Slides from a seminar by Lucian Parshall to the NZ Ministry of Education on Thursday 21 January 2010

1. Keep in Perspective
“Not everything that counts
!can be counted.
!And not everything that
!can be counted - counts.”
Albert Einstein

2. Keep in Perspective
“Not everything that counts
!can be counted.
!And not everything that
!can be counted - counts.”

3. How important is a DSS?

4. How important is a DSS?
Imagine the CEO of a large enterprise with:
53,800 employees

5. How important is a DSS?
Imagine the CEO of a large enterprise with:
53,800 employees
2,600 branch offices

6. How important is a DSS?
Imagine the CEO of a large enterprise with:
53,800 employees
2,600 branch offices
$6.4 billion annual budget

7. How important is a DSS?
Imagine the CEO of a large enterprise with:
53,800 employees
2,600 branch offices
$6.4 billion annual budget
758,000 customers

8. How important is a DSS?
Imagine the CEO of a large enterprise with:
53,800 employees
2,600 branch offices
$6.4 billion annual budget
758,000 customers
What strategic information might
this CEO expect to be available?

9. How important is a DSS?
Imagine the CEO of a large enterprise with:
53,800 employees
2,600 branch offices
$6.4 billion annual budget
758,000 customers
What strategic information might
this CEO expect to be available?
Would an 18 month delay in
finding out how many employees
left the company be OK?

10. How important is a DSS?
Imagine the CEO of a large enterprise with:
53,800 employees
2,600 branch offices
$6.4 billion annual budget
758,000 customers
What strategic information might
this CEO expect to be available?
Finding out how a customer
performed on an evaluation -
six months later?

11. How important is a DSS?
Imagine the CEO of a large enterprise with:
53,800 employees
2,600 branch offices
$6.4 billion annual budget
758,000 customers
What strategic information might
this CEO expect to be available?
Not knowing the location or the
age of the technology in your
branch offices?

12. How important is a DSS?
Imagine the CEO of a large enterprise with:
53,800 employees
2,600 branch offices
$6.4 billion annual budget
758,000 customers
What strategic information might
this CEO expect to be available?
We find ourselves in the
Information Age with an aging
information system

13. What are the needs of the
educational community?

14. Current Education Data Sets
Performance Infrastructure
Personal
Student
Finance Foreign

15. Current Education Data Sets
Performance Infrastructure
Personal
Student
Finance Foreign

16. Current Education Data Sets
Performance Infrastructure
Personal
Student
Finance Foreign
Promotes narrow decisions based on information
extracted from one or two functional data-sets
(finance and assessment)

17. Current Education Data Sets
Performance Infrastructure
Personal
Student
Finance Foreign
Redundant data entry is common
Disconnected data increases resources needed
Collections become costly and inefficient

18. Educational Data Warehousing
Performance
TID (10 digit) Student Personnel
NCLB math TID (10 digit) Admin Unit No.
Teacher SS# ........ Supply
NCLB read
LEA Number Teacher SS# IHE Unit No,
........
........ Teacher Assign ........
AP score
Stud gender ........ SS#
PSAT math
Stu Grade lvl Type of Cert ........
........
Stu FTE ........ IHE Endorsement
........
ACT enroll ........ Cert Exp Date
Admin Unit No.
Finance
LEA Number
School
........
Infrastructure Per Pupil
Admin Unit No. Total Rev
Data Partnerships
........ ........
Technology Avg Salary
Crime/Safety
Foreign Data
Gov Data Operating Bdgt
Admin Unit No.
........ ........
Admin Unit No. ........
........
........ Employment
Bld Age
Live Births NCES
........
GPS system University
Title I
Num Arrests
Cong Dist

19. Educational Data Warehousing
Performance
TID (10 digit) Student Personnel
NCLB math a location Admin Unit No. Supply
that:
TID (10 digit)
Teacher SS#
NCLB read ........
Integrates information from . . . . . . . .
........ Teacher SS# disparate
LEA Number
........
Teacher Assign
IHE Unit No,
AP score
systems into a total view of Cert . . . . . . . .
PSAT math . . . and a common
Stud gender
Stu Grade lvl
Type
..... SS#
........
foundation for understanding student
........ Stu FTE
........
........
Cert Exp Date
IHE Endorsement
ACT enroll
performance and school improvement
Admin Unit No.
Finance
LEA Number
School
........
Infrastructure Per Pupil
Admin Unit No. Total Rev
Data Partnerships
........ ........
Technology
Crime/Safety and Foreign Data Avg Salary
Operating Bdgt
Gov Data Admin Unit No.
Provides for Admin Unit No. set. .of. .definitions
........
........
a common . . . . ........
Becomes the Live .Births.
Bld Age sole . source Employment
... . .
of reusable data
NCES
........
Improves timeliness and utility of reports
Title I
GPS system University
Num Arrests
Cong Dist

20. What is a Data Warehouse?

21. What is a Data Warehouse?
DW is not just storage but the
tools to query, analyze and
present information on the web.

22. What is a Data Warehouse?
DWs have many definitions
- with these similarities:
Subject oriented - gives information about a person instead
of operations.
Integrated - a variety of sources are merged into a whole.
Non-volatile - provides users with a consistent picture over
specified time periods.
Robust architecture - that allows concurrent access by a
multiple number of users with frequent queries.
Quality data - valid and reliable data that promotes
confidence in DW and forms the nucleus of information
used by the educational community.

