HMIS DATA QUALITY Flashcards
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
Perception of the data’s
appropriateness to serve its
purpose in a given context
data quality
aspects of data quality
accuracy accessibility appropriate presentation completeness consistency relevance reliability update status
LQAS means
LOT QUALITY ASSESSMENT SAMPLING
Tool that allows the use of small random samples to distinguish between different groups of data elements with high and low data quality
LOT QUALITY ASSESSMENT SAMPLING
RDQA means
ROUTINE DATA QUALITY ASSESSMENT
Simplified version of the Data Quality Audit (DQA) which allows programs and projects to verify and assess the quality of their reported data
ROUTINE DATA QUALITY ASSESSMENT
RDQA
RDQA OBJECTIVES
- verify rapidly
- implement
- monitor
the quality of reported data for key indicators at selected sites and the ability of data-management systems to collect, manage, and report quality data
verify rapidly
corrective measures with action plans (one of RDQA Objectives)
implement
capacity improvements and performance of the data management and reporting system to produce quality data (under RDQA objectives)
monitor
A project management tool that shows how a project will evolve at a high level
IMPLEMENTATION PLAN
Helps ensure that a development team is working to deliver and complete tasks on time
IMPLEMENTATION PLAN
IMPLEMENTATION PLAN KEY
CONCEPTS
(1) Define Goals/Objectives
(2) Schedule Milestones
(3) Allocate Resources
(4) Designate Team Member Responsibilities
(5) Define Metrics for Success
Answers the question
“What do you want to accomplish?”
Define Goals/Objectives:
Outline the high level
schedule in the implementation phase.
• Schedule Milestones:
Determine whether you
have sufficient resources, and decide how you will
procure what’s missing.
Allocate Resources
Create a general team plan with the 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
refers to the decomposition of fields into component parts and formatting the values into consistent layouts based on industry standards and patterns and user-defined business rule
Parsing and Standardization
- Modification of data values to meet domain restrictions
- Constraints on the integrity of other rules that define data quality as sufficient for the organization
generalized “cleansing”
This is the identification and merging related entries within or across data sets
matching
Refers to the analysis of data to capture statistics or metadata to determines the quality of the data and identify data quality issues
profiling
The deployment of controls to ensure conformity of data to business rules by the organization
monitoring
Enhancing the value of the data by using related attributes from external sources such as consumer demographic attributes of geographic descriptors
enrichment
Focus on Data Quality Management (DQM), which generally integrate profiling, parsing, standardization, cleansing and matching processes
APPLICATION / SCOPE OF DATA QUALITY TOOLS
A class of problem solving methods aimed at identifying the root causes of the problems or events instead of simply addressing the obvious symptoms
ROOT CAUSE ANALYSIS
Useful for getting to the underlying causes of a
problem
5 WHYS ANALYSIS (ASK WHY 5 TIMES)
• By identifying the problem, and then asking “why”
five times - getting progressively deeper into the
problem, the root cause can be strategically
identified and tackled
5 WHYS ANALYSIS (ASK WHY 5 TIMES)
Aimed to find various modes for failure within a system. It requires several steps for execution:
- All failure modes (the way in which an observed failure occurs) must be determined.
- How many times does a cause of failure occur?
- What actions are implemented to prevent this cause from occurring again?
- Are the actions effective and efficient?
FAILURE MODE AND EFFECTS ANALYSIS
FMEA
Operates using the Pareto principle (20% of the
work creates 80% of the results)
PARETO ANALYSIS
Utilized when there are multiple potential causes to a problem
pareto analysis
Uses boolean logic to determine the root causes of an undesirable event.
FAULT TREE ANALYSIS
This technique is usually used in risk analysis and safety analysis.
FAULT TREE ANALYSIS
Used when many problems exist and you want to get to the root causes of all the problems
CURRENT REALITY TREE
CRT
Analyzes a system at once
CRT
will group causes into categories including: ▪ People ▪ Measurements ▪ Methods ▪ Materials ▪ Environment ▪ Machines
FISHBONE OR ISHIKAWA OR CAUSE-AND-EFFECT DIAGRAMS
KEPNER-TREGOE TECHNIQUE
Also known as rational process is intended to break a problem down to its root cause
deals with diagnosing the
causes of recurrent problems
RPR PROBLEM DIAGNOSIS
Information culture affects the information use outcomes
SUSTAINING CULTURE OF INFORMATION USE
3 Phases:
• Discover - team members gather data and analyze their findings
• Investigate - a diagnostic plan is created and the root cause is identified
through careful analysis of the diagnostic data
• Fix - the problem is fixed and monitored to ensure that the proper root cause
was identified.
RPR PROBLEM DIAGNOSIS
