Data Warehouse – National State
and Local Perspective
Presenters:
Mark Takayesu, Orange County Department of Child
Support Services, Mtakayesu@css.ocgov.com
Tank Waklee, California Department of Child
Support Services, Tank.Waklee@dcss.ca.gov
Jennifer Effie, San Diego Department of Child
Support Services, Jennifer.Effie@sdcounty.ca.gov
Data Warehouse – National
State and Local Perspective
What Is A Data Warehouse?
While states vary in definitions – A Data
Warehouse is a Computer System that stores
information to facilitate reporting and
analyzing data to obtain information and
facilitate decision making
Source – “Enhancing Child Support Efforts: Summary of Data Warehouse Efforts
in Nine States” – The Lewin Group 2006
Data Warehouse – National
State and Local Perspective
Want to cook Pumpkin Ravioli with Sage
Brown Butter for an important client for
dinner?
Data Warehouse – National
State and Local Perspective
Data Warehouse-National
State and Local Perspective
Data Warehouse – National
and State Perspective
• Data Modeling Workgroup – Sponsored by
the Federal Office of Child Support
Enforcement – Facilitated by Richard Ordowich
• Began Meeting in March of 2009
• Objective -Develop a standard data
warehouse model and data definitions, and
provide a forum for data warehouse sharing
between states
• Have a deliverable within 12 months
• Developed based on business need
Data Warehouse – National
and State Perspective
• Participating States and Organizations – 14 states
•There is no “one size fits all” data warehouse
OCSE
ACF
Vermont
Massachusetts
Illinois
Virginia
Colorado
Texas
Hawaii
Washington
Louisiana
Minnesota
California
Arizona
North Dakota
Nevada
Data Warehouse – National
and State Perspective
Washington
Policy development research and statistical
analysis: help staff understand cause and effect
analysis (how case workers actions affect
outcomes).
Cost Avoidance Studies
Illinois
Ad-Hoc reports for managers to filter through
cases requiring action. Monthly Management
Dashboard Reporting
Minnesota
Operational Research Analysis, Fraud
Investigation, Quality Assurance Review
Hawaii
Business Process Management (streamline
staff’s work processes, customer service etc.)
Quality Assurance, Performance Measurement
Vermont
Data Mining – medical support (focus on cases
with highest potential for medical support), audit
prediction, payment prediction need for
intervention, operational reports for supervisors,
Dashboard, Ad Hoc reporting
Data Warehouse –
Vermont Example
Data Warehouse –
Vermont Example
Data Warehouse –
Vermont Example
Data Warehouse –
Vermont Example
Data Warehouse –
Vermont Example
Data Warehouse –
Vermont Example
Data Warehouse –
Vermont Example
Data Warehouse –
Project Objectives
• Agree to start with the OCSE – 157
• Develop a model based on
–Dimensions
–Facts
Data Warehouse –
Project Objectives
Examples of Dimensions And Facts
Dimension Examples (descriptive values)
Time
Year, Month, Day, etc
Case Info
Interjurisdictional Code (International/Tribal/Interstate/No
Jurisdiction)
IV-D/Non IV-D Code
Interjurisdictional Status (Initiating/Responding)
Person Info
Person Role Code (CP/NCP/Child/Employee)
Medical Coverage Code (Y/N)
Data Warehouse –
Project Objectives
Examples of Dimensions And Facts
Fact Examples (numeric values used in calculations)
Case counts
Person counts
Current support due at month end $
Current support distributed at month end $
Total arrears due at month end $
Arrears distributed at month end $
Data Warehouse –
Project Objectives
Line Identifier
Line 2
Line 2a
Line 2b
Description
Cases WithSupport Orders Established
Interstate Cases Initiated
Interstate Cases Received
Ar
re
ar
s
Cu
r
Su rent
pp
or
t
ed
Ob
lig
at
Pa
re
nt
Fact Areas
ag
e
OCSE 157 Annual Report Fact Descriptions
Data Warehouse –
Orange County Example
Interstate Initiating vs. Responding
Monthly Percent of Current Support Collected
Initiating = 8,345 Cases, Responding = 3,807 Cases
100.0%
90.0%
80.0%
70.0%
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
Initiating
55.7%
44.3%
Oct-08
60.5%
51.4%
39.4%
Nov-08
45.0%
Dec-08
Responding
55.1%
54.9%
40.4%
40.3%
Jan-09
Feb-09
56.9%
58.8%
43.7%
Mar-09
55.0%
55.2%
42.9%
40.0%
42.8%
Apr-09
May-09
Jun-09
Data Warehouse –
Orange County Example
Interstate Initiating
Monthly Percent of Current Support Collected
Current, Former Never
80.0%
60.0%
40.0%
20.0%
0.0%
Oct-08
Nov-08
Dec-08
Jan-09
Feb-09
M ar-09
Apr-09
M ay-09
Jun-09
Current
26.7%
28.1%
28.4%
28.7%
24.0%
27.9%
24.2%
22.3%
27.1%
Former
44.3%
40.5%
46.3%
40.3%
42.7%
47.4%
47.2%
43.0%
46.9%
Never
48.9%
41.5%
48.4%
43.6%
42.6%
44.8%
44.4%
42.2%
43.4%
CSE BDDR
•
•
•
•
•
•
Why
What
Where
Who
How
And then Some
Why
• Why we Got Here!
