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Data is a Team Sport: Data-Driven Journalism

Dirk Slater - June 20, 2017 in Community, Data Blog, Event report, Research

Our podcast series that explores the ever evolving data literacy eco-system. Cut and paste this link into your podcast app to subscribe: or find us in the iTunes Store and Stitcher.

In this episode we speak with two veteran data literacy practitioners who have been involved with developing data-driven journalism teams.

Our guests:

  • Eva Constantaras is a data journalist specialized in building data journalism teams in developing countries. These teams that have reported from across Latin America, Asia and East Africa on topics ranging from displacement and kidnapping by organized crime networks to extractive industries and public health. As a Google Data Journalism Scholar and a Fulbright Fellow, she developed a course for investigative and data journalism in high-risk environments.
  • Natalia Mazotte is Program Manager of School of Data in Brazil and founder and co-director of the digital magazine Gender and Number. She has a Master Degree in Communications and Culture from the Federal University of Rio de Janeiro and a specialization in Digital Strategy from Pompeu Fabra University (Barcelona/Spain). Natalia has been teaching data skills in different universities and newsrooms around Brazil. She also works as instructor in online courses in the Knight Center for Journalism in the Americas, a project from Texas University, and writes for international publications such as SGI News, Bertelsmann-Stiftung, Euroactiv and Nieman Lab.

Notes from this episode

They both describe the lessons learned in getting journalists to use data that can drive social change. For Eva, getting journalists to work harder and just reporting that corruption exists is not enough, while Natalia, talks about how they use data on gender to drive debate and discussion around equality. What is critical for democracy is the existence of good journalism and this includes data-driven journalism that uncovers facts and gets at the root causes.

Gaps in the Data Literacy EcoSystem:

Natalia points out that corporations and government has the power because they are data-literate and can use it effectively, while people in low-income communities, such as favela’s really suffer because they are at the mercy of what story gets told by looking at the ‘official’ data.

Eva feels that there has been too much emphasis on short-term and quick solutions from individuals who have put a lot of money in making sure that data is ready and accessible.  Donors need to support more long-term efforts and engagement around data-literacy.

Adjusting to a ‘post-fact’ world means:

Western journalists have spent too much time focusing on reporting on polling data rather than reporting on policies and it’s important for newer journalists to understand why that was problematic.

In Brazil, the main stream media is focusing on ‘what’s happened’ while independent media is focusing on ‘why it’s happened’ and this means the media landscape is changing.

They also talked about:

  • Ethics and the responsibility inherent in gathering and storing data, along with the grey areas around privacy.
  • How to get media outlets to value data-driven journalism by getting them to understand that people are increasingly getting their ‘breaking news’ from social media, so they need to look at providing more in-depth stories.

They wanted to plug:

Readings/Resources they find inspiring for their work.

Resources contributed from the participants:

View the online conversation in full:

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Data is a Team Sport

Dirk Slater - June 16, 2017 in Announcement

A series of online conversations examining the data literacy ecosystem.

In this series we seek to capture learnings about the ever-changing field of data literacy and how it is evolving in response to concepts like ‘big data’, ‘post-fact’ and ‘data cofusion’.  This open research project by School of Data, in collaboration with FabRiders, will produce a series of podcasts and blog posts as we engage data literacy practitioners with particular expertise within the ecosystem (e.g., investigative journalism, advocacy and activism, academia, government, etc) in conversation. 

Check out our podcasts on enabling learning and data-driven journalism.

You can join the conversation (see RSVP below) and provide inputs into the research we are conducting. During each online conversation we will give participants an opportunity to ask questions and share their own insights on the topic.

Our next conversation will focus on advocacy organisations and their role in the eco-system on Friday June 23rd at 7:00 PDT, 10:00 EDT, 15:00 BST, 16:00 CEST, 17:00 EAT/Istanbul, 19:30 India  & 21:00 Bangkok with:

  • Milena Marin is Senior Innovation Campaigner at Amnesty International. Milena has over seven years’ experience working at the intersection of technology, data and social good. She is currently working with Amnesty International, leading Amnesty Decoders – an innovative project aiming to engage digital volunteers in documenting human right violations using new technologies. Previously she worked as programme manager of School of Data where she trained and mentored numerous NGOs and journalists around the world to make the most of their data and reach new audiences. She also worked for over 4 years with Transparency International where she supported TI’s global network to use technology in the fight against corruption.
  • Sam Leon, is Data Lead at Global Witness. His work focuses on the use of data to fight corruption and how to turn this information into change making stories. He is currently working with a coalition of data scientists, academics and investigative journalists to build analytical models and tools that enable anti-corruption campaigners to understand and identify corporate networks used for nefarious and corrupt practices. He previously worked at the Open Knowledge Foundation leading the organization’s work on data literacy for human rights groups and journalists. He has an undergraduate degree in Philosophy from Cambridge University and a Masters in the History of Ideas from University College London.

