HMIS DATA QUALITY
LESSON 8
HANNAH JOY ROCHA, RMT
DATA QUALITY
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 SYSTE M.
ASPECTS OF DATA
QUALITY
ACCURACY
COMPLETENESS
UPDATE STATUS
RELEVANCE
CONSISTENCY
RELIABILITY
APPROPRIATE PRESENTATION
ACCESSIBILITY
Having quality data means that the data is useful and consistent.
Data cleansing can be done to raise the quality of available data
DATA ACCURACY USING LOT QUALITY ASSESSMENT
SAMPLING (LQAS)
•
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
•
Using small samples make conducting surveys or supervision more efficient.
ROUTINE DATA QUALITY ASSESSMENT (RDQA)
•
a simplified version of the Data Quality Audit (DQA) which allows programs
and projects to verify and assess the quality of reported data
•
aims to strengthen data management and reporting system
DEVELOPMENT IMPLEMENTATION PLAN
IMPLEMENTATION PLAN
•
a project management tool that shows how a project will evolve at a high
level
•
ensures that a development team is working to deliver and complete tasks on
time
•
Development of Implementation plan is important to ensure that
communication between those involved in the project will not encounter any
issues and work will be delivered on time
Key Components
•
DEFINE GOALS/OBJECTIVES: “What do you want to accomplish?”
•
SCHEDULE MILESTONES: Outline the high level schedule in implementation
phase
•
ALLOCATE RESOURCES: Determine whether you have sufficient resources,
decide how you will procure missing
•
DESIGNATE TEAM MEMBER RESPONSIBILITIES: Create a general team plan
with overall roles that each team member will play
•
DEFINE METRICS FOR SUCCESS: How will you determine if you have achieved
your goal?
DATA QUALITY TOOLS
•
analyzes information and identifies incomplete or incorrect data
•
Parsing and standardization: decomposition of fields into component parts and formatting values
into consistent layouts based on industry standards and patterns and user-defined business rules.
•
Generalized “cleansing”: modification of data values to meet domain restrictions, constraints on
integrity, or other rules that define data quality as sufficient for the organization
•
Matching: identification and merging related entries within or across data sets
•
Monitoring: deployment of controls to ensure conformity of data to business rules set by the
organization
•
Enrichment: enhancing the value of the data by using related attributes from external resources
such as consumer demographic attributes or geographic descriptors.
APPLICATION/SCOPE OF DATA QUALITY TOOLS
•
First generation data quality tools was characterized by dedicated data cleansing
tools designed to address NORMALIZATION and DE-DUPLICATION.
•
DATA QUALITY MANAGEMENT (DQM) - generally integrate profiling, parsing,
standardization, cleansing, and matching processes.
ROOT CAUSE ANALYSIS
•
A class of problem-solving methods aimed at identifying the root causes of the
problems or events instead of simply addressing the obvious symptoms
•
Aims to improve quality of products by using systematic ways in order to be
effective
TECHNIQUES IN ROOT CAUSE ANALYSIS
•
Ask “WHY” 5 times: done by identifying the problem at hand and progressively
unveiling the underlying cause by asking why 5 times
•
Failure Mode and Effects Analysis (FMEA): determine failure modes, determine no
of times a cause of failure occurs, identify actions implemented to prevent
recurrence, check if actions are effective and efficient
•
Pareto Analysis: 20% work creates 80% results. Helpful in multiple causes of a
problem
•
Fault Tree Analysis: root cause are determined using “Boolean Logic”. Listed in
diagram shaped inverted tree. Used in risk analysis and safety analysis.
TECHNIQUES IN ROOT CAUSE ANALYSIS
•
Current Reality Tree (CRT): getting the root cause of all problems all at once.
“If-then” statements are used.
•
Fishbone or Ishikawa or Cause-and-Effect Diagrams: categorizes causes into
people, measurements, methods, materials, environment, machines
•
Kepner- Tregoe Technique: “Rational Process”. breakdowns the problem to its
root cause/s by not only identifying the causes but by appraising the situation
as well.
•
RPR Problem Diagnosis:”Rapid Problem Resolution” Problems are diagnosed
in three phases Discover, Investigate, Fix