Empowering those that don't "speak" data
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(Slides from my workshop at the 2018 UN World Data Forum) Data is too often used “about” people, rather than “with” people. Are you looking for novel ways to engage and empower populations that don’t “speak” the language of data? This workshop session will introduce the Data Culture Project – our suite of hands-on, arts-based, activities that bring people together around data with a goal of empowerment. We’ll do two of our participatory hands-on activities in this workshop – building data sculptures and brainstorming questions to ask a dataset. These activities can be run individually within a project scope to involve various low-data-literacy stakeholders, or can be strung together to do things such as design and paint community-driven data murals. You will walk away with concrete approaches to engaging constituents at various points within your data lifecycle, and inspirational examples of how to empower those that don’t otherwise “speak” data. Only through collaborative outreach, engagement, and capacity building can we truly “leave no-one behind” in the data revolution.
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Empowering those that don't "speak" data
- 1. Empowering Those That Don’t “Speak” Data
- 2. Recognizing Data as an Asset improve operations spread the message bring people together
- 3. Learning to Speak Data
- 4. Barriers technical jargon lack of confidence technical expertise IT-centric thinking perceived irrelevance organizational silos budgetary constraints boring https://medium.com/@rahulbot/building-a-data-culture-4f5c116448fc
- 5. Separate Technology from Process Excel doesn’t help you learn to ask better questions. R won’t pick the most appropriate chart for telling your story. Tableau doesn’t tell you which narrative arc will convince your audience.
- 6. Putting it Into Practice
- 7. Opportunities for Engagement Asking questions Gathering data Finding a story Telling your story Trying it out Group brainstorming Participatory collection Inclusive analysis Collaborative creation Realistic evaluation
- 9. Meet People Where They Are Sketching a Data Story Building a Data Sculpture D’Ignazio, C., & Bhargava, R. (2016). DataBasic: Design Principles, Tools and Activities for Data Literacy Learners. The Journal of Community Informatics, 12(3). Bhargava, R., & D’Ignazio, C. (2017). Data Sculptures as a Playful and Low-Tech Introduction to Working with Data. Presented at the Designing Interactive Systems, Edinburgh, Scotland. Making Arguments with Data
- 10. Paper Spreadsheets Introduce people to data, data cleaning, data-types, and each other
- 11. Data Sculptures Use physical craft materials to quickly find and tell a story in 3D
- 12. A Case Study - WFP Maryna Taran
- 13. WFP Data Literacy Maryna Taran, Field Data Coordinator
- 14. The World Food Programme (WFP) is the world's largest humanitarian agency fighting hunger worldwide, delivering food assistance in emergencies and working with communities to improve nutrition and build resilience. WFP is funded entirely by donations from governments, companies and private individuals. 4
- 15. WFP is headquartered in Rome. We have a global presence that includes: • 83 Country Offices worldwide • 6 Regional Bureaux (Bangkok, Cairo, Dakar, Johannesburg, Nairobi and Panama) • 14 offices in world capitals • A vast supply chain logistics network that enables us to quickly deliver life-saving food assistance anywhere in the world • Specialist centres for innovation and development of sustainable solutions to hunger. GLOBAL PRESENCE 6
- 16. Data Literacy in WFP 16 Started in 2017 Building a Community of Practice • Role of the Data Coordinators • Data Fellows Community Operationalizing the Concept • Data Literacy Sessions • Data Week 2017 • Data Mapping Missions: assessment, literacy, tools • Data Viz Challenges
- 17. Data Literacy in WFP 17
- 18. Applying what we’ve learned 18
- 19. Speaking Data
- 20. Opportunities for Engagement Asking questions Gathering data Finding a story Telling your story Trying it out Group brainstorming Participatory collection Inclusive analysis Collaborative creation Realistic evaluation
- 21. Empowering Those That Don’t “Speak” Data Rahul Bhargava rahulb@mit.edu @rahulbot datacultureproject.org datatherapy.org databasic.io
Editor's Notes
- Data is key to unlocking new insights, ideas and possibilities. But experts estimate that only one-third of us can confidently understand, analyse and argue with data. This keynote will address why Data Literacy must be regarded as a critical consideration for creating a data-driven culture and arm attendees with pragmatic advice and recommendations.
- All this stuff is created wiith Catherine D’Ignazio from Emerson College. I’ve been doing workshops with orgs on data literacy for 10 years. The first one was terrible, but I’ve learned a lot since then. Usually data efforts focus on the first two, but we focus on the third – bringing people together
- This is the key point.
- There are lots of barriers to accomplishing those. Checkout my recent blog post for details on these. I’m sure many of you have experienced these; they are sourced from our 10 years of workshops with industry, academia, and non-profits.
- People think about data as a technology problem, but building a data culture requires you to decouple technology from process. You need to work on both, otherwise you’re just building technological fluency and not helping your team learn to read, write, and argue with data.
- The process of working with data is ripe with opportunities for engagement with otherwise marginalized populations (that’s don’t “speak” data)
- A lightweight, self-service curriculum of activities you fan run with people of different literacy levels
- This is some of what it looks like. The arts is the best tool we have for bringing people together
- Some concrete example from industries and collaborations
- Some concrete example from industries and collaborations
- Some concrete example from industries and collaborations
- The Yemen example is one of the discussions that came up after we had a sketch a story activity in RBC. We discussed how thinking through the colours and symbols when analysing text could have been applied in this use case. Ask the participants what other local examples they can share.
- The Yemen example is one of the discussions that came up after we had a sketch a story activity in RBC. We discussed how thinking through the colours and symbols when analysing text could have been applied in this use case. Ask the participants what other local examples they can share.
- The Yemen example is one of the discussions that came up after we had a sketch a story activity in RBC. We discussed how thinking through the colours and symbols when analysing text could have been applied in this use case. Ask the participants what other local examples they can share.
- Some concrete example from industries and collaborations
- The process of working with data is ripe with opportunities for engagement with otherwise marginalized populations (that’s don’t “speak” data)