Data Warehousing: Changing Campus Culture
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Copyright 2005 Ora Fish RPI
Data Warehousing: Changing Campus Culture
Ora Fish, Data Warehouse Program Manager
Rensselaer Polytechnic Institute
Copyright 2005 Ora Fish RPI
Rensselaer Polytechnic Institute (RPI)
“We are the first degree granting technological university in the English-speaking world”
Research University with programs in Architecture, Arts, Engineering, Humanities, Science, and Social Sciences Rensselaer enrolls over 7,500 undergraduates, graduate, and working professionals.
Over 450 Rensselaer faculty members include National Science Foundation Presidential Faculty Fellows, members of the National Academy of Engineering, the National Academy of Sciences, and other eminent professional organizations.
Founded in 1824 by Stephen Van Rensselaer
Copyright 2005 Ora Fish RPI
Fundamental Problem
Operational systems are not designed for information retrieval and analytical processing
Copyright 2005 Ora Fish RPI
History of DW at Rensselaer Fall 1998- Summer 2001: Looking for solution Fall 2001: Budgets are approved Fall 2001 - Jan 2002: Building infrastructure Jan 2002 – today: Delivering Enterprise Wide Warehouse
with the following areas: Finance Positions Human Resources Student Enrollment Admissions Graduate Financial Aid Undergraduate Financial Aid Research (pre award, post award) Institute Advancement (in progress)
Copyright 2005 Ora Fish RPI
Data Warehouse group
Part of the Administrative Computing within the Division of Chief Information Office
Total of eight employees Responsible for addressing campus
reporting and analytical needs http://www.rpi.edu/datawarehouse/
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Our constituency
Administrative leadership: President, VP of Finance, VP of Student Life, Provost, Dean for Graduate Admissions, Controller, Registrar, Dean of Enrollment, VP of Research, AVP of Budgets, etc.
Academic leadership: Deans and Department Chairpersons, Research Center Directors
Core Administration: Institutional Researcher, Director of Budgets, Director of Enrollment, Registrar, Director of Research Administration, etc.
Core Administration Personal: responsible for carrying out centralized functions such as registration, admissions, payroll, etc.
Campus Administrative Personal - Graduate Coordinator’s Assistant, Business managers across campus, Coaches, etc.
Faculty
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Viewpoint
Regardless of how well designed our star schemas are or how well the dimensions are conformed, to be effective in addressing campus decision support and analytical needs the Data Warehouse should be viewed as a service addressing information quality and campus culture
Copyright 2005 Ora Fish RPI
Viewpoint
The true benefits can be achieved only when the new technology is adapted and becomes part of our business routine:
Penetration takes time Brings transformational changes to
Processes and Culture
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Successful Data Warehouse implementation
Clear set of Goals and Objectives Sponsorship Budgeted Dedicated staff Strong alliance between IT and Business Implemented as a Service Proved implementation methodology Addresses Information Quality Serve as a catalyst for change
Copyright 2005 Ora Fish RPI
The Fundamental Goal
The fundamental goal of the Rensselaer Data Warehouse Initiative is to integrate administrative data into a consistent information resource that supports planning, forecasting, and decision-making processes at Rensselaer.