– Reports requests take too long
– Query requests take too long
– Not enough State Staff resources
to fulfill these requests
– Listening to you (Top 5)
– This is YOUR System/YOUR Data
What
• What is BDDR
– “Back Door Data Repository”
– Raw Data from CSE
– De-normalized where possible
• Based on Counties perspective
– Name competition
What BDDR is Not
• Buzz Word - Data Warehouse
• Core data is not
– Aggregated
– Summarized
– Some History
What Software
• How can I access BDDR
– MS Access
– SQL Server Mgmt Studio
– Toad
– SAS
– Fox Pro
– Anything that allows port 50217
What’s in BDDR Now
•
•
•
•
•
Approx 75 Tables
Core CSE Data
1257
34/35
Misc –
– Dax Reports
– EI
– CSLN
• What ever data you want! - - Did I
just say that!!!
What Means What
What’s in BDDR
• Types – Append, Complete,
Snapshot
Where
• BDDR is a 2005 SQL Server
located in Rancho Cordova
OTECH Data Center
• Uses the DTS Network (not
internet)
• SQL Port 50217
Who
• Who is Using BDDR
How
• How do I get access
• Two users per county
• How does the data get there
eBHC
OTECH
DCSS Project Office
Future Plans Near
• More Data
– Employer Demographics
– Task
– Guideline Calculator
– What ever the Counties Request
• Data Dictionary
• Reporting Services
• BDDR Blog
Future Plans Near
Future Plans Far
•
•
•
•
AIX
DB2
Fully Automated ETL
Faster loads from CSE
– Bi-weekly
– Weekly
– Daily
Future
Now
System Performance
Data Analysis
• Quantitative vs. Qualitative Evaluation
– Quantitative: 1257 line comparisons
between counties, months, years, etc.
– Qualitative: Why are the case counts and
dollars moving up or moving down?
– the goal is to discover the story behind the
numbers…that’s what BDDR gives us
Access to Data
•
•
•
•
•
Identifies problems
Maximizes resources
Communicates project results
Enhances decision making ability
Allows for continuous program
improvement
Pieces of a Puzzle
• Querying & analyzing data is part
science and part creative writing
– Success requires time, patience, &
vision
– Collaboration among IT, Analysts,
Child Support Staff, other counties, &
“puzzle-solvers”
Information Sharing
• Sharing information and resources is
key to our success
– BDDR Sharing Forum & PAW Blog
– Data Dictionary
– OC & SD Documentation
– Case Management Tool (Los Angeles)
– County SMEs
San Diego Documentation
41
Data Groupings
•
•
•
•
•
•
•
Case
Participant
Legal
Accounts/Payments
Public Assistance
34/35 and 1257
Reference Info
Case Data
•
•
•
•
•
•
•
•
•
Case and Account Summary
Case Status History
Case Type History
Case Management History
Case Assignment History
Interstate Perspective
Events
Review & Adjustment
Early Intervention Activity Logs
Participant Data
•
•
•
•
•
•
•
•
•
•
•
Case Participant
Participant Relationship
Participant Demographics
Demographic Source History
Phone Numbers
Prisoner Information
Income
Employer
Wage Assignment
License
Liens
Legal Activity
• Court Case
• Legal Activity (including BK, WC,
Contempt, Liens, and Support)
• Service Activity
• Support Terms
Accounts & Payments
• Case Accounts
• Account Transactions and Summary
Transactions
• Charging Instructions
• Logical & Physical Collections
• Remittance Advice
• Disbursement
• Payables
Public Assistance
•
•
•
•
Participant Public Assistance
Public Assistance Case
Public Assistance Case Balance
Public Assistance Case Transactions
CS 34 & 1257
• CS 34/35
– Collections
– Disbursements
– Undistributed Collections
• 1257
– FFY Detail for 2006/2007, 2007/2008,
and 2008/2009
– Financial Detail
– FFY Summary
Building a Query
Assessing MNO Child Only Cases using
Microsoft Query Tool
49
Building a Query
50
Building a Query
51
Building a Query
52
Building a Query
53
Building a Query
54
Building a Query
55
Parting thoughts…
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•
•
•
•
Find a tool that works for you
Don’t reinvent the wheel
Ask questions
Share Ideas
Study, study, study!!!
Thank you
Questions?
(Besides why Tank is named Tank”!)