What is Management Reporting from a Data Warehouse and What Does It Have to Do with Institutional Research?

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Full text

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What is Management Reporting from a

Data Warehouse and What Does It

Have to Do with Institutional Research?

Emily Thomas

Stony Brook University

AIRPO Winter Workshop January 2006

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Data to Information

Historically

Standard paper reports

Systems Special reports written by programmers of

record A few direct users IR extracts

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Data to Information

Current Technology

Data Many

On-line warehouse self-service

transaction users

processing Institutional

systems reporting including

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Hypothesis

• New information technologies are creating greater

demand for information and the need for data warehouses.

• Building a data warehouse requires transforming raw data

into reporting measures and categories.

• Meeting the demand for information requires creating

useful reports and report templates.

• Institutional researchers are experts at displaying

information and constructing reporting variables.

• Therefore participating in the development of institutional

reporting programs is a new role for institutional research.

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Questions

• What is a data warehouse?

• What kinds of reporting does higher

education do with warehouse data?

• What roles are institutional researchers

playing in the development of institutional reporting programs?

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What is a data warehouse?

A data warehouse is a subject oriented,

integrated, non-volatile, and time variant

collection of data in support of management’s decisi`ons.

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What is a data warehouse?

A data warehouse is a subject oriented, integrated, non-volatile, and time variant

collection of data to describe an organization’s activities and support of management’s

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What is a data warehouse?

A data warehouse is a subject oriented, integrated, non-volatile, and time variant collection of derived data to describe an organization’s activities and support of management’s decisions.

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What is a data warehouse?

A data warehouse is a subject oriented, integrated, non-volatile, and time variant

collection of derived data that are managed and institutionally recognized as a shared data

resource used to describe an organization’s activities and support of management’s

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What is a data warehouse?

A data warehouse is a subject oriented, integrated, non-volatile, and time variant

collection of data in support of management’s decisions.

(W.H. Inmon, Building the Data Warehouse)

A data warehouse is a subject oriented, integrated, non-volatile, and time variant

collection of derived data that are managed and institutionally recognized as a shared data

resource used to describe an organization’s

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“There is no question that the power user is the most important person in the corporation in

regard to establishment of the data warehouse and the unleashing of the power of informational processing.” (Inmon 1994,116)

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Reporting Categories

• Operations monitoring: Which students are ready

to be cleared for graduation?

• Operations analysis: Which students were affected by an error in processing graduation clearance?

• Management reporting: How many students

graduated in each major in each of the last five years?

• Management analysis: Did the graduation GPA of

recent graduates vary with whether they entered as freshmen or transfer students?

• Analytics: Did a new freshman program improve

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Star Schema Data Model

Instructor Dimension Student Dimension

Instructor ID Student ID

Name Fact Table Name

Department Student ID Class

Title Course ID Major

Full/Part-Time Instructor ID

Location ID Course Dimension Location Dimension Term Course ID

Location ID Credits Course Title

Room Grade Course Department

Building Gen Ed Indicator

Example:

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Reporting Matrix

Operations monitoring Operations analysis Management reporting Management Analysis Analytics Detail or aggregates Ad hoc or recurring Who does it?

Output format Distribution Tools User skills Data timing Data sources Data access

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Reporting Matrix:

Contents and Repetition

Contents

Repetition Recurring Ad hoc Recurring Ad hoc Detail to support action on

individual students

Aggregate data to describe performance or trends

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Reporting Matrix:

Reporting Contents and Repetition

Reporting category Operations monitoring Operations analysis Management reporting Management Analysis Contents

Repetition Recurring Ad hoc Recurring Ad hoc Detail to support action on

individual students

Aggregate data to describe performance or trends

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Reporting Matrix:

Who Does the Reporting?

