DATA SCIENCE AND BENEFICIARY CENTERED DESIGN FOR BETTER DEVELOPMENT OUTCOMES Madhoun and Wheeldon 1 Copyright: Mast Measurement and Management May 2022 DATA SCIENCE AND BENEFICIARY CENTERED DESIGN FOR BETTER DEVELOPMENT OUTCOMES 1 Copyright: Mast Measurement and Management. August 2022 Walid Madhoun walid@triplem.io Johannes Wheeldon jwheeldon@gmail.com 1. Introduction International development organizations, whether bilateral or multilateral, employ relatively common approaches and tools to design, manage, and assess the effectiveness of their development interventions. These include the results framework, social and environmental safeguards, and other performance measurement instruments. Development organizations are constantly improving their processes and tools, thus giving rise to innovative variations in management and operations. The recent emergence of data science is an example of such innovation. As a promising tool to help better understand the contribution of the development sector, data science will reduce the costs of data collection and analysis, thus freeing professionals to focus on design, creative solutions, and more efficient implementation. Additionally, data science applications can be used to better explain what happened in projects, why it happened, who benefited, and who did not. There are different definitions of Data Science - some broad, some narrow. Data science is defined broadly for this article. It includes both data 1 We would like to acknowledge and express gratitude for the valuable insights and advice provided by Mr. Michael Wodzicki (Development Consultant), Mr. Andrej Hudoklin (COO at ADD Business Solutions), and Mr. Marko Skufca (Business Solutions Director at ADD Business Solutions). Abstract This paper is a proposal to rethink the results framework of development organizations. The paper proposes an integrative approach to development with the context of the Design Thinking Approach and the use of data science. This paper presents the potential for data science and design thinking to transform evaluation, including data collection, extraction, management, integration, analysis, interpretation, and reporting. Projects serve as the main delivery mechanism by which development mandates are achieved. The proposed integrative framework is more favorable than current approaches because it allows for adjustments at the project level. The proposal seeks to reframe the results framework by better defining levels of outcomes and substantiating (quantifying) the contribution of individual projects to overall institutional goals. This proposal, while presenting concrete suggestions, is the foundation for further interdisciplinary studies; the subject requires detailed design, feasibility of assumptions, and piloting to test the mechanisms proposed herein. Data science in this article includes data management and analysis, covering the process from collection to reporting. 1. Big Data: Massive data collected through various transactions, whether a single source or multiple sources. 2. Data Warehouse: Derivate of big data but structured, transformed, and prepared for analysis. 3. Data Science Applications: Artificial Intelligence or Machine Learning are two common examples of advanced applications. The whole process from collection to analysis and reporting relies on the availability, ability, and capacity to collect high-quality data. DEFINING DATA SCIENCE