ICTlogy

City leaders panel: local issues and open data solutions, lessons learned, and setting short and long terms priorities
Moderator: Alex Howard, Sunlight Foundation, What Works Cities initiative

Juan Prada, City of Montevideo (Uruguay)

The main reason to open data is the belief that data belongs to the citizen, not to agencies. Why should the citizen be charged for the use of their own data?

In 2008 the government began its open data strategy. After an initial publication of data, the government focused on enabling the creation of open data based services developed by the citizens, such as the adaptation of Fix My Street for Montevideo.

The service behind the open data initiative acts as thus, as a service, and so has a help-desk and an analysis unit to monitor usage and make proposals of new data sets to be published, etc.

Víctor Morlán, City of Zaragoza (Spain)

Same belief as Uruguay: access to data and public information is a basic right for citizens.

Now, all services that the City Council website creates use open data as a main source. Thus, there is no need to maintain different databases and services: open data becomes useful for the City Council itself.

Privacy is dealt with in the open data initiative, and everything that is published has gone through a thorough process of compliance with the law.

Stephane Contre, City of Edmonton (Canada)

After a first deployment, the big effort now has been publishing a “data analytics website” so that people that are neither tech-savvy or data-savvy can query the data themselves.

One of the big impact has been the internal use of open data. Using specific algorithms you can use open data to improve municipal services.

Keynote: Amen Ra Mashariki, Chief Analytics Officer, New York City

After several milestones on open data and open governmetn, in 2015 New York City released its Open Data For All programme. Its aim, to actually increase the use and reuse of open data by all citizens, not just a bunch of them. Open data has to be available for all, meaning that data should be able to be used by anyone, anywhere and anytime.

Start with users

NYC made some research on who the users were and how did they use data.

Human-centered design was applied to improve the portal, and think on the portal not as a repository, but as a service.

In partnership with New York University, the portal made that you —as an individual, as a community— you could find yourself in the data, you have to feel that you are represented there. If a policy is implemented and you are not there, the policy will not affect you. So, main issues/problems/needs were identified and the date was put into motion to illustrate or lay the foundations of these issues and the policies to address them.

For instance, an Open Data Powerty model was designed using data on community concerns, infrastructures, representation, demographics, etc.

Encourage purposeful engagement

e.g. Organise hackathons and other ways of constructive engagement that has a meaning not for the city, but for the individual citizen too.

Empower agencies

Agencies have many missions and goals, and opening data usually is not one of them. Thus, they will not dedicate a part of the budget to it, no matter how insistent you are on that. So, how do we bring agencies to open up data? And make it meaningful to them?

First thing is to address standards. Try and have agencies applying standards in their data management, so that they can be reused elsewhere, or that they can “talk” to other data sets. This will sooner or later create synergies and help agencies not to open data but to achieve their own goals, which is what they really care about.

Treat publishing as the middle of opening data

When you get data from an agency, most of it does not make sense to you, out of the agency’s context. So you partner with them and try to understand their data so that you can bring them to light. For the agency, publishing data is the end; for you, publishing data is just the middle, as there is a lot of work to be done still.

Integrate Open Data into citywide processes

Case: The NYPD Was Systematically Ticketing Legally Parked Cars for Millions of Dollars a Year — Open Data Just Put an End to It. If citizens can have access to open data, they can help improve the city in many ways. So, it is not only about “data journalism” and publishing news, etc. but also about engaging in citizen processes.

You have to work to change the complexion of the community. You have to work to empower people to believe that they can make a change, that they can participate, that they can help to improve the city.

Learn, test, standardize — and learn again

Reflect about the whole process and improve it.

Random notes during the session:

Strengthening global coordination

Coordination for what? Why coordination?

Strengths

• Ability to bridge the top-down approach with the bottom-up approach.

Weaknesses

• Regional representatives

Opportunities

• Need for asset-mapping in communities to create communities.

Threats

• Making connections shouldn’t be left to chance.
• We talk about open data but have no open resources or open tools.

The role of multi-donor coordination in open data field

Strengths

• Emerging network.
• Different expertise.
• Research done.

Weaknesses

• How donors communicate with each other.
• Target countries.
• Mainstream use of data.

Opportunities

• Better use of open data.
• Leverage existing investments in the field.
• Sectorial problem solving.
• Consolidate as a resource.

Threats

• Different proposals to different donors
• Make it sustainable
• Proliferation of global initiatives that forget about the local
• Disperse information

Exploring how we can improve gender outcomes and drive new gender and data projects

Strengths

Weaknesses

• Gender unbalances can mask the real impact of open data.

Opportunities

Threats

Random notes during the session:

Setting up regional initiatives

Stop talking about open data and begin talking about issues.

Open data should not be an end in itself.

How do we cope with encouraging regional initiatives while keeping the interest in the local impact. How do we keep the regional nodes locally grounded.

• Having other stakeholders in the projects so that they can make it socially sustainable, like data journalists.
• Connect researchers and practitioners.
• Establish fellowship programmes.
• Repository of best practices.
• We have to do the open data revolution from below. We have to start infiltrating the system.

Instead of putting out solutions, put the spotlight on problems, create conditions/spaces where problems can be shared: crowdsourcing, crowdfunding, hackathong, etc. so that people that have the solutions can joint the people that have the problems. Putting out the solutions will not necessarily attract people with the problems.

• Be more able to connect people: citizens, sectors, governments, etc.
• Pre-events have been very successful.
• Have to make connections more continuously, with consistent engagement.

More collaboration.

More sectorial work. Infiltrate sectors. Adaptive approach.

Better research. Map out good practices.

Re-use of apps.

Supporting Governments

What format is useful, what content.

How to go beyond the programme.

• Not lobbying, but helping governments to manage change.
• Focus on use, focus on the demand side. How do we stimulate the demand?
• Sector focused initiatives.
• How to embed our thinking as a tool to achieve governments’ goals.

The technicalities of open data is not the most important thing.

Help communities how to have good ideas and how to use open data to achieve them.

Having a champion is very important.

The best way to foster the supply side is to support the demand side.

We have to stop talking about ‘open data’ and focus instead on vertical communities: e.g. open data and agriculture, open data and transportation, etc.

In some countries is not anymore a matter of evangelization: governments are convinced. Thus, it is again a matter of technology, of providing (technical) solutions.

We have to institutionalize open data initiatives, make them mainstream at the highest government levels.

How to institutionalize it, though? Help governments to actually implement things.

• Political will and capacities.
• Continuity, stability.
• Think about demand and how to contribute to it: developers, data scientists, etc.

Still working in silos. Support open culture across governments.

Use and reuse of data

If government discloses only what’s convenient, this leads nowhere.

Demand mapping: open data availability is different from usefulness.

Citizens don’t necessarily use data –> build capacity of intermediaries.

Offline engagement is critical.

Open data for transparency, how to?

Fellow selection is critical.

Reuse will benefit from problem-solving.

Data collection is a crucial skill. Data literacy.

Data collaboration.

Measurement and impact

Institutionalization.

How governments are investing, what actions they are taking.

Indices.

None of these indices are talking to each other. How to define measuring.

Infomediaries/intermediaries.

Data does not produce impact: people produce impact.

What is the immediate outcome and what is the long-term effect of open data.

Different levels of impact: economic impact, social impact, etc.

One of the first places to look for impact is in the flow of feedback and what the demand side is saying about open data.