Successful Data Warehouse Implementation Brings Changes to Campus Culture, Processes,

and People

Ora Fish

Rensselaer Polytechnic Institute

Agenda

Alignment between the Information Technology and the Business

Development Methodology Lessons LearnedDemonstration Q & A

RensselaerPolytechnic Institute

(RPI)• “We are the first degree granting technological

university in the English-speaking world”• Research University• Total Students 8,265

– Graduates: 1,378– Undergraduates: 5,164– Education for Working Professionals: 1,723

• Faculty - 450

Founded in 1824 by Stephen Van Rensselaer

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/

Fundamental Problem

Operational systems are not designed for information retrieval and analytical processing

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.

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

Implementing Data Warehouse

Technology

Alignment

Business

CampusCulture

Information Quality

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

Culture

• Promotes fact based decisions

• Requires lowering the walls across organizational boundaries

• Understanding the business enterprise across different functional areas

• Analytical culture requires different set of skills

Before, During, and After the implementation

How does the IT leads and effectively aligns Technology, Information Quality, and Campus Culture before, during, and after the Data Warehouse implementation

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

Adaptation and Growth

Training

DW Program Timeline

Infrastructure Planning/StaffingSoftwareDatabase/HardwareProduction Platform

Policy Data PolicyDatamarts Finance/Research Req

Position Cntrl/LaborHuman ResourcesEnrollmentGrad Financial AidUndergrad Fin AidAdmissionsContracts & GrantsUG Prospect Fin Aid PI/Fund Info AccessAlumni/Advancemnt

Operations SupportSoftware UpgradeDatabase UpgradeHardware Growth

RolloutDev & Test

FY02 FY03 FY04 FY05

Building DW Foundation

• Organizational Structure

• Project framework and high level plan

• Building Technical Infrastructure

• Develop Data Policies and Procedures

Project Organizational Structure

Sponsorship Group(Business & IT)

Steering Committee(Business & IT)

Implementation groups(Business & IT)

Approves Next Areas

Forming Implementation groups; Defining scope and deliverables

Data Warehouse Group(IT)

Implementation issues

Alignment between the IT and the Business

High Level Analysis and Prioritization process

CONSTITUENCIES

BUSINESS PROCESSES

Enrollment Analysis X X X X X X X X

Student Pipeline Analysis X X X X X

Faculty Workload Assessment X X X X X X

Financial Analysis X X X X

Contract and Grants Analysis X X X X X

Proposal Pipeline Analysis X X X X

Financial Analysis - Research X X X X X X

Graduate Financial Aid X X X

Alumni Demographics and Tracking X X X X X X

Alumni Contact Management X X

Human Resources X X X X X

Facilities Management X X X

Prioritization Process

Value toRensselaer(Culture)

Feasibility (determined by Data Quality and availability)

HighLow

High

FM

HR

PP

GF

AC

FW

FR SP

AD

EA

FA

CG

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 – Brio, Dash Boards

• Desktop Front End tools – Brio, Excel

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

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, Information Quality, and Campus Culture

Modeling: Kimball’s Bus Architecture to Subject oriented Data Marts and the Conformed Dimensions

Student Enrollment Model – one row per enrolled student per term

Student EnrollmentSnapshot

Student Dimension

Class Dimension

Student countMatriculated countCredit Hours RegisteredCredit Hours AttemptedCredit Hours EarnedOverall GPATerm GPATuition Amt ChargedTuition Fees ChargedTuition Amount BilledTuition Fees Billed

Student Key

Academic Term KeyPrim Program Major Grp KeySec Program major Grp KeyStudent keyStudent Cohort KeyClass KeyStudent Faculty Advisor Key

Class KeyAcademic ProgramBridge Dimension

Student AcademicProgram Key

Academic TermDimension

Academic Term Key

Student FacultyAdvisor Bridge

Student Faculty AdvisorKey

Student CohortBridge

Student Cohort Key

Faculty Advisor Key

Cohort Key

Student Enrollment Snapshot

Highlights of the Development – ETL Process

In addition to the data staging and development processes:

