Big Data to Data Science: Moving from ‘What’ to ‘How’ in the MERL Tech Space
Big data is a big topic in other sectors but its application within MERL is limited, with most reports focusing more on its potential rather than actual use. This paper, “Big Data to Data Science: Moving from ‘What’ to ‘How’ in the MERL Tech Space” probes trends in the use of big data by a community of early adopters working in monitoring, evaluation, research, and learning (MERL) in the development and humanitarian sectors. We focus on how MERL practitioners actually use big data and what encourages or deters adoption.
First, we collated administrative and publicly available MERL Tech conference data from the 281 sessions accepted for presentation between 2015 and 2018. Of these, we identified 54 sessions that mentioned big data and compared trends between sessions that did and did not mention this topic. In any given year from 2015 to 2019, 16 percent to 26 percent of sessions at MERL Tech conferences were related to the topic of big data.
Our quantitative analysis was complemented by 11 qualitative key informant interviews. We selected interviewees representing diverse viewpoints (implementers, donors, MERL specialists) and a range of subject matter expertise and backgrounds. During interviews, we explored why an interviewee chose to use big data, the benefits and challenges of using big data, reflections on the use of big data in the wider MERL tech community, and opportunities for the future.
Our findings indicate that MERL practitioners are in a fragmented, experimental phase, with use and application of big data varying widely, accompanied by shifting terminologies. One interviewee noted that “big data is sort of an outmoded buzzword” with practitioners now using terms such as ‘artificial intelligence’ and ‘machine learning.’ Our analysis attempted to expand the umbrella of terminologies under which big data and related technologies might fall. Key informant interviews and conference session analysis identified four main types of technologies used to collect big data: satellites, remote sensors, mobile technology, and M&E platforms, as well as a number of other tools and methods. Additionally, our analysis surfaced six main types of tools used to analyze big data: artificial intelligence and machine learning, geospatial analysis, data mining, data visualization, data analysis software packages, and social network analysis.
We also took an in-depth look at barriers to and enablers of use of big data within MERL, as well as benefits and drawbacks. Our analysis found that perceived benefits of big data included enhanced analytical possibilities, increased efficiency, scale, data quality, accuracy, and cost-effectiveness. Big data is contributing to improved targeting and better value for money. It is also enabling remote monitoring in areas that are difficult to access for reasons such as distance, poor infrastructure, or conflict.
Concerns about bias, privacy, and the potential for big data to magnify existing inequalities arose frequently. MERL practitioners cited a number of drawbacks and limitations that make them cautious about using big data. These include lack of trust in the data (including community mistrust); misalignment of objectives, capacity, and resources when partnering with big data firms and the corporate sector; and ethical concerns related to privacy, bias, and magnification of inequalities. Barriers to adoption include insufficient resources, absence of relevant use cases, lack of skills for big data, difficulty in determining return on investment, and challenges in pinpointing the tangible value of using big data in MERL.
Our paper includes a series of short case studies of big data applications in MERL. Our research surfaced a need for more systematic and broader sharing of big data use cases and case studies in the development sector.
The field of Big Data is rapidly evolving, thus we expect that shifts have happened already in the field since the beginning of our research in 2018.
Download the paper here.
The State of the Field series includes four papers:
MERL Tech State of the Field: The Evolution of MERL Tech: Linda Raftree, independent consultant and MERL Tech Conference organizer.
What We Know About Traditional MERL Tech: Insights from a Scoping Review: Zach Tilton, Michael Harnar, and Michele Behr, University of Western Michigan; Soham Banerji and Manon McGuigan, independent consultants; and Paul Perrin, Gretchen Bruening, John Gordley and Hannah Foster, University of Notre Dame; Linda Raftree, independent consultant and MERL Tech Conference organizer.
Big Data to Data Science: Moving from “What” to “How” in the MERL Tech Space: Kecia Bertermann, Luminate; Alexandra Robinson, Threshold.World; Michael Bamberger, independent consultant; Grace Lyn Higdon, Institute of Development Studies; Linda Raftree, independent consultant and MERL Tech Conference organizer.
Emerging Technologies and Approaches in Monitoring, Evaluation, Research, and Learning for International Development Programs: Kerry Bruce and Joris Vandelanotte, Clear Outcomes; and Valentine Gandhi, The Development CAFE and Social Impact.