Process for Data Quality Assurance
[Pages:26]"Good decisions require good data"
Process for
Data Quality Assurance
at Manitoba Centre for Health Policy (MCHP)
Mahmoud Azimaee Data Analyst at ICES
Literature and Resources
? CIHI Data Quality Framework, (2009
edition)
? UK's NHS Data Quality Reports ? Handbook on Data Quality
Assessment Methods and Tools,
(European Commission)
? Handbook on Improving Quality by
Analysis of Process Variables ,
(European Commission)
? Data fitness
(Australian National Statistical Service)
Data Quality at MCHP
1. Data Quality Indicators 2. Rating System
? CIHI Data Quality Framework,
(2009 edition)
3. Data Quality Report
? UK's NHS Data Quality Reports
4. Practical Approach 5. Automation
? Cody's Data Cleaning Techniques Using SAS, (by Ron Cody)
Data Quality Indicators and Rating
? Example:
System
? Completeness: Rate of missing values for all data elements.
? Consistency : Agreement with registry database.
MCHP Data Quality Framework:
Data Quality Assurance
Accuracy
Database Level (In Data Management)
Research Level (In a Specific Research Projects)
Internal Validity
External Validity
Timeliness Interpretability
Accuracy
Reliability
Completeness
(Missing Values)
Correctness
(Invalid codes, Invalid Dates, Out of Range, Outliers and Extreme Observations)
Internal Consistency
Stability Across Time
Linkability
Identifying Units of Analysis (Persons, Places, Things, ...)
Level of Agreement With the Literature and available reports
Time to Acquisition
Time to Release
Currency of Data
Availability and Quality of:
Documents , Policies and Procedures, Formats Libraries, Metadata, Data Model Diagrams
Completeness
Measurement Error
Level of Bias
Degree of Problems with Consistency
Level of Agreement With Other Databases
Data Management Process at MCHP
1. Formulate the Request and Receive the Data
Check the data sharing
agreements
Liaise with the source agency to acquire available data, data model diagram, data dictionary, documentation about historical changes in data content, format, and structure, data quality reports
Prepare the data request
letter
Receive the data and associated documentation
2. Become Familiar with Data Structure and Content
Review provided documentation
If required, create a data model for the original data
If receiving test data, test it and send feedback to the source agency
3. Apply SAS Programs
Apply Normalization or De-normalization as required
Normalization can be defined as the practice of optimizing table structures by eliminating redundancy and inconsistent dependency
Apply data field and SAS format
standards
Install on SPD server
(This includes indexing, sorting and clustering)
Create Metadata
If there is a problem, liaise with the source
agency
4. Evaluate Data Quality
Test the installed data using standardized protocol
Identify solutions to address deficiencies in data quality
Prepare data quality report for addition to standard documentation
5. Document Data
Including original documents, data model diagram, SPDS data dictionary, history, file variations and structural changes, revisions and common problems and data quality report, where available
6. Release Data to Analyst(s) and Researcher(s)
Meet with programmer(s) and researcher(s) to present data structure and content
How to Present Data Quality Results?
? CIHI Data Quality Report
? UK's NHS Data Quality Report
? VODIM Test Analysis Methodology
? Valid ? Other ? Default ? Invalid
? Valid ? Invalid ? Missing ? Outlier
VIMO!
? Missing
VIMO Table
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