Data Demand and Use Curriculum – Facilitator’s Guide

Author(s):

Year: 2018

Abstract:

Significant human and financial resources have been invested worldwide in the collection of data to measure
public health at various levels, such as within a specific population, facility, or community. However, this
information is not always used efficiently to inform policy and programmatic decision making. Health programs
and policymakers often fail to link evidence to decisions about how to address public health issues. As a result,
they are not able to respond most effectively to priority needs of the populations they serve.

Using data effectively for evidence-based decision making involves the following considerations:

• How does information flow up to decision makers, and how do they use it to make their decisions?
• In what contexts are data collected and decisions made?
• What are the organizational infrastructures and technical capacities of those who generate and use data?

This curriculum and facilitator’s guide was designed to explain the conceptual basis for training on data demand and use (DDU) within an organization or program, or at the national, state, or district levels of government. It provides tools created by MEASURE Evaluation to facilitate the use of data in decision making. The goal is twofold:

• Improve the understanding of the role of data in decision making, the context of decision making, the determinants of data use, and the importance of data sharing and feedback.

• Build skills for applying DDU tools.

Ideally, this training course should be delivered to teams from the same organization or government level. Each team should include both data users and data producers. Data users are health professionals, policymakers, and other key health decision makers who use data to inform the design, implementation, monitoring, and improvement of health programs. Data producers are professionals who acquire and analyze health data and prepare them for distribution to users. These professionals may be monitoring and evaluation (M&E) specialists, data clerks, and researchers. This team approach to training ensures that all data producers and professionals involved understand their respective roles in DDU and how their roles interact with each other.

When team training is not feasible, this course may be used in separate trainings for data users and data producers from the same organization. To minimize costs, this type of training can be conducted as an add-on to previously scheduled meetings. In such a case trainers should emphasize the links between data users and data producers.