Reporting category Operations monitoring Operations analysis Management reporting Management Analysis Analytics

Objective Support for

conclusions Question type Recurring Ad hoc Recurring

Typical reporters Functional area staff Functional area technical experts Core management such as dept. chairs Management analysts Institutional researchers Ad hoc

Detail to support action on individual students

Aggregate data to describe performance or

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Reporting Matrix:

Typical Output, Distribution, Skills, Tools

Reporting category Operations monitoring Operations analysis Management reporting Management Analysis Analytics

Typical output lists/counts lists/counts tables/graphs tables/graphs tables/graphs

statistics

Distribution system output system output paper report/

web paper report

text document Tools pre-program m ed que rie s s tandard re ports OLAP SQL re porting s oftw are analytic s oftw are s tandard re ports OLAP das hboards re porting softw are s preadshe e ts re porting s oftw are analytic s oftw are User skill Low us e of pre -program m e d re ports /queries / cubes High

us e raw data via SQL or s im ilar

extraction

Low

eas y inform ation acces s

Mode rate

m anipulation of raw data w ith a

re porting tool

Ve ry high

s tatistical analys is and

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Reporting Matrix:

Data Timing, Data Sources and User Access

Reporting category Operations monitoring Operations analysis Management reporting Management Analysis Analytics Data timing real time/

daily extract real time/ daily extract snapshots/ longitudinal data snapshots/ longitudinal data snapshots/ longitudinal data Data sources transaction system/ warehouse transaction system/ warehouse cubes from warehouse or data marts warehouse/ data marts warehouse/ longitudinal data marts

Data access all or

restricted veiw all

all or restricted view

all or

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Trends

• Information culture and data availability

generate increased demand for information.

• Web-based report delivery and user-friendly

tools facilitate self-service reporting.

• Increased reporting generates interest in

institution-wide reporting solutions.

• New transaction systems add data complexity

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New Roles for Institutional Research

• New responsibilities for designing and implementing

disseminated management reporting systems

• New responsibilities for shared data designs

including data warehouses

• New means of ensuring the accuracy of

management information: within the data source

• Less staff time devoted to meeting simple data

requests

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IR and IT

Historical Shared

Responsibilties Responsibility?

Operational reporting

IT Operational data Management reporting

Wide data disseminatation

--data extracted and transformed for reporting Analytics

IR Extracted/constructed data --wide dissemination

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Institutional Research Contributions

• Assessing reporting needs

• Advocating for new forms of information delivery • Defining an institutional reporting strategy/program • Defining warehouse variables and table structure • Selecting an institutional reporting tool

• Designing standard reports or templates

• Managing a management information delivery

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Two Types of Best Practice?

(1) Fully-developed data warehouse

• Core of an institutional reporting program • Source for all or most reporting

• Well-developed data model

• Fully defined and documented data management

procedures

• Substantial institutional commitment and staff

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Two Types of Best Practice?

(2) Pragmatic low-budget approach

• Build something.

• Identify the data needed to meet key reporting

needs

• Create tables to meet those needs

• Clean, expand, integrate, and document the

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Sources

The Data Warehousing Institute. http://tdwi.org

Davenport, TH (1997). Information Ecology: Why Technology is Not

Enough for Success in the Information Age. New York and Oxford:

Oxford University Press.

Greenfield, L (1995). The Data Warehousing Information Center. www.dwinfocenter.org.

Inmon, WH (1996). Building the Data Warehouse. New York: John Wiley & Sons, Inc.

Inmon WH and RD Hackathorn (1994). Using the Data Warehouse. New York: John Wiley & Sons, Inc.

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Sources

Kimball, R, M Ross and W Thornthwaite (1998). The Data

Warehouse Lifecycle Toolkit: Expert Methods for Designing, Developing, and Deploying Data Warehouses. New York: John

Wiley & Sons, Inc.

Kimball, R and M Ross (2002). The Data Warehouse Toolkit: The

Complete Guide to Dimensional Modeling (second edition). New

York: John Wiley & Sons, Inc.

Sanders, L (editor), How Technology is Changing Institutional

Research. New Directions in Institutional Research, 103. Fall 1999. Serban, AM and J Luan. Knowledge Management: Building a

Competitive Advantage in Higher Education. New Directions in

Institutional Research, 113, Spring 2002.

Wierschem, D, R McBroom and J McMillen. Methodology for

Developing an Institutional Data Warehouse. AIR Professional File 88, 2003.

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Hypothesis

• New information technologies are creating greater

demand for information and the need for data warehouses.

• Building a data warehouse requires transforming raw data

into reporting measures and categories.

• Meeting the demand for information requires creating

useful reports and report templates.

• Institutional researchers are experts at displaying

information and constructing reporting variables.

• Therefore participating in the development of institutional

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References

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