23. What is a Data Warehouse?
DWs have many definitions
- with these similarities:
Subject oriented - gives information about a person instead
of operations.
Integrated - a variety of sources are merged into a whole.
Non-volatile - provides users with a consistent picture over
specified time periods.
Robust architecture - that allows concurrent access by a
multiple number of users with frequent queries.
Quality data - valid and reliable data that promotes
confidence in DW and forms the nucleus of information
used by the educational community.

24. What is a Decision
Support System?
DSS is a process used by the
educational community (with support
of the data warehouse) that
transforms data into a knowledgebase
that will support decision-making.

25. DSS starts with a: What is a Decision
Problem + Support System?
administration
=
Data

26. DSS starts with a: What is a Decision
Problem + Support System?
administration
=
Data + dissemination =
Information

27. DSS starts with a: What is a Decision
Problem + Support System?
administration
=
Data + dissemination =
Information
+ social
discussion =
Knowledge

28. DSS starts with a: What is a Decision
Problem + Support System?
administration
=
Data + dissemination =
Policy/Action Information
+ social
discussion =
+ community
Knowledge
response =

29. DSS starts with a: What is a Decision
Problem + Support System?
administration
=
Data + dissemination =
Policy/Action Information
+ wisdom to ask a more + social
complex question =
discussion =
+ community
Knowledge
response =

30. 12 Steps to Creating the DSS
These steps are a
combination of buying and
building that depend on
time and money

31. 12 Steps to Creating the DSS
Education Community
Involvement
These steps are a
combination of buying and
building that depend on
time and money

32. 12 Steps to Creating the DSS
Education Community
Involvement
Conceptual Agreement
DRA/DBA Staffing
Meta Data
DRA
Security/Confidentiality
Unique ID#
Edit check/ETL Time from
Operation to
Dup Res
These steps are a Analysis
BI tool DBA
combination of buying and
building that depend on
time and money Data
Warehouse
Data Mining
Information
Democracy
Training
Decision Support System

33. 12 Steps to Creating the DSS
Education Community
Involvement
Conceptual Agreement
DRA/DBA Staffing
Meta Data
DRA
Security/Confidentiality
Unique ID#
Edit check/ETL Time from
Operation to
Dup Res
Analysis
BI tool DBA
Looks linear - is
multidimensional
Data
Warehouse
Data Mining
Information
Democracy
Training
Decision Support System

34. 12 Steps to Creating the DSS
Education Community
Involvement
Conceptual Agreement
DRA/DBA Staffing
Meta Data
DRA
Security/Confidentiality
Unique ID#
Edit check/ETL Time from
Operation to
Dup Res
Analysis
BI tool DBA
Factor in the
fatigue-fizzle function Data
Warehouse
Data Mining
Information
Democracy
Training
Decision Support System

35. 12 Steps to Creating the DSS
Education Community
Involvement
Conceptual Agreement
DRA/DBA Staffing
Meta Data
DRA
Security/Confidentiality
Unique ID#
Edit check/ETL Time from
Operation to
Dup Res
Analysis
BI tool DBA
Factor in the
fatigue-fizzle function Data
Warehouse
Information
Escape velocity Data Mining
Democracy
Training
Decision Support System

36. 12 S
S
teps
DS

37. Step #1
Concept Formation
Initiation Project
Phase Planning
Execution/
Closeout Control
12 S
S
teps
DS

38. Building the Framework
for the DW and DSS

39. Building the Framework
for the DW and DSS
Must have conceptual agreement on the:

40. Building the Framework
for the DW and DSS
Must have conceptual agreement on the:
Design of support system

41. Building the Framework
for the DW and DSS
Must have conceptual agreement on the:
Design of support system
Policy to protect data

42. Building the Framework
for the DW and DSS
Must have conceptual agreement on the:
Design of support system
Policy to protect data
Advisory committee(s)

43. Building the Framework
for the DW and DSS
Must have conceptual agreement on the:
Design of support system
Policy to protect data
Advisory committee(s)
Buy or build and

44. Building the Framework
for the DW and DSS
Must have conceptual agreement on the:
Design of support system
Policy to protect data
Advisory committee(s)
Buy or build and
Funding

45. Building the Framework
for the DW and DSS
Must have conceptual agreement on the:
Design of support system
Policy to protect data
Advisory committee(s)
Buy or build and
Funding

46. DSS Design: Best Practice

47. DSS Design: Best Practice
Who? Whom? Where? With? What?
School Codes
When?
Data Warehouse

48. DSS Design: Best Practice
Who? Whom? Where? With? What?
Decision Decision
Support Support
Users Tools
Used for: Used how:
Operation Data mining
Management School Codes Analysis
Policy Makers Ad-hoc query
Instruction Off-line
Research
When? manipulation
Data Warehouse
Foreign data
i.e. Employment, Higher Ed

49. DSS Design: Best Practice
Data Democracy
Web Interface
Who? Whom? Where? With? What?
Decision Decision
Support Support
Users Tools
Used for: Used how:
Operation Data mining
Management School Codes Analysis
Policy Makers Ad-hoc query
Instruction Off-line
Research
When? manipulation
Data Warehouse
Foreign data
i.e. Employment, Higher Ed

50. DSS Design: Best Practice
Data Democracy
Web Interface
Who? Whom? Where? With? What?
Decision Decision
Support Support
Users Tools
Used for: Used how:
Operation Data mining
Management School Codes Analysis
Policy Makers Ad-hoc query
Instruction Off-line
Research
When? manipulation
Data Warehouse
Foreign data
i.e. Employment, Higher Ed
h y As professionals, we need to make informed
W decisions, anticipate their impact on
education and design appropriate policy.

