptance/Project Review
Develop and approve specific security
policy
Modeling
Subject-based data
marts
Star Schemas
Conformed dimension
Graduate Financial Aid Data Model
One row per student per term per support type
Graduate Support Snapshot
ACADEMIC TERM
DIM
Academic Term Key
Student Dimension
Student Key
Fund Dimension
Fund Key
Organization
Dimension
Org Key
Student Cohort
Bridge
Student Cohort Key
Cohort Key
Student Faculty
Advisor Bridge
Student Faculty Advisor
Key
Faculty Advisor Key
Graduate Support
Snapshot
Academic Term Key
Prim Program Major Grp Key
Sec Program Major Grp Key
Student key
Student Cohort Key
Class Key
Fund Key
Org Key
Account Key
Program Key
Activity Key
Grant Key
Student Faculty Advisor Key
Account Dimension
Account Key
Program Dimension
Program Key
Class Dimension
Class Key
Tuition Assistance Amt
Tuition Assistance Fees Amt
Tuition Assistance Disb. Amt
Tuition Assistance Fees Disb. Amt
Tuition Assistance Expensed Amt
Degree Completion Amt
Degree Completion Disb. Amt
Stipend Amt
Activity Dimension
Activity Key
GFA Support Type
Dimension
GFA Support Type Key
Grant Dimension
Grant Key
Academic Degree
Bridge Dimension
Student Degree Key
Student Enrollment Model – one
row per enrolled student per term
Student Enrollment Snapshot
Academic Term
Dimension
Academic Term Key
Student Dimension
Student Key
Student Enrollment
Snapshot
Academic Program
Bridge Dimension
Student Academic
Program Key
Academic Term Key
Prim Program Major Grp Key
Sec Program major Grp Key
Student key
Student Cohort Key
Class Key
Student Faculty Advisor Key
Class Dimension
Class Key
Student Cohort
Bridge
Student count
Matriculated count
Credit Hours Registered
Credit Hours Attempted
Credit Hours Earned
Overall GPA
Term GPA
Tuition Amt Charged
Tuition Fees Charged
Tuition Amount Billed
Tuition Fees Billed
Student Faculty
Advisor Bridge
Student Faculty Advisor
Key
Faculty Advisor Key
Student Cohort Key
Cohort Key
Summary GFA Model
one row per graduate student per term
Graduate Student Count Enrollment Snapshot
Academic Term
Dimension
Academic Term Key
Student Dimension
Student Key
Student Enrollment
Snapshot
Primary Program
Dimension
Student Academic
Program Key
Academic Term Key
Prim Program Major Grp Key
Sec Program major Grp Key
Student key
Student Cohort Key
Class Key
Student Faculty Advisor Key
GFA STATUS
GFA Key
Primary Funding
Source
Student count
IRA count
ERA count
TA count
Fellowship count
Scholarship count
Self supported count
Cumulative terms enrolled
Cumulative terms affiliated
Current Tuition Amt Charged
Current GFA Stipend Amt
Student Faculty
Advisor Bridge
Student Faculty Advisor
Key
Faculty Advisor Key
Funding Key
Development - ETL
Data Staging Design and Development
Design & Develop Aggregation Process
Develop Data Quality Assurance
Processes
User testing testing and testing
…..
