1. SCASBO
Data Collection Points of Failure and Impact on Poverty Identification
Data Governance
Fiscal Impact Exemplar

2. Essential Questions Think – Pair - Share
What are the key data elements that impact funding in your district?
Who owns the data accuracy for those elements?
Who does the data impact?

3. SCDE DATA Definition District and/or School Training
Report delivered on time to intended audience
SCDE
DATA
Definition
Program Office data validation and report generation
Program Office data request to ORDA
District and/or School Training
ORDA reception of data
Program Office reception of data
Data formatting
(example PowerSchool)
Data transmission to SCDE
Collection of data at school level

4. Data Governance Formalizing of Data Processes and Reporting
Accuracy (are the data correct?)
Coherence (does the data mean the same thing across districts?)
Timeliness (is the collection cycle aligned with the reporting expectations)
Stewardship (who is shepherding data through the data cycle?)
Security (what is the appropriate level of access to this data?)

5. Food and Nutrition Example Data Cycle (process)
Supplemental Nutrition Assistance Program (SNAP)
Protected from identification
Source Department of Social Services
Matched From DSS to SCDE name match
Address match to identify balance of students in the household
File is distributed by school to food and nutrition systems
Moved by Level Data into PowerSchool (end of current process)

6. Food and Nutrition (future)
Level Data through PowerSchool moves Migrant, Homeless, Foster, SNAP and TANF data to single table (hidden)
Determines if a student is a pupil in poverty
Data returns to SCDE
For funding (Finance Office)
For grants (Grants Office)
For accountability (Research and Data Analysis)
For loan forgiveness (South Carolina Student Loan)
For program evaluation (varies)

7. People Responsible for Poverty Indicator
Points of failure
Match not correct by Data Management
SNAP not claimed at Food and Nutrition
Address incorrect in PowerSchool (who owns this)
Migrant not identified
Foster not identified
Homeless not identified

8. Interventions at Points of Failure
Data Match Quality Control (SCDE)
Food and Nutrition training
Address (??)
Migrant training and valid migrant id match to Federal Database
Foster training and match to DSS file
Homeless training

9. Role Clarification Training Program Office vs Training PowerSchool
Follows current definitions and reporting requirements in order to meet appropriate guidelines
Meets with PowerSchool clerk to identify data to collect and format required
Meets with State counterparts to train elements supplied to PowerSchool staff
PowerSchool
Customize PowerSchool to receive new data
Train PowerSchool clerks on new data fields
Collect and report to program offices

10. Data Governance by District Identification of Report Validation
Who in the District can validate the quality of the data produced?
Who can see this report data based on program requirements?

11. Examples of Validation Opportunity
Area of potential loss of revenue
Large
Medium
Small
Missing Primary EFA 14 17 2 3 39
Misaligned EFA codes 50
Potentially incorrect Entry Code 1 20
Missing State Student Number 10 6 27
ExitDate/ExitCode Mismatch 34
Incorrect ExitDate
Excluded from State Reporting 11
Overall Analysis (Random sampling of two small, medium, and large districts)
Any number greater than zero MAY be negatively affecting funding. Data entry omissions or mistakes in entry can affect either of two main factors in revenue: the identification of proper EFA coding and the amount of ADM identified for a school Monetizing the errors will help magnify the importance of getting this data correct.
. By creating additional validation rules, we will only help districts even more to identify any shortfalls or potential loss of revenue.