Your hosts:

RSVP to join:


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Data is a Team Sport: Enabling Learning

Dirk Slater - June 6, 2017 in Community, Event report, Research

Our podcast series that explores the ever evolving data literacy eco-system. Cut and paste this link into your podcast app to subscribe: or find us in the iTunes Store.

In this episode we speak with two veteran data literacy practitioners who have been involved with directly engaging learners to get beyond spreadsheets to build confidence and take agency in their own learning.

Our guests:

  • Rahul Bhargava is a researcher and technologist specializing in civic technology and data literacy. He creates interactive websites used by hundreds of thousands, playful educational experiences across the globe, and award-winning visualizations for museum settings. As a research scientist at the MIT Center for Civic Media, Rahul leads technical development on projects ranging from interfaces for quantitative news analysis to platforms for crowd-sourced sensing.
  • Lucy Chambers initially embarked on a career as a journalist, she took a few turns which lead to a career at Open Knowledge teaching journalists how and why to work with data. She was one of the editors of the Data Journalism Handbook. She later lead the highly successful School of Data programme which extended technical training to non-profit organisations. Lately, she has focussed on delivery of software projects as a product manager. Most recently, she has been working in West Africa on health related software.

Notes from this episode

Rahul described methods to data novices to think more creatively by drawing and using a gallery of their artwork to build confidence to think more critically. He says that this experience is what led to the creation of, a website designed specifically to engage learners.

Lucy tells of School of Data’s initial struggles with setting up a one-size fits all online curriculum. They learned through focus groups and testing that a tool-based approach was not helpful or achievable. Instead they needed a people based approach. They then turned to developing a fellowship programme which is very much at the core of the School of Data network.

Both of our guests had strong opinions about building data literacy culture in organisations. A common mistake is made by letting the IT Department provide data training.  Organisations often produce unhelpful data metrics and dashboards that don’t actually help staff get a full picture of progress.

Gaps in the Data Literacy EcoSystem:

  • Toolbuilders not understanding and not building for learners.
  • NGO’s not testing out data driven messages with their audiences before they release them.

Adjusting to a ‘post-fact’ world means:

  • We need to make sure that people understand that data is not necessarily truth, that it is often used as rhetoric and that it carries bias. Data sets should have a biography attached.
  • Narrative wins, so the data presentation methods where the audience is bombarded with facts and figures just doesn’t work. We have to spend more time pulling out the compelling narrative from the data.

They wanted to plug:

  • Rahul is building a co-hort around further development of Ping him via twitter to get more information on that.
  • Lucy’s blog is Tech to Human and she writes about her work and what she’s learning. She is working on a project for MySociety called EveryPolitician and writing about it on Medium.

Readings/Resources they find inspiring for data literacy work.

View the full online conversations:

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Research Results Part 6: Data Literacy Research References and Resources

Dirk Slater - February 11, 2016 in Uncategorized

Even though work in the field of data literacy can feel a bit lonely at times, truth is it is not entirely new and undocumented. During the research process that has been described over these blog posts, we have been lucky to come across valuable sources of information on the topic – researchers and practitioners have devoted writing time to data literacy in civil society and in academia.

To close off the blog posts sharing our main findings, we found it suitable to share a bit of information about the resources that informed the process.

A quick dive into the history of data literacy

Even though data literacy efforts in civil society might seem recent, they fit into a much longer history of numeracy, statistical literacy (and, of course, literacy in general). When looking into the broader literature, we found articles devoting time to narrow and define this field, especially as compared to others. We recommend taking a look at:

For a shorter (but comprehensive) account of broader research in this field, we found Data Pop Alliance’s Beyond Data Literacy: Reinventing Community Engagement and Empowerment in the Age of Data to be illuminating.

The origins of School of Data

If you were around in School of Data in 2012, the information in tis section might be redundant for you… but many of the newer School of Data community members haven’t had the chance to learn how it all started.

We also want to point out to Sam Leon’s blog post talking about his embedded fellowship in Global Witness – one of School of Data’s first experiments with longer term processes.

