You are browsing the archive for advocacy.

Data is a Team Sport: Mentors Mediators and Mad Skills

Dirk Slater - August 7, 2017 in advocacy, community, Data Blog, data literacy, Data Maturity, DataKind UK, Emma Prest, Event report, Fabriders, Intermediaries, mentoring, Service Organisations, Team Sport, Tin Geber

Data is a Team Sport is our open-research project exploring the data literacy eco-system and how it is evolving in the wake of post-fact, fake news and data-driven confusion.  We are producing a series of videos, blog posts and podcasts based on a series of online conversations we are having with data literacy practitioners. To subscribe to the podcast series, cut and paste the following link into your podcast manager : http://feeds.soundcloud.com/users/soundcloud:users:311573348/sounds.rss or find us in the iTunes Store and Stitcher. This episode features:
  • Emma Prest oversees the running of DataKind UK, leading the community of volunteers and building understanding about what data science can do in the charitable sector. Emma sits on the Editorial Advisory Committee at the Bureau of Investigative Journalism. She was previously a programme coordinator at Tactical Tech, providing hands-on help for activists using data in campaigns. 
  • Tin Geber has been working on the intersection of technology, art and activism for most of the last decade. In his previous role as Design and Tech Lead for The Engine Room, he developed role-playing games for human rights activists; collaborated on augmented reality transmedia projects; and helped NGOs around the world to develop creative ways to combine technology and human rights.
In this episode we take a deep dive into how to get organisations beyond ‘data literacy’ and reach ‘data maturity’, where organisations understand what is good practice on running a data project.  Some main points:
  • A red flag that indicates a data project will end in failure is when the goal is implementation of a tool as opposed to a mission-critical goal.
  • Training in itself can be helpful with hard skills, such as how to do analysis, but in terms of running data projects, it takes a lot of hand-holding and mentorship is a more effective.
  • A critical role in and organisations is people who can champion tech and data work, and they need better support in that role.
  • Fake news and data-driven confusion has meant the need for understanding good data practice is even more important.

DataKind UK’s resources:

Tin’s resources:

Resources that are inspiring Emma’s Work:

Resources that are inspiring Tin’s work:

  • DataBasic.io – A a suite of easy-to-use web tools for beginners that introduce concepts of working with data
  • Media Manipulation and Disinformation Online – Report from Data and Society on how false or misleading information is having real and negative effects on the public consumption of news.
  • Raw Graphs – The missing link between spreadsheets and data visualization

View the full online conversation:

Flattr this!

Data is a Team Sport: Mentors Mediators and Mad Skills

Dirk Slater - August 7, 2017 in advocacy, community, Data Blog, data literacy, Data Maturity, DataKind UK, Emma Prest, Event report, Fabriders, Intermediaries, mentoring, Service Organisations, Team Sport, Tin Geber

Data is a Team Sport is our open-research project exploring the data literacy eco-system and how it is evolving in the wake of post-fact, fake news and data-driven confusion.  We are producing a series of videos, blog posts and podcasts based on a series of online conversations we are having with data literacy practitioners. To subscribe to the podcast series, cut and paste the following link into your podcast manager : http://feeds.soundcloud.com/users/soundcloud:users:311573348/sounds.rss or find us in the iTunes Store and Stitcher. This episode features:
  • Emma Prest oversees the running of DataKind UK, leading the community of volunteers and building understanding about what data science can do in the charitable sector. Emma sits on the Editorial Advisory Committee at the Bureau of Investigative Journalism. She was previously a programme coordinator at Tactical Tech, providing hands-on help for activists using data in campaigns. 
  • Tin Geber has been working on the intersection of technology, art and activism for most of the last decade. In his previous role as Design and Tech Lead for The Engine Room, he developed role-playing games for human rights activists; collaborated on augmented reality transmedia projects; and helped NGOs around the world to develop creative ways to combine technology and human rights.
In this episode we take a deep dive into how to get organisations beyond ‘data literacy’ and reach ‘data maturity’, where organisations understand what is good practice on running a data project.  Some main points:
  • A red flag that indicates a data project will end in failure is when the goal is implementation of a tool as opposed to a mission-critical goal.
  • Training in itself can be helpful with hard skills, such as how to do analysis, but in terms of running data projects, it takes a lot of hand-holding and mentorship is a more effective.
  • A critical role in and organisations is people who can champion tech and data work, and they need better support in that role.
  • Fake news and data-driven confusion has meant the need for understanding good data practice is even more important.

