dwdw dwwgrespons-20070529b-1

7/28/2019 dwdw DWWGRespons-20070529b-1

1/24

A Roadmap for

Data Warehouse,

Reporting and Analyticsat Georgetown

A Response to the Report of the Data

Warehouse Working GroupRon Allan, Dave Lambert, Matt McNally, Piet

Niederhausen


2/24

The growing importance of data in the life of theinstitution

Introduction to key concepts ..developing a common vocabulary

Some important issues facing forward

What can we learn from the experience of others

Addressing some short term tools issues

A proposed roadmap forward

A discussion about resources

Overview


3/24

Increasingly our executive leaders are demanding informationand analysis to support strategic decisions.

To assure efficiency and competitiveness of our day-to-day

operations, managers and directors require a constant flow ofreliable information.

Hardly a month passes that a new reporting requirement isntimposed from an external source.

..and even more are on the way.

Georgetown, like the rest of higher education, is being hit by aperfect storm of data issues.

But

Building the information-driven

university


4/24

Complexity of cataloging all data fields

Complexity of authorization: the Who sees what?

Complexity of access: the How do I see what?

No single management or reporting tool meets therange of information access needs

Regulatory issues: FERPA, HIPAA & G-L-B

Data security

Data accuracy issues: Sarbanes-Oxley

Confusing concepts and vocabulary

There are a multitude of

complicating issues


5/24

Common concepts and

vocabulary From the initiation of our first efforts in building data

warehouses, we have been hampered by a confusion inconcepts and vocabulary.

We are not alone in that regard There are still major disagreements among thought leaders (including

academics)

Vendors have unleashed an array of products with conflicting namesand capabilities.

One vendors warehouse is anothers ODS. There is every evidence that confusion will get muddier rather than

clearer with the accelerating consolidation in the market.


6/24

Reporting and analytics

Business systemsLook up and edit individual records.

Execute tasks and record transactions.

Operational data storesAccess data from one business domain.

Create production reports.

Integrated data environment (e.g., EDW)Correlate data across systems.

Report on historical data over time.

Create university-level reports.

AnalyticsCreate metrics based on institutional goals

Monitor performance using dashboards.

Conduct institutional research.


7/24

Business systems

Business systems contain transactional dataupdated by staff and end-users via self-service.

Access control is fine-grained (down torecords and fields).

Detailed knowledge of a system is required

to manage its data. How data is collected and stored affects the

ability to do reporting later.


8/24

Operational Data Stores

Each data store contains a copy at a point-in-time of transactional data from a

business domain, updated periodically.

Data stores are primarily used forproduction, line-of-business reporting.

Access control is modeled on business

systems, managed by database notreporting tools.

Domain knowledge and reporting skills arerequired to create reports.


9/24

Integrated data environment

Contains data extracted from many business domains, storedover time.

Some data is transformed to make key data consistent acrossdomains.

Multiple reporting tools should be supported.

The same access controls should be enforced across all

reporting tools. Access control is inherently less fine-grained than

underlying business systems to enable broader vision.

Reporting is constrained by how the data was originallycollected and stored.

Extraction,

transformation,

and load

(ETL)

Consistent

access

controls

Reporting

tools

Data stores Integrated data

environment


10/24

Analytics

Goals must be defined to establish measurableperformance metrics and research questions.

An analytics environment provides dashboardsthat show current status compared with a defined

goal. An analytics environment also provides the ability

to run hypothetical scenarios and projections forinstitutional research.

Institutional,

program, and

project goals

Performance

metrics

Research

questions

SVP Provost VP Dean

Dashboards

Analytics Scenarios Projections


11/24

Differentiators

Intended for Type of data Access to data

Required

expertise

Business

systems

Staff and

administrators

Individual

records and

transactions

Fine-grained

access controls

Detailed

knowledge of

base system

Operational

data stores

Staff and

administrators

Production data

from one

domain

Domain specific

fine-to-medium-

grained access

controls

Domain

knowledge and

reporting skills

Integrated

dataenvironment

Administrators

and executives

Data across

systems, overtime

Access by

domain orbusiness entity

Domain

knowledge

Analytics Executives and

institutional

research

Metrics and

trends over

time

Broad, high-

level access

High-level

domain

knowledge


12/24

Overview

Business systems

Operational data

stores

Integrated data

environment

Analytics

Student

Advance-

ment

Human

Resources

Endowment

Mgt

Financials

Research

Mgt

Benefits

Space/

Facilities

Faculty

Service

Mgt

Student

Advance-

ment

Human

Resources

Endow-

ment Mgt

Financials

Research

Mgt

Benefits

Space/

Facilities

Faculty

Service

Mgt

Reporting

tools

Institutional,

program, and

project goals

Performance

metrics

Research

questions

Dashboards

Analytics

Data access

policy

Data

administration

Data

governance


13/24

Status

Business systems

Operational Data

Stores

Integrated data

environment

Analytics

Student

Advance-

ment

Human

Resources

Endowment

Mgt

Financials

Research

Mgt

Benefits

Space/

Facilities

Faculty

Service

Mgt

Student

Advance-

ment

Human

Resources

Endow-

ment Mgt

Financials

Research

Mgt

Benefits

Space/

Facilities

Faculty

Service

Mgt

Reporting

tools

Institutional,

program, and

project goals

Performance

metrics

Research

questions

Dashboards

Analytics

Data access

policy

Data

administration

Data

governance


14/24

Issues

Establishing a culture and organizational focal point for information-based analytics in a consensus system

Performance analysis

Institutional research

Assuring current and future systems support DW/Analytics. The Financial system is a particular focus

In reference to the model, the max above is constrained by the minbelow

Establish mechanism(s) to address:

Data access policy

Data governance

Data administration

Building a long-term funding strategy

Some foundational elements are in the investment plan

Missing the dedicated resources per application area

In both UIS and Functional areas.