Note: the Data Warehouse serves the needs
for ad-hoc analysis and reporting of various
groups of users
 Testers are: Deans, Cabinet, Financial
Managers, Core Administration offices…
 Testing period is an opportunity to create
more definitions, groupings, and
transformations…
Prior To User Acceptance Testing
Identify Testing Candidates - key users identified
in the scope
Train Users in Brio
Transfer of Knowledge from Developer to
Testing Group
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Sample Reports
Document Data Mart Description
Document Standard Naming Conventions
Document Common Uses for Each Star Schema
User Set-Up
Testing sessions
Allocating time slots
Targeting – aiming to produce results
Verifying that the models do address the
need
Great opportunity to bridge diverse groups
Defect/Enhancement Log
Date Reported
Priority Level (i.e. High, Medium, Low)
Defect and/or Enhancement Description
User Reporting Defect and/or Enhancement
Defect/Enhancement Status
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Incoming
Pending
Work In Progress
User Acceptance Testing
Closed
Focus Group
Assigned To
Resolution
User Assigned To Test Resolution
Recording Metadata
User driven effort
Stored in Informatica repository
Accessed via Brio
Development - Securing Data
Marts
Ensure that the subject oriented Data
Policy is defined
Technically feasible
Approved
Build Security Front End application
Data Security options
Securing schemas
Securing facts only
Securing dimensions only
Securing both facts and dimensions
Nuts and Bolts of the Data Base
Security
Data Base security applies to all individuals given either direct access
to the warehoused data or given permissions to process Brio dynamic
reports
Organization Managers And Financial Managers will have access to
the warehoused financial data based on the following criteria
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All financials posted against that Org
All funds listing that Org as a home Org (in cases of research funds, this
defines where the research is brought into)
All funds listing the PIs (or the Financial Manager) associated with that
Org as fund financial managers. (Resolves the Multi-disciplinary issue)
All funds and orgs listing that Org as a predecessor in either one of the
above three cases.
Administrative role: Individuals might be granted access to additional
funds and org based on their needs and their role within Rensselaer.
Position Control and Labor Data
Policy Overview
Already have access to Labor data in Banner
Completed DW training
Access to Budgets and Labor data for all Funding,
Employees, or Positions owned by their Organization
as following:
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Funding: All actual and budgeted labor expenses posted
against their Organization
Employees: All actual and budgeted labor expenses
associated with the Employees reporting to their Organization
within the timeframes of the employees’ employment in the
Organization.
Positions: All actual and budgeted labor expenses associated
with Positions owned by their Organizations.
All of the above within their Organizations’ hierarchy.
HR Security Policy Overview
Access to Employee Information
Level 1
(Fund Fin. Mgr)
Level 2
(Department/
Division or School
Manager)
Level 3
(HR)
Name
Yes
Yes
Yes
RIN
Yes
Yes
Address
No
Yes
Yes
Yes
Age
No
No
Yes
Ethnicity
No
No
Yes
Marital Status
No
No
Yes
Gender
No
No
Yes
No
Yes
No
Yes
Yes
No
Yes
Yes
Employee Effective Dates and Status
No
Yes
History: Name Changes
Yes
Yes
Yes
History: RIN Changes
Yes
Yes
Yes
History: Demographic Changes
No
No
Yes
Benefits Eligibility Categories
No
Yes
Yes
Hire Date(s)
No
Yes
Yes
T ermination Data
Employee T ype (Full/Part T ime, FLSA
Status, Employee Category)
No
Yes
Yes
No
Yes
Yes
Portfolio and Department Data (Current)
Yes
Yes
Yes
Portfolio and Department Data (History)
No
No
Yes
Category
Citizenship
Deceased
Veteran Status
Yes
Yes
Enrollment and Graduate Financial
Aid Data Policy Overview
Access to aggregate data is based on
“need to know”
Access to student identifiable information
is restricted as following:
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Sponsoring graduate students
Major
Advisement
Central administration/management of the
University
Access to Undergraduate Financial
Aid
Restricted to very few positions within:
President Office
Institutional Research
Students Records and Financial Services
Financial Aid Office
Development – Front End
Dash Board Design and Development –
Joint effort with Core Administration
Training testing groups in Brio
Develop first version of Brio dynamic
documents and publish via Portal – Joint
effort with Core Administration
Campus Rollout
Defining roles and responsibilities
Who will have initial access to what
Develop Roll out strategy
Setting expectations
Designing and carrying out Training
Programs
Communicate
Executive briefings
During Training
Campus orientations
Wed site
Any possible vehicle ….
