User demand driven and machine-readable open data

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Open data is undergoing a paradigm change where the focus is shifting to user demand driven publication of data in machine-readable formats, with open standards and licenses that is appropriate for its application area. This is often refereed to as “liquid information” or “liquid data” which can be read about in this report from McKinsey’s 2013. The report address the potential value that can be achieved if standards, formats and metadata are functional for its intended use. Open data 2.0 is another emerging term which refers to data that is being made available based on demand and provides means for participation and collaboration, where users can report suggestions for improvement and provide feedback on flawed data. Continue reading “User demand driven and machine-readable open data”

Postman OAuth 2.0 guide for Västtrafik API

We are currently working on evaluating the API for public transportation data and journey planing in the Gothenburg and west-coast area of Sweden. We used the API client Postman to access the transportation data. The current guide for the API portal is only in Swedish and we stumbled upon some problem setting up the OAuth 2.0 authentication. Which is why we thought of sharing how to configure the authentication, so you can start exploring the data. Continue reading “Postman OAuth 2.0 guide for Västtrafik API”

Development phases for building services on open data

Previous article described various obstacles commonly related to service development based on open data. Obstacle is the first dimension of a model intended to gather and categories problems related to third-party development based on open data. The second dimension of the model concerns different phases that third-party developers goes through when creating services. Continue reading “Development phases for building services on open data”

Common obstacles related to service development based on open data

Studies carried out by Viktoria Swedish ICT, on behalf of the Swedish Transport Administration (Trafikverket) shows that service development based on open data are consistent with various obstacles. To better understand problems related to the use of open data when developing services, a project was initiated 2014 to explore how owners and providers of open data could create better value for service developers. The premise for the project was to view open data as a service and study what created  value for third-party developers. The reason for the focuses on third-party developer is that they portray a heterogeneous user group with a wide array of incentives for using open data. Continue reading “Common obstacles related to service development based on open data”

Open data and value generating mechanism

For third-party developer to be effective in their work creating product and services that uses open data. The providing organisation of data must provide value generating mechanism that facilitate and support their effort. One recent study done by Viktoria Swedish ICT in collaboration with the Swedish Transport Administration (STA), investigated which mechanism that third-party developers values when drafting services that uses open data.

The study conducted in-depth interviews with enterprises and individuals that uses traffic information from STA and has developed a embryo for a model that points-out process and principals that are important to support third-party developer working with open data. The value generating mechanism that emerged during the interviews with third-party developers related with supporting functions that are common in a service-provider and consumer relationship. These are documentation, customer support and relation etcetera. Other supporting mechanism are more specific and concerns data format and level of data resolution that third-party developer need to handle when working with API:s and web services. One of the findings in the study was that although open data is per definition free service doesn’t change the fact that customers need support from the service-provider.

Future post will in detail explain each value generating mechanism more in detail. The list below show the mechanism that emerged from the study, notice that list can change since this is ongoing research.

  • Data format
  • Data resolution
  • Refinement of service content
  • Support and documentation
  • Customer care and partnership
  • Openness and transparency

Open data, what economical and social value does it generate?

Clear Byte work on making information sharing smart and at the same time create a visual understanding of content. One valuable source of data that more people should take advantage of is open data. Open data exist in different shapes and forms but are mainly provided by public sector thought open API:s and web services. In Europe there is a EU directive that recommends and guides governmental organisations to make more data available.

Open data is estimated to have great economical and social value. McKinsey Global Institute estimate that the potential value of data in seven domain areas has could be over $3 trillion only in the US. The first day that Obama took office he signed an executive order to make all governmental data open and machine-readable, i.e. open data. That has today lead to the US are amongst the top three countries in the world in providing open data.

We intend to post a number of articles that focusing on how governmental organisations that provides open data can create a greater value for third-party developers. Third-party developers are a groups of heterogeneous actors that spans from hobby programmer, small start-up companies to global enterprises. The reason that we focus on third-party developers is that this groups is the most important one to create the alleged economical and social value. Next post will address value creating mechanism that facilitates uptake and usage of open data services.

 

Engage citizens to contribute in social development

This is the third article (link to article #1 and #2) on how to create open dialogue and citizen’s interaction by using crowdsourcing in public sector.

In previous articles we have looked at real examples how to solve information and knowledge problems with crowdsourcing in the public sector. In this article we shall look at other examples where crowdsourcing has been applied and present a typology (classification) that is based on work by Brabham (Daren C. Brabham).

