Just wanted to share back some notes on what we did and some general takeaways from the data champions face to face workshop on building a data culture in community foundations.
In attendance were: Gareth from UKCF, Steve from Essex CF, Nicola from Devon CF, Mor from 360 Giving and Myself.
We started out by talking in pairs about recent learning experiences, and once those had been shared, I asked everyone to rate the following learning methods from most effective to least effective:
- Reading step by step instructions
- Listening to a lecture
- Watching and listening to a presentation
- Watching a video ‘how to’
- Teaching others
- Discussion with peers
- Personal Experience
There were some controversies around what was the least effective. ‘Listening to a lecture’ or ‘watching and listening to a presentation’ were at the bottom for most. Though making lectures and presentations entertaining, improved their effectiveness. While, ‘Discussion with peers’, ‘Teaching Others’ and ‘Personal Experience’ was seen as being highly effective. I think it was Steve, who threw in ‘Problem Solving’, which had agreement as being the most-effective.
Which ended up being a perfect segue way into reviewing some research into how adults learn. Malcolm Knowles did some ground-breaking research in the 60’s & 70’s that led to the theory of Andragogy, or how adults learn. To summarise his theory:
- Adults need to understand and accept the reason for learning a specific skill.
- Experience (including error) provides the basis for learning activities.
- Adults need to be involved in both the planning and evaluation of their learning.
- Adult learning is problem-centered rather than content-oriented.
- Most adults are interested in learning what has immediate relevance to their professional and social lives.
We then did a quick review of an application of the Andragogy theory in ADIDS as a teaching format, particularly in a workshop setting.
- Activity: The session begins with an activity that is connected to the topic of the session. This is meant to introduce the topic to the participants using interactive exercises. Trainers / facilitators design this beforehand to illustrate some of the issues that they want the participants to start thinking about.
- Discussion: In this part of your session, everyone talks about what they thought of the activity they just completed. The trainer / facilitator should prepare questions to guide the activity.
- Input: This is usually the lecture part of the session. The trainer presents on issues, sub-topics and more advanced concepts related to focus of the session
- Deepening: In technical training, this is usually the hands-on segment of a session. This is where the participants will get to put what they are learning to use
- Synthesis: A good training habit is to always summarize the session. Talk about what happened in the session, some of the results of the discussion, what issues were discussed, what solutions were made, and give some more time for participants to ask more questions before the session is closed.
Why did we spend the first part of a ‘building a data culture’ workshop on adult learning? Because building a data culture is really about building a learning culture. As we have noted throughout this initiative, data is a journey that often starts with a question but then leads to further questions and even more data. Learning how to be a more effective community foundation is at the core of it all.
We did a review of the data workflows from the previous workshop, and explored the various roles in each step.
We visited Heather Leson’s Data Literacy work at the International Federation of the Red Cross/Red Crescent Society and how she categorises diverse audiences across the different sectors and regions of her work.
- Data Curious need an ‘on ramp’ to learn and be exposed to the data basics.
- Data Advocate sees relevance and and wants to improve their skills.
- Data Active are motivated to self-learn and are on their way to being a ‘data-leader’.
- Data Ready are ‘trainers’ or ‘data leaders’ who lead data-driven project and mentor colleagues.
These key data user profiles inform the resources she develops, like the Data Playbook, along with future training planning.
Heather’s profiles inspired us to do a deeper dive into understanding our audiences and in particular the individuals we need to engage.
Looking back at the data workflow and the roles needed, we identified key individuals that we needed to more deeply engage on using data for learning and built user personas for those individuals based on the following template:
- Name:
- Role:
- Data Level:
- Skillset:
- Values - how do they want to be seen?
- What problems are they solving? What are their motivations for using data?
- How do they apply data to their jobs?
- What are their barriers for using data?
- How do they communicate with others?
- What are the opportunities to engage them on data? What skills are they keep to learn?
- What can they teach others?
Each of the Data Champions then produced three user profiles on individuals in their community foundation that will be key for building a data/learning culture. Those profiles are not being shared for obvious reasons.
We had a discussion about what Data Champions need to build a data culture in each of their community foundations. Key points:
- Get CF Boards to ask the questions and then engage them in the data journey
- Define problems that we want to use data to solve
- Carve-out space to actively discuss data and how it is being used.
- Use weekly meetings
- Be wary of knowledge silos when growth happens and use the learning frame to break through them.
- Get high level buy-in on learning and the data journey
- Think about data for leadership.
We took a look at some key data culture resources that were developed for other sectors to determine their usefulness for community foundations:
We wrapped up the workshop with each of the Data Champions sharing their next steps for building a data culture when they got back. Each talked about ways to be more intentional about surfacing learning opportunities in routines that already exist in the organisation, and ways to engage their colleagues on better defining problems they want to solve.
We also proposed using ‘What Does a Data Culture Look Like’ for the frame of the workshop at the UKCF Conference in September.