Initial Tiered Access – Who will
have access to what
Cabinet; Deans;
Department Chairs;
Center Directors
Dash
Board
Department Financial Managers
Information
published
In Brio
documents
Finance Administration
Portfolio Financial Managers
Data in the Warehouse
Brio Products Overview
(Brio Intelligence)
Brio Desktop User (i.e.,
Brio Explorer, or Designer)
Data Warehouse
Connects to the DW without Portal
Brio Portal
Portal and Insight
are also available
to Desktop users
via the Web
Finance
Data Mart
Position
Control
Data Mart
Graduate
Financial
Aid
Student
Enrollment
Brio Insight User
Web
Connects to the
DW with Insight
and Portal via the
Web
Folders, Published
Documents,
Personalized Content,
Dashboards, ERD
Brio
Insight
Other Data
Marts
Each user will have separate Portal and database usernames and passwords. The
Portal login provides the user with access to published content based on a security
profile. The database login is necessary to extract data from the Data Warehouse.
Brio Portal
Allows users to
access published
documents
(e.g., BQYs, Brio
manuals,
training
documents) and
personalize their
content
Executive Dashboard Overview
Accessed via the Portal
High-level, graphical
views of Portfolio-specific
data
Designed primarily for
executive use, though
available to other users
as well
Comprised of monthly
summary data, refreshed
nightly
Dashboard Help: http://www.rpi.edu/datawarehouse/dw-help-dashboards.html
Campus Rollout Assumptions
Training is mandatory at all levels.
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Several levels of training will be offered to campu
in Brio tools, Data, and Data Policies.
Joint effort between DW Group and Core
Administration
Portfolio Financial Managers responsibilities:
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Rollout within Portfolio
Training within Portfolio
Data Warehouse Cascaded
Rollout Strategy
1. Core Administration
2. Portfolio Level
(Cabinet,
Deans, Portfolio Managers)
3. Department Level
(Directors,
Center Directors, Department Chairs,
Department Financial Managers)
4. Other
Training Methodology
Training
Required
High
Track 1
Primarily Portfolio Financial
Managers who will build adhoc queries and reports (i.e.,
Brio documents) from data
mart star schemas and meta
topics.
Brio 101
Level 1:
Data Mart
Basics
Track 2
Medium
Department Financial
Managers who will work
primarily with pre-built Brio
documents.
Brio 101
Level 1:
Portfolio/Dept-Specific PreBuilt Docs
Track 3
Low
Designed for Executive users,
this track focuses on
Dashboards and the Brio
Portal.
Dashboard & Portal training
One-on-one or small group format
Level 2:
Advanced Brio
Documents
Setting and Communicating
Expectations
Communicate to Institute Executives
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Creating an Information Revolution
Changing culture
Top down approach is needed
Recognize Barriers
Ask for commitment
Recognizing Barriers
People’s resistance to a new tool
Expectations on information availability and
usability for decision making are low
Habit of relying on Central Administration to
provide information, or on their own sources
(many versions of the ‘truth’)
People will need to acquire new job skills
Job expectations will need to change
How to get there ….
Common Vision:
One version of the truth
Data Experts across campus and across organizational
boundaries
Data Experts: Portfolio Financial Managers or Equivalents will be
expected to:
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access data
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create reports
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perform analysis
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enable/train Portfolio end-users
Training approach
Evaluating skill levels: Surveys before training
Measuring satisfaction with training
program: Overall satisfaction with program content is
very high: 91% gave the highest survey rating.
Partnering with HR – The DW training was
included in the appropriate Performance Evaluations /
Job Descriptions and course offerings
Measuring access levels – generating log files
Utilizing Web – self help, registration,
communication
Status of the Data Warehouse
Initiative
Development
Data Access Policy
Operations
Campus Rollout
DW Program Timeline
FY02
Infrastructure Planning/Staffing
Software
Database/Hardware
Production Platform
Policy
Data Policy
Datamarts
Finance/Research
Position Cntrl/Labor
Human Resources
Enrollment
Grad Financial Aid
Undergrad Fin Aid
Contracts & Grants
Admissions Pipeline
Operations Support
Software Upgrade
Database Upgrade
Hardware Growth
Req
FY03
Dev & Test
FY04
Rollout
FY05
Data Warehouse Operations
Support
Transitioning from Development to Operations
Portal Administration
Dash Board maintenance
Data Marts maintenance
Users support
Data Base Administration
Brio documents development, support, and
administration
Informatica Administration
On – Going Training
Functional Training
Brio training
Refreshers courses: Finance/Research,
Labor, HR, Enrollment, GFA, etc.
