Data Qualityu Flashcards
Numbers, words or images that have yet to be analyzed to answer a specific questiom.
Data
Produced through processing, manipulating, and organizing data to answer questions, adding to knowledge of the receiver.
Information
it is the overall utility of a datasets as function of its ability to be processed easily and analyzed for a database, data warehouse or data analytics system.
Data Quality
7 Aspects of Data Quality (ACRCRPA)
Accuracy, Completeness, Relevance, Consistency, Reliability, Presentability and Accessibility
Indicates whether the data is free from significant errors and whether the numbers seem to make sense.
Accuracy
Indicates whether there is enough information to draw a conclusion about the data and whether enough individuals responded to it to ensure representative.
Completeness
Refers to the degree to which data are important to users and their needs.
Relevance
Considers the extent to which data is collected using the same process and procedures by everyone doing the collecting and in all locations over time.
Consistency
Determined by the degree to which measurements are similar (consistent) on repeated measurement
Reliability
Degree from which the data is easily understood and well organized.
Presentability
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 Assurance Sampling
Steps in Applying LQAS:
- Define the service to be assessed. (e.g. DQA of DHIS)
- Identify the unit of interest.
- Define the higher and lower threshold of performance.
- Determine the level of acceptable error.
- Determine the sample size and decision rule for acceptable errors.
- Identify of the number of errors observed.
It is a simplified version of the Data Quality Audit tool which allows programs and projects to verify and assess the quality of their reported data.
Routine Data Quality Assessment (RDQA)
3 Objective of RDQA
- RAPIDLY VERIFY the quality of reported data of key indicators at selected sites.
- IMPLEMENT corrective measures with action plans for strengthening data management and reporting system and improving data quality.
- MONITOR capacity improvements and performance of data management and reporting system and produce quality data.
A project management tool that illustrates how a project is expected to progress at a high level.
Implementation Plan
It helps ensure that a development team is working to deliver and complete tasks on time.
Implementation Plan
it answers the question “ what do you want to accomplish?”
Define Goals/Objectives
Outline the high level schedule in the implementation phase
Schedule Milestone
Determines 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 analyzes information and identifies incomplete or incorrect data.
Data Quality Tool
It follow after the complete profiling of data concerns, which could range anywhere from removing abnormalities to merging repeated information.
Data Cleansing
Refers to the decomposition of fields into components parts and formatting the values into consistent layouts based industry standards and patterns and user-defined business rules.
Parsing and Standardization
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
The identification and merging related entries within or across data sets
Matching
The deployment of controls to ensure conformity of data to business rules set by the organization.
Monitoring
Enhancing the value of the data by using the related attributes from external sources such as the consumer demographic attributes or geographic descriptors.
Enrichment
refers to the analysis of data to capture statistics or metadata to determine the quality of the data and identify data quality issues.
Profiling
A problem solving method that identifies the root case of problems or events instead of simply addressing the obvious symptoms.
Root Cause Analysis
Aims to find various modes of failures within the system. It is used when there is a new product or process or when there are changes or updates in a product and when a problem is reported through customer feed back.
Failure Mode And Effect Analysis
It uses the Pareto principle (20% of work produces 80% of results) It is used when there are multiple potential causes to a problem.
Pareto Analysis
It is used in risk and safety analysis. It uses the boolean logic to determine the root cause of an undesirable event
Fault tree analysis
It is used when the root causes of multiple problems need to be analyzed all at once. The problems are listed down followed by the potential cause for a problem.
Current Reality Tree
It is also called Ishiwaka or cause-and-effect diagram. It categorizes the cause and sub causes of a problem. This useful in grouping cause into categories.
Fishbone Diagram
It breaks a problem down to its root cause by assessing a situation using priorities and orders of concerns for a specific issues
Kepner-Tregoe-Technique
Discover, Investigate, Fix
Rapid Problem Resolution Diagnosis
Data gathering and analysis of the findings
Discover
Creation of diagnostic plan and identification of the root cause through careful analysis of the diagnostic data
InvestigateF
Fixing the problem and monitoring to confirm and validate that the correct root cause was identified.
Fix