Academic research meets data literacy work

Data literacy training efforts in civil society are similar to some of those documented by academic researchers, and that’s why we decided to take a look at how they are being discussed in the literature. Sources that we recommend:

Data literacy in civil society

Perhaps not in journal articles, but civil society organizations and individuals around the world have also devoted efforts to the documentation of their work in the field. Some of the highlights:

Thank you for participating and following the data literacy research process we underwent! Our blog post series has now been completed and we encourage you to take a look at it. If you want to send feedback or get in touch, please do so at dataliteracy [at]

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Research Results Part 5: Improving Data Literacy Efforts

Dirk Slater - February 5, 2016 in Research

As technologies advance and the accessibility of data becomes ubiquitous, data literacy skills will likely gain increasing importance. The School of Data training resources have already laid an important foundation for social change efforts to harness data and improve their impact. Going forward, School of Data local communities will have to take into account their role as stewards of the curriculum, and continue to develop and incorporate new learnings as access to data continues to increase.

From what we (Mariel Garcia, and myself) have learned by conducting this research, we make the following recommendations:

  • Training the trainers: The School of Data curriculum is the foundation for much of the Data Literacy training that is happening both inside and outside the School of Data network, as reported by interviewees; it would make sense to focus efforts on preparing materials not just for learner consumption, but also in a curriculum format for trainers.
  • More research on pedagogical methods: Additional research and establishment of effective pedagogical methods of data literacy training would be beneficial – many interviewees mentioned the importance of this topic, and yet had no resources to share about it. In this regard, Peer to Peer University is the one participant that has invested most resources into this understanding, and is a great ally going forward in this area.
  • More knowledge-sharing within the network: In this regard, the School of Data network also functions as a ‘community of practice’ for trainers who are sharing advice and tips on providing data literacy training, but this could be strengthened by actively promoting conversations around the topics covered in this research.
  • Measuring the impact: As with different initiatives, impact evaluation is an area in which data literacy work can still grow. Both the School of Data local communities and data literacy related organisations need much stronger articulations of their long-term goals and intended impact in the short term.  School of Data events might be a good space to have the necessary conversations to find frameworks of evaluation that work for different work formats and budgets. Some organizations outside of the School of Data network (IREX and Internews) have worked extensively on this, and could be good references going forward.
  • Promoting long term engagements: It appeared during the research that only older and established organisations had started long term projects and engagements related to data literacy. Consequently, it might make sense for School of Data to help smaller and newer organisations within its community to start and sustain long term engagements, by helping them find the necessary resources. This could provide an important focal point for collaborations within the network as it will likely yield important learnings.
  • Data literacy at the organisation level: Articulate how individual data literacy training can complement and support long term engagements that will lead to organisational data literacy. Building local fellowship programs that can engage social change organisations over the long-term and build their capacity to utilise data in their campaigns will likely lead to deeper alliances and joint funding opportunities.
  • Better collaboration with outside partners: The project would stand to benefit from more linkages and collaborations with academia, open data-related civil society efforts. Additionally, more efforts can be made to improve the accessibility of the School of Data curriculum, methodologies and trainings. This will likely lead to more diverse and sustainable funding.

The goal of this research was to empower the School of Data Steering Committee to take strategic decisions about the programme going forward along with helping the School of Data network members build on the successes to date. We hope that in providing this research and recommendations in an accessible format, both School of data and the wider network of data literacy practicioners will benefit from it. Hopefully, these research results will complement and contribute to the School of Data’s goal of improving the impact of social change efforts through data literacy.

In our next and final blog post, we will present a list of resources and references we used during our research.

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Research Results Part 4: Which Business Models for Data Literacy Efforts?

Dirk Slater - January 29, 2016 in Research

After researching the definitions of data literacy, along with the methodologies and impact of data literacy efforts, we looked into the question of business models: are there sustainable business models that can support data literacy efforts in the long term? Along with looking at how data literacy efforts can support themselves financially, we also looked for opportunities for linkages with other efforts.

No clear business model for sustainability

Many of the School of Data local communities and external organisations that provide data literacy training are using a mix of foundational funding and fee for service to sustain themselves. The organisations using this model are opportunistic in getting organisations and individuals to pay for trainings when they can, but a lack of understanding about data processes among clients is often a problem. At this point, there is not a clear ‘sustainable business model’ that would direct data literacy organisations towards longevity. To understand better the business models of established organisations working at the intersection of technology and social change, we looked at a two of them: Aspiration and Tactical Technology Collective. 