52. Steering Committee
Oversight to design of the DSS

53. Steering Committee
Oversight to design of the DSS
Local district policy concerns

54. Steering Committee
Oversight to design of the DSS
Local district policy concerns
Meta Data modification

55. Steering Committee
Oversight to design of the DSS
Local district policy concerns
Meta Data modification
Standard reports, and

56. Steering Committee
Oversight to design of the DSS
Local district policy concerns
Meta Data modification
Standard reports, and
Long term funding

58. Cost Savings?
(OCIO-USED)
Warehouse/DSS
initiative
Current costs
(paper and mail)
Break
even
2001 2002 2003 2004 2005 2006

59. Cost Savings?
(OCIO-USED)
Warehouse/DSS
initiative
Current costs
(paper and mail)
Break
even
2001 2002 2003 2004 2005 2006
“We spend a lot of resources on an existing
data edifice that isn’t very useful”

60. 12 S
S
teps
DS

61. Step #2
DRA & DBA
12 S
S
teps
DS

63. Partnership on Both
Sides of the
Keyboard

64. Partnership on Both
Sides of the
Keyboard
DRA: modifies and DBA: technical
enforces standards implementation
that sustain the of the data
DSS environment - warehouse
chairs data environment -
managers group chairs IT group

66. DRA and DBA Collaboration
User
requirements
Feature
expectation (DRA) Critical
Divergence
IT Development
Cycle (DBA)
18 Mo 30 Mo
Time

67. DRA and DBA Collaboration
User
requirements
Feature
expectation (DRA) Critical
Divergence
IT Development
Cycle (DBA)
18 Mo 30 Mo
Time

68. DRA and DBA Collaboration
User
requirements
Feature
expectation (DRA) Critical
Divergence
IT Development
Cycle (DBA)
18 Mo 30 Mo
Time

69. DRA and DBA Collaboration
User
requirements
Feature
expectation (DRA) Critical
Divergence
IT Development
Cycle (DBA)
18 Mo 30 Mo
Time
Outcome of building the DW within time frame:
Data Warehouse will run 12-15 years - whereas
Current apps last 6-7 years (with patches)

70. 12 S
S
teps
DS

71. Step #3
Define the Data
12 S
S
teps
DS

72. Meta Data
Data about the Database
in the Data Warehouse

73. Meta Data
Data about the Database
in the Data Warehouse
to define Meta Data Pupil
Personnel
break task into
Student
Meta Data
Human
Manual
logical support Personnel Resources
groups
Meta Data
Manual
Finance
Meta Data promotes the -
Finance
Meta Data Office
Manual
• common understanding by users
• data interchange with other agencies Test
Performance
Meta Data Company
Manual
School
Facilities
Meta Data
Manual
Manager

75. Meta Data Online Manuals
Student
Performance
Personnel
Finance
School
Infrastructure

76. Meta Data Online Manuals
Student
Performance
Personnel
Finance
School
Infrastructure
Employment
Higher Education

77. Meta Data Online Manuals
Name of Field
Student
Field Number
Technical Information
Number of characters: (length) SIF name:
Blanks: (not accepted, null) XML tag: < >
Performance Field type: (alpha, numeric, character)
Record position: (35-39)
Warehouse name:R/Ecode
Warehouse type: VCAR
Progam Information
Code format:
Personnel
Definition:
Finance Elements (variables):
School
Date Information
Submission: Effective: Reporting Period:
Infrastructure Revised: Discontinued: ?
Edits
Employment
Error traps: Fatal Error:
Cross field edits: Warning:
Historical Information
Higher Education
Form number replaced: Used for:
Statutory requirement: Report number:

78. Step #4
Maintaining Security
and Confidentiality
12 S
S
teps
DS

79. Protection is both
sides of the keyboard

80. Protection is both
sides of the keyboard
System Security (DBA)
Identification (confident of who)
Authentication (confident of source)
Authorization (grant access rights)
Access control (user profiling)
Administration (security procedures)
Auditing (monitoring and detection)

81. Protection is both
sides of the keyboard
Confidentiality (DRA)
Established FERPA policy
Unique NSN w/check sum
Statistical disclosure (<6)
System Security (DBA) Human subject review policy
Purge and destruction
Identification (confident of who) Set levels of access & audit
Authentication (confident of source)
Authorization (grant access rights)
Access control (user profiling)
Administration (security procedures)
Auditing (monitoring and detection)

82. Step #5
Unique Testing ID (NSN)
12 S
S
teps
DS

84. Test Identification Number:
Production
Record Warehouse
layout layout
First name TID (10 digit)
Last name First name
Date of Birth Last name
Gender Date of Birth
……… Gender
FTE ………
……… FTE
Grade ………
Race/Ethnic Grade
……… Race/Ethnic
……… ………
………