Copyright 2005 Ora Fish RPI
Data Warehouse Objectives
Serve as an information hub for Administration as well as the Academic Schools
Transform Data into Information with embedded business definitions
Informative - Meta Data Intuitive for end user to perform ad-hoc
queries and analysis Adequate response time - Retrieved within
seconds
Copyright 2005 Ora Fish RPI
Business Sponsorship
Lack of Business SponsorshipPrototype
Shop around and identify area where it ‘hurts’ Build a prototype and invite vendors to participate Market to the business side
Engage and build awareness Facilitate a visit to the peer institution Invite peer institution to your campus
Be aware of offering temporary solutions Costly in a long run Will have dissatisfied customers
Wait for leadership to change
Copyright 2005 Ora Fish RPI
Lack of IT Sponsorship
Typical reasons are: Lacking knowledge and/or expertise, Do not have necessary resources; Not enough demand or pressure from the top
Possible steps: Secure funding Bring in outside help with knowledge transfer Build Prototype as a joint venture Engage and Build awareness Emphasize partnership Engage Leadership (Business Sponsor) in
setting IT priorities
Copyright 2005 Ora Fish RPI
Budget
Budget is the true indication of sponsorship support and priority
Hardware and software for Production, Test, and Training environment
Data base servers Data base licenses ETL Front-end Personnel Education and travel Consulting services Contingency
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Dedicated Staff
Need dedicated personnel to carry out the following functions Project Manager/Champion DBA Modeler ETL developers Front end developers Software administration and installation Desktop support Customer support Campus training Business staff and Power user
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Alignment between the IT and the Business in DW implementation
Technology
Alignment
Business
CampusCulture
Information QualityArchitecture
Copyright 2005 Ora Fish RPI
Information Quality
Accurate, Reliable, Consistent, Relevant
Re-enforce common definitions Set up processes to identify and clean
erroneous data Set up processes to gather relevant data Define policies on who will have access
to what information
Copyright 2005 Ora Fish RPI
CultureFrom Transaction Processing Environment to Decision Support Environment
The goal is to build analytical culture that values and promotes usage of information in decision making
Copyright 2005 Ora Fish RPI
CultureFrom Transaction Processing Environment to Decision Support Environment
Promotes fact based decisions where value is placed on decisions made through usage of information vs. supply of data
Lowers the walls across organizational boundaries and promotes understanding of the business enterprise across different functional areas
Analytical culture requires different set of skills
Copyright 2005 Ora Fish RPI
Our Approach
The approach to addressing campus informational needs can not be:
A Project A Product
It is a service
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Implementing Data Warehouse
Build Technical Architecture Establish Services in support of campus
community Build Processes ensuring Data Quality Work with campus Leadership on
addressing campus analytical culture
Copyright 2005 Ora Fish RPI
Methodology Addresses long term solution Enterprise wide integrated data warehouse vs.
Departmental data mart Use methodology with proven success i.e. learn from
others Overall long term planning with short time to delivery Has to include all aspects of DW implementation
Architecture addressing transformations, meta data, security, delivery
Campus rollout and training Information Quality Communication Support
Copyright 2005 Ora Fish RPI
Implementation Methodology
Build DW Foundation
DevelopSubject Oriented
Data Marts
Release Data Martto the Campus
Next Data MartRelease Data Mart
To the Core AdministrationData stewards
Maintenance and Support
Campus Communication
Continuing Adaptation and Growth……
Training
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DataWarehouse
SourceDatabase
SourceDatabase
OtherSources
(e.g. files,spreadsheets)
ETL
DataMart
Data Cube
Web ClientInterfaces
ApplicationServers
OperationalData Store
DesktopInterfaces
• operational systems• transactional systems
• extraction• transformation• modeling• loading
• central repository• subject-based data marts•Conformed dimensions• metadata
• user-facing applications
• business intelligence• decision-support• OLAP• querying• reporting
Decision SupportServers
Metadata
DATA SOURCES DATA ACQUISITION DATA WAREHOUSE DATA DELIVERY DATA CONSUMPTION
Technical Architecture
Copyright 2005 Ora Fish RPI
Building DW Foundation - Technical Architecture Inventory