Aspiration is a US-based NGO that operates globally, providing a range of services to build capacity and community around technology for social change. Over the last decade, under Allen Gunn’s (Gunner) leadership, Aspiration has gained a strong reputation for delivering trainings and events that focus on strategic and tangible outcomes while strengthening communities of practice.  Aspiration has championed an approach known as participatory events, developing knowledge-sharing and leadership development methodologies that prioritize active dialog-based learning. The philosophy and design focus on maximizing interaction and peer learning while making spare use of one-to-many and several-to-many session formats such as presentations and panels. Aspiration has been able to scale their model across a range of meeting sizes and purposes, from smaller team strategy sessions and retreats to large-scale events, such as the Mozilla Festival, which brings together over 1,500 participants.

Aspiration’s services are in high demand, with clients ranging from both small civil society organisations to larger international NGO’s and foundations. They have seen a gradual reduction in reliance on grants, and now generate the majority of their funds through earned income from strategic consulting services, events, and trainings. In order to scale service delivery, program staff have all been trained in the unique skill sets required for delivering participatory events and providing strategic services within the Aspiration frame of analysis. The organization now has five full-time staff able to deliver both live events and strategic services.

Tactical Technology Collective

Fee for service work on utilising data in social change has had an increased market in the civil society sector over the last five to seven years. However, many social change organisations have been unaware of the amount of resources and effort it takes to analyse and produce outputs such as data visualisations and info-graphics. A more mature organisation with experience in utilising information and technologies in activism, Tactical Technology Collective, set up a social enterprise, Tactical Studios, to undertake data-driven projects for large-scale NGO’s. They attempted to better educate clients by engaging them in creation of design briefs and a more intentional process. Tactical Studios was marginally successful as most advocacy and activist connected organisations look for quick and low-cost solutions to their data visualisations. 

Using collaborations and linkages to improve the understanding of data needs

A hopeful example in developing the capacity of organisations to understand the amount of resources needed to utilise data is in the School of Data’s Embedded Fellowship with Global Witness. Through a six-month engagement, the fellow, Sam Leon, was able to provide data trainings at all levels of the organisation – from senior management to the front line staff. This has helped the organisation, rather than just the individuals, to improve its data literacy.  What this points to is a need to differentiate between individual data literacy and organisational data literacy. While the School of Data curriculum addresses individual data literacy, efforts like the fellowship programme, that have long term engagements succeed in building organisational capacities. Being more intentional in articulating both the difference and how they complement each other will likely lead to a greater ability to raise funds and develop deeper relationships with allies.

Other potential areas for linkages and collaboration on the School of Data Curriculum that could lead to greater sustainablity for data literacy organisations:

    • Schools and universities who are interested in expanding their course offerings to better address data literacy amongst students. An opportunity for chapters is to work with local academia in adapting the School of Data curriculum to address the needs of students who will potentially be using open-data in their careers. Teachers are also in need of training on the pedagogy in regards to understanding data and it’s contexts, as opposed to understanding how to use tools. Academic grants and funding could support this adaptation.
    • Civil society efforts that are working towards the release of data, particularly by governments, for use in the public domain. One area that has a strong need for greater data literacy is the open governments, transparency and accountability movements, whose area of expertise is in pressuring governments through advocacy campaigns to release data. Many do not have capacity to provide training to those who might actually use the data. In this regard, a conclusion of the International Open Data Conference in 2015 (as stated in its final report) poses the need for work to identify and embed core competencies for working with open data within existing organizational training, formal education, and informal learning programs.
    • Development initiatives, particularly those that are focused on supporting an emerging private sector that will be inspired by data for use in innovation.  Access and use-ability of open data could be exploited by the private sector in ways that could expand data literacy in emerging economies.  Current development initiatives could greatly benefit from engaging with School of Data chapters and engaging with the curriculum.

In order to sustain a long term data literacy initiative, it is likely that funding will need to come from a mix of foundational funding and fee for service work through expanding the diversity of clients, collaborations and linkages. As open-data usability and access continues to improve, it will be critical that Data Literacy organisations stay on top of future trends and continue to shape their curriculum to meet the needs of the communities they aim to serve. Hopefully, funders and social change organisations will also continue to evolve in their understanding of the importance of data and the resources involved in making it useful to stakeholders. As a network, the School of Data local communities will need to share information about how they grow and evolve sustainable business models.

In our next blog post ‘Recommendations for Improving Data Literacy Efforts’ we will discuss the conclusions that we have made as a result of undertaking this research effort.

