Monthly Archives: February 2017

Open Data Camp 4: bigger, better, wetter

This post is a repost of Giuseppe’s Medium blog post

I am slowly coming back to life after Open Data Camp. Being in Cardiff was amazing, if not for the weather, which is partly to blame for my current heavy cold. I have not been that wet since I walked up the Snowdon six years ago. Despite the weather, this Open Data Camp has been probably the most amazing we have run since starting in Winchester two years ago — with some caveats. Here are some stats coming from the participants who shared their data (>50%).

The highest participation ever recorded at an Open Data Camp

The most mind-boggling figure from this camp is the total number of attendees: we checked in 125 people on day one, and 103 people on day two (most of them, but not all, returners from day one). To put things in perspective, the highest participation on any day one had been at Open Data Camp 3 in Bristol, with a total tally of 93.

What’s more striking is the very low drop-out rate. We counted 134 unique attendees out of 147 tickets sold. In the unconference industry, a drop out rate of 30% is considered normal, and ours was only 9%. A 91% attendance level for a free event is something I would have never expected. It is testament not just to Open Data Camp being a great event — hey, I’m blowing my own trumpet here! — but to the community being very committed to attending.

Most of the UK was covered

Look at the pin map on the left (or play with Angharad’s interactive map). People who declare their travel origin are from all over the place: Sunderland to the North, Norwich to the East, Hastings to the South. However, the map on the right suggest the magnitude of attendance is higher somewhere in the South of the country. Let’s make a chart…

 
Participant origin in the UK

We can still do better, location wise

Looking at the data, it is evident that most attendees come from the English South or Wales. Is transport an issue? Potentially. However, one of the ideas behind Open Data Camp is in fact to bring Open Data around the country, rather than getting people to attend, so I would not be extremely negative about it. If anything, these chart suggest where to bring Open Data Camp next — if almost 70% of the attendees come from Wales and the South (including London), we should focus on making the next event happen where we only have few attendees: Yorkshire, the North East, Scotland, Northern Ireland.

Attendance by town and region of origin

Some people travelled a long distance

I had a huge grin on my face when I realised we had attendees from 4 continents. The non-European attendees were just 3, but their contribution was really useful. Hearing about Open Data in Seattle and Bangalore is certainly something that can make UK Open Data better.

The median distance travelled was 86 miles, which is more or less how far Southampton, Oxford, or Plymouth are from Cardiff.

 People travelled some distance to get to Open Data Camp

Diversity

Of course, some thought needs to be given to the diversity of Open Data Camp. The organising team did relatively well on gender balance, with over half of the members being women. So I was a bit disappointed upon realising that overall the event saw twice as many men as women (I leave those who did not declare their gender here in the chart, as I think this might be another symptom we need to address). What was your feeling as an attendee?

Open Data Camp seems to be pretty well received among people of a diverse range of ages, but if there is anything we can do to improve please let us know.

We have no data about ethnic background at this stage, but it might be something we would need to monitor in the future.

Gender and age data

On the way to Open Data Camp 5

It’s going to be difficult to beat Open Data Camp 4:

  • the biggest Open Data Camp so far
  • the first in a (former) parliament building
  • the first with armed police
  • the first in which I pitched a session (pushed by Jeni — and actually this was the first session I ever pitched at an unconference…)

When we started organising Open Data Camp in late 2014, I was skeptical: I thought this would be a one-off event and I was resigned that interest would move elsewhere. Instead, if I can summarise Open Data Camp 4 in one key learning point, I can say that interest in Open Data is getting hotter and hotter.

There were many users of data, new to the community, who are extremely keen on data releases and clear, open processes; there was an incredibly well attended session, “Open Data for beginners”, which had to be repeated due to demand; we had, for the first time, data journalists attending, interested in keeping pressure on the government to publish data timely and accurately; we had professionals who work in fact-checking, now using government data to partially automate their fact-checking processes.

Equally, there is a demand for better data, open standards, and clear processes by veterans of Open Data. Open Data shouldn’t come at a massive expense to the taxpayer, but I still think it is beneficial to the efficiency of the public sector if processes that generate data are made clear, de-duplicated, and documented — and the Open Data agenda has clearly been pushing in this direction.

It seems evident to me that Open Data needs to be consolidated — and, preferably, approach releases from a problem-driven perspective (as I somewhat suggest here) — but it is also evident that the community is becoming richer thanks to people belonging to different areas of expertise, interest, and activism, starting to join in. I look forward to continuing the discussion at the next camp.

What makes for a good API?

One of the first questions to come up on day two of Open Data Camp was “what is an API?” One of the last issues to be discussed was “what makes a good API?”

 

Participants were asked for examples of application programming interfaces that they actually liked. The official postcode release site got a thumbs up: “It was really clear how to use it and what I’d get, and I can trust that the data will come back in the same way each time.”

Continue reading What makes for a good API?

A tale of two datasets

Controversially, Gavin Freeguard, head of data and transparency at the Institute for Government, was allowed a PowerPoint presentation at Open Data Camp 4. However, it was in a good cause.

His slides enabled him to give some concrete examples of the data in the Whitehall Monitoring Project, which he runs. The project monitors the shape and size of government, the morale of civil servants, and other factors.

Continue reading A tale of two datasets

Sustaining Senior Sponsorship

If sponsorship is taken away – there must have been sponsorship before. So why does it go away? Understanding that might help.

 

Why we lose sponsorship

  • Short attention spans
  • Whitewashing, which they move on
  • People over-promising, and the results not matching that.

The enthusiasm needs to be sustainable.

Continue reading Sustaining Senior Sponsorship

Better local government through open data

Local government seems to be in a perpetual state of competition – while the most efficient use of resources would be to collaborate. So how could open data help facilitate that?

 

One attendee talked about formalised co-ordination roles. There have been some pockets of good stuff: the Cabinet Office nominated over a dozen councils as their open data champions, with some mixed results. Redbridge’s data sharing platform DataShare, part funded by the LGA, seems to be well-liked by those who have used it. Some other user authorities are using it – but it’s often not as well implemented as the Redbridge implementation.

Continue reading Better local government through open data

What is data, open data… and what on earth is an API?

Day two of Open Data Camp in Cardiff opened with another session on the basics. What is open data, who can use it and what is it useful for?

More open data for newbies

Also, going back a step: “What is data?” Session participants suggested that while the public or ‘newbies’ might equate data with statistics, ‘data’ was much broader than that. It might be the raw data – or numbers – on which the stats were based. But it might also be text, or photographs.

Continue reading What is data, open data… and what on earth is an API?