ERP – Banner from SCT ETL – Power Center from Informatica Data Base – Oracle 9i Models – Star schemas with conformed
dimensions Web Front end tools – Hyperion
Performance Management (Brio), Dash Boards
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Building DW Foundation – Data Security, Privacy and Access Policy
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
Key oversight principle: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
Security& Privacy Access
& Use
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Building Subject Oriented Data Marts
Determining Constituency Forming Implementation
Group Conducting interviews Defining Scope and
Timelines Modeling Extracting, Transforming,
and Loading Data Develop Security system Testing
Identify information gaps Identify erroneous data Reinforce common definitions Establish processes to
identify and clean erroneous data
Establish processes to capture missing data
Develop and approve Data Security Policy
Record Meta Data – stored in Informatica repository and accessed with Brio
Alignment between the Technology and Information Quality
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Catalyst of Change
Requires marketing and PR Communications Cheerleading Support at the Executive levels Lead by individual respected by all Offering campus training programs “Carrots and sticks” Re-examine existing processes: (month-
end reporting)
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Rollout
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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
Copyright 2005 Ora Fish RPI
Developing Common Vision
One version of the truth – Warehoused Information was recognized as the only official source of data
Data Experts across campus and across organizational boundaries
Partnering with Human Resources – The DW training was included in Performance Evaluations and Job Descriptions
Training is mandatory at all levels
Copyright 2005 Ora Fish RPI
Communication and Buy-into
Executive briefings: Emphasized changes in analytical culture Recognized Barriers Emphasized that top down approach is needed and ask
for commitment Demonstrated new capabilities via Dash Boards Demonstrated ad-hoc capabilities people within their
organization have Campus orientations
Demonstrated analytical capabilities Introduced training programs and the rollout strategy Communicated Data Policies
Wed site
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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. Faculty
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Data Mart Release to the Core Administration
Utilizing Data Mart for internal operations More changes to the Data Mart are expected
Establishing data cleanups queries and procedures
Preparing for Campus release: Developing campus training program:
Developing and publishing Dash Boards, and Brio dynamic documents
Developing operational training
Information Quality
Impacting Culture
Copyright 2005 Ora Fish RPI
Initial Tiered Access – Who will have access to what
Dash Board
Analysis performed on the pre-published dynamic
documents
Ad-Hoc capabilities retrieving information from the Data Warehouse
Cabinet; Deans; Department Chairs; Center Directors
Core AdministrationPortfolio/Division level
Department level
Trai
ning
Hig
h
Low
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Copyright 2005 Ora Fish RPI
Common Usage
Dash Boards
Simple click away access to the most common topics for analysis
Pre build dynamic queries
Build to address specific needs for information
Ad-Hoc
Meta Topics and published Stars
Ad-Hoc functionality within specific topic
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Training Mix
Brio 101 Basic navigation and
mechanics Brio 201
Advanced analytics and reports
Data Training Data mart basics, BQYs,
and star schemas Operational Training
Focuses on practical applications , delivered by business owners
Study Halls Informal, open agenda
Best Practices Demonstration of best
practices, delivered by business owners
One-on-Ones Used to address
specific reporting/analytical needs
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Training Program Overview
Track 2
Track 3
Track 1
Brio 101
Brio 101Level 1:
Portfolio/Dept-Specific Pre-Built Docs
Level 1: Data Mart
Basics
Level 2: Advanced
Brio Documents
Dashboard & Portal trainingOne-on-one or small group format
Operational Training
Ongoing Follow-up
High
Medium
Low
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Training Philosophy
The goal of the training program goes beyond teaching the mechanics: Need to sell the Brio tool and the project Need to educate on the benefits of the DW Need to emphasize that Banner and the DW
are complementary systems, i.e.,
Need to continue and inspire!We are changing our analytical
culture!