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Research Results Part 3: Measuring the Impact of Data Literacy Efforts

Dirk Slater - January 21, 2016 in Research

As there are a wide range of methodologies for achieving data literacy in social change efforts, there is also a range of approaches to determining effectiveness. The degree to which a data literacy practice has the capacity to measure effectiveness is largely based on that practice’s maturity level. Participants who work for older, established organizations reported devoting considerable resources to M&E, whereas most individuals and participants from smaller organizations recognized that they were very limited in their evaluation possibilities. Methodologies for evaluating efficacy of data literacy efforts are not standardised. To measure the impact of data literacy work in environments with limited resources, participants in our research focus on the following sources of information:

  • Analysis of data outputs: some of the participants mentioned the relative ease to measure the impact of data work (as compared to other ICT-related initiatives) because there will be outputs that you can analyze qualitatively.
  • Sentiment analysis: Data literacy trainers frequently mentioned the importance of measuring outcomes by trying to get a feel for the reactions of people before ending a workshop, particularly in processes where follow-up is unlikely.
  • Having an eye for the manifestation of organizational (vs individual) change: For some trainers, the true impact of data literacy work can be seen in how data work becomes internalised in an organization’s programmes and staffing.
  • Direct skills assessment: Perhaps difficult to evaluate without exams, some participants rely on self-reporting from their beneficiaries and try to compare pre and post surveys to see the impact of particular training processes.

Diversity in approach

More recently established data literacy efforts are using basic evaluation forms distributed at the end of their trainings. Data literacy trainers frequently mentioned the importance of measuring outcomes by trying to get a feel for the reactions of people in their workshops. This involves including questions about the setup and fulfilment of expectations in post-workshop surveys, but also looking for signs of independent work and pondered questions. A few of the participants consider these signs of engagement are crucial in processes where follow up isn’t likely.

Code for Africa has developed a robust set of success indicators that allow them to chart a path towards success such as data responsibilities being included in organisational job descriptions. For some practitioners, the true impact of data literacy work can be seen in organizational change. Will someone be hired to do data work in the organizations? Do senior executives value data work more and are they willing to allocate more funds for this type of work?

Some of the participants mentioned the relative ease to measure the impact of data work (as compared to other ICT-related initiatives) because there will be outputs that you can analyze qualitatively. A couple of organizations mentioned detailed analysis frameworks to measure the quality of stories in data journalism, for example – employing local data journalists who could evaluate stories from before and after the processes took place to compare the performance of beneficiaries.

Some participants rely on self-reporting from their beneficiaries (through surveys that ask questions on their level of comfort/knowledge on specific skills) – and try to compare pre and post surveys to see the impact of particular training processes.

Even though most participants had given thought to monitoring and evaluation in a way or another, few of them had developed frameworks to use before, during and after the implementation of a project or program. Many of these efforts need more opportunity to articulate what success would look like for their project, and then work backwards to understand what steps and endeavours they need to accomplish to attain that success.

Determining effective ways to measure impact

During a workshop on impact assessment provided to data literacy practitioners connected to the School of Data network in March of 2015, it was determined that the term ‘impact assessment’ may not be an appropriate term, as it implies more robust and resource intensive endeavours that is often applied when evaluating public policy. There was a strong desire for lightweight methodologies that will help them learn how to improve offerings that will deliver greater impact in the long term. They determined that the methodology should contain some basic elements, such as baselines, working with beneficiaries to establish indicators, having feedback loops, articulating clear and transparent goals, having consistency throughout their programs and taking the time to document.

While some exchange between data literacy practitioners has begun around methodologies for evaluation that leads to learning and improved projects, there needs to be continued dialogue in the School of Data network to determine effective ways of measuring impact.

In our next post, ‘Sustainable Business Models for Data Literacy Efforts’, we will explore viable models and opportunities for data literacy practitioners to fund and support their work.


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Research Results Part 2: The Methodologies of Data Literacy

Dirk Slater - January 14, 2016 in Research

After exploring how to define data literacy, we wondered about the reality of the work of data literacy advocates. Which methodology do they use and what does it look like in practice? Unsurprisingly, there is a wide range of methodologies in use across different groups. Each of them fits the available opportunities, time and resources.

Short term efforts


A large part of the training done by the data literacy advocates surveyed take the form of workshops. Participants agreed that workshops (rather than talks) were a good way to promote practical learning, and also the short timeframe allowed for the participation of individuals and organizations with resource constraints. Some workshops can be delivered inside of conferences and events (ranging from two hours to a half-day), and their learning goals are largely limited to introducing the basic topics to participants. Some of those workshops can be multi-day, ranging from two to five days: they allow for a longer exposure to processes and provide enough of a foundation to start concrete data projects. Multi-day workshops may also be augmented with follow-up sessions designed to provide guidance and support during the life of a data project.