DataKind UK’s resources:

Tin’s resources:

Resources that are inspiring Emma’s Work:

Resources that are inspiring Tin’s work:

  • DataBasic.io – A a suite of easy-to-use web tools for beginners that introduce concepts of working with data
  • Media Manipulation and Disinformation Online – Report from Data and Society on how false or misleading information is having real and negative effects on the public consumption of news.
  • Raw Graphs – The missing link between spreadsheets and data visualization

View the full online conversation:

Flattr this!

Some Misconceptions about Data Journalism

Adam Kariv - October 27, 2016 in advocacy, Data Journalism, Hacktivism, journalism, Open Data

This blog originally appeared on Medium, https://medium.com/@adam.kariv/some-misconceptions-about-data-journalism-8c911e743ef8#.begd19gf4

 

In the past few years, a new discipline in journalism is slowly getting more and more followers — a discipline commonly known as ‘data journalism’. These so-called ‘data journalists’ are usually envisioned as the younger, tech savvy journalists, ones that are not afraid to analyse data, understand how computer code works and simply love these colourful and detailed visualisations.

On the other end of the scale are the non-data-journalists . We usually imagine them, still using a phone and Rolodex as they simply don’t get email — and the last technological leap they made was when the mechanical typewriters were replaced by computerised word processors.

Moving away from these simplistic (even stereotypical) dichotomies into a better understanding of what a data journalist actually looks like, will do justice to the actual hard-working data-journalists out there as well as take this movement forward and make it more open and inclusive.

The Python vs. Rolodex dilemma

Let’s begin with the ground truth about the journalism trade: Journalism is all about telling a story, and the best stories are ones that revolve around humans, not numbers.

This basic fact was true a hundred years ago, and is not about to change — even if technology does. For this reason, the best journalists will always be the masters of words; those who have the best understanding of people and what makes them tick. It is the unfortunate truth that the benefit of knowing how to work with data will always come after that.

Don’t get me wrong, there’s certainly a place for all the ‘visualisation-oriented journalists’ (or “visi-journalists”). That’s because sometimes the data is the story. Sometimes, the fact that some new data is available to the public is newsworthy. Sometimes, some hard-to-find, hidden links in a large dataset are the scoop. Sometimes, a subject is too technical and complex that only a super-interactive visualisation is the only way to actually explain it. But most times, this is not the case.

So we have on one end of the spectrum, that old school journalist with her Rolodex, holding a precious network of high-ranking sources. On the other extreme, a journalist that also codes and wrangles data, trying to find a corruption case by sifting through publicly available data using a custom made Python script. But in between these two extremes, lies a vast range of hard-working journalists, reporting on the day to day happenings in politics, economy, foreign affairs and domestic issues. These journalists don’t have any sources in any high places, and have never heard of Python.

Yet, this majority of journalists is mostly ignored by the data journalism movement — which is a shame, as these are the ones most likely to benefit from it and advance it the most.

A website is not a source

Flashback to five years ago — I’m one of the few founding-volunteers of an open-data NGO in Israel, “The Public Knowledge Workshop”. One of our first projects was called “The Open Budget” — a website who took the publicly available (but hard-to-understand) national budget data and presented it in a feature-rich, user friendly website.

At that time, we tried to meet with as many journalists as we could to tell them about the new budget website — and not many would spare an hour of their busy schedules for some geeks from an unknown NGO. We would show them how easy it was to find information and visualise it in an instant. Then we would ask them whether they might consider using our website by themselves for their work.

A common answer that took me by quite a surprise always went along the lines of “That is very nice indeed but I don’t need your website as I have my sources in the Ministry of Finance and they get me any data I need”. The fact that the data was lying there, within a mouse-click’s reach, and they still wouldn’t use it — simply baffled me. It took me some time to understand why it made perfect sense.