85. Test Identification Number:
Production
Record Warehouse
layout layout TID rules:
• Only assigned to one student (is unique).
First name TID (10 digit) • Number and name can be confirmed as
Last name First name being correct (verified via check sum).
Date of Birth Last name • Meets criteria as an identifier (is valid).
Gender Date of Birth • Has no intrinsic meaning (is nominal).
……… Gender • Can be substituted for a student’s name
FTE ……… (is not personally identifiable).
……… FTE • Permanent over the life-cycle of the
Grade ……… student (0-21 for special education).
Race/Ethnic Grade • Is returned and used by all local
……… Race/Ethnic education agencies (is ubiquitous).
……… ……… • Issued only by the SEA (is restricted).
……… • Accessible by selected SEA employees
only (is confidential).

86. Test Identification Number:
Problems
10 digit
Check Sum
First name
Last name
Constant Date of Birth
Gender
………
FTE
………
Variables Grade
Race/Ethnic
………
………
ID#
Admin Unit #

87. Test Identification Number:
Problems
10 digit
Check Sum
First name First name
Last name Last name
Constant Date of Birth
Moves
Date of Birth
Gender Gender
……… ………
FTE FTE
……… ………
Variables
Variables Grade
Race/Ethnic change
Grade
Race/Ethnic
……… ………
……… ………
ID# ID#
Admin Unit # Admin Unit #

88. Test Identification Number:
Problems
10 digit
Check Sum
First name First name
Last name Last name Need other constant:
Constant Date of Birth
Moves
Date of Birth Date of Immunization
Gender Gender Place of Birth
……… ………
Birth Cert Number
FTE FTE
……… ………
Variables
Variables Grade
Race/Ethnic change
Grade
Race/Ethnic
……… ………
……… ………
ID# ID#
Admin Unit # Admin Unit #

89. 42 states use a unique
student identifier (DQC)
How constructed How issued
(NCES) (NCES)
ISD
Combination (1)
of fields (5)
Soc Sec
Number (8) LEA (9) SEA (20)
SSN plus
algorithm (1) Other (9)
Random School (2)
number (8) Other
(4)

90. Crossing over from aggregate
to single record
Data reliability
and validity
Aggregate
collection
Time

91. Crossing over from aggregate
to single record
Data reliability
and validity
Single record
collection
Aggregate
collection
Time

92. Crossing over from aggregate
to single record
Data reliability
and validity
Single record
collection
Aggregate
collection
Time

93. Crossing over from aggregate
to single record
Data reliability
and validity
Single record
collection
Aggregate
collection
Time

94. Aggregated Data can be Misleading
Classrooms: District A Classrooms: District B
Class size! Reading Class size! Reading
! 10! 8.04 ! 14! ! 8.1
! 8! 6.95 ! 6! ! 6.13
! 13! 7.58 ! 4! ! 3.1
! 9! 8.81 ! 12! ! 9.13
! 11! 8.33 ! 7! ! 7.26
! 14! 9.96 ! 5! ! 4.74
! 6! 7.24 ! 10! ! 9.14
! 4! 4.26 ! 8! ! 8.14
! 12! 10.84 ! 13! ! 8.74
! 7! 4.82 ! 9! ! 8.77
! 5! 5.68 ! 11! ! 9.26
Classrooms: District C Classrooms: District D
Class size! Reading Class size! Reading
! 10! 7.46 ! 8! 6.58
! 8! 6.77 ! 8! 5.76
! 13! 12.74 ! 8! 7.71
! 9! 7.11 ! 8! 8.84
! 11! 7.81 ! 8! 8.47
! 14! 8.84 ! 8! 7.04
! 6! 6.08 ! 8! 5.25
! 4! 5.39 ! 19! 12.5
! 12! 8.15 ! 8! 5.56
! 7! 6.42 ! 8! 7.91
! 5! 5.73 ! 8! 6.89

95. Aggregated Data can be Misleading
Classrooms: District A Classrooms: District B
Class size! Reading Class size! Reading
! 10! 8.04 ! 14! ! 8.1
! 8! 6.95 ! 6! ! 6.13
! 13! 7.58 ! 4! ! 3.1
! 9! 8.81 ! 12! ! 9.13
! 11! 8.33 ! 7! ! 7.26
! 14! 9.96 ! 5! ! 4.74
! 6! 7.24 ! 10! ! 9.14
! 4! 4.26 ! 8! ! 8.14
! 12! 10.84 ! 13! ! 8.74
! 7! 4.82 ! 9! ! 8.77
! 5! 5.68 ! 11! ! 9.26
Classrooms: District C Classrooms: District D
Class size! Reading Class size! Reading
! 10!
! 8!
7.46
6.77
! 8!
! 8!
6.58
5.76
! Avg. classrooms != 11
! 13! 12.74 ! 8! 7.71 ! Avg. class size != 9.0
! 9! 7.11 ! 8! 8.84
! 11! 7.81 ! 8! 8.47 ! Avg. reading score != 7.5
! 14!
! 6!
8.84
6.08
! 8!
! 8!
7.04
5.25
Four districts are similar
! 4! 5.39 ! 19! 12.5
! 12! 8.15 ! 8! 5.56
! 7! 6.42 ! 8! 7.91
! 5! 5.73 ! 8! 6.89

