This post is part of the Q&A Series with the Data Ambassadors from DataDive2013. You can also read an interview with the fraud and corruption data ambassadors, a recap of Data Dive 2013, and watch the presentations from the weekend.
Photo credit: Itir Sonuparlak
During DataDive 2013, each project had an assigned data ambassador, a leader to guide and direct the research and efforts of the teams. In the days following the DataDive, we spoke with two of the data ambassadors from the fraud and corruption related projects to learn more about their experiences. Read their responses below and join the conversation in our comments section.
- Taimur Sajid develops financial models to asses risk for a financial firm and acted as a data ambassador during the DataDive.
- Marc Maxson is an Innovation Consultant with Global Giving and brought his Heuristic Auditing Tool to the DataDive.
This post is part of the Q&A Series with the Data Ambassadors from DataDive2013. You can also read an interview with the poverty data ambassadors, a recap of Data Dive 2013, and watch the presentations from the weekend.
Data Ambassadors posing at the end of DataDive 2013. Photo Credit: Carlos Teodoro Linares Carvalho.
During DataDive 2013, each project had an assigned data ambassador, a leader to guide and direct the research and efforts of the teams. In the days following the DataDive, we spoke with four of the data ambassadors from the poverty projects to learn more about their experiences. Read their responses below and join the conversation in our comments section.
- Monique Williams is an independent consultant and a statistician at the U.S. Government Accountability Office. She led and represented the UNDP Resource Allocation team.
- Nick McClellan is the web production editor for the New America Foundation and he represented the Night Illumination team.
- Max Richman is an independent consultant who provides research and technology services to non-profits, foundations and governments focused on international development. He led the Website Scraping team.
- Tom Levine works in data analysis and he represented the Arabic Tweets project.
Image Credit: World Bank Flickr
Why worry about the demand for open data?
When it comes to open data, much has been done around what we can publish, but much more can be done on identifying what others might need and want. Many open data initiatives have been started as supply-driven efforts seeking to increase transparency and leverage new information dissemination technologies - and that’s been a good way to start. However, being supply-driven is not the only way forward – a genuinely demand-driven approach would allow data providers to respond to, rather than anticipate, the data needs of users.
So what is the demand for open data? This is a simple question that is difficult to answer. Unearthing even elements of the answer would help to increase understanding, inform the continued practical growth of open data efforts and activities, and hopefully result in more relevant, accessible, and widely-used data.
Photo Credit: Neil Fantom
A more detailed recap will follow soon but here’s a very quick hats off to the about 150 data scientists, civic hackers, visual analytics savants, poverty specialists, and fraud/anti-corruption experts that made the Big Data Exploration at Washington DC over the weekend such an eye-opener.
If you haven’t registered yet for the Big Data Exploration event at the World Bank on March 15-17, you really should. After stops in Venice, Vienna, and a pre-event at Washington DC, data divers assemble at DC this weekend to take another crack at issues related to poverty measurement, plus fraud/anti-corruption in operations and to demonstrate whether and how practitioners can use big/open data to get results for traditionally knotty development problems (which are relatively difficult or expensive to resolve using standard techniques).
If you're playing catch up, read more about the plans and potential impact for future Data Dives. Also have a look at what colleagues at a Data Dive in Venice accomplished by analysing World Bank contracts and vendors. And now, read the cross-posted blog below from UNDP's Giulio Quaggiotto and World Bank's Prasanna Lal Das who ask: Would You Give Up Your Personal Data for Development?
Update: the event will be webstreamed on World Bank Live.
This is an exciting time to be in the data business. There have never been more groups, from such different backgrounds, with a passion for producing and using data for the public good.
Let’s say you are in the middle of what others may call ‘nowhere’ and need information on the Bank’s work in the vicinity before an upcoming meeting with local officials. Or you are a civil society organization rep and want to make sure that the numbers you have about a particular project are the same as what the Bank reports (and if not, you want to know why not).
Your laptop is no good because - it is the middle of nowhere after all! - and you can only rue your decision to leave your stack of papers behind.
What do you do? Well, the answer might be in your pocket.
It took longer than we'd hoped but it's finally here - the new World Bank Finances app answers many of the questions you asked after the release of the first version last year (click here to download the new version for Android; an updated iOS version will be out soon).
It was awesome.
Here are seven things I learned:
1) Iteration is the path to perfection
By now you’ve heard of Nate Silver - the statistician behind FiveThirtyEight and a near-perfect prediction of the 2012 US elections. What you may have missed is the best interactive graphic of the year - the New York Times’ “Paths to the White House” built with Mike Bostock’s D3:
Shan Carter from the NYT graphics team showed how newspapers have struggled to represent the potential scenarios and actual outcomes of US elections ever since the late 19th century. His team eventually came up with the graphic above, but see how many revisions they went through to get there:
That’s 257 revisions. As early as version 15, you can see the core idea. At version 81, it looks almost done, but it takes another 157 revisions and that extra attention to detail, high production values and pride in your work to be at the top of your game like this.
Lesson: Iterate and aim high: editors are your friends, they’ll make your work stand out. Also: this is the benchmark for what a good data visualization looks like - if you can’t honestly say what you’re doing is at least this good, iterate.