Advance curses
DW User Support levels
Signing up for sessions
Creating user profiles/security
Installs
Publishing requests
General problems/questions
5-10 Emails Daily
2-4 Calls Daily
1-2 Major problems that need extensive work
from developers/front-end technical support
on a daily basis.
Data Policy Administration
Each Data Policy is administered by the
appropriate Committee appointed at the VP
level
Requests outside the policy are submitted in
writing to the Data Warehouse group
The Committee has the discretion to either
authorize/deny access or recommend access
to the appropriate VP depending on the nature
of the request.
The respective portfolio owner are notified of
access granted.
Maintenance
DW Maintenance - DM Review
Month in Production
Role
1-3
4-6
7+
Business Staff
10%
10%
10%
Power user
60%
45%
20%
Data Warehouse Administrator
75%
50%
30%
OLAP/Reporting Tool Administrator/Developer
80%
40%
30%
Data modeler
20%
10%
5%
ETL Specialist
75%
50%
25%
Development DBA
50%
25%
10%
Operating System Administrator
20%
10%
5%
Operations
25%
25%
10%
Production DBA
20%
10%
10%
DM Review 2003 Resource Guide
RPI Resources
Role
Business Staff
Power user
IT Group
Data Warehouse Management
OLAP/Reporting Tool Administrator/Developer
1
0.5
Data modeler
0.25
ETL Specialist
3.5
DBA
0.5
Operating System Administrator
Operations (Desktop, Security set-up, etc.)
Training
Customer Support
Total
0.25
0.5
1
0.5
8
Benefits Gained
Empowers decision-makers
Redirects costly personnel hours
Enhances institutional
effectiveness
Improves integrity and conformity
of campus-wide information
Promotes the “no walls” culture.
Improves data quality over time.
Kirsten M. Volpi,
Assistant VP/Controller
“...There has been analysis that we have not been able to get
at before because the data was not retrievable in a fashion
conducive to perform analytics. For instance, we have
begun utilizing the warehouse to analyze the indirect cost
yield on our research grants. This data was not readily
available before.”
“We are also using the warehouse not only for analytics but for
reports to assist with monitoring compliance with internal
policies, assisting with data gathering for external surveys,
as well as assisting with automating certain processes
(encumbrances for graduate financial aid).”
Eileen G. McLoughlin,
Director of Financial Planning & Budget
“The Budget material was consolidated two weeks sooner than the
previous years. Many factors contributed to the success, however a
significant contributor was the data warehouse allowing the Budget
Office to provide data and analysis of the data to decision makers
faster than in the past.”
“…reinforces the “no walls” culture – i.e. as the warehouse becomes
known as the one and only data source – this will contribute towards
individuals recognizing that we are one organization with one
version of the truth.“
“…Improved quality over time, integrity, conformity – as data is
viewed and questioned issues have and will come to the surface on
processes that impact data. This has occurred in the budget office,
accounting practices have been simplified so the resultant data is
more easily interpreted”
Diane Veros,
Director Research Accounting
“The Data warehouse along with the BRIO software has
proven to be an extremely useful tool for providing
information for reporting, monitoring and analysis. BRIO
queries and pivot tables have definitely helped to make
some of our work more efficient and effective. We have
developed queries for monitoring reports, verifying data
integrity, and analysis that before would have required
days, weeks, or even months working with IACS to
program and develop. Once developed, those older
reports (and/or the data in them) would have allowed
limited access to campus, and another user might have
started from scratch to produce a similar report. The
data warehouse provides a consistent data stream that
allows all campus users to view and analyze the same
information in many alternative ways.”
