All Rights Reserved 2002, iStrategy Consulting January 16, 2003 Mark Max, Managing Partner Adding...

All Rights Reserved 2002, iStrategy Consulting

January 16, 2003January 16, 2003

Mark Max, Managing PartnerMark Max, Managing Partner

Adding Value to the Data Warehouse:Adding Value to the Data Warehouse:

Utilizing OLAP Technology and Utilizing OLAP Technology and Analytical ApplicationsAnalytical Applications

Mark Max BioMark Max Bio

• B.S. Accounting & M.S. Business – University of Maryland

• University of Maryland, Instructor• 20 years Consulting, Corporate, Software

Vendor Work Experience• Started iStrategy Consulting in 1999

– Maryland based consulting firm specializing in Business Intelligence and Data Warehousing

– Principals have been working in BI for 15+ years– Experience in BI/DW for higher education– Launching new DW/Analytical Application for Higher

Education in Q1 2003

email: [email protected]


Discussion PointsDiscussion Points

1. Information Delivery Challenges2. Data Warehousing and Business

Intelligence Technology3. Higher Education Analytical

Application Framework4. Demonstration5. Q&A


Shift Towards Information Based Shift Towards Information Based Management – High Visibility AreasManagement – High Visibility Areas

• Recruiting Effectiveness• Retention• Enrollment Funnel• Student Demographics• Course Planning• Resource Management• Outcomes Management• Compliance Reporting• Early Intervention• Key Performance Indicators


Emerging Strategies in in Higher EducationEmerging Strategies in in Higher Education

Strategic Enrollment Management (SEM)“Strategic Enrollment Management is a comprehensive process designed to achieve and maintain the optimum recruitment, retention, and graduation rates of students where ‘optimum’ is defined within the academic context of the institution”.

Strategic Planning Engine (SPE) “The heart of the Strategic Planning Engine links strategic

decision making with organizational key performance indicators (KPI's).”

from Michael G. Dolence & Associates

These processes require information!These processes require information!


Typical Reporting ChallengesTypical Reporting Challenges

• No central repository of official information – many non-integrated systems and databases

• Databases are structured for transaction processing, audit trail and operational needs; they are not organized for ease of reporting!

• Lack of standardized metrics and information rules (e.g., how is retention % calculated?)

• Some information needs require data from multiple systems (e.g., Cost per Student)

• Many informal databases and spreadsheets used by individuals for reporting, analysis, external reporting

• No standardized tools for reporting and analysis

Student Admin

HumanResources

Housing/Judicial

Alumni

Financials

Recruiting


Application Reporting ComplexityApplication Reporting Complexity

• Student Administration application database structures are very complex

• Reporting requires queries for database extracts – need to know SQL language

• Reporting results are subject to: – a) users understanding of

database structure, – b) “interpretation” of query

criteria, and – c) proper SQL syntax.

• Its easy to get the wrong answer!

• No easy way to combine data across multiple systems and database.

• Limited number of people who know how to query databases


The ImpactThe Impact

• No ability for self service access to information – users are totally dependent upon others to produce information

• Time consuming, manually intensive process to produce reports

• Different people produce reports with the same information but have different results– What is the real answer?– How do you know the information is

correct?• Have to repeat the same time

consuming process each time you want a report

• No time available for analysis because of the extensive time required to produce information


Data Warehousing and Data Warehousing and Business Intelligence ArchitectureBusiness Intelligence Architecture

OLAPTools

RelationalQuery & Reporting Tools

AnalyticalApplications

Data SourcesData Sources

Data/Application ServersData/Application Servers

Business IntelligenceBusiness Intelligence

Data Mining

DataMart

DataMart

DataMart

Enterprise Data Warehouse

DataMart OLAP OLAP

E

T

L

OLAP OLAP

Departmental Data Marts

E T L

ETL – Extraction, Transformation and Load

OLAP Server

Data WarehouseData Warehouse

Financials/HR

Student

Other


2002 Higher Education ERP Survey2002 Higher Education ERP Survey

Source: The Promise and Performance of Enterprise Systems, 2002 ECARS Research Study by Dr. Robert Kvavik (500 Institutions surveyed)

• 39% of institutions surveyed have implemented or are in the process of implementing a Data Warehouse

• 37% of institutions surveyed plan to implement a Data Warehouse within the next three years, with almost 1/3 of the projects beginning in 2003


Recipe for FailureRecipe for Failure

• Start by looking for application data to source a DW

• Move as much transactional data as possible into a “warehouse database”

• Purchase a relational reporting or query tool

• Send users to training

-- This approach rarely works! ---- This approach rarely works! --


Big Difference between Big Difference between Data vs. Information vs. KnowledgeData vs. Information vs. Knowledge• Data – raw facts that have been collected,

processed, stored, but not organized to convey meaning.

