Student Admin Data Reporting from PeopleSoft SA DePaul University: Three years, three (concurrent) approaches! Jim Janossy and Russ Patterson DePaul Information.

Slides:



Advertisements
Similar presentations
Pennsylvania BANNER Users Group 2006 Integrate Your Decision Support with Cognos 8.
Advertisements

Task Force on Reporting Strategies February 6, 2003 Student Administration Laura Stoll.
WHY CMS? WHY NOW? CONTENT MANAGEMENT SYSTEM. CMS OVERVIEW Why CMS? What is it? What are the benefits and how can it help me? Centralia College web content.
Database Management3-1 L3 Database Management Santa R. Susarapu Ph.D. Student Virginia Commonwealth University.
Page 2 Agenda Page 3 History –Blue Print, 2000 –GIS Process 1.2, 2001 (training only) –GIS Process 2.0, (ITIL based - not implemented) –Supply/Demand.
CUMREC 2002 © 2002 Information Services at DePaul University 1 Growing an Economical Student Admin Datamart Using Common Tools Jim Janossy Russ Patterson.
Technical BI Project Lifecycle
SERVING CORPORATES AND INDIVIDUALS ©2012 BUSINESS REPORTING MANAGEMENT SERVICES, INC WELCOME.
Selecting a Business Intelligence Standard for Higher Education Mid Atlantic Educause Conference Baltimore, Maryland Baltimore, Maryland January 10, 2006.
Workload Management BMO Financial Group Case Study IRMAC, January 2008 Sorina Faur, Database Development Manager.
Access 2007 Product Review. With its improved interface and interactive design capabilities that do not require deep database knowledge, Microsoft Office.
Chapter 3 Database Management
The Hierarchy of Data Bit (a binary digit): a circuit that is either on or off Byte: 8 bits Character: each byte represents a character; the basic building.
Database Management An Introduction.
Tools You Own Maggie Moehringer AIRPO, June 2006.
Database Management: Getting Data Together Chapter 14.
Student Information system
Data Warehousing at Notre Dame October 7, 2004 Dale Carter, Manager, Decision Support Jared Barnard, Database Administrator.
Dana C. Voss Manager, Decision Support Services, UIS University Information Technology Services INDIANA UNIVERSITY May 2003 Copyright Dana C. Voss, 2003.
Data Warehousing: Defined and Its Applications Pete Johnson April 2002.
State of Connecticut Core-CT Project Query 4 hrs Updated 1/21/2011.
XP Information Information is everywhere in an organization Employees must be able to obtain and analyze the many different levels, formats, and granularities.
Beyond the Campus Gates: Bringing Alumni, Parents, and Prospects into the Campus Portal William P. Wilson Mark R. Albert John C. Duffy Gettysburg College.
Welcome to the Minnesota SharePoint User Group. Introductions / Overview Project Tracking / Management / Collaboration via SharePoint Multiple Audiences.
BUSINESS INTELLIGENCE/DATA INTEGRATION/ETL/INTEGRATION AN INTRODUCTION Presented by: Gautam Sinha.
Data Warehouse Tools and Technologies - ETL
Setting up a National Warehouse of Official Statistics in India P C Mohanan Deputy Director general National Statistical Organisation Ministry of Statistics.
What is Business Intelligence? Business intelligence (BI) –Range of applications, practices, and technologies for the extraction, translation, integration,
1 CADE Finance and HR Reports Administrative Staff Leadership Conference Presenter: Mary Jo Kuffner, Assistant Director Administration.
Lighting up “Fermidash” – Fermilab’s Executive Dashboard
University of North Dakota Office of Institutional Research November 8, 2013 Drivers get ready - new dashboards are coming your way! Presented at the.
SharePoint 2010 Business Intelligence Module 2: Business Intelligence.
Database Systems – Data Warehousing
Chapter 5 Lecture 2. Principles of Information Systems2 Objectives Understand Data definition language (DDL) and data dictionary Learn about popular DBMSs.
Classroom User Training June 29, 2005 Presented by:
 Introduction Introduction  Purpose of Database SystemsPurpose of Database Systems  Levels of Abstraction Levels of Abstraction  Instances and Schemas.
Best Practices for Data Warehousing. 2 Agenda – Best Practices for DW-BI Best Practices in Data Modeling Best Practices in ETL Best Practices in Reporting.
GBA IT Project Management Final Project – “ FoodMart Corp - Making use of Business Intelligence” July 12, 2004 N.Khuda.
Fundamentals of Information Systems, Fifth Edition
South Africa Data Warehouse for PEPFAR Presented by: Michael Ogawa Khulisa Management Services
Data Warehouse Overview September 28, 2012 presented by Terry Bilskie.
ETL Overview February 24, DS User Group - ETL - February ETL Overview “ETL is the heart and soul of business intelligence (BI).” -- TDWI ETL.
Using SAS® Information Map Studio
INFORMATION SERVICES Decision Support Office Evolution of Business Intelligence.
IBIS-Admin New Mexico’s Web-based, Public Health Indicator, Content Management System.
CSS/417 Introduction to Database Management Systems Workshop 4.
Database Design and Management CPTG /23/2015Chapter 12 of 38 Functions of a Database Store data Store data School: student records, class schedules,
Introduction – Addressing Business Challenges Microsoft® Business Intelligence Solutions.
Robin Mullinix Systems Analyst GeorgiaFIRST Financials PeopleSoft Query: The Next Step.
6.1 © 2010 by Prentice Hall 6 Chapter Foundations of Business Intelligence: Databases and Information Management.
REPORTING RIGHT NOW WITH ARGOS JESSICA ASHBROOK – LANSING COMMUNITY COLLEGE BRIAN STEVENS – EVISIONS ACCOUNT EXECUTIVE.
By N.Gopinath AP/CSE. There are 5 categories of Decision support tools, They are; 1. Reporting 2. Managed Query 3. Executive Information Systems 4. OLAP.
1 Technology in Action Chapter 11 Behind the Scenes: Databases and Information Systems Copyright © 2010 Pearson Education, Inc. Publishing as Prentice.
Building Dashboards SharePoint and Business Intelligence.
Chabot – Las Positas Community College District Reporting Strategy.
DATA RESOURCE MANAGEMENT
© 2006 Pearson Education Canada Inc. 3-1 Chapter 3 Database Management PowerPoint Presentation Jack Van Deventer Ward M. Eagen.
© 2003 Prentice Hall, Inc.3-1 Chapter 3 Database Management Information Systems Today Leonard Jessup and Joseph Valacich.
Business Intelligence Training Siemens Engineering Pakistan Zeeshan Shah December 07, 2009.
Presenter : Ahmed M. Mosa User Group : SQLHero. Overview  Where is BI in market trend  Information Overload  Business View  BI Stages  BI Life Cycle.
Library Online Resource Analysis (LORA) System Introduction Electronic information resources and databases have become an essential part of library collections.
1 Visalia Unified School District Principal & Area Administrator Service Request Approval Processing Using The SRTS November 16, 2005 Administrative Services.
WELCOME. Successful Collaboration between IR and IT in Building a SAS Dashboard at the El Paso Community College.
SAP BI – The Solution at a Glance : SAP Business Intelligence is an enterprise-class, complete, open and integrated solution.
1 Copyright © 2008, Oracle. All rights reserved. Repository Basics.
Information Systems in Organizations
Moving the Needle Conference 2017
Data Warehouse Overview September 28, 2012 presented by Terry Bilskie
MANAGING DATA RESOURCES
Data Warehousing Concepts
Presentation transcript:

