Lesson 8: HMIS Data Quality Flashcards
the overall utility of a datasets as a function of its ability to be processed easily and analyzed for a database, data warehouse, or data analytics system
data quality
what are the aspects of data quality?
accuracy
completeness
update status
relevance
consistency
reliability
appropriate presentation
accessibility
it can be done to raise the quality of available data
data cleansing
true or false: having the data means that the data is useful and consistent
true
a 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 (LQAS)
a simplified version of tool of the data quality which allows programs and projects to verify and assess the quality of their reported data.
routine date quality assessment (RDQA)
it aims to strengthen their data management and reporting systems.
routine data quality assessment (RDQA)
a project management tool that shows how a project will evolve at a higher level.
implementation plan
it helps ensure that a development team is working to deliver and complete tasks on time
implementation plan
what are the key components in an implementation plan?
- define goals/ objectives
- schedule milestones
- allocate resources
- designate team member responsibilities
- define metrics for success
answers the question “what do you want to accomplish?”
define goal/objectives
it outlines the high level schedule in the implementation phase.
schedules milestones
it determines whether you have a sufficient resources, and decide how you will procure what is missing
allocate resources
it creates a general team plan with overall roles that each member will play
designate team member responsibilities
how will you determine if you have achieved your goal?
define metrics for success
it analyzes information and identifies incomplete or incorrect data
data quality tool
it refers to the decomposition of fields into component and formatting the values into consistent layouts based on industry standards and patterns and user-defined business rules
parsing and standardization
it means the modification of data values to meet domain restrictions, constraints on integrity, or other rules that define data quality as sufficient for the organozation
generalized cleansing
is the identification and merging related entries within or across data sets
matching
is the deployment of controls to ensure conformity of data to business rules set by the organization
monitoring
it is enhancing the value of the data by using related attributes from external sources such as consumer demographics attributes or geographic descriptions
enrichment
a class of problem-solving methods aimed at identifying the root causes of the problems or events instead of simply addressing the obvious symptopms
root cause analysis
a system failure may take place in varying modes, and a well-known techniques used to identify these modes
failure mode and effects analysis
a technique that does not only work for a clever kid wanting to get his or her way but can also help in identifying the root causes of a problem
five whys analysis
what are the techniques in root cause analysis?
- ask why 5 times
- current reality tree
- fishbone or ishikawa or cause and effect diagrams
- kepner-tregoe technique
- RPR problem diagnosis
- Pareto analysis
- Failure mode and effects analysis
- Fault tree analysis
RPR stands for:
rapid problem resolutions
what are the 3 phases in RPR problem diagnosis
discover
investigate
fix
this is where designated workers gather data and analyze their findings
discover
team members come up with a diagnostic plan and carefully analyze the diagnostic data to identify the root cause
investigate
the problem is fixed and continuously being monitored to double check if the correct root cause was determined.
fix