Nevertheless, we would offer ourselves to these journalists as domain experts in understanding and analysing government data (or even knowing where to find that data) — and as volunteer ‘data wranglers’. In theory, it was supposed to be a mutually beneficial relationship: they needed help with getting the right data in their stories, and we were a young NGO, hungry for some media spotlight. In practice, this situation resulted in too many articles where we would do the work but would not be credited for it. Journalists would ask for some budget related data analysed for an article with a tight deadline. We would do our part, only to find the data attributed in the printed paper to the Ministry of Finance. As annoying as it was, they would always claim that they cannot give us credit as “No one knows who you are. We need someone with some credibility”…

Getting an answer is a human thing

So what is the reason, really, that journalists will not use an official government open-data web-site to get data and for fact-checking?

I remember one time a journalist calling me with a very simple question:

– ‘Can you tell me the total size of this year’s national budget?’ – ’Sure, but did you try our website? It’s the one single big number right there on the homepage.’ – ‘Umm… there are a few other numbers there. Can you please copy-paste the correct one and send it to me in an email?’

And so I did.

Was that reporter lazy? Perhaps. But it wasn’t just that. As it turns out, it’s not just a matter of credibility — it’s also a matter of attribution. Journalistic reporting is a delicate art of telling a narrative using only “facts”, not the journalist’s own personal opinions. Journalistic facts (which may be just someone else’s opinion) need to always be attributed to someone, be it a person or an organisation.

So you’d get sayings similar to this: ‘according to this NGO, spending on health in the national budget is 20%’. This sort of wording leaves room for other parties to claim the analysis was wrong and the actual number is different. It keeps journalists free from biases — and from accusations of such biases — while still promoting a specific world view.

The only catch is that this only works if they are solely reporting these interpretations — not making them.

Getting the right answer is also a human thing

As time passed and the number of journalists seeking our help constantly grew, a new understanding slowly emerged. We were no longer just the geeks with the best budget data in town, but we became also the geeks that know the most about the intricacies of the budgeting cycle, tenders and procurement processes.

Geeks in action

All of a sudden we were able to answer more vague questions from journalists. Take this question as an example – “how much money is a specific company getting from the government?”. To answer that, you first need to know what options there are to ‘get money from the government’ (there are at least three or four). Then you need to know how to query the data correctly to find the actual data rows that answer the question. You might find that a single company is in fact more than one legal entity. You could discover that it’s being called differently in different data sources. Some data sources might contain data that’s partly overlapping. And after all that work you still need to produce an answer that is (most likely) correct and you can wholeheartedly stand behind it.

Getting to such a level of expertise is not something that happens in a day. This is another reason why open-data portals are simply not that useful for journalists. Even if the journalist has a clue as to which dataset contains an answer to her question — which is rarely the case, nor that a single dataset will hold the answer — it’s not enough to see the data, you need to make sense out of it. You need to understand the context. You need to know what it really means — and for that, you need an expert.

When Open Data takes the Lead

With deep knowledge of data, arrive interesting findings. Most are standard cases of negligence with public funds. Some are interesting insights regarding money flows that are only visible when analysing the ‘big picture’. Only rarely you find small acts of corruption. We believed that each of these findings was newsworthy, and we would try to find journalists that might take our leads and develop them into a complete story.

But hard as we tried, our efforts were in vain — none of the methods we tried seemed to be working. We tweeted our findings, wrote about them in our blog, pushed them hard through facebook — we even got a Telegram bot pushing algorithmically detected suspicious procurements in real time! But journalists were not impressed.

On other instances, we managed to get a specific journalist interested in a story. The only problem was that sometimes they would hold on that piece of information for weeks without doing anything with it until it became irrelevant — thus losing our chance to use it anywhere else.

At that point we decided to get some help from an expert, and hired a PR manager to help our efforts to get the message across. Seeing him work with journalists left me in awe: his ability to match the right story to the correct person, ensure we were always credited properly, that stories were written promptly was something we’d never seen. And the best part was how he was leveraging his many connections to make journalists come to us for the next story instead of the other way round.

But he also made us change our ways a little bit — as good leads needed to be kept secret until a good match was found. Exclusivity and patience bought us larger media coverage and a wider reach — but with the price of compromising on our open-data and transparency ideologies.

Data is a Source

Back to present day.