Student Admin Data Reporting from PeopleSoft SA DePaul University: Three years, three (concurrent) approaches! Jim Janossy and Russ Patterson DePaul Information Services Ed Schaefer, Enrollment Management Research

Jim Janossy - DePaul Information Services –Student datamart development and popularization Ed Schaefer - Enrollment Management and Marketing, Reporting and Research –Informatica ETL and BI decision and future DW Russ Patterson - DePaul Information Services –Informatica ETL and BI decision and future DW Presenters and Panel

Start simple and stay focused Establish a naming convention early Prototype and pilot first Keep security simple (ACAD_GROUP) Roll out useful things as you go “Market” to users, do ongoing training Get your documentation onto the web! Cutting to the chase...

Student datamart –PS table extracts packaged for easy use by 160 casual users –Aim: day-to-day meat-and-potatoes selection and contact data for the enrolled and active student population –Not intended for statistical reporting Enrollment Management Research databases –Current statistical reporting and census captures –Admission reporting and day-to-day college admin stats –Marketing measures and EM decisionmaking OIPR databases –Moderate to long term statistics and trends –IPEDS reporting Three level of approach

Cadre of willing users Increased user self-service willingness and capability Ability to integrate SA, HR, Finance Staged replacement of initial products Justification for a higher level ETL and BI Leads to...