97. Reports using Disaggregated Data
10 10
5 5
District A District B
10 20 10 20
10 10
Individual
reading scores
5 5 Four districts are
District C District D very different
10 20 10 20

98. Step #6
Cleaning the Data
12 S
S
teps
DS

100. Quality Data

101. Quality Data
Reasons for poor quality of data:
Absence of definitions
Unclear definitions
Lack of human resources
Inconsistent collections cycles (not ongoing)
Insufficient time
Inadequate training on entry and data traps
Lack of data integration
Fear of 'punishment' (look bad syndrome)

102. Quality Data
The key elements that improve the quality of what is being collected
include:
• Consistency. Data fields must have a standardized definition
so that each entity can be collected from each district in a
systematic manner.
• Timeliness. There is no efficiency in gathering statewide data
that reflects a one-time need or an unusual piece of
information. Do a survey.
• Reliability. The data set should reflect a dependable
measurement of every entity from one collection cycle to
another (i.e., data has accuracy regardless of who enters it.)
• Validity. A data element must reflect a logical and
meaningful description of an entity and should not be
subject to interpretation (i.e., data has utility to answer the
question being asked.)

103. Step #7
Resolving Duplicates
12 S
S
teps
DS

104. Thresholds and Assigning ID numbers
True False
Match
Non-
match

105. Thresholds and Assigning ID numbers
True False
Match is true - are
the same student
(assign same ID#)
Match
Pat ! Smith! M! 1/19/60
Pat! T! Smith! M! 1/19/60
Non-
match

106. Thresholds and Assigning ID numbers
True False
Match is true - are
the same student
(assign same ID#)
Match
Pat ! Smith! M! 1/19/60
Pat! T! Smith! M! 1/19/60
Non-match is true - are
different students
Non-
(assign different ID#s)
match
Pat ! Smith! F! 1/19/60
Pat! T! Smith! ! 1/19/61
Patrick ! Smith ! M! 1/19/60

107. Thresholds and Assigning ID numbers
True False
Match is true - are Match is false - are
the same student different students
(assign same ID#) (assign same ID#)
Match
Pat ! Smith! M! 1/19/60 Patricia! Smith! F! 1/19/60
Pat! T! Smith! M! 1/19/60 Pat! ! Smith! ! 1/19/60
Non-match is true - are
different students
Non-
(assign different ID#s)
match
Pat ! Smith! F! 1/19/60
Pat! T! Smith! ! 1/19/61
Patrick ! Smith ! M! 1/19/60

108. Thresholds and Assigning ID numbers
True False
Match is true - are Match is false - are
the same student different students
(assign same ID#) (assign same ID#)
Match
Pat ! Smith! M! 1/19/60 Patricia! Smith! F! 1/19/60
Pat! T! Smith! M! 1/19/60 Pat! ! Smith! ! 1/19/60
Non-match is true - are Non-match is false -
different students are the same student
Non-
(assign different ID#s) (assign different ID#s)
match
Pat ! Smith! F! 1/19/60 Pat ! Smith! M! 1/19/60
Pat! T! Smith! ! 1/19/61 Patrick! Smith! ! 1/19/60
Patrick ! Smith ! M! 1/19/60 Pat ! ! Smyth! M! 1/19/60

109. Thresholds and Assigning ID numbers
True False
Match is true - are Match is false - are
the same student different students
(assign same ID#) (assign same ID#)
Match
Pat ! Smith! M! 1/19/60 Patricia! Smith! F! 1/19/60
Pat! T! Smith! M! 1/19/60 Pat! ! Smith! ! 1/19/60
Non-match is true - are Non-match is false -
different students are the same student
Non-
(assign different ID#s) (assign different ID#s)
match
Pat ! Smith! F! 1/19/60 Pat ! Smith! M! 1/19/60
Pat! T! Smith! ! 1/19/61 Patrick! Smith! ! 1/19/60
Patrick ! Smith ! M! 1/19/60 Pat ! ! Smyth! M! 1/19/60
Error Creep

110. Step #8
Select a BI Tool
12 S
S
teps
DS

111. Task #1:
Create Model
Software &
Hardware

112. Task #1:
Create Model
Software &
Hardware
On scalable, normalized,
symmetric multiprocessing
architecture

113. Task #2: Set up a ‘road map’

114. Task #3:
Choose a
BI tool

115. Task #3:
Choose a
BI tool

116. Step #9
Data Warehouse
12 S
S
teps
DS

117. Benefits of DW:

118. Benefits of DW:
Reduction of paper forms
Savings from data duplication
Best use of technology
Sole source of reusable data
Common set of definitions
Integrated environment of core data
Breaks cycle of low quality data
Answers that took months take days
Reports that took days take minutes

119. Data Democracy for the
Educational Community
Ad-hoc
Reports
Pre-
defined
Simple - Query Sophisticated
one time - ongoing

120. Data Democracy for the
Educational Community
Ad-hoc
Leg
isla rs
tive Re searche
Ai d
es
Finance
Officers
Reports
rs
ito
A ud
General
Public Reporters
Pre-
defined
Simple - Query Sophisticated
one time - ongoing

