HIS Lesson 8 Flashcards
has become a major concern for
large companies especially in the areas of customer
relationship management data integration,
and regulation requirements.
Data quality
is the overall utility of a dataset(s) as
a function of its ability to be processed easily and
analyzed for a database, data warehouse, or data
analytics system.
Data quality
signifies the data’s appropriateness
to serve its purpose in a given context.
Data quality
Aspects of Data Quality
- Accuracy
- Completeness
- Reliability
- Relevance
- Consistency
- Presentability
- Accesability
is a tool
that allows the use of small random samples to
distinguish between different groups of data elements (or
lots) with high and low data quality.
Lot Quality Assurance Sampling (LQAS)
methodology provides real-time planning and
management information. It uses small sample
sizes to classify health or administrative
geographical areas , to inform if these areas
have achieved or not a pre-determined target
for a given indicator.
Lot Quality Assurance Sampling (LQAS)
tool is a simplified version of the Data Quality Audit
(DQA) tool which allows programs and projects to
verify and assess the quality of their reported data.
Routine Data Quality Assessment (RDQA)
is a multipurpose tool that is most
effective when routinely used.
Routine Data Quality Assessment (RDQA)
is a project management
tool that illustrates how a project is expected to progress
at a high level.
Implementation plan
Address the question,
“What do you want to accomplish?”
Define Goals/Objectives
Outline the deadline and
timelines in the implementation phase.
Schedule Milestones
Determine whether you have
sufficient resources, and decide how you will
procure those missing.
Allocate Resources
Create a
general team plan with overall roles that each team
member will play.
Designate Team Member Responsibilities
How will you determine
if you have achieved your goal?
Define Metrics for Success
analyzes information and
identifies incomplete or incorrect data.
Data Quality Tool