• Develop Data Quality Assurance Processes• Ensure transformations are captured• Capture data at the lowest level – no one ‘trusts’

statistics only without the supportive details• Initial and Incremental Loads

Development - Securing Data Marts

• Working with each portfolio, the IT role was to ensure that the subject oriented Data Policy is: Defined, Approved, Technically feasible, and Consistent Across The Board

• Build Security Front End application• Security Options:

– Securing schemas– Securing facts only– Securing dimensions only– Securing both facts and dimensions

Securing Facts Only

Financial Transaction FactOrg KeyFund KeyAcct KeyTime Key------------------------$

Time DimTime KeyCalendar YearCalendar monthCalendar dayDateFiscal yearFiscal Period

Account DimAcct KeyAcct codeAcct DescriptionAcct TypeVarious IndicatorsVarious attributes

Organization DimOrg KeyOrg codeOrg DescriptionOrg Financial ManagerTypeVarious IndicatorsVarious attributes

Fund DimFund KeyFund codeFund DescriptionFund Financial ManagerTypeVarious IndicatorsVarious attributes

Securing Only Identifiable Information

Student Enrollment Fact------------------------CountMatriculated CountHours RegisteredHours AttemptedCredit Hours EarnedTerm GPAOverall GPATuition PaidEtc….

Time DimTime KeyCalendar YearCalendar monthCalendar dayDateAcademic TermAcademic YearSnapshot Type

Student DimStd KeyNameDemographicsGeographic InfoMinority IndCitizenship reporting IndVarious IndicatorsVarious attributes

Student Advisor BridgeAdvisor KeyNameWeight FactorTypeRankDiscipline attributesTitleSchoolDepartmentVarious attributes

Academic Program DimAcademic Prg KeyTypeClassificationMajor/Minor/Concentration TypeMajor/Minor/Concentration Desc.CampusSchoolDepartmentOfficial Headcount IndicatorWeight FactorVarious attributes

Class DimClass KeyClass codeClass Descr

Managing Testing Sessions

• Allocating time slots• Focused – aiming to produce existing reports

and Queries• Verifying that the models do address the need• Opportunity to create more definitions,

groupings, and transformations• Great opportunity to bridge diverse groups• Further Enforce Common Definitions • Further Identify Information Gaps

Alignment between the Technology, Information Quality, and Campus Culture

Campus Communications During Testing Period

Meeting one-on-one with Campus executives (Cabinet, Deans, etc.)

– Getting feedback early on

– Engaging

– Marketing

Data Mart Release to the Core Administration/Data Steward

• 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

Campus Rollout

• Developing Roll-Out strategy

• Defining roles and responsibilities

• Defining initial access level

• Recognize barriers and Setting expectations

• Designing Training Programs

• Communicate to the Campus

Alignment between the Technology, Information Quality, and Campus Culture

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

Initial Tiered Access – Who will have access to what

Dash Board

Informationpublished

In Brio documents

Data in the Warehouse

Cabinet; Deans; Department Chairs; Center Directors

Core AdministrationPortfolio/Division level

Department level

Trai

ning

Hig

h

Low

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

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

Communication

• 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

– Demonstration were carried out by the original testing group– Introduced training programs and the rollout strategy– Communicated Data Policies

• Wed site

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

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

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!

Adaptation and Growth

Adaptation and Growth

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

Adaptation and GrowthChanges 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

Adaptation and Growth 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

Adaptation and GrowthCultural Changes

• Empowers decision-makers: getting accustomed to information availability

• Promotes the “no walls” culture• From ‘MY Data’ to ‘Our Information’ - Data

Stewards role in improving data quality, integrity, and conformity

• Fact based decision making• Redirecting costly personnel hours • Enhancing institutional effectiveness

Lessons Learned

• Building Data Warehouse is far more than a technical endeavor: the alignment between the right technology, information quality, and campus culture has to be addressed before, during, and after the data warehouse implementation through planning, development, testing, rollout, training, and adaptation stages

• Business Sponsorship – is a must

Lessons Learned

• 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

Data Warehouse development• Dash Boards – unless it is visible it is not there

Questions ???

Ora Fish

fisho2@rpi.edu

?