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
is the enhancement of the value of the data by using related attributes from external sources
Enrichment
which allow the
optimization of the alimentation process
Extract, Transform, Load (ETL) tools
which generally integrates profiling, parsing,
standardization, cleansing, and matching processes.
Data Quality Management
is a problem solving
method that identifies the root causes of problems or
events instead of simply addressing the obvious
symptoms.
Root cause analysis
s is among the core building
blocks in the continuous improvement efforts of an
organization in terms of its operation dynamics,
especially in the way it handles information.
Root cause analysis
aims to find various modes for failure within a system
Failure Mode and Effects Analysis (FMEA)
- It is used when there are
multiple potential causes to a problem. - uses the Pareto principle
which states that 20 percent of the work creates 80
percent of the results.
Pareto analysis
is used in risk
and safety analysis. It uses of Boolean logic to
determine the root causes of an undesirable event.
Fault Tree Analysis
is used when the root
causes of multiple problems need to be analyzed all
at once. The problems are listed down followed by
the potential cause for a problem
Current Reality Tree (CRT)
The diagram looks like a
fishbone as it shows the categorized causes and subcauses of a problem. This diagramming technique is
useful in grouping causes into categories
Fishbone or Ishikawa or Cause-and-Effect Diagram
breaks a problem
down to its root cause by assessing a situation using
priorities and orders of concern for specific issues.
Kepner-Tregoe Technique
- Another technique for root cause analysis.
- which diagnoses the
causes of recurrent problems
Rapid
Problem Resolution
data gathering and analysis of the findings
Discover
creation of a diagnostic plan and identification
of the root cause through careful analysis of the diagnostic
data
Investigate
fix the problem and monitor to confirm and validate
that the correct root cause was identified.
Fix
