HMIS Data Quality Flashcards
these are numbers, words or images that have yet to be organized or analyzed to answer a specific question
data
produced through processing , manipulating and organizing data to answer questions, adding to knowledge of the receiver
information
data quality has been a major concern for who
large companies especially in the areas of customer relationship management (CRM)
data integration
regulation requirements
it is the overall quality of datasets as a function of its ability to be processed easily and analyzed for database, data warehouse or data analytics system
data quality
briefly explain the aspects of data quality
CCAAARRU
consistency
-data collected using the same process and procedures
completeness
- significant no. of info/data to draw conclusion and whether enough indivs responded = ensure representativeness
accuracy
- data is free from significant errors and whether the no. seems to make sense
accessibility
appropriate presentation (presentation)
- easily understood and well organized
(ex: table/ graphs)
relevance
- important to users n their needs
reliability
update status
it is a tool that allows the use of small random samples to distinguish between different groups of data elements with high and low quality data
lot quality assessment sampling (LQAS)
LQAS is used by
health managers and supervisors
using small samples makes conducting surveys or supervision more efficient
The concept and application of LQAS technique has been adopted in the context of …
The adaptation was comprised of
District health information system (DHIS) data quality assurance
Designing health facilities
DHIS monthly reports
Sections of monthly reports
Group of data elements as ‘Lots’ to provide representative samples for data quality assurance of DHIS
steps in applying Lot quality assurance sampling
Define the service to be assessed ( eg. DQA of DHIS)
Identify the unit of interest (what department, facility, hospital?)
Define the higher and lower threshold performance
Determine the level of acceptable error
Determine the sample size and decision rule for acceptable errors
Identify the no. of errors observed
It is a 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) Tool
briefly explain the objectives of RDQA
- verify rapidly 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 - implement corrective measures with action plans for strengthening the data management and reporting system and improving data quality
- monitoring capacity improvements and performance of the data management and reporting system to produce quality data
what are some uses of RDQA Tool
routine data quality check ups as part of ongoing supervision
initial and follow-up assessments of data management and reporting systems
strengthening program staff’s capacity in data management and reporting system
prep for formal data quality audit
external assessment by partners of the quality of data
briefly explain implementation plan
it s a project management tool that shows how a project will evolve at a high level
helps ensure that a development team is working to deliver and complete tasks on time
visual paradigm, 2009
what are the key components/ steps of an implementation plan
- define goals/ objectives
- SMART - schedule milestone
- timeline
- guideline
- gannt chart - allocate resources
- have sufficiennt resources
- decide how to procure wht is missing - designate team member responsibilities
- plan with overall roles for each member to play - define metrics for success
- determine if achieved goal
it analyzes information and identifies incomplete or incorrect data
data quality tool
it is such data that follows after the completion of profiling of data concerns, which dould range anywhere from removing abnormalities to merging repeated information
data cleansing
briefly explain the data quality tools as being used to address the data quality problem
according to Gartner 2017
- parsing and standardization
- generalized “cleansing”
- matching
- profiling
- monitoring
- enrichment
it 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 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 organization
generalized “cleansing”
it is the identification and merging related entries within or across data sets
matching
it is the analysis of data that captures stats or make data to determie the quality of data and identify the data quality issues
profiling
it is the deployment of control 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 …
enrichment
consumer demographic attributes
geographic descriptors
it is 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
