Lesson 8 Flashcards
(35 cards)
Is 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
It is a perception of the data’s appropriateness to serve its purpose in a given context.Having quality data means that the data is useful and consistent. Data _____ can be done to raise the quality of available data (Rouse, 2005).
Data quality
cleansing
Aspects of data quality
Accuracy Completeness Update status Relevance Consistency Reliability Appropriate Presentation Accessibility
Is 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.
Lot Quality Assurance Sampling (LQAS)
The concept and application of LQAS technique has been adopted in the context of _____ data quality assurance
(District Health Information System) DHIS
Define the service to be assessed
identify the unit of interest
Define the higher and lower thresholds of performance
determine the level of acceptable error
From a table, determine the sample size and the decision rule for acceptable errors to declare an area as performing “below expectations’’
The number of errors observed (mismatched data elements will determine reliably If the facility is performing above or below expectations).
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. It also aims to strengthen their data management and reporting systems.
Routine Data Quality Assessment (RDQA)
objectives routine data quality assessmnet
Verify rapidly
Implement
Monitor
Examples of RDQA use case
Routine data quality checks as part of an ongoing supervision
Preparation for a formal data quality audit
A _____project management tool that shows how a project will evolve at a high level. An _____helps ensure that a development team is working to deliver and complete tasks on time (Visual Paradigm, 2009)
An Implementation Plan
The ______ is important to ensure that the communication between those who are involved in the project will not encounter any issues and work will also be delivered on time. The plan validates the estimation and schedule of the project plan.
Development of Implementation Plan
Implementation Plan has the following key components:
Define Goals/Objectives: “What do you want to accomplish?
Schedule Milestones:Outline the deadline and timelines in the implementation phase
Allocate Resources: Determine whether you have sufficient resources, and decide how you will procure what’s 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?
analyzes information and identifies incomplete or incorrect data.
data quality tool
By maintaining data ___, the process enhances the reliability of the information being used by a business.
integrity
Refers to the decomposition of fields into component parts and formatting the values into consistent layouts
Parsing and standardization
Means the modification of data values to meet domain restrictions that define data quality as sufficient for the organization
Generalized”cleansing”
This is the identification and merging of related entries
Matching
Refers to the analysis of data to capture statistics or metadata
Profiling
The deployment of controls to ensure conformity of data to business rules
Monitoring
Enhancing the value of the data by using related attributes from external sources
Enrichment
But during the last 10 years, it was observed that there is a generalization of_____ (ETL) tools which allowed the optimization of the alimentation process
Extract, Transform, Load
Recently, these tools started to focus on ____, which generally integrate profiling, parsing, standardization, cleansing and matching processes (Goasdue, Nugier, Duquennoy, and Laboisse, 2007).
Data Quality Management (DQM)
Is among the core building blocks in the continuous improvement efforts of the organization.
Root cause analysis
Techniques in root cause analysis
Ask Why 5 Times Failure Mode and Effects Analysis (FMEA) Pareto Analysis Fault Tree Analysis Current Reality Tree (CRT) Fishbone or Ishikawa or Cause-and-Effect Diagrams Kepner-Tregoe Technique RPR Problem Diagnosis