We still meet journalists on a regular basis. and although it’s now easier to get their attention, most of them would still start our meetings with a skeptical approach. They look as if they wonder ‘what are they trying to sell me?’ and ‘how on earth these geeks could have anything to do with my work?’.

But then we start talking — first we tell them about our different projects and areas of expertise, and the conversation flows to what they’re interested in: what are the ideas they’re trying to promote? which big projects they’ve always dreamt of doing but never had the data? They tell us about all their attempts to get data from the government through FOIA requests that ended in hitting brick walls.

That’s usually the point where I take out my laptop. They seem baffled when I start typing a few SQL commands on my terminal, and utterly surprised when after two or three minutes I present them with a graph of what they were looking for. “Wow, I didn’t know it was even possible… and all of that just from data that’s out there?” they say, with a smile and a new sparkle in their eyes. And that’s when I know — a new data-journalist was born.

Every once in a while, a beautifully interactive data visualisation project is published by one of the media outlets. Everybody applauds the “innovative use of the medium” and the “fine example of data-journalism” — and I’m also impressed! — but to me this is simply forgetting all these other journalists who made that leap into the world of data.

These journalists understand that leads come not just from sources in the government, but also from algorithms analysing CSV files. They cautiously learn to link to the government data portals as proof for their claims. They take data and make it a part of their story.

These are the true heroes of the data-journalism revolution. And the motto of this revolution cannot be ‘Visualise More!’ or ‘Use Big Data!’ — it must be: ‘Data is a Source’.


Thanks to Paul Walsh for the encouragement and to Nir Hirshman for being that awesome PR guy…

New Report: “Changing What Counts: How Can Citizen-Generated and Civil Society Data Be Used as an Advocacy Tool to Change Official Data Collection?”

Jonathan Gray - March 3, 2016 in advocacy, citizen data, citizen generated data, civil society, civil society data, Data Journalism, Data Revolution, Featured, Open Data, Open Government Data, Policy, public information, research

Screen Shot 2016-03-01 at 09.42.12 copy Following on from our discussion paper on “Democratising the Data Revolution”, today we’re pleased to announce the release of a new report titled “Changing What Counts: How Can Citizen-Generated and Civil Society Data Be Used as an Advocacy Tool to Change Official Data Collection?”. Undertaken as a collaboration between Open Knowledge and the CIVICUS DataShift, the report contains seven case studies accompanied by a series of recommendations for civil society groups, public institutions and policy-makers. The case studies cover data collection initiatives around a wide variety of different topics – from literacy rates in East Africa to water access in Malawi, migration deaths in Europe to fracking pollution in the US. It was researched and written by myself, Danny Lämmerhirt and Liliana Bounegru. We hope that it will contribute to advancing policies and practices to make public information systems more responsive to the interests and concerns of civil society. You can download the full report here. Here is an excerpt from the introduction:
The information systems of public institutions play a crucial role in how we collectively look at and act in the world. They shape the way decisions are made, progress is evaluated, resources are allocated, issues are flagged, debates are framed and action is taken. As a United Nations (UN) report recently put it, “Data are the lifeblood of decision-making and the raw material for accountability.”1 Every information system renders certain aspects of the world visible and lets others recede into the background. Datasets highlight some things and not others. They make the world comprehensible and navigable in their own way – whether for the purposes of policy evaluation, public service delivery, administration or governance. Given the critical role of public information systems, what happens when they leave out parts of the picture that civil society groups consider vital? What can civil society actors do to shape or influence these systems so they can be used to advance progress around social, democratic and environmental issues? This report looks at how citizens and civil society groups can generate data as a means to influence institutional data collection. In the following pages, we profile citizen generated and civil society data projects and how they have been used as advocacy instruments to change institutional data collection – including looking at the strategies, methods, technologies and resources that have been mobilised to this end. We conclude with a series of recommendations for civil society groups, public institutions, policy-makers and funders. The report was commissioned as part of a research series by DataShift, an initiative that builds the capacity and confidence of civil society organisations to produce and use citizen-generated data. It follows on from another recent discussion paper from Open Knowledge on what can be done to make the “data revolution” more responsive to the interests and concerns of civil society,2 as well as a briefing note by DataShift on how institutions can support sustainability of citizen-generated data initiatives.3