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
is the process of
detecting and correcting corrupt or inaccurate
records from a record set, table, or database and
refers to identifying incomplete, incorrect,
inaccurate or irrelevant parts of the data and then
replacing, modifying, or deleting the dirty or
coarse data.
Data cleansing
the process
enhances the reliability of the information being
used by an organization
maintaining data integrity
is a technologyenabled discipline in which business and
information technology work together to ensure
the uniformity, accuracy, stewardship, semantic
consistency and accountability of the
enterprise’s official shared master data assets.
Master data management
involves combining data
residing in different sources and providing users
with a unified view of them.
Data integration
refers to the decomposition of fields into component parts and formatting the values into consistent layouts
Parsing and Standardization
is the modification of data values to meet the domain restrictions, constraints on integrity
Generalized Cleansing
is the identification merging of related entries within across data sets
Matching
refers to the analysis of data to capture statistics or metadata to determine the quality of data and identify data quality issues
Profiling
refers to the deployment of controls to ensure conformity of data to business rules set by the organization
Monitoring