121. Data Democracy for the
Educational Community
Ad-hoc
Leg
isla rs
tive Re searche
Finance
Ai d
es
u ll
Officers P
As system is
Reports
rs
ito used one will
A ud
find a need to
store data not
being captured
sh
General
Reporters
Pre- P u
Public
defined
Simple - Query Sophisticated
one time - ongoing

122. Push example:
one time - pre defined

123. Push example:
one time - pre defined
School report card
• School Size: small vs. large schools
• Spending: percent of budget on staff salary
• Safety: rate of expulsions and degree of crime
• Technology: ratio of pc's to students & connectivity
• Class Size: teacher-student ratio, average size
• Staff Turnover: rate and attendance
• Advanced Placement: number passing test
• Test Scores: gaps in State performance test
• College Acceptance Rate: percent taking ACT, PSAT
• Graduation/Dropout Rates: number taking GED
• Satisfaction: teachers, parents and students

124. Pull example:
ongoing - Ad hoc
Significant Usable

125. Pull example:
ongoing - Ad hoc
Significant Usable
The largest class size in high school is the 9th grade Not
No
really

126. Pull example:
ongoing - Ad hoc
Significant Usable
The largest class size in high school is the 9th grade Not
No
really
Some 9th grades have a disproportionate number
Possibly No
of Hispanics

127. Pull example:
ongoing - Ad hoc
Significant Usable
The largest class size in high school is the 9th grade Not
No
really
Some 9th grades have a disproportionate number
Possibly No
of Hispanics
Many female Hispanics in the 9th grade are
Possibly Yes
retained due to poor science skills

128. Pull example:
ongoing - Ad hoc
Significant Usable
The largest class size in high school is the 9th grade Not
No
really
Some 9th grades have a disproportionate number
Possibly No
of Hispanics
Many female Hispanics in the 9th grade are
Possibly Yes
retained due to poor science skills
Hispanics in the 8th grade had fewer computers in
science classrooms and more teachers who do not Yes Yes
have a teaching major in science

129. The DW Backbone:
The Sole Authority for the Educational Community
NCLB School
Accreditation
Crime/
Safety
Quality
Workforce
AYP State Report
Card
Title II
(IHE)
IDEA
Fiscal
Trends

130. The DW Backbone:
The Sole Authority for the Educational Community
NCLB School
Accreditation
Crime/
Safety
Quality
Workforce
AYP State Report
Card
Title II
(IHE)
IDEA
Fiscal
Trends

131. Step #10
Data Mining
12 S
S
teps
DS

132. Data re-construction

133. Data re-construction
Undirected and exploratory
knowledge discovery

134. Data re-construction
Undirected and exploratory
knowledge discovery
Sequencing: order of
patterns or groups

135. Data re-construction
Undirected and exploratory
knowledge discovery
Framing: using past
data to predict trend
Sequencing: order of
patterns or groups

136. Data re-construction
Undirected and exploratory
knowledge discovery
Framing: using past
data to predict trend
Sequencing: order of
patterns or groups
Clustering: assembling
unforeseen groups

137. Data re-construction
Undirected and exploratory
knowledge discovery
Framing: using past
data to predict trend
Sequencing: order of
patterns or groups
Clustering: assembling
unforeseen groups
Drilling:
interactive
discovery

138. Multidimensional
Ad-hoc Analysis
Student
Technology
Infrastructure
Performance

139. Multidimensional Single Parent Homes
Ad-hoc Analysis Live
Births
Student
Technology
Infrastructure Millages
Passed
Performance

140. Multidimensional Single Parent Homes
Ad-hoc Analysis Live
Births
Ethnic change
Student and growth by
enrollment
Technology
Infrastructure Millages
Passed
Performance

141. Multidimensional Single Parent Homes
Ad-hoc Analysis Live
Births
Ethnic change
Student and growth by
enrollment
Technology
Infrastructure Millages
Performance Passed
by gender
by PCs
Performance

142. Multidimensional Single Parent Homes
Ad-hoc Analysis Live
Births
Ethnic change
Student and growth by
enrollment
Technology
Infrastructure Millages
Performance Passed
by gender
by PCs
Trends and Projections
Performance

143. Multidimensional Single Parent Homes
Ad-hoc Analysis Live
Births
Ethnic change
Student and growth by
enrollment
Technology
Infrastructure Millages
Performance Passed
by gender
by PCs
Trends and Projections
Performance Similar districts
that passed bonds
by month
over past 3 yrs
by ethnicity
by building
by grade

144. Step #11
Conduct Training
12 S
S
teps
DS

145. The ultimate goal of training is to have
everyone who touches the data at every
level know what is expected of them, so
that the data that are submitted will be
the valid and reliable.

146. Training
Training must also include detailed
procedures, for example:

147. Training
Training must also include detailed
procedures, for example:
Who gets notified when an error
is discovered and how is the notification done?

148. Training
Training must also include detailed
procedures, for example:
Who gets notified when an error
is discovered and how is the notification done?
What is the procedure for making corrections of data within
an agency (i.e., who actually makes them and retransmits
the new error-free data)?

149. Training
Training must also include detailed
procedures, for example:
Who gets notified when an error
is discovered and how is the notification done?
What is the procedure for making corrections of data within
an agency (i.e., who actually makes them and retransmits
the new error-free data)?
Who reviews, verifies or signs off on the cleaned data?

