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Challenge

Challenge
  • Organizations working in international development are increasingly required to provide independent and rigorous evidence on the outcomes of their work.

 

  • NGOs or other development organizations that deliver services to thousands of beneficiaries and customers generate vast quantities of data, but this data is often of low quality and under-utilized.

 

  • The M&E departments of development organizations often focus on donor reporting with a limited emphasis on generating actionable learnings to improve service delivery and the impact of their initiatives. 

Innovation

Innovation
  • Through our embedded research advisory service – which directly targets development organizations, NGOs, social enterprises, and government agencies - we empower our clients to design and implement a rigorous research & learning agenda customized to their strategic, operational and informational needs.

 

  • Laterite currently has an independent research team embedded in TechnoServe's Coffee East Africa programme. The core objectives are to maximize learning, improve the research function and inrease the harmony between implementation and research.

Outcome

Outcome
  • The project is ongoing, but to date we have achieved:
    (i) better alignment of research and implementation activities;
    (ii) improved research design and sampling;
    (iii) introduction of better processes and systems across six countries (Ethiopia, Kenya, Democratic Republic of the Congo, Uganda, Zimbabwe and Puerto Rico) to improve data quality, including audit;
    (iv) training of TechnoServe’s M&E teams on using Python, Stata, Survey CTO and research methods; 
    (v) provision of ongoing technical guidance on all research outputs; 
    (vi) substantial improvements to data quality monitoring on all data collection efforts through real-time dashboards, and meta-data and picture audits; and
    (ii) consolidation all of TechnoServe’s historic data from coffee farmers into a master database to generate new insights about agronomy practices across countries and over time.

     
  • In terms of learning, we are using existing and ongoing data collection efforts to better profile TechnoServe’s farmer-clients and how their profiles relate to their agronomy practices. We are also using historic data to better learn what works and what does not for TechnoServe's clients.

 

  • In the future, our efforts will focus on testing new approaches together with TechnoServe to ensure that research and data increasingly feed in to operations.