Define Open Data
Open Data is data that is made available by organisations, businesses and individuals for anyone to access, use and share, without restrictions from copyright, patents or other mechanisms of control.
Paradoxically, the growth of the open data movement is paralleled by a rise in intellectual property rights.
Aiming for transparency and collaboration.
‘Good’ Open Data must be published so that it:
- can be linked to, so it is easily shared and discussed
- is in standard, structured format, so easily processed
- guaranteed availability and consistency over time
- so that others can rely on it
- is traceable, rightback to where it originates
- so others can work out whether to trust it
Open Data & Governments
To maximise the visibility of Open Data, governments (like the EU) have created portals, where Open Data from different agencies are collated for access:
- http://data.europa.eu/euodp/en/data/
In 2011, the EU estimated that Open Data publication could add €40 billion per year to the economy.
Several laws and policies have been introduced since.
Is Open Data in conflict with profits?
- In the Digital Age, economic data depends on data and BI
- data that has been collated, processed and analysed, and sometimes used to create additional new data sources, metrics, or services.
- To be sustainable, organisations need to be able to profitfrom their activities
- this can be achieved through selling proprietary (owned) data in the digital economy
Reasons for organisations to publish open data
Publishing your own Open Data externally (outbound).
- Outbound experimentation: Let potential collaborators or customers experiment with your data, to explore a partnership with you.
- Legal Transparency: Publishing Open Data can address regulatory requirements
- CSR: Supply NGOs and charities with access to your Open Data to assist their missions.
- Support start-ups: free data useful for them.
- ‘Freemium’ opportunities: Free access to your Open Data to attract customers. May charge them if they decide to make heavy/frequent downloads of your data or use ‘value added’ features.
- Low cost growth: Use collaborators with access to your Open Data to develop new apps or websites with no or limited expense to you. They can then pass web traffic and customers directly to you, in exchange for a fee, or pay per click.
- Internationalisation: As above, to attract overseas customers.
Reasons to use Open Data
Using an external organisation’s Open Data (inbound).
- Innovate: Create new products and services using novel Open Data sources
- Reduce costs: Use free Open Data instead of buying proprietary data
- Organisational efficiency: benchmark your own KPIs and drive internal change
- Experimentation: Develop new potential products
3 benefits to public bodies using Open Data
Achieving common goals: Publishing Open Data and sharing municipal data with other public or private organisations can support all stakeholders in reaching their common aims, e.g. in becoming a smart city
Efficiency: Savings can be achieved by sharing Open Data across government agencies, at local, regional and country levels.
- avoids agencies collecting or purchasing data
- reduces data replication
- potentially enhances services
Complementary outsourcing: Government agencies often have limited budgets. Non-public organisations can use open data to help develop new services and apps for citizens.
TfL Open Data - Case Study
Benefits
Citymapper and other apps can use the rea-time data to save time.
- Saved time for passengers.
- Better routes, more updates
- Creates commercial opportunities for third party developers.
- Leveraging value and savings from partnerships. TfLreceives back significant data on areas it does not itself collect data (e.g. crowdsourced traffic data). This allows TfLto get an even better understanding of journeys in London and improve its operations.
Open Data & Start-ups
Benefits
- Low cost/free resource
- Flexibility and innovation
- Agile startups can experiment and pivot with new products that use Open Data.
- Collaboration
- Small businesses can grow by working with other Open Data organisations.
- Some publishers of Open Data look to SMEs and start-ups to become affiliates
Challegenes
- Absorptive capacity:
- limitations in obtaining the skills and knowledge to understand the context of Open Data sources and to integrate it into their products and services.
- Competitors:
- may imitate your products and services by accessing the same Open Data
Open Data & Big Organisations
Benefits
Save money and innovate:
- Open Data is free and can be used for new ideas.
‘Freemium’ promotions:
- Corporates are able to use Open Data as a trial or promotional strategy.
One-off projects:
- Data may be used in consultancy projects that provide new insights
Benchmarking:
- Benchmark KPIs against competitors using Open Data
Transparency, Compliance:
- Publishing Open Data can address regulatory requirements
Challenges
Agility:
- Larger businesses are usually less flexible than start-ups
- Hence, less capable to do entirely new practices based on Open Data
Cultural barriers:
- Established organisations often find opening their data to all external users daunting
- Many business made a business around selling proprietary data, don’t understand benefits of Open Data
- Open Data may require substantial changes to the business
Open Data & Social Enterprises
Low cost:
- helps them to create innovative apps and services which generate income for use in the creation of social value
Civic innovation:
- Social enterprises can improve government agency transparency through the development of new citizen-friendly apps and services
Benchmarking and one off projects
- KPIs comparison
- Open Data for research and campaign for change
Open Transport Net
Turn your open geospatial data into insights and easy-to-read, visually appealing maps. Help your city or business to solve transport related challenges by applying innovative insights and co-creating new services together with developers, data providers community representatives.
Evaluating sources of Open Data
Licensing:
- Open Data source licensed?
- Can you use it to meet your needs?
Provenance:
- Who is the publisher of the data source? Trust?
- Do you expect the organisation to be in existence in the next week, month or year?
Publication frequency:
- Is the data source published on a regular basis?
- Last publishing?
- Punctuality of publishing?
Data analysis:
- Do basic statistical checks.
- Are all the fields in the dataset complete?
- Are there any outliers?
- Is the dataset clean?
- Is the data formatting consistent?
Proprietary back-ups:
- Is there a proprietary dataset you could purchase as a back-up in case the Open Data source is discontinued?
Cleaning costs:
- Sometimes Open Data needs ‘cleaning’.
- This may undermine the case for using Open Data
Licensing and Open Data
Open Data licenses tell you what the data can be used for.