150. Training
Training must also include detailed
procedures, for example:
Who gets notified when an error
is discovered and how is the notification done?
What is the procedure for making corrections of data within
an agency (i.e., who actually makes them and retransmits
the new error-free data)?
Who reviews, verifies or signs off on the cleaned data?
Who provides technical assistance to the end user?

151. Training
Training must also include detailed
procedures, for example:
Who gets notified when an error
is discovered and how is the notification done?
What is the procedure for making corrections of data within
an agency (i.e., who actually makes them and retransmits
the new error-free data)?
Who reviews, verifies or signs off on the cleaned data?
Who provides technical assistance to the end user?
What is the procedure to ensure a new copy of the data is
retained for auditing?

152. Training
Training must also include detailed
procedures, for example:
Who gets notified when an error
is discovered and how is the notification done?
What is the procedure for making corrections of data within
an agency (i.e., who actually makes them and retransmits
the new error-free data)?
Who reviews, verifies or signs off on the cleaned data?
Who provides technical assistance to the end user?
What is the procedure to ensure a new copy of the data is
retained for auditing?
Who receives confirmation that the file has been received as
specified?

153. Training
Training must also include detailed
procedures, for example:
Who gets notified when an error
is discovered and how is the notification done?
What is the procedure for making corrections of data within
an agency (i.e., who actually makes them and retransmits
the new error-free data)?
Who reviews, verifies or signs off on the cleaned data?
Who provides technical assistance to the end user?
What is the procedure to ensure a new copy of the data is
retained for auditing?
Who receives confirmation that the file has been received as
specified?
Who secures the data and maintains confidentiality?

154. Reallocation of Resources
Have
Have multiple
collections -
use once
disregard
Data collection, Analysis Reporting Decision
error checks, support and
and clean-up shared data

155. Reallocation of Resources
Have Want
Have multiple
collections -
use once
disregard
Data collection, Analysis Reporting Decision
error checks, support and
and clean-up shared data
Staff training - shifts from front to back end

156. Step #12
The DSS
12 S
S
teps
DS

157. Step #12
The DSS
Providing access to critical information for
driving, managing, tracking, and measuring
institutional policies and goals.
12 S
S
teps
DS

158. The first decision of the
DSS is to make a decision
Transactional Cyclical

159. The first decision of the
DSS is to make a decision
Transactional Cyclical
Realtime Points in time
Day to day operations Historical
Updates daily/weekly Updates quarterly
7X24 6X18
Read/write Read only
Short term data retention Long-term (longitudinal)
Mission critical queries Strategic-analytical queries
More open access paths More restricted access
Standardized reports Adhoc reports
Server based Warehouse technology

160. DSS: Helps Anticipate Issues
Problem
Anticipation
Policy Policy
Repercussion Forecasting
Problem
Reaction

161. DSS: Helps Anticipate Issues
Problem
Anticipation
Policy Policy
Repercussion Forecasting
Current
Problem
Reaction

162. DSS: Helps Anticipate Issues
Problem
Anticipation
Need
to be
Policy Policy
Repercussion Forecasting
Problem
Reaction

163. DSS: Helps Anticipate Issues
Problem
Anticipation Cannot
anticipate
with only
Need ‘required’
to be data
Policy Policy
Repercussion Forecasting
Problem
Reaction

164. Help Anticipate
Impact of Policy:
Class Size

165. Help Anticipate
Impact of Policy:
Class Size
Achievement Issues - Does reducing the class size below
grade 4 improve achievement on the state performance test?

166. Help Anticipate
Impact of Policy:
Class Size
Achievement Issues - Does reducing the class size below
grade 4 improve achievement on the state performance test?
Teacher Supply/Demand Issues - Will the current production of
teachers by IHEs meet the increased need for staff at the LEAs?

167. Help Anticipate
Impact of Policy:
Class Size
Achievement Issues - Does reducing the class size below
grade 4 improve achievement on the state performance test?
Teacher Supply/Demand Issues - Will the current production of
teachers by IHEs meet the increased need for staff at the LEAs?
Fiscal Issues - Using average salary, will there be sufficient
funds for LEAs to hire additional staff?

168. Help Anticipate
Impact of Policy:
Class Size
Achievement Issues - Does reducing the class size below
grade 4 improve achievement on the state performance test?
Teacher Supply/Demand Issues - Will the current production of
teachers by IHEs meet the increased need for staff at the LEAs?
Fiscal Issues - Using average salary, will there be sufficient
funds for LEAs to hire additional staff?
Infrastructure Issues - Do buildings have the space for
additional classrooms?

169. Help Anticipate
Impact of Policy:
Class Size
Achievement Issues - Does reducing the class size below
grade 4 improve achievement on the state performance test?
Teacher Supply/Demand Issues - Will the current production of
teachers by IHEs meet the increased need for staff at the LEAs?
Fiscal Issues - Using average salary, will there be sufficient
funds for LEAs to hire additional staff?
Infrastructure Issues - Do buildings have the space for
additional classrooms?
Trend Issues - Will improved achievement impact employment,
graduation or adult life roles?

