Data Parties I Have Known: Lessons Learned and Best Practices for Success

An increased focus on data-driven decision making in Cooperative Extension requires that Extension professionals more fully engage with data analysis and interpretation. In response to this need, the use of data parties has become increasingly popular for involving stakeholders in data analysis and, more importantly, for interpretation of data (Franz, 2013). As data parties become more commonly used by Extension professionals, best practices are emerging from lessons learned to improve the process of and enhance the outcomes from these events.

Lessons Learned

Over the last half decade, I have experimented with a variety of approaches to conducting data parties to engage stakeholders in data analysis and improve interpretation of data from program evaluations, research, needs assessments, and other data collection processes. The data party steps and guiding questions previously suggested (Franz, 2013) are a good start, but the event is more effective when taking into account the following tenets:

• Goals matter! Be intentional and forthright about the reasons for conducting a data party. Do you want to enhance inclusion, increase buy-in, improve data analysis and interpretation quality, consider implications of the data, build public relations, engage new stakeholders, increase social capital, strengthen community development, or accomplish other goals? All aspects of party planning, implementation, and evaluation need to specifically match those goals.
• Context matters! The party participants, announcement/invitation, environment, and language shape the process and the results. Familiarity with the participants and their preferences is critical for having the right people attend and for beneficial engagement during the event. I have learned to refrain from being too academic or data wonky for most audiences. I also have found that people love a good party, especially one that is timely. Sometimes the parties I facilitate are instead called a tailgate or a celebration.
• Questions matter! Create and order questions that specifically produce what you want to produce. Are you looking for data analysis and interpretation, or do you also want participants to make recommendations for next steps? Productive questions I have added to data parties include "What three things in the data are the most important to pay attention to and why?," "What do these data suggest should happen next and why?," and "What would you disagree with or add to the data or the discussion based on your experience or other data you have seen?"
• Process matters! Strong facilitation and focus group interview skills are critical for a productive data party. It is important to decide whether the process will be rigid or flexible. A facilitator needs to quickly build rapport with the participants, build a sense of community, and elicit useful feedback from participants. A successful process also relies on having the right size group and important incentives to enhance participant engagement. Often a short party (1–2 hr) is better than a long one (Franz, 2011; Krueger & Casey, 2015; Piercy, Franz, Donaldson, & Richard, 2011).

Best Practices

Several best practices for successful data parties have arisen from the lessons learned over the last 5 years. They are as follows:

• Intentionally and selectively involve important partners in designing, implementing, and assessing the data party. This tactic helps set specific and clear goals and guiding questions, gets the right people participating in the event, and improves the results. Partners tend to supplement one another's strengths for a more inclusive and successful process.
• Provide a short summary of the party goals, process, and data to participants a few days before the event so that participants can think deeply about them and prepare if they wish. It is often necessary to provide time before the data are analyzed at the party for participants to read the summary as they may not have taken time to do so prior to the event.
• Split groups of 10 or more participants into subgroups for data analysis and interpretation to fully engage participants. This approach requires providing a trained facilitator for each subgroup and a main facilitator to provide leadership for the whole-group process (i.e., welcome, introductions, overview, instructions for subgroups, monitoring of subgroups, whole-group discussion, closure, and thank you).
• Move beyond data parties that focus only on data analysis. Stakeholders often have important interpretations of data and insightful implications from those interpretations.
• Use data displays that summarize data instead of sharing raw data to catalyze analysis and interpretation (Carls & Griffin, 2016). I have used posters on the walls, place mats, table tents, and other data visualization tools.
• Be serious about throwing a great party that appeals to everyone. Themed invitations, balloons, food, party favors, and other party features create a festive environment and often make others in the building who were not invited curious about the event. Do not underestimate the power of the party to become a great tool for promoting your project, partnerships, and products.
• Cancel or postpone the party if you do not have trained facilitators or the right participants. No party is better than one that limps along, is not inclusive, or does not help you reach your goals.

Summary

Effective Extension professionals know that successful educational opportunities require intentional planning, implementation, and evaluation (Franz, Garst, & Gagnon, 2015). A data party is an educational opportunity and so requires the same intentionality. As the use of data parties by Extension professionals has increased, lessons have been learned and best practices discovered to improve their effectiveness. In particular, these include designing the questions guiding the party discussion more intentionally to better fit the specific goals of the process, engaging a planning team of key partners, setting a theme and party environment that appeals to the participants and fits the context, providing appropriate data visualization tools, and using strong facilitation practices.

References

Carls, E., & Griffin, T. (2016). Developing interactive website charts for Extension clientele using Google Docs. Journal of Extension, 54(5), Article 5TOT3. Available at: https://www.joe.org/joe/2016october/tt3.php

Franz, N. (2011). The unfocused focus group: Benefit or bane? The Qualitative Report, 16(5), 1380–1388.

Franz, N. (2013). The data party: Involving stakeholders in meaningful data analysis. Journal of Extension, 51(1), Article 1IAW2. Available at: https://www.joe.org/joe/2013february/iw2.php

Franz, N., Garst, B., & Gagnon, R. (2015). The Cooperative Extension program development model: Adapting to a changing context. Journal of Human Sciences and Extension, 3(2), 3–12.

Krueger, R., & Casey, M. (2015). Focus groups: A practical guide for applied research (5th ed.). Thousand Oaks, CA: Sage Publications.

Piercy, F., Franz, N., Donaldson, J., & Richard, R. (2011). Consistency and change in participatory action research: Reflections on a focus group study about how farmers learn. The Qualitative Report, 16(3), 820–829.