The following short term workshops were mentioned:

  • 2 hour workshops: they are the ones that take place in conferences, and which only provide space for an introduction (but require few resources to make happen)
  • Half day workshops: they are seen as good for introductions to topics, as well as spaces to do practical work. For example: Data Therapy’s workshops.
  • 2 to 3-day workshops: they provide enough practice time to make it feasible to start specific projects
  • 5-day workshops: two of the organizations surveyed mentioned them as great opportunities to go through entire processes (like the data pipeline) with workshop participants
  • 10-day workshops: the longest workshop format that was mentioned in interviews; they provide enough space to go through entire processes as well as work on specific projects from scratch.

The Data Pipeline

In regard to the content of these workshops, they often start with what participants described as “data basics” (what is data, what is data journalism, etc). After this introduction, it is common for trainers to explain the process of working with data. Here, a recurring concept is the School of Data pipeline, as shown in the illustration. It is a pedagogical device created to show that “data wrangling takes place in several stages; in order to process data, it must be moved through a ‘pipeline’. […] Raw data must usually travel through every stage of the pipeline – sometimes going through one stage more than once” (Newman, 2012). While the data pipeline is heavily promoted and used by the School of Data network, participants outside of the network were found to use the pipeline model to describe the type of content they cover in workshops.

Beside the data pipeline-like methodologies, another specific type of exercice that came up during the research was the “reverse engineering” of data exercises: deconstructing existing examples to explain how they came to be and make them more relatable for the trainees.

Community events

Along with workshops, community events have developed as a way to have a more social component to events, while getting informal but practical help and advice.

  • Data clinics: those events provide space where people can develop their data skills, ask questions related to their own data and get help with challenges they are facing in their data projects
  • Data meetups: many organizations that do trainings also devote resources to hosting informal meetups where people can share learnings on their own data projects, along with getting insights into other data projects.


Another intensive format is the Datathon, which is based on the concept of hackathons. They are often named “data expeditions”, “data dives”, “data quests” and are popular with data literacy practitioners, along with individuals who have more established data-literacy skills. Quoting a participant, “acquiring data skills requires short, intensive bursts of focus from a group, rather than the type of attention you would have during 6-8 weeks with sessions that last a couple of hours per week”.

Medium term efforts

Some of the formats do not require individuals to be removed from their workplace. The training is brought to them, either physically at their workplace, or online, allowing participation from their place of work. The online format is generally conducted over a period of several weeks or months, a few hours a week.

  • 5-week newslab model: in contexts where journalists cannot leave their newsroom, trainings can be brought to the newsroom.
  • 4-week training: in opposition to the hypothesis that led to the birth of data expeditions, this model relies on a relatively modest demand of time each week, and relies on the accumulation of practice over four weeks.
  • 1-week workshop with follow up: when there is interest in supporting a long term process, but offline training can’t be sustained over a long continuous period, follow up sessions can extend the process.

Some interviewees mentioned paying special attention to the need of developing communities of practice with alumni, in order to provide spaces where they can continue to develop expertise and learn from each other’s experiences.

Long term efforts

On the longer end of the scale, the long term efforts correspond to immersive endeavours where an individual is placed within a data project lasting anywhere from five months to a year. This takes the form of either fellowship programs, allowing an individual to gain expertise by being placed within a data project, or a mentorship program, where an individual with data expertise is placed within a data project to help build the skills of staff while working side by side.

  • Fellowship programs: tend to last from 5 months to a year. Some participants favor fellowship programs for data journalists in environments where such intensive involvement is not disruptive to the media industry. School of Data has experience in this regard, too, with its own group of School of Data fellows.
  • Research processes: some participants sustained long term capacity building through a research process that they documented. For communities that must collect and analyse their own data in the face of other challenges, such as marginalisation and illiteracy, an approach of a multi-year engagement towards empowerment can be successful. An example of this can be found in the FabRiders’ blog post series What we learned from Sex Workers.
  • Six-month projects: rather than doing it through workshops, data literacy training can take place in the form of involvement in specific projects with the guidance of a mentor.  

Online vs offline

Most of the aforementioned formats (except for the follow-up community) take place primarily offline regardless of the duration, but some online formats were brought up by participants – primarily by those whose native language isn’t English, and whose communities don’t have a wealth of data literacy resources in their language. Some mentioned MOOCs (one of the participants ran a five-week MOOC on data journalism – the first one ever in Portuguese, the language spoken in her country, Brazil); others, dedicated websites (one of the participants was prompted by the desire to introduce trends she admired in other contexts, while translating the resources that could aid in this adoption); webinars as an attempt to replicate brief offline trainings, and paying attention to social media content as a source of learning.

