HMIS Data Quality

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.

Aspects of Data Quality

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.

The quality of the reported data for key indicators at selct sites

Corrective measures with action plans for strengthening the data management and reporting system and improving data quality

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

is a project management tool that shows how a project will evolve at a high level.

Answers the question “What do you want to accomplish?”

Outline the high level schedule in the implementation phase.

Determine whether you have sufficient resources, and decide how you will procure what’s missing.

Create a general team plan with overall roles that each team member will play.

How will you determine if you have achieved your goal? (Smartsheet, 2017).

analyzes information and identifies incomplete or incorrect data. Cleansing such data follows after the completion of the profiling of data concerns, which could range anywhere from removing abnormalities to merging repeated information.

Refers to the decomposition of fields into component parts and formatting the values into consistent layouts based on industry standard and patterns and user-defines business rules

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

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

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

The development of controls to ensure conformity of data to business rule set by the organization

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

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 is among the core building blocks in the continuous improvement efforts of the organization.

getting progressively deeper into the problem, the root cause can be strategically identified and tackled.

is a technique which is aimed to find various modes for failure within a system. FMEA requires several steps for execution:

operates using the Pareto principle (20% of the work creates 80% of the results).