Brabham has analyzed and carried out a study on how crowdsourcing has been used to collectively develop and vote for the best design of the bus and stops in Salt Lake City, Utah. But first we shall try to describe the difference between social media and crowdsourcing services. A fundamental characteristic that describes crowdsourcing is that it is based on a collective task that is produced, distributed across the network, a result that accrues to both parties. What we have described in previous articles. Social media, however, need not result in an outcome that accrues to the collective, their natures is more of giving users some form of self-affirmation or provide a communication channel. It is possible to solve problems through social media, depending on the type of problem. But crowdsourcing services YouBongo and others, are often better suited to solve specific tasks and problems.

To sum this difference, one can say that social media serves as a communication channel and that crowdsourcing is a process to solve a problem or produce something collective that creates value for all parties involved. NextStopDesign (nextstopdesign.com) was a competition aimed for everyone that wanted to submit design proposals for the next generation of bus stops that was launched in 2009 in Salt Lake City.

NextStopDesignCitizens had the opportunity to register and submit design proposals. In total citizens submitted 260 design proposals and a total of 3187 users registered to vote for the submitted proposals. No cash or reward was offered top three proposals that received the most votes and selected winners during the contest. Brabham’s article (Crowdsourcing public participation in transit planning) from 2010 describes theories and analyzes the contestant’s interviews about why they spent their own time to develop and produce design proposals for the contest. The result of what motivated citizens to contribute were distributed in a couple of areas that Brabham discuss in his article, a quick summary of the conclusion where;

  • Increased career opportunities by developing their own skills and portfolio of design work
  • Gain industry recognition
  • Contribute to a collective effort, inspired by other people’s posts and discussions
  • Amusing task where users had free opportunity to express their ideas

To successfully engage citizens is no simple task, but there is enough empirical evidence and a study of using crowdsourcing in varies situations to provide indications of success factors leading to desired result. The example on how to engaging citizens to help with design proposals are only one of several examples of successful projects where crowdsourcing has been used. The design proposal project fits into the Brabham’s category of public consultation, in his typology of areas where crowdsourcing is being used.

Typology of areas where crowdsourcing is often being used;

Typology of crowdsourcing applications
Typology of crowdsourcing applications

Manage information and knowledge problems with crowdsourcing

This is the second article (Article # 1) on how to source the crowd and create value for public sector.

Crowdsourcing is an online-based process based on the combination of two perspectives in order to create the desired affect and benefit. One perspective is from the part that initiates the task and wants to get some task done. The control lies with the part that defines the task of when, how and when it should be done. This approach is called top-down and means that the affect of the task goes to those who initiate it. For example, it may be when an organisation wants to communicate an advertising campaign via social media to generate as many “likes” as possible. The task brings only value to the initiated part and no real value to the crowd that perform the task.

The second perspective focuses on the community and what the collective wants to achieve. The task can be initiated by individuals in the collective, but where others will join to complete the task. When the task is completed the result and value benefits the collective, this approach is called bottom-up. An example of this is Wikipedia, where anyone can join and create and edit other people’s articles. The benefits and value benefits the collective that carry out the task, as well as anyone else who are interested in the outcome.

 

Process perspective
Process perspective

The basic idea of ​​crowdsourcing is to create value for both the initiator and executor. One factor to consider is whom has control of the media (on-line tool) used for communication, if it is driven by a neutral part or the initiator. If only one is in control of the communication, the other part could perceive that the process is less open and less inclined to contribute.

Information and Knowledge management

By using crowdsourcing for collecting and structuring information, public organisation can acquire knowledge with significantly less resources than performing the task on their own. A prerequisite for success is that the description of the task must be clear, what is to be collected, how it’s gone be collected and when it will be collected. The goal of the task must equally clear and communicated before the work begins. The dissemination of the result or the affect of the completed task is also important. These are some factors that will determine how many people that will buy in and find the task interesting enough for using their own time to contribute to the task.

One example of successful knowledge management is the U.S. Patent Office (USPTO) project called Peer-to-Patent. This case shows that users can perform complex patent issues, provided they have the perquisite knowledge of the field at hand. What case shows is how the collective helps the authority to determine whether the patent application is a new innovation or builds on previous knowledge or technology. This project was so successful and save the Agency substantial resources and now part of organisations operation. The project has also spread to Australia, Japan, South Korea and the UK.