Choosing an effective format

Despite the wide range of formats that are used to help build data literacy, the selected format for an event often comes down to two factors: the availability of funding and the amount of time participants are willing to invest. In some contexts, journalists and/or activists can’t give up more than two days at a time; in others, they can give up to half a year. The least disruptive formats are chosen for each community. There is a noticeable difference in the types of actors and the engagements they will favor. Larger and older organizations favor intensive, long term processes with relatively few beneficiaries; smaller and younger organizations or individuals favor short-term trainings to reach larger audiences.

Interviewees focused on developing developing data literacy capacity in both individuals and organisations favor providing experiential, project-driven work. Often it’s about providing people with a dataset and getting them to develop a story from it; other times, it’s hands-on training on different parts of the data pipeline. Most interviewees so far have made an emphasis on the importance of providing opportunities for hands-on experience with data.  They also strive for having concrete outcomes for their trainings where participants can see immediate impact of their work with data.

Curriculum Sources

The research participants mentioned several sources of inspiration around data literacy, which can be found below:

In our next post ‘Measuring the Impact of Data Literacy Efforts’ we will look at how data literacy practitioners measure and evaluate their methodologies and efforts.

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Research Results Part 1: Defining Data Literacy

Dirk Slater - January 8, 2016 in Impact

Thanks to the efforts of governments, organizations and agencies to make their information more transparent, the amount of open data has increased dramatically in recent years. Consequently, interest has arisen in the practitioners who develop data literacy, which they do often through international, collaborative networks of like-minded actors.

The work of School of Data has emerged in a context where different fields (from Information and Communications Technology (ICT) for change activism to data journalism curriculum creation in universities) have seen resources devoted to the transmission of skills related to data use in different journalism and advocacy contexts. ‘Data literacy’ has emerged as a term to refer to the umbrella of initiatives, though not without challenges (Data-Pop Alliance, 2015). What does the concept exactly mean?

‘Data literacy’ can be defined in terms of skills (‘the ability to use and analyse data’), and this can inspire different analysis on each component to those skills. However, attempts to define the term can also allude to the social transformations that can be sought through it, especially when seen through the lens of the history of literacy (Data-Pop Alliance, 2015).

Furthermore, once we accept a definition of ‘data literacy’, how does it coexist with discussions such as the difference between ‘statistical literacy’ and ‘statistical competence’ (“what every college graduate should know” vs “what we hope a business statistics student will retain five years later”, as Moore distinguishes –as cited by Schield, 2014–), or with the general concept of data awareness (as discussed by Rumsey, 2002)?

‘Data literacy’ as a concept stems from old visions of numeracy and information literacy; however, researchers who have examined current work in this field have categorized the approaches to define data literacy as the ability to read, work with, analyze and argue with data (Bhargava and D’Ignazio, 2014), as well as “the desire and ability to constructively engage in society through or about data” (Data-Pop Alliance, 2015). We consider both dimensions, skills and social engagement, are a good foundation to discuss the aims and practices of the School of Data community.

The underlying concept of data literacy that each actor holds will determine aspects of their methodology at the individual and collective levels. Inspired by the categorization done by Bhargava and D’Ignazio, in our interviews we asked participants questions to get insight on their visions of data literacy and the aims of their work. The following abilities were mentioned by two or more participants:

  • Knowing how to find information in different ways. This includes being able to track down sources of existing data, but also knowing how to collect it if it doesn’t exist yet.
  • Being able to apply critical thinking skills to data. This ranges from the ability to do data quality assessment or contextualizing specific information to other aspects of processes related to data-related work, such as the ethics of handling data.
  • Being able to ask questions to the data (and then finding an answer). Related to the last ability, different participants mentioned the ability to ask questions to data as one of the goals of data literacy trainings – even if they don’t go as far as finding the answers for them, though ideally they would.
  • Being able to find specific outputs (such as stories or visualizations) in data. Apart from the ability to ask and answer questions, a topic that recurred among participants from the field of data journalism was the importance of finding stories and other journalistic outputs.
  • Being able to use it to advance one’s own goals. Whether it is specifically more in-depth research, or generally better and more data-driven storytelling or campaigning, the link between data and action was evident in many of the interactions we had with the participants.
  • Feeling comfortable around data and working with it. At times as an intermediate aim to lead to the other abilities mentioned in this section, and at other times as an end in itself, many participants mentioned the importance of promoting comfort around data (and bringing down the psychological barriers that exist between people and data).
  • Being able to do basic statistical analysis with data. Even though more technical aspects of data literacy came up at different points (for example, the need to know how to clean data), the only one that was recurring was the ability to work with basic statistics.

