CREATING SOCIAL VALUE THROUGH THE USE OF OPEN

DATA

A dissertation submitted to the University of Manchester

for the degree of Master of Science

in the Faculty of Engineering and Physical Sciences

2015

By

Hafiz Usman Mohammed

School of Computer Science

2

Contents

Declaration................................................................................................................................ 4

Intellectual Property Statement .............................................................................................. 5

Acknowledgement .................................................................................................................... 6

List of Tables ............................................................................................................................ 7

List of Figures ........................................................................................................................... 7

List of Abbreviations ............................................................................................................... 8

Glossary of Terms .................................................................................................................... 9

1. Introduction ........................................................................................................................ 11

1.1. Research Motivation and Questions .......................................................................... 12

1.2. Aim And Objectives .................................................................................................. 12

1.3. Deliverables ............................................................................................................... 13

1.4. Report Structure ........................................................................................................ 13

2. Background ........................................................................................................................ 15

2.1. Open Data: A Brief History ...................................................................................... 15

2.2. Open Data .................................................................................................................. 15

2.3. Open Government Data ............................................................................................. 17

2.4. Formats of Open data. ............................................................................................... 18

2.5. Open Data Ecosystem ............................................................................................... 21

2.6. People/Businesses using Open Data ......................................................................... 24

2.7. Benefits of Creating Value using Open Data ............................................................ 26

2.8. Barriers to Adopting Open Data ................................................................................ 27

2.9 Overcoming the Barriers to Adopting Open Data ..................................................... 28

2.10 How Open Data Leads to Value Creation ................................................................. 29

2.11 Value from Data to Knowledge ................................................................................ 30

2.12 Technologies Used to Provide Value ........................................................................ 30

2.13 Tools for Each Technology. ...................................................................................... 35

2.14 Summary ................................................................................................................... 37

3. Social Value Creation ........................................................................................................ 38

3.1 Social Value .............................................................................................................. 38

3.2. Importance of Social Value Creation ........................................................................ 39

3.3 Case Studies: Analysis of Products and Services Driven by Open Data .................. 40

3.4 Summary ................................................................................................................... 46

3

4. Framework ......................................................................................................................... 47

4.1 Developing a Framework for Creating Social Value ................................................ 47

4.2 Explaining the Framework ........................................................................................ 48

4.3 Summary ................................................................................................................... 49

5. Evaluation ........................................................................................................................... 50

5.1 Evaluating the Framework ........................................................................................ 50

5.2 Value Created for Stakeholders ................................................................................. 52

5.3 Barriers to Developing Skill Route and How to Overcome. ..................................... 52

5.4 Summary ................................................................................................................... 52

6. Conclusion, Limitations and Future Work ..................................................................... 54

6.1 Conclusion ................................................................................................................. 54

6.2 Limitations ................................................................................................................ 54

6.3 Future Work .............................................................................................................. 55

References ............................................................................................................................... 56

Appendix ................................................................................................................................. 65

4

Declaration

I hereby declare that no portion of the work referred to in this dissertation has been submitted in support of an application for another