- Sometimes only requires referencing the source when using the data
- Some dont allow data modifications, or use it commercially.
- Can have legal consequences…
Creative Commons Licensing - Key Characteristics
- A simple, standardized way to grant copyright permissions to creative work
- Gives access to a pool of content that can be copied, distributed, edited, remixed, built upon, all within the boundaries of copyright law.
- Licensees must credit the licensor, use copyright notices and link to the license from copies of the work
Three “Layers” of licenses of CC
1. Legalese (aka Legal Code)
- Each license begins as a traditional legal tool for lawyers.
Human readable (aka Commons Deed)
- most creators, educators, and scientists are not lawyers,
- a handy reference for licensors and licensees, summarizing and expressing some of the most important terms and conditions.
Machine readable
- The final layer ensures that software knows what kind of liscens it’s dealing with
- important f or search engines, copying, discovery, and distribution of works, etc.
6 liscens categories of CC
Commercial
- This license lets others distribute, remix, tweak, and build upon your work, even commercially, as long as they credit you for the original creation. This is the most accommodating of licenses offered. Recommended for maximum dissemination and use of licensed materials.
- This license lets others remix, tweak, and build upon your work even for commercial purposes, as long as they credit you and license their new creations under the identical terms. This license is often compared to “copyleft” free and open source software licenses. All new works based on yours will carry the same license, so any derivatives will also allow commercial use. This is the license used by Wikipedia, and is recommended for materials that would benefit from incorporating content from Wikipedia and similarly licensed projects.
- This license allows for redistribution, commercial and non-commercial, as long as it is passed along unchanged and in whole, with credit to you.
Non-commercial
Weget into more restrictive areas here, where the stuff is only used for non-commercial use
- This license lets others remix, tweak, and build upon your work non-commercially, and although their new works must also acknowledge you and be non-commercial, they don’t have to license their derivative works on the same terms.
- This license lets others remix, tweak, and build upon your work non-commercially, as long as they credit you and license their new creations under the identical terms.
- This license is the most restrictive of our six main licenses, only allowing others to download your works and share them with others as long as they credit you, but they can’t change them in any way or use them commercially.
Open Government License
A simple set of terms and conditions that facilitates the re-use of a wide range of UK public sector information free of charge.
Risks in consuming Open Data
Format change:
- A publisher may alter how their dataset is formatted. Especially disruptive when working with real-time data.
Copycat:
- Competitors can access the same data
- Responose: keep innovating
Open Data closure:
- Source may be discontinued
- Response: backups and using many sources
Legal:
- (accidentially) not complying with licenses
- if a customer uses your service and is injured due to inaccurate open data, you might be blamed
Ethical:
- Data accurate?
- Anonymised?
Risks in publishing Open Data
Reputation/brand:
- You, or a collaborator, could undermine your brand (inaccurate data)
Legal:
- If an organisation uses your Open Data in an app, and it is inaccurate, what would happen if a consumer suffers a loss or injury?
Revenue Loss:
- You may publish Open Data that previously generated revenue as ‘closed’ proprietary data.
- If you are not careful with timing, you could lose revenue
- business model changed insufficiently
Copycat:
- Some competitors may try to copy/imitate your products and services by using your published Open Data.
- Be selective on what data you share
- Robust Open Data licensing
- Keep innovating
Fraud:
- Criminals could create a full fake website that copies your business, using your published Open Data.
How can ODI mitigate Open Data risks?
ODI’s Open Data Certificate programme.
- Certifies your published data sources meeting international standards.
- Assures that you are publishing quality data that is sustainable
- Reassure users of your data sources.
Open Data Business Models
Open-closed hybrid:
This type of Open Data business model can be used to create a new business, or an existing business can ‘pivot’ to adopt this model, if it does not already use Open Data.
The Open–closed hybrid is quite straight forward and effectively uses inbound and outbound Open Data to develop new products and derive revenue. The business is not necessarily reliant on Open Data and this model can be used to slowly introduce Open Data to an organisation. Over time, the organisationcan consume and publish more Open Data as required.
Inbound Open Data is used to develop new products that could not have been created before Open Data was made available, particularly if the products are reliant on Open Data from government sources. A business may also develop new products that would not be financially viable if Open Data was not available and proprietary data has to be used instead.
Outbound Open Data may be published to attract potential consumers and developers into using that source, although the business may charge for complementary data sources that provide more detail than the Open Data source, or which are better formatted or suited to a user’s needs.
Open affiliate model: This Open Data business model is well suited to start-ups with limited resources, who are seeking to develop services based on the published Open Data of a larger organisationthat has an established brand.
The start-up will begin by affiliating with the larger Open Data publisher. The start-up will then use the Open Data to create a new site, app or service that will promote and sell the products and services of the larger Open Data publisher. The start-up receives a percentage share of the sales made on the larger Open Data publisher’s behalf.
Alternatively, the start-up may create a website or app that attracts web traffic that can be forwarded to the Open Data publisher.
Open freemium model: In the Open freemium model, Open Data is used to create free products and apps to attract customer users. As the Open Data is free, costs to the business are low. Once attracted to the free product, a customer may then be tempted to pay for additional ‘value added’ services and extra features.Forexample, you could provide a weather app which is free, but where the forecast is only available in 2 hour increments. By paying for additional features, a user could receive weather updates in 15 minute increments with automatic notifications of any changes in the weather forecast.
The Open freemium data generator model is slightly different. Customers get access to a free app or service. By using the service or app, and potentially adding registration details, customer data is generated and collected for the app. This data can then be sold to another business directly, or combined with other data sources to generate revenue.
Unsurprisingly, both variants of this model could be merged together, to benefit from freemium customer product sales and commercial data sales. However, you would need to make clear to consumers how their data will be used (remember the GDPR).