Other general considerations

  • It’s a non-linear process. Two participants pointed out that it was important in their work not to view data literacy as a linear process, or a binary (“you are data literate or you aren’t”); they view data literacy as a process that involves very different actions depending on the context and needs of each individual (or group).
  • Data literacy can be promoted and assessed at the individual level, but also in groups (such as organizations or communities). When asked what data literacy looked like at the organization level, participants mentioned buy-in and engagement from different parts of the organization (including the senior staff). The proper allocation of resources to this type of work depends on an understanding of data work and its genuine possibilities.
  • An aim of data literacy work is to expand existing markets. In the case of data journalism, different participants mentioned data literacy work as a way to help journalists produce content that will bridge the gap between them and information they can act upon (a hypothesis based on solutions journalism, which is journalism that aims at covering solutions to social problems, for example). Also, as a way to increase the demand for open data.

It’s important to understand how the various actors in the field use the term ‘data literacy’ and in particular, how that impacts training and knowledge sharing goals. As the use of data becomes more ubiquitous in social change efforts, it is likely that the definition will continue to narrow and be as recognisable as terms like ‘computer literacy’.  

In our next post ‘Data Literacy Methodologies’ we will look at how data literacy practitioners reinforce their own definitions through their training and knowledge sharing practices.

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Our Data Literacy Research Findings

Dirk Slater - January 8, 2016 in Impact, Update


In 2015 School of Data started its first research project to understand data literacy efforts around the world. In the lead up to the publication of the final report, we’re publishing a series of blog posts to share our findings. The goal is to provide them in an accessible format, benefitting both data literacy practitioners and a wider network of peers. Hopefully, this examination of techniques and methodologies currently employed by actors within and outside the network can provide with a pool of knowledge to be used in building and developing data literacy efforts.

For this research project we aimed to examine the effectiveness of current data literacy efforts, particularly in relation to social change work. This research is specifically aimed to empower the School of Data Steering Committee to take strategic decisions about the programme going forward and along with the School of Data network members, build on the successes to date.  We specifically looked to answer the question: What are the recurring topics when speaking about data literacy in social change/justice work?

We have conducted a series of semi-structured interviews with data literacy practitioners, and desk research to collect data and literature on data literacy. This has been analysed with the goal of improving data literacy practice in the short term, informing efforts to provide data literacy in the long run.

In the coming weeks we will be sharing our findings here under the following topics:

  1. Defining Data Literacy – January 8th
  2. Data Literacy Methodologies – January 14th
  3. Measuring the Impact of Data Literacy Efforts – January 21st
  4. Which Business Models for Data Literacy Efforts? – January 28th
  5. Improving Data Literacy Efforts – February 4th
  6. List of resources we used during our research – February 11th


Mariel Garcia provided research assistance and Dirk Slater from FabRiders provided research advisory. Guidance for their work was provided by Marco Pires, School of Data Coordinator; Milena Marin, former School of Data Coordinator and Katelyn Rogers, Project Manager at Open Knowledge International.

We are especially thankful to the following people who advised us during this process:

  • Javiera Atenas (Management Science and Innovation Department, University College London, United Kingdom),
  • Becky Faith (Department of Computing and Communications, Open University, United Kingdom),
  • Rahul Bhargava (Center for Civic Media, Massachusetts Institute of Technology, United States),
  • Silvana Fumega (University of Tasmania, Australia)
  • Fabrizio Scrollini (Iniciativa Latinoamericana por los Datos Abiertos, Uruguay).

The following people were gracious enough to provide us with insightful interviews that helped us develop our research:

  • Allen Gunn, Aspiration;
  • Ariel Merpert, Chequeado;
  • Emma Prest, Data Kind UK;
  • Eva Constantaras, Internews;
  • Fabio Campos, Oi Futuro;
  • Gabriela Rodriguez;
  • Jason Norwood-Young, Raymond Joseph and Jennifer Walker, Code for South Africa;
  • Juan Manuel Casanueva, SocialTIC;
  • Maya Ganesh, Tactical Technology Committee;
  • Natalia Mazotte, School of Data Brazil;
  • Nisha Thompson, Data Meet;
  • Rahul Bhargava, Data Therapy;
  • Rebecca Kahn, P2P University;
  • Ye Sheng, IREX;
  • Zara Rahman, the engine room.

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