170. Impact on State Standards

171. Impact on State Standards
Efficiency of System
Inputs Process Outputs

172. Impact on State Standards
Efficiency of System
Inputs Process Outputs
Input issues:
fiscal resources
teacher supply
building structure
technology
poverty

173. Impact on State Standards
Efficiency of System
Inputs Process Outputs
Input issues:
fiscal resources
teacher supply
building structure
technology Process issues:
poverty crime and safety
prof development
attendance
teacher experience
student performance

174. Impact on State Standards
Efficiency of System
Inputs Process Outputs
Input issues: Output issues:
fiscal resources college entrance
teacher supply graduate numbers
building structure retention rates
technology employment
Process issues:
poverty crime and safety
prof development
attendance
teacher experience
student performance

175. Impact on State Standards
Effectiveness of System
Efficiency of System
Inputs Process Outputs Outcomes
Input issues: Output issues:
fiscal resources college entrance
teacher supply graduate numbers
building structure retention rates Outcome issues:
employment works with others
technology Process issues: acquires information
poverty crime and safety understands inter-relationships
prof development allocates resources
attendance works w/variety of tech
teacher experience
student performance Impact Policy

176. Impact on State Standards
Effectiveness of System
Efficiency of System
Inputs Process Outputs Outcomes
Output issues:
college entrance
graduate numbers
retention rates Outcome issues:
pa ct employment works with others
im acquires information
i
ot
ll n ith o nly understands inter-relationships
allocates resources
W yw ata works w/variety of tech
lic re d’ d
p o ui
req Impact Policy
‘

177. pa ct
im
i
ot
ll n ith o nly
W yw ata
lic re d’ d
p o ui
‘ req

178. Finding the Balance
Required Desired
Data Data
Social Integration
Mandatory Vocational Orientation
Measurement in volume Use of Time
(amounts, avg., ranks, percents) Daily Living Skills
Realistic Mobility
Use of Environmental Ques

179. Finding the Balance
Required Desired
Data Data
Social Integration
Mandatory Vocational Orientation
Measurement in volume Use of Time
(amounts, avg., ranks, percents) Daily Living Skills
Realistic Mobility
Use of Environmental Ques
The DSS must help policy makers find a
comfortable balance between
acceptable risks and benefits.

180. Helps in Data Discovery
Input Process Output Outcomes
Issues Issues Issues Issues
General Public
Parents
Teachers
Standards moves from
Support Staff
efficiency to effectiveness
Admin/Boards
State
Legislators
Others

181. One Last Time
Web Front End
District Users
Upload data formats Public Portal Access
Correct duplicate data Predefined Report Cards
FERPA requests Limited queries
Dashboard/Scorecard

182. One Last Time
Web Front End
District Users
Security Upload data formats Public Portal Access
Correct duplicate data Predefined Report Cards
FERPA requests Limited queries
Dashboard/Scorecard
File ETL:
Developer Applications
• Student
• Assessment
• Finance
• Professional
Student IDs
Match & Merge
Check Sum
Audit (FERPA)
Error reports

183. One Last Time
Web Front End
District Users
Security Upload data formats Public Portal Access
Correct duplicate data Predefined Report Cards
FERPA requests Limited queries
Dashboard/Scorecard
File ETL:
Developer Applications
• Student Reliable/
• Assessment Valid
• Finance
• Professional
Student IDs
Match & Merge
Check Sum WAREHOUSE
Audit (FERPA)
GPS
Error reports
School Meta
Codes Data

184. One Last Time
Web Front End
District Users
Security Upload data formats Public Portal Access
Correct duplicate data Predefined Report Cards
FERPA requests Limited queries
Dashboard/Scorecard
File ETL:
Developer Applications
• Student Reliable/
• Assessment Valid Data Mart
• Finance
• Professional
Student IDs
Match & Merge
Check Sum WAREHOUSE
Audit (FERPA)
GPS DoE Users
Error reports Generate Report Card
School Meta Federal: EDEN, NCLB, IDEA
Codes Data
Skopus
Issue Assessment IDs

185. Current problem:
data rich and information poor

186. Current problem:
data rich and information poor
Data
Silos
Department

187. Current problem:
data rich and information poor
Data Gap:
Silos
Lack of confidence
No trust in system
Have a low ROI
Department Educational
Community

188. Solution
Data
Democracy
Data
Warehouse Secure
Scalable
Flexible
Finance
Apply information
Personnel Meta Data Scho ol and facilitate
Meta Data
decision-making
Manual Meta Data P
ent erfor
Stu d ta Manual Manual m
Meta ance
D a Dat
Meta al Manu a
Manu al
Department Educational
Community

189. Solution
Data
Democracy
Data
Warehouse Secure
Scalable
Flexible
Finance
Apply information
Personnel Meta Data Scho ol and facilitate
Meta Data
decision-making
Manual Meta Data P
ent erfor
Stu d ta Manual Manual m
Meta ance
D a Dat
Meta al Manu a
Manu al
Department Educational
Community

191. Lucian_Parshall@ameritech.net

192. Without
Data
You’re Just
Another Person
With an Opinion

193. We find
ourselves in an
Information Age
with an aging
information
system

194. Decisions
begin with
good data

195. Most of the fun using the
DSS is not finding the
answer to your question -
it’s finding the new
questions you don’t have
the answers to.