At the AESP 2019 Annual Conference, I met Carmen Best, a woman I have admired from afar for quite some time. Although she and I frequent the same industry conference circuit, I had never had the opportunity to connect with her. During our first conversation, the topic quickly turned to embedded research and evaluation (ER&E). While pondering how to talk about the value of ER&E, Carmen suggested the term ‘actionable intelligence’. Brilliant!
Actionable intelligence. Useful information that can be quickly acted upon[i]. This is exactly the goal of embedded research and evaluation.
Researching if, how, and when ER&E to drive actionable intelligence is being used led me to a paper from Fourth Quadrant Partners titled Emergent Learning: A Framework for Whole-System Strategy, Learning, and Adaptation. This summary quote from that paper highlights how I feel about ER&E within the energy conservation programming field (in particular, the part in italics), “While approaches like systems mapping, scenario planning, and appreciative inquiry have been put forward as useful approaches to expanding perspectives and seeing whole systems, the field needs a framework for going beyond these planning tools in order to actually create the conditions in which emergence can happen – by expanding agency beyond the walls of the funder, distinguishing between goals and strategies, encouraging experimentation around strategies, and supporting whole-system learning, which requires shorter, faster, more rigorous real-time learning and more cross-pollination among peers.”[ii]
Emergence. Embedded research and evaluation brings together researchers/evaluators, program staff, and other stakeholders that can inform and benefit from the research. Engaging the broader community of stakeholders can ensure research objectives that matter most are explored, and it can lead to a deeper understanding of the results of that research.
Experimentation. Experimenting with both delivery strategies and research methods are at the heart of embedded research and evaluation.
Shorter, faster, more rigorous real-time learning. Actionable intelligence is the difference between ‘knowing’ and ‘understanding’. Engaging stakeholders in the discussion of outcomes takes the conversation from ‘knowing’ what happened to ‘understanding why it happened’.
These are key components of ER&E.
In the Next Issue
In the next issue, I will explore emergent learning frameworks.
[i] Pearson S., Watson R., Actionable Intelligence, Chapter 1 - New age of warfare (2010). https://www.sciencedirect.com/science/article/pii/B9781597495967000012).
[ii] Marilyn J. Darling, Sparkes Guber H., Smith J. A., Stiles J. E. M. (2016). Emergent Learning: A Framework for Whole-System Strategy, Learning, and Adaptation. https://scholarworks.gvsu.edu/cgi/viewcontent.cgi?referer=&httpsredir=1&article=1284&context=tfr.
About This Blog
We are on the brink of an evaluation renaissance. Smart grids, smart meters, smart buildings, and smart data are prominent themes in the industry lexicon. Smarter evaluation and research must follow. To explore this evaluation renaissance, I am looking both inside and outside the evaluation community in a search for fresh ideas, new methods, and novel twists on old methods. I am looking to others for their thoughts and experiences for advancing the evaluation and research practice.
So, please…stay tuned, engage, and always, always question. Let’s get smarter together.