# Presentation: 196448

## Data Quality Case Study

- Prepared
- by
- ORC Macro

## Background
Data Correction
Tracking system
SAS AF query application
Guidelines
Profile Analysis
SSNs
Names

- Data Correction
- Tracking system
- SAS AF query application
- Guidelines
- Profile Analysis
- SSNs
- Names

- Data Correction

## Profile Analysis—SSNs

## Profile Analysis—SSNs

**Shared SSNs (n=7,100)**

- Different Names
- 27%

- Candidates for Correction

- Same or Similar Names
- 73%

- Candidates for Collapse

## Profile Analysis—Names

## Profile Analysis—Names

## Definition
Statistics
Status

- Statistics
- Status

- OLTP—Commons Cases

## Identifying the extent of the problem
Investigating based on type of error
Validating the investigation
Implementing the change
Tracking the identification, investigation, validation, and implementation

- Investigating based on type of error
- Validating the investigation
- Implementing the change
- Tracking the identification, investigation, validation, and implementation

- Data Correction

## PERSON_ID=3070908—PPRF record

Identification of problem
Two different middle initials found
Investigation of problem
TA module
Scripts run
Validation of information
Name, SSN, degree(s), grant(s)
Sources

- Identification of problem
- Two different middle initials found
- Investigation of problem
- TA module
- Scripts run
- Validation of information
- Name, SSN, degree(s), grant(s)
- Sources

- Data Correction—An Example

## PERSON_ID=3070908—PPRF record

Implementation of correction
Grants report submitted to NIH OD
Tracking of correction
Internal tracking system
Post-correction
Loss of control of data

- Implementation of correction
- Grants report submitted to NIH OD
- Tracking of correction
- Internal tracking system
- Post-correction
- Loss of control of data

- Data Correction—An Example

## Developing a
Data Quality Business Plan

- Data Quality Business Plan

## Focus of Our Activities

- Examination of the Database, Procedures, and Interface

- Development of Modified Use Cases

- Unified Modeling Language

- Identification and Extraction
- of Business Rules

- Identification of Business
- Model

## Data Quality Issues

- Type-over of information
- Generation of duplicate persons
- Collapsing
- Changes in degree and address data
- Generation of orphans

## Type-Over Practices 

- Intentions:
- Assign a new principal investigator (PI) to a grant
- Change the name of a PI on a grant
- Correct a misspelled name
- Consequences:
- Inclusion of incorrect information in a person profile
- Absence of linkages between PIs and grant applications
- Creation of false linkages between PIs and grant applications

## Factors Affecting Quality

- Relatively easy access to person-related data elements
- Lack of self-validation routines
- Interface issues

## Solutions

- Restricted access
- Quality control validation
- Interface simplification
- Self-validation algorithm

## Data Quality Validation

- Who does it?
- ICs
- A Quality Assurance group
- Other

- How is it done?
- Staging areas
- Manual and intelligent filtering
- Architecture

## GM Module Screen COM1100

**GM Module Screen COM1100**

## Self Validation

- Name-matching algorithm
- Consistency checking

## Higher-Level Analysis

- The following are being examined relative to their effect on quality:
- 	Commons interface with IMPAC II
- 	Database redundancy
- 	Business rules in the database
- 	Master person file
- 	Front-end design
- 	Human factors
- 	Ownership

## Development of a
Data Quality Model

- Data Quality Model

## Evaluate the different identification algorithms currently in use for IMPAC II
Develop identification algorithm(s) and procedures
Serve as consultant and guarantor of efficacy of algorithm implementation

- Develop identification algorithm(s) and procedures
- Serve as consultant and guarantor of efficacy of algorithm implementation

- Major Goals

**Quality improvements plan for personal identifiers**

## Understanding the technical infrastructure
Identification of specific areas of concern
Development/proposal of data quality expectations
Development/proposal of appropriate, acceptable solutions

- Identification of specific areas of concern
- Development/proposal of data quality expectations
- Development/proposal of appropriate, acceptable solutions

- Moving Forward

## Outline
Definition
Rules
Risks and Costs
NIH Expectations
Process
Measurements/Metrics
Testing
Continuous Improvements
Conclusions

- Definition
- Rules
- Risks and Costs
- NIH Expectations
- Process
- Measurements/Metrics
- Testing
- Continuous Improvements
- Conclusions

- Data Quality White Paper

- Knowledge assets are very real and carry tremendous value.

## Conclusion

- Data Quality Expectations

- Develop-
- ment/Proposal
- of Appropriate, Acceptable Solutions

- Identification of Specific Areas of Concern

- Understanding the Technical Infrastructure

- Examination of the Database, Procedures, and Interface

- Development of Modified Use Cases

- Unified Modeling Language

- Identification and Extraction
- of Business Rules

- Identification of
- Business
- Model