HMIS DATA QUALITY Flashcards
data quality has become a major concern for large companies like in the areas of;
Customer Relationship Management (CRM)
data integration
regulation requirements
overall utility of a dataset (s) as a function of its ability to be processed easily and analyzed for a database.
Data Quality
it is done to raise the quality of available data
Data Cleansing
Aspects of Data Quality
accuracy
completeness
update status
relevance
consistency
reliability
appropriate presentation
accessibility
LQAS?
Lot Quality Assessment Sampling
allows the use of small random samples to distinguish between different groups of data elements
LQAS
tool that is simplified version of the data quality audit
routine data quality assessment
it allows programs and projects to verify and assess the quality of their reported data.
Routine Data Quality Assessment
Objectives of RDQA
Verify Rapidly
Implement
Monitor
aims to strengthen their data management and reporting systems
RDQA
the potential users of RDQA
program managers
supervisors
M & E staff at national and subnational levels
donors and stakeholders
a project management tool that shows how a project will evolve at a high level. the plan validates the estimation and schedule of the project plan
Implementation Plan
It helps ensure that a development team is working to deliver and complete tasks on time. people involved in the project will not encounter any issues
Implementation Plan
Implementation Plans Key Components
Define Goals and Objectives
Schedule Milestones
Allocate Resources
Designate Team Member Responsibilities
Define Metrics for Success
It 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 is 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
it analyzed information and identifies incomplete or incorrect data
data quality tools
analyze the problem by parts and one by one
Parsing
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
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”
identification and merging related entries within or across data sets
Matching