Jeff Tanis,
Manager of Financial Operations
School of Science
“The time it has taken me to gather information has been
cut by at least half. I now query the warehouse--where
previously I had to initiate many e-mails and phone calls
to collect what I needed. Last month while doing a
research expenditure analysis, it took me a matter of
hours--where in the past it took days to get what I
needed.”
“While doing a research expenditure analysis last month I
identified a substantial amount of research expenditures
on other schools grants using School of Science Orgs. I
could not have identified and subsequently corrected
these errors without the use of the Data Warehouse.”
Helen Grzymala,
Associate Director Budget
“As we roll the Finance Data Mart out to all Portfolio Financial
Managers, the Budget Office will be providing more and more
reports via the Data Warehouse. Portfolios will be able to see the
various reports that are prepared on an Institutional level for the
data. We will be able to have ongoing, meaningful discussions
about the data, rather than how to get the data and how to
manipulate it.”
“The Data Warehouse will result in a change in job expectations for
both the Budget Office and the Portfolio Financial Managers. The
forecast and budget process will evolve to a more analytical review
of history and a fact-based projection of the future. Users will move
from simply ‘crunching the numbers’ because they will have more
time and because more data is actually available. Once the
Contracts and Grants information is available, the research units will
be able to track activity right from the pre-proposal stage thru the
award close out. Using this data, trending and other analysis will
follow, leading to more accurate forecasts and budgets.”
Benefits
User Name
Task Performed
Pre Data Warehouse
Implementation
Post Data Warehouse
Implementation
Sandra
Redemann
Butcher
Portfolio YTD
Analysis
Half Day to retrieve and
compile information
manually.
Seconds to retrieve
information from the Data
Warehouse.
Gina Ricci
Report Analysis
Multiple Truths existed
across campus. Multiple
information sources
existed, which destroyed
data integrity and
conformity.
One Truth exists. One
information source
promotes a common
understanding of the data
and allows users to derive
at the same conclusions.
Tanya
Struzinsky
Available
Balance Report
2 Weeks to retrieve and
compile information
manually.
30 Minutes to build the
report in the Data
Warehouse, which can be
refreshed daily in Seconds.
Donna
Tomlinson
Org 3 Year
Comparison By
Account Group
Not Readily Available
Readily Available on
demand.
Benefits
User Name
Task Performed
Pre Data Warehouse
Implementation
Post Data Warehouse
Implementation
Jeff Tanis
Research
Expenditure
Analysis
Multiple Days to retrieve
data from multiple sources
and compile information
manually.
Few Hours to retrieve
information from the Data
Warehouse.
Diane Veros Data Integrity
Verification
Days, Weeks or Months to
develop reports to ensure
data integrity or to perform
analysis.
Few Hours to develop
reports in the Data
Warehouse to ensure data
integrity or to perform
analysis.
Tanya
Struzinsky
Credit Card
Transaction
Reconciliation
2-3 Hours to compile,
review and verify credit
card transactions for each
user.
5 Minutes to retrieve,
review and verify credit
card transactions for each
user.
Sandra
Redemann
Butcher
Month-End
Report
2 Days to retrieve and
verify program and activity
codes in order to ensure
accurate results.
2 Hours to retrieve
information from the Data
Warehouse. No data
manipulation required.
Program metrics
Web access only (not including desktop or Dash Boards users)
Timeframes: January 27 – July 31
Financial Analysis Web Access
Lessons Learned
Picture worth thousand words – prototype
Funding (time, resources, and dollars)
Business Sponsorship – find the Champion and promote
them
Properly designed Organizational Structure helps to
navigate political obstacles
Partnership with the Business users – build it alone and
they will never come
Identify your Business ‘Stars’ as early as possible
JAD and RAD approaches are best fitted for the iterative
DW development
Dash Boards – unless it is visible it is not there
Building Data Warehouse is far more than a technical
endeavor it is all about changing the culture
Questions ???
Ora Fish
[email protected]
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