15/24

Experience of Peer Institutions

MIT

George Washington (GW)

University of Pennsylvania (Penn)

Yale


16/24

Sample of Peers

MIT Penn GWU Yale

Data

Governance

No

Comprehensive

Approach

No

Comprehensive

Approach

No

Comprehensive

Approach

No

Comprehensive

Approach

DataAdministration

Tradit. Aspects(catalog,

definition

Covered

DesignatedData

Administrator

No DesignatedData

Administrator

Conducted byBusiness

Service Data

Users

Data

Warehouse

Best developed

in Higher Ed

Kimball

Well Developed

Moving from

Inmon toKimball

Well Developed

Kimball

Sporadically

Developed

Adhoc

Analytics/

Dashboards

Dashboards

Extent in Non-

Executive

Community

Dashboard

Analyst

Assigned

Board has 1

Dashboard

Managers have

Dashboards

Executives Do

Not


17/24

Sample of Peers

MIT Penn GWU Yale

Data

Ownership

Not an Issue:

Trustees respn

for all aspects

of data

Institution Owns

the Data

Data Owned by

Relevant

Individual

Data Owned by

Relevant

Business

Manager

Data Access Granted byTrustee

Granted byData Steward

Granted byData Owner

Granted byData Owning

Departments

Staffing 7 FTEs in

Central

Technology Org

11 FTEs in

Central

Technology Org

5 FTEs 12 FTEs in

Central

Technology Org

Funding

Model

Formal Part of

IT Budget

Ad Hoc:

Overhead

funding to date

Combination of

formal and

project funding

Formal Budget

Process


18/24

Conclusions from peer analysis

Peer institution beset by the same demands andchallenges.

Many have launched data-related initiatives similar tothose at GU.

Most have made more progress on dataadministration and access policy.

Most have moved further up the integrated DWlayer..

..but with selected data

We could not find a really good exemplar of an R1

university that has a comprehensive solution


19/24

Status of our current toolset

ETL tool: Informatica

Top of the line

Query Tool: Cognos Version out of date

Web reports successful

Security is administered in Cognos rather than underlyingdatabases

This is a significant constraint

We have no standard tool for Analytics at this point

SAS is often used for statistical processing

Nothing available for true analytics


20/24

Open tools issues

ETL tool: Stay with Informatica or move to OraclesWarehouse Builder

This is Banners tool

Query Tool: Cognos

Given the market is in flux we can:

Stand pat for awhile (3 years?)

This is what SunGardHE decided to do

Would require upgrade to latest release

Tool choice for analytics

SAS is the industry leader, but there are others we shouldlook at:

Hyperion, Banner Analytics, etc.


21/24

Addressing tools issues

Establish Analytics (business intelligence)tools evaluation committee.

Make recommendations on ODS and IDE tools:Investigate Analytics tools options.

Lay the ground work for next generation querytool choice

As market stabilizes.

Members from UIS and user communities.


22/24

Proposed Calendar (1)

Replace SIS with Banner

Define FMS then Replace

Stabilize & Enhance Space

Migrate HRIS off Mainframe

2007 2008 2009 2010 2011Business Systems Context

Review interim ETL and reporting tools

Establish Secure Reporting Environment

Deploy 2nd Generation ODSs for

Student, Financials, HR, and Space

2007 2008 2009 2010 2011Refresh or Build ODSs


23/24

Proposed Calendar (2)

Establish Data Administrator

Build and Hire Data Admin team

Develop 1st generation data inventory

Establish Data Governance committees

Develop Data Access policies

2007 2008 2009 2010Data Administration

Develop 1st generation IDEs

Develop 1st generation Analytics

Integrated data environment & Analytics

Hire IA and Reporting Vacancies

UIS and Business Division Staffing


24/24

Organizational Model

Data Access, Analytics, Self Service, Enterprise Web

Associate Director,

Information Access

Web & Data

Architect

BusinessAnalyst

Data

Engineer

Data

Engineer

Application

Programmer

DataAdministrator

Application

Programmer

Technical Manager,

Information Access

Application

Programmer

Director, EETS

DBA

AVP, NCS

DBAsApplications, Managers

Financials, HR, Space, Student

Application

Teams

Data Access

Policy

Working

Group

Data

Governance

Committee

Business

Reporting

Analyst

Business

Reporting

Analyst

Business

Reporting

Analyst

Business

Reporting

Analyst

Business

Reporting

Analyst

Business

Reporting

Analyst

Business Divisions

Business Divisions

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