• Information – a collection of data organized in a manner to be meaningful to a recipient.

• Knowledge – information combined with understanding, experience, accumulated learning, and expertise relevant to a problem, decision, or process.

Data Transformation, Derivation and Data Transformation, Derivation and Aggregation are necessary, along with a Aggregation are necessary, along with a self service access capability!self service access capability!


DW Casual User vs. Power UserDW Casual User vs. Power User

• Different audiences with different:Different audiences with different:– Information needs– Analytical capabilities– Technical aptitudes– Level of insight into application data– Time constraints

• 80% – 90% of information consumers are casual users

Need to consider both in technology decisionsNeed to consider both in technology decisions


Confusing BI Product SpaceConfusing BI Product Space

• 25 to 50 legitimate vendors; many overlapping products that may appear similar but are fundamentally different

• Reporting vs. Analytics – there’s a big difference!• Relational vs. OLAP Technology

– MOLAP vs. ROLAP vs. HOLAP– Multidimensional Presentation vs. OLAP engine

• Products/Vendors: Front-end only vs. Back-end only vs. Both• Open vs. Proprietary platforms• Web vs. Client Server

– HTML vs. Rich web client (JAVA, Active-X)• Open component architecture vs. self contained products

– Portal integrationConclusionsConclusions There’s no magic product that does it all!There’s no magic product that does it all! Understand your user base, information needs and Understand your user base, information needs and

objectives before selecting BI technologyobjectives before selecting BI technology


Why OLAP Technology?Why OLAP Technology?

• Multi-dimensional presentation is the natural orientation for business information and analysis– Intuitive and easy to use– Hides user from underlying relational data

model• OLAP Technology is very fast

– Most reports run within 1-3 seconds– Speed advantage substantial in highly

aggregated reports such as multi-year trends

– Without OLAP, the burden is on the developer to build the aggregation

• Enables calculations that are impractical using relational technology – e.g., moving averages, prior period %

change• Produces consistent information

– Pre-calculated results– Not subject to unexpected SQL query

behavior


Aggregation Management:Aggregation Management:Relational Summary Tables ScenarioRelational Summary Tables Scenario• Fact table with four dimensions• Each dimension has four levels in its hierarchy (e.g.,

Time: Section, Course, Subject, All)• How many summary fact tables are required to support

every combination of dimension level?

255

• If you don’t build 255, how many should you build and which ones?

• What if you have a 20 dimensional Student Term Fact Table?

• OLAP Technology makes aggregation management very easy!


Why an Analytical Application? Why an Analytical Application? (vs. Reporting Tools)(vs. Reporting Tools)• Casual Users – majority of information users (80 – 90 %) are

casual users who will have difficulty mastering a reporting tool. An Analytical Application will be much easier to use and be more highly utilized

• Hide Database Complexity – most reporting tools require the user to understand the reporting database content and relationships. An analytical application enables casual users to get information without understanding the underlying database and functionality of reporting tools

• Guided Analysis – an application framework provides the opportunity to guide users through an analytical process and better leverage the metrics and analytical capabilities inherent in the solution

• Personalization – provide users with the ability to personalize their content and interface

• Embed Customized Analytical Functionality – enables customized application functionality to be integrated with reporting (e.g., Student Peer Group Analysis)


What the experts are saying!What the experts are saying!

“ ...most decision support software is gathering dust on office bookshelves” “Whether you build and/or buy, the key is to … deliver a robust analytic application that delivers the information and analysis that business users need.”

