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Data Viz that Works

Facilitating HIV Program Targeting

I wanted to pass along a post from the Data Use Network on a recent paper titled Data Visualization That Works: Facilitating HIV Program Targeting. We often share examples of data viz work that we admire (or are confused by) but it's great to see more research around the driven factors behind successful visualizations in our health programs.

 

MEASURE Evaluation recently published this working paper on data visualization that works: case studies and considerations on facilitating HIV program targeting.

 

The paper discusses three examples of successful data visualizations that have been used for program targeting and decision making and highlights one dashboard that is currently in development.

 

The authors found that in order to create a successful data visualization for decision making, several things are necessary:

  1. Collaboration between data users and data producers, 
  2. Flexibility in the development of proxy indicators through estimation or modelling techniques, 
  3. Training on data visualization interpretation,
  4. Standardized data sources, 
  5. High quality data, and
  6. Software sustainability. 

The full text of the paper can be found here.

I wanted to pass along a post from the Data Use Network on a recent paper titled Data Visualization That Works: Facilitating HIV Program Targeting. We often share examples of data viz work that we admire (or are confused by) but it's great to see more research around the driven factors behind successful visualizations in our health programs.

MEASURE Evaluation recently published this working paper on data visualization that works: case studies and considerations on facilitating HIV program targeting.

The paper discusses three examples of successful data visualizations that have been used for program targeting and decision making and highlights one dashboard that is currently in development.

The authors found that in order to create a successful data visualization for decision making, several things are necessary:

  1. Collaboration between data users and data producers, 
  2. Flexibility in the development of proxy indicators through estimation or modelling techniques, 
  3. Training on data visualization interpretation,
  4. Standardized data sources, 
  5. High quality data, and
  6. Software sustainability. 

The full text of the paper can be found here.

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