Copyright 2005 Ora Fish RPI
Addressing Information Quality
Establishing processes to capture erroneous and inconsistent data ETL process to identify errors Rejecting data Load data and clearly label errors
Data Audit processes Ensuring that the loaded data reconciles back to
the operational systems
Data
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Addressing Information Quality
Establishing Data Stewards roles and responsibilities
The overall data integrity and conformity by instilling business practices and procedures to identify and correct erroneous and inconsistent data recorded in ERP systems
Ensuring that Meta-data is up-to-date Operational Training in information applicability and usage Establishing processes to capture and maintain data necessary
to support decisions Enforcing Common Definitions by facilitating agreement across
organizational boundaries
Data
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Establishing services and support
Assessments of information needs Expansion and enhancement of Warehoused
Information Expansion and enhancement of Information
Delivery solutions Process re-engineering Monitoring data quality Support Assessment, Planning, and Analysis Offering full spectrum of campus training
programs
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Establishing services and support
Transitioning from Development to Operations Front-End (Hyperion Performance Suite)
Administration ETL (Power Center) Administration Desktop Support and Administration Data Base Administration Dash Board maintenance Brio documents development, support, and
administration Customer Support
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Catalyst of Change
Processes and Culture
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Changes in our Processes
Some examples on utilization of the warehoused information in our operations:
Assessment and Planning Enrollment Planning Committee meeting utilizes the
enrollment and the admission data in setting the enrollment targets and financial aid goals as they discuss the incoming class (how we did, quality, numbers, diversity, etc)
Retention analysis – analyzing the admissions data to better understand how well the incoming class may be retained next year
Assessment of Employee retention Assessment of Faculty renewal program
Copyright 2005 Ora Fish RPI
Changes in our Processes
Forecasting: Forecast current year sponsor research expenditures. Forecast graduate financial aid commitments Utilize past enrollment, retention, and financial aid information to forecast current and
future year financial aid commitments to determine the affordability of various discount rates
More accurately forecast research awards Utilizing historical research ‘success rates’ in projecting cost sharing commitments
Monitoring and compliance: Daily monitoring of budgets and expenditures from higher levels down to the specifics Monitor and review project to date budgets Monitoring positions budgets vs. actuals and in conjunction with estimated future earnings
are accurately projecting balances Monitoring the allocation of graduate financial aid
Operations Financial information is used in preparing and analyzing the financial statements,
reconciling between the sub-ledger and general ledger, reviewing payroll allocations Credit card reconciliation
Copyright 2005 Ora Fish RPI
Cultural Changes
Empowers decision-makers: Getting accustomed to information availability
Promotes the “no walls” culture: Performing analysis that could never been done before
From ‘MY Data’ to ‘Our Information’ Data Stewards role in improving data quality,
integrity, and conformity Fact based decision making How do we now redirect these costly
personnel hours Enhanced institutional effectiveness
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Assessing Data Warehouse
Penetration and Adoption Number of users trained and their role in
organization Number of distinct users connected monthly Number of monthly connection Requests for changes and enhancements Satisfaction surveysValue Shifting IT resources from reporting to other value
added activities Productivity savings on the business side Savings realized by better more informed access to
information
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The Dreaded Return on Investment
Calculating ROI Savings in personnel and processing More Effective Financial Aid packaging Effective recruitment strategies Identification of retention issues to
target More fiscal responsiveness
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Benefits
Fosters data integrity and conformity One version of the truth Helps to identify erroneous and
inconsistent data Establishing ‘data cleanup’ proceduresValue shifts from data supplier to
analysisTestimonials
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What’s Next
Cultural shifts: Are we Higher Education and non for profit or Business?
Performance planning processes and assessments
Cultural shifts towards developing Goals, Objectives, measuring outcomes
KPI, Scorecards, Metrics
Administrative Leadership
Academic Leadership
Faculty Operational users
Business Analysts
Pre populated Generic Campus Wide Dash Boards
Pre populated Specific Dash Boards
Research financials Dash Board
Pre build dynamic queries;Meta Topics
Ad-Hoc
As a single source with common definitions, the Data Warehouse is a solid foundation for Scorecards and
KPI
KPI Scorecards Planning and Assessment
KPI, Scorecards Planning and Assessment
Simple Budgeting Planning
Exceptions Alerts Notification
Advance Analytics AssessmentVisualizationMining
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Informational Resources
The data warehouse toolkit (Ralph Kimball)
The data warehouse lifecycle toolkit (Ralph Kimball)
Data warehouse design solutions (Christopher Adamson & Michael Venerable)
Copyright 2005 Ora Fish RPI
Informational Resources
Become a member of the data warehouse institute
Visit http://www.datawarehousing.com maintained by DataMirror
Subscribe to listserv from EDUCAUSE http://www.educause.edu/memdir/cg/cg.Html
Visit other schools web sites via http://www.Georgetown.edu/users/allanr/dwconfig/
Copyright 2005 Ora Fish RPI
Questions ???
Ora Fish
fisho2@rpi.edu
?
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