Wayne Eckerson, Director of Education and Research for The Data Warehousing Institute (TDWI)


Analytical Application for Higher EducationAnalytical Application for Higher Education

• Information Scope– Serve a broad audience: institutional research, management

reporting, compliance reporting, operational analysis – Span complete student lifecycle: admissions, enrollment,

course activity, graduation– Address key objectives: recruiting effectiveness, retention,

student achievement, course curriculum and schedule• Provide self service access to information:

– Intuitive and easy to use (the basics are simple)– Minimal training required – Easy to deploy

• Functionality:– Interactive standard reports and charts,– Guided Analysis,– Key Performance Indicators (KPIs),– Personalized Dashboard (KPIs and Charts)– Ad hoc analysis,– “Actionable” analytical tools (e.g., support early intervention

through student risk analysis, student peer group analysis)


Higher Education Analytical ApplicationHigher Education Analytical Application

InstitutionalResearch

AcademicAffairs

AdmissionsOffice

StrategicPlanning

Deans/Assoc. Deans

DepartmentChairs

Registrar’sOffice

ComplianceReporting

FinancialAid

Administrative Departments

StudentTerm

ClassOffering

StudentClass Enr.

Admissions

Graduation

FacultyTerm

GuidedAnalysis

AnalyticalAnalyticalModulesModules

DownloadDownloadExtractsExtracts

Key PerfKey PerfIndicatorsIndicators

ComplianceComplianceReportsReports

StandardStandardReportsReports

PersonalPersonalDashboardDashboard

Ad HocAd HocAnalysisAnalysis

PersonalPersonalReportsReports

Analytical Application

Data Warehouse

Information Delivery Engine Information Consumers


DemonstrationBackground Information


Student Administration Information Student Administration Information CategoriesCategories

1. Admissions2. Student Demographics3. Enrollment Trends4. Retention5. Class Offering and Utilization6. Student Class Enrollment7. Student Performance8. Student Risk Analysis9. Student Peer Group Analysis10.Graduation11.Faculty Information


Student AdministrationStudent AdministrationDimensional Data ModelDimensional Data Model

ClassOffering

Student Term

Student Class

Enrollment

Admissions

Graduation

FacultyTerm

Fact Areas

Admissions:• Application Method• Applicant Home State• Prior Applicant Ind.• Applicant Fin Aid Interest• Applicant Housing Interest• Recruiting Category• Applicant Status• Admit Category• CohortFaculty Attributes:• Faculty• Faculty Ethnicity• Faculty Gender• Faculty Rank• Tenure StatusGraduation:• Graduated Indicator• Degree• Years to Graduate

Institutional:• Term• School/Major• Academic Department Student Term:• Academic Level• Academic Standing• Student Term Status• FT/PT IndicatorClass/Grade:• Subject/Class• Course Level• Class Type• Grade• GPA BandStudent Attributes:• Student• Student Citizenship• Student Ethnicity• Student Gender• Student Home State

DimensionsDimensions


User Interface TerminologyUser Interface Terminology

• Grid/Chart• Presentation Orientation: Rows, Columns, Pages• Dimension/Measures• Hierarchy• Drill Down• Page Selection• Rotate• Dimension Filtering

– Top/Bottom Ranking– Exception based selection

• Drill to Detail


Application DemonstrationApplication Demonstration


Technology ArchitectureTechnology Architecture

Microsoft SQL Server

Windows 2000 Server

Microsoft Analysis Services

ProClarity Analytical Server

Microsoft IIS Web Server


Operational Databases

Flat Files

Relational WarehouseDimensions/Attributes Star Schema Fact Tables

Data Transformation Services (DTS)

Staging Tables

Bulk Load Process


Edit & Transformation


Microsoft Analysis Server (OLAP) Cubes

Microsoft SQL Server Data Warehouse

1

2

3

Data Warehouse ArchitectureData Warehouse Architecture

Student Admin Application

DW Build Process1. Bulk load data from

transaction system into temporary staging tables (most recent n terms)

2. Perform edit, data derivation and relational DW build transformations

3. Build aggregate OLAP cubes


Keys to SuccessKeys to Success

• Set reasonable expectations– It’s impossible to address every imaginable information need– It’s better to successfully deliver 80% - 90% of the

requirements than to deliver nothing– Continue to expand scope based on needs

• Target a quick success story• Ensure that the casual users have an application interface

that is:– Simple to use– Fast– Supports analytics as the user skills develop

• Design must incorporate transformation of data to a dimensional data model

• Provide a good support infrastructure

All Rights Reserved 2002, iStrategy Consulting January 16, 2003 Mark Max, Managing Partner Adding...

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