Operational Managers; using data for better decisions 2008 Annual Child Support Training Conference & Expo California Department of Child Support Services

What is Data Analysis?  Data analysis is the act of transforming data with the aim of extracting useful information (Wikipedia).  Data Mining: statistical and logical analysis of large transactional data, looking for patterns that can aid decision making.

Management Decisions  What decisions can be made from data? Understanding what influences performance Understanding what influences performance Understanding how it influences performance Understanding how it influences performance Resource Allocation Resource Allocation Business Practice Business Practice

Performance Improvement  Need To Be Specific  Can it be Measured?  How Do You Measure it?

Ask The Right Questions Research Example  Do You Really Want To Win The Lottery?  The Wal-Mart Example  The Health Care Example

CSE Data  CSE Reports  Hyperion Tool (BQY’s)  Database Warehouse

Levels of Data Analysis Levels of Data Analysis Level 1 = User Level Case look up Level 2 = System reports Collection Totals Level 3 = Aggregate System Reporting CS 1257 Level 4 = Aggregate Data Reporting Comparative Data Analysis Level 5 = Analytics ProgramEvaluation Examples of

Levels of Data Analysis Levels of Data Analysis Level 5 = Analytics Level 4 = Aggregate Data Reporting Reporting Level 3 = Aggregate System Reporting Level 2 = System reports Level 1 = User Level

Bringing It All Together  Tools – Microsoft Excel and Access  Pre and Post Designs – What Are They?  Examine Difference Between Groups  Impact to FM3, FM4, and Net Distributed Collections

The Credit Card Example  Does Implementing a Credit Card Solution Bring in New Money?  Overall Impact to FM3

The Credit Card Example

Pre and Post Designs

The Credit Card Example

The Early Intervention Example  Pre-Default Intervention  Understanding The Data Makes A Difference

Examine Difference Between Groups

SET (Special Enforcement) Program  Overview of Orange County’s SET  Challenges of SET  What Are The Right Questions  How Can We Improve Performance

SET (Special Enforcement) Research Methodology  Goals of Project – Research Questions  Data Sources = Court Calendar, ARS Financial Data.  Design = Financial Patterns 12 months Pre vs. 12 months Post Hearing  Group Identification = Judgment Debtor/Job Seek, Overlap, Contempt

SET Results

* Extrapolated value based on FFY 2007 number of initial settings multiplied by the FFY 2006 average per case collection.

SET Results * Data for FFY 2007 represents payments through January 2008.

SET Results * Data for FFY 2007 represents payments through January 2008.

How Was The Information Used?  From the Results What Decisions Were Made Regarding the SET Program?  How Does The Information Impact The Program?

Judicial Modifications (Review and Adjustment)  Overview of Orange County’s R&A  Challenges of R&A  What Are The Right Questions  How Can We Improve Performance

Program Improvement Approach Extract data from Court Calendar and ARS. Measure impact to Federal Measure 3. Adjust business practice for continuous performance improvement. How do Judicial Modifications impact Federal Measure 3?

Judicial Modification Research Methodology  Goals of Project – Research Questions  Data Sources = Court Calendar, ARS Financial Data.  Design = Financial Patterns 6 months Pre vs. 6 months Post Hearing  Group Identification = 5 Groups

Judicial Modification Research Methodology FFY 2004  Groups Identified (Type of Modification) Upward ( $0 Support to Dollar) Upward ( $0 Support to Dollar) Upward (Dollar Support to Higher Dollar) Upward (Dollar Support to Higher Dollar) Downward (Dollar Support to $0 Support) Downward (Dollar Support to $0 Support) Downward (Dollar Support to Lower Dollar) Downward (Dollar Support to Lower Dollar) No Change (Support Order Remains Same) No Change (Support Order Remains Same)

For FFY ,391 Modifications Were Examined No Change 614 or 25.7% Upward (zero to dollar) 257 or 10.7% Upward (dollar to dollar) 771 or 32.2% Downward (dollar to dollar) 560 or 23.4% Downward (dollar to zero) 189 or 7.9%

Upward Modifications (N=771) Federal Measure 3 6 Months Pre Mod 6 Months Post Mod Variation% Change CS Due$1,451,736$2,488,645$1,036, % CS Distributed$1,193,019$1,894,937$701, % % CS Distributed82.2%76.1%-6.1%-7.4% 82.2% 76.1% 0.0% 20.0% 40.0% 60.0% 80.0% 100.0% 6 MONTHS PRIOR TO MOD 6 MONTHS POST MOD

Downward Modifications (N=560) Federal Measure 3 6 Months Pre Mod 6 Months Post Mod Variation% Change CS Due$1,741,245$1,074,975-$666, % CS Distributed$864,868$753,703-$111, % % CS Distributed49.7%70.1%20.4%41.0% 49.7% 70.1% 0.0% 20.0% 40.0% 60.0% 80.0% 100.0% 6 MONTHS PRIOR TO MOD6 MONTHS POST MOD

Type of Modification Measure 6 Months Pre Mod 6 Months Post Mod Variation% Change Upward (zero to dollar) N = 257 CS Due.$1,162,677.. CS Distributed.$528,021.. % CS Distributed.45.4%.. Downward (dollar to zero) N = 189 CS Due$257, CS Distributed$38, % CS Distributed15.0%... No Change N = 614 CS Due$244,110$211,373-$32,737*-13.4% CS Distributed$107,427$132,251$24, % % CS Distributed44.0%62.6%22.6%42.3% * The total current support due variance is due to accrual credits/negative adjustments. Other Areas of Modifications

FFY 2007 Results  11.5% Reduction in Total Modifications in FFY 2007 (2,117) vs. FFY 2004 (2,391).  FFY 2007 Had A Higher Proportion (49.4%) of Upward Modifications (dollar to dollar) vs. FFY 2004 (32.2%).  Similar Results As FFY 2004 (Upward Modifications Improved Collections, Downward Modifications Improved FM3.  No Change Group Still Witnessed Improved Collections.

How Was The Information Used?  From the Results What Decisions Were Made Regarding Judicial Modifications?  How Does The Information Impact The Program?

SET Tool Demonstration

Contact Information  Mark Takayesu (714) ;  Patty Welch (714) ;  Steve Eldred (714) ( );