Student datamart Access, SQR EMR databases Perl, SQL-Server, Access OIPR databases ODBC, SQL-Server, DTS Cognos Leads to... Decision: Acquire a standard ETL and BI Need for portal delivery Need for administration via web Need for dashboards Credits acquired with EPM

Reporting instance PeopleSoft Student Admin system Common base: Reporting instance is a full copy of the on-line system

Three level of approach Student datamart –PS table extracts packaged for easy use 160 casual users –Aim: day-to-day meat and potatoes selection and contact data for the enrolled and active student population –Not intended for statistical reporting Enrollment Management Research databases –Current statistical reporting and census captures –Admission reporting and day-to-day college admin stats –Marketing measures and EM decisionmaking OIPR databases –Moderate to long term statistics and trends –IPEDS reporting

Limitations Limitations spreadsheet maximum 65K rows spreadsheet maximum 65K rows hard to learn data structures! hard to learn data structures! awkward outer joins awkward outer joins PS-Query 338 users Data

?

?

Low- hanging fruit!

What users did What users did PS-Query 338 users Data Access

Datamart approach Datamart approach PS-Query 338 users Data Datamart Access

You can combine! You can combine! PS-Query 338 users Data Datamart Access Links to spreadsheets Links to tables Local tables Data

!

Datamart Access spreadsheet maximum 65K rows spreadsheet maximum 65K rows hard to learn data structures! hard to learn data structures! awkward outer joins awkward outer joins Why datamart? Why datamart?

Student datamart functional look 22 tables 22 tables Enrolled student data Enrolled student data Assemble data for convenient use Assemble data for convenient use Focus on 20% to meet 80% needs Focus on 20% to meet 80% needs Row control by ACAD_GROUP Row control by ACAD_GROUP

PeopleSoft operator id and password

Usage

Student datamart technical look Separate Oracle database Separate Oracle database Oracle define, create, security Oracle define, create, security Extract data with Access or SQR Extract data with Access or SQR Load with SQR or SQL*Loader Load with SQR or SQL*Loader Pilot first, engage users, perfect it Pilot first, engage users, perfect it

Example: Best Contact Data Table ADDRESSESNAMESPERSONAL_PHONE _ADDRESSES HR: employees CBORD: dorm assigns Barat dorm assigns DP701A Best Contact Data

One datamart table!

Goals Clear documentation Clear documentation Clear naming convention Clear naming convention Create simple data structures Create simple data structures Interpret coded values for use Interpret coded values for use Use common tools, common skills Use common tools, common skills

For table building... Common tools Common skills Common cents ODBC MS-Access SQR SQL*Loader Oracle roles

Common tools Common skills Common cents Existing skills MS-Access Excel Basic PC training Views by college For users...

Common tools Common skills Common cents For DePaul... Low cost Short lead time Low overhead Meets 80% need In place now

For more info... See the DePaul datamart web site at

Data access blossoms with DePaul’s datamart! The DePaul University Student Datamart contains data about currently enrolled students extracted from the PeopleSoft student administration system and staged for quick retrieval to meet adhoc information reporting needs. The datamart is provided for use as a regular resource to administrative personnel in the university community. This web site provides background information, instructions for gaining access to the mart, and documentation for the 23 datamart tables. Questions? OverviewAccessExamplesTrainingDocumentation If the information at the selection buttons above still leaves you with questions or you need additional help, please Jim Janossy, Russ Patterson, or Gino Kao. To Table of Contents

Overview: How the Datamart grows! DePaul Information Services began analysis and construction of the student datamart in 2001 after implementation of the PeopleSoft student administration system. The datamart is housed as a special collection of data tables in an Oracle database. Users gain access to the datamart via ODBC connection and typically use Microsoft Access as their data extraction and reporting tool. The core of datamart users is 50 administrative personnel in all nine college of the university. Follow the flowers to see how the datamart grows! Questions? IntentTable listTable loading If the information at the selection buttons above still leaves you with questions or you need additional help, please Jim Janossy, Russ Patterson, or Gino Kao. To Home Page User reactionScopeExtraction

Access to the Datamart Access to the student datamart is provided to administrative personnel whose job responsibilities require the ability to acquire and use student information in their daily work. All datamart users must be authorized PeopleSoft system users, and in addition must file a request for mart access. Datamart users in college offices receive access to data for students enrolled in their respective colleges, while executive department users can access student data across the university. Questions? View controlRequest form If the information at the selection buttons above still leaves you with questions or you need additional help, please Jim Janossy, Russ Patterson, or Gino Kao. To Home Page Tom Paetsch, Data Administrator Enrollment Management DetailsAccess policy

Information Extraction Examples “Since gaining access to the student datamart, I have been able to do information extractions I previously had to depend on programmers to do. Using the mart has made it much faster and easier for me to get data I need!” says Cheryl Barkby of DePaul’s ID Card Services Division. “I typically need to identify the enrolled student population that meets requirements for the U-Pass program, and obtain their addresses and process interface files to the CTA. The datamart really helps me do my work!” Questions? Linking to PS-Query spreadsheets Frequently asked questions Labels If the information at the selection buttons above still leaves you with questions or you need additional help, please Jim Janossy, Russ Patterson, or Gino Kao. To Home Page Cheryl Barkby, Analyst ID Card Services SamplesHow to...Reports

Datamart Training The student datamart was designed specifically to ease the burden of data access to a complex student administration system. A major effort in the design was directed toward extracting and staging the data that experience has shown most college offices need to conduct their day-to-day work effectively. In order to help college office administrative personnel use the datamart effectively, we’re providing a number of training resources in collaboration with ongoing Human Resources office software skills training. Questions? ODBC connectionSchedule If the information at the selection buttons above still leaves you with questions or you need additional help, please Jim Janossy, Russ Patterson, or Gino Kao. To Home Page Happy campers in classroom training! Suggested preparation Links to training sites

Datamart Documentation The content of each datamart table is documented in a spreadsheet that shows the column name, format of the column, and the PeopleSoft table and column from which the data is drawn. You’ll also find access here to explanations of certain data columns as well as the SQL used to extract and form the datamart tables. Review, comments, and suggestions concerning this documentation and table formation logic is welcome. Questions? Table definitions Table formation SQL If the information at the selection buttons above still leaves you with questions or you need additional help, please Jim Janossy, Russ Patterson, or Gino Kao. To Home Page Gino Kao Programmer, Infrastructure Group Data explanations

Datamart Documentation The content of each datamart table is documented in a spreadsheet that shows the column name, format of the column, and the PeopleSoft table and column from which the data is drawn. You’ll also find access here to explanations of certain data columns as well as the SQL used to extract and form the datamart tables. Review, comments, and suggestions concerning this documentation and table formation logic is welcome. Questions? Table definitions Table formation SQL If the information at the selection buttons above still leaves you with questions or you need additional help, please Jim Janossy, Russ Patterson, or Gino Kao. To Home Page Gino Kao Programmer, Infrastructure Group Data explanations Data origin documentation

Documentation

Datamart Documentation The content of each datamart table is documented in a spreadsheet that shows the column name, format of the column, and the PeopleSoft table and column from which the data is drawn. You’ll also find access here to explanations of certain data columns as well as the SQL used to extract and form the datamart tables. Review, comments, and suggestions concerning this documentation and table formation logic is welcome. Questions? Table definitions Table formation SQL If the information at the selection buttons above still leaves you with questions or you need additional help, please Jim Janossy, Russ Patterson, or Gino Kao. To Home Page Gino Kao Programmer, Infrastructure Group Data explanations SQR source code download

What do users say about the student datamart ? “We’ve found that while we can look up students one at a time online using PeopleSoft, we can use the datamart to access data to get lists of enrolled students and related information, without having to ask for special programming in each case!” Mike Medin ID Card Services Office Tanicha Hart College of Liberal Arts and Science “We’re using the student datamart to identify incoming freshmen and prepare mailings to them. We have conducted in-house datamart training sessions and find that getting people up to speed on datamart access is easy and quick to accomplish!” DePaul University Copyright 2003 DePaul University Chicago, Illinois USA

Charles Moore School for New Learning Marcelo Lanzarotti Information Services Division “The datamart lets us get data for operational reporting and analysis that we just couldn’t get before! And it has given me new opportunities to learn modern data access techniques and presentation. My new skills have allowed me to grow in areas that are also essential for higher education achievement. Everyone can benefit from the Datamart's user friendly interface!” “The datamart lets us retrieve student information to meet adhoc requests from many users quickly and efficiently. We handle over 300 requests a year using the mart, and this is only a small part of what we do in this area of information services.” DePaul University Copyright 2003 DePaul University Chicago, Illinois USA

Jennifer Hoover College of Liberal Arts and Sciences “As a frequent user of the Student Datamart tables for nearly a year, I find it a highly reliable, integral and overall indispensable resource for generating a diverse collection of student reports. Moreover, the datamart immensely reduces turnaround time for my report requests. Reports that formerly required three or four separate queries in PeopleSoft Query can now be completed right in Access by way of the datamart tables, often from only one query! Within the College of LA&S departments now receive more detailed and accurate quarterly reports about their students. Usage of the student datamart played a large part in these reporting improvements.” DePaul University Copyright 2003 DePaul University Chicago, Illinois USA

“The student datamart is a great tool for our information gathering needs. The aggregated data allows for the creation of much simpler queries than can be written in PeopleSoft Query. We can create easy-to-access queries and reports that are much simpler to understand, change and run for users of different skill levels. Whether it is targeted mailings or analyzing student history, we are continually finding new uses for the datamart that allow us to better serve our student population!” Mark McMurray School of Computer Science, Telecommunication, and Information Systems “The datamart is very useful to SNL in our day-to-day operation since it provides a fast and convenient way to extract data we need for decision-making. We look forward to using the datamart even more to meet many of our needs for information about our classes and students!” Doug Murphy Senior Assistant Dean School for New Learning DePaul University Copyright 2003 DePaul University Chicago, Illinois USA

Three level of approach Student datamart –PS table extracts packaged for easy use 160 casual users –Aim: day-to-day meat and potatoes selection and contact data for the enrolled and active student population –Not intended for statistical reporting Enrollment Management Research databases –Current statistical reporting and census captures –Admission reporting and day-to-day college admin stats –Marketing measures and EM decisionmaking OIPR databases –Moderate to long term statistics and trends –IPEDS reporting

Department in Enrollment Management (EM) –Integrates traditional enrollment services (admission and financial aid, for example) with our university’s marketing and communication activities, as well as alumni and career networks –EM’s Goal: Improve and enhance DePaul’s competitive market position and prominence in Chicago, the nation, and the international community EMR’s Goal: Provide timely information that is valuable to understanding and enhancing DePaul's market position and prominence –Reporting –Research Enrollment & Marketing Research

Reporting –Admissions Yield Reports Prospect Reports Mailing Lists –Enrollment Weekly Enrollment Comparisons Daily Enrollment Reports Research –Prospect Analysis (prospects to enrolled) –Market Analysis (program success) Enrollment Data Capture began in 1990 Admission Data Capture began in 2000 Some Research/Reports required data from both captures –Trick was getting the two to tie-out –And getting the data to the requestor quickly EMR Information Needs

PeopleSoft Daily Enrollment Daily Admissions ETL Oracle PERL, T-SQL, Stored Procs Adm Reports Specialized Adm DBs Enr Reports TEAMS Elite Housing DB Current Future SQL Server EMR Data Warehouse T-SQL & Stored Procs Specialized Research DBs Specialized Reports Research & Report Oriented ETL SQL Server Enrollment & Marketing Research Enrollment Management DePaul University Data Stores

Three level of approach Student datamart –PS table extracts packaged for easy use 160 casual users –Aim: day-to-day meat and potatoes selection and contact data for the enrolled and active student population –Not intended for statistical reporting Enrollment Management Research databases –Current statistical reporting and census captures –Admission reporting and day-to-day college admin stats –Marketing measures and EM decisionmaking OIPR databases –Moderate to long term statistics and trends –IPEDS reporting

Census Files created from Oracle via ODBC and SQL-Server DTS Integrated Finance, Student, Instruction, and Faculty data serves as official stats Student data set is consistent with historical categories at aggregate level Student census files coordinated with EMR Reports developed in varying degrees of sophistication (reports to OLAP cubes) Office of Institutional Planning and Research (OIPR)

Cognos is used against SQL-Server Reports have 3 levels: –OLAP driven static web reports with no interactivity (put up reports quickly) –OLAP driven web reports with drop downs. (customized web reports with interactivity) –OLAP interactive browser: fully analyze data from basic browsing to data mining Office of Institutional Planning and Research (OIPR)

Warehouse PowerCenter Meta Data Repository PowerAnalyzer Meta Data Repository Multiple schemas HR Finance SA Non-PS PowerCenter Client Tools PowerAnalyzer Reporting Web Server PowerCenter ETL EPM BI ETL Informatica Data Warehouse PowerCenter/PowerAnalyzer + PS EPM Portal delivery!

!