Data Quality

Data

Data

numbers word or images that have yet to be organized or analyzed to answer a specific question

Information

Produced through processing, manipulating, and organizing data to answer questions adding to knowledge of the receiver

Data Quality

it is 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

Accuracy, Completeness, Update status, Relevance, Consistency, Reliability, Appropriate presentation, and Accessibility

Aspects of Data Quality

Accuracy

indicates whether the data is free from significant errors and whether the numbers seem to make sense

Completeness

indicates whether there is enough information to draw a conclusion about the data and whether enough individuals responded to it to ensure representativeness

Relevance

refers to the degree to which data are important to users and their needs

Consistency

considers the extent to which data is collected using the same process and which procedures by everyone doing the collecting and in all locations over time

Reliability

determined by the degree to which measurements are similar (consistent) on repeated measurements

Appropriate presentation

degree from which the data is easily understood and well organized

Lot Quality Assurance Sampling

is a tool that allows the sue of small random samples to distinguish between different group of data elements with high and low data quality

Lot Quality Assurance Sampling

LQAS

Step by step of LQAS

-define the service to be assessed (DQA of DHIS)

-identify the unit of interest

-define the higher and lower thresholds of performance

-determine the level of acceptable error

-determine the size sample size and decision rule for acceptable errors

-identify the number of errors observed

Routine Data Quality Assessment

RDQA

Routine Data Quality Assessment

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

Verify Rapidly

• The quality of reported data for key indicators at selected sites;

• 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

Monitor

Capacity improvements and performance of the data management and reporting system to produce quality data.

• Verify Rapidly

• Implement

• Monitor

Objectives of RDQA

Development Implementation Plan

-project management tool that illustrates how a project is expected to progress at a high level

-helps ensure that a development team is working to deliver and complete tasks on time

Development Implementation Plan

DIP

Steps of DIP

-define goals/objectives

-schedule milestone

-allocate resources

-designate team member responsibilities

-define metric for success

Data Quality Tool

it analyzes information and identifies incomplete or incorrect data

Data Cleansing

can be done to raise the quality of available data

Define goals/objectives

address the question "what do you want to accomplish?"

Schedule milestone

Outline the deadline and timeline in the implementation phase

Allocate resources

determine whether you have sufficient resources and decide how you will procure those 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?

Parsing and Standardization

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

Generalized "cleansing"

is the modification of data values to meet domain restrictions constraints on integrity or other rules that define data quality as sufficient for the organization

Matching

is the identification and merging of related entries within or across data sets

Profiling

refers to the analysis of data to capture statistics or metadata to determine the quality of the data and identify data quality

Monitoring

refers to the deployment of controls to ensure conformity of data to business rules set by the organization

Enrichment

is the enhancement of the value of the data by using related attributes from external sources such as consumer demographic attributes or geographic descriptors

Generalization of ETL

tools which allow optimization of the alimentation process

Extract, Transform, Load

ETL

Root Cause Analysis

is a problem solving method that identifies the root case of problems or events instead of simply addressing the obvious symptoms

Failure Mode and Effect Analysis

-aims to find various modes of failures within a system

-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

FMEA

Pareto Analysis

-uses 20% work produces 80% of result

-used when there are multiple potential causes to a problem

Fault Tree Analysis

-is used in risk and safety analysis

-uses boolean logic to determine the root causes of an undesirable event

Fault Tree Analysis

FTA

Current Reality Tree

CRT

CRT

-used when the root causes of multiple problems need to be analyzed all at once

-problems listed down followed by the potential cause for a problem

Fishbone Diagram

-aka Ishiwaka or cause and effect diagram

-categorizes the causes and sub causes of problem

-is useful in grouping causes into categories

Kepner-Tregoe Technique

-breaks a problem down to its root cause by assessing a situation using priorities and orders of concern for a specific issues

Rapid Problem Resolution Diagnosis

RPR

RPR

diagnoses the problem by

-discover

-investigate

-fix

Discover

data gathering and analysis of findings

Investigate

creation of diagnostic plan and identification of the root cause through careful analysis of the diagnostic data

Fix

Fixing the problem and monitoring to confirm and validate that the correct root cause was identified

Information Culture

is determined by the ff variables:

-mission

-history

-leadership

-employee traits

-industry

-national culture

-can also be cognitive and epistemic

Sustaining culture of information use

suggest that in order to have a sense of information attitudes and values managers should consider taking the pulse of information of their organizations

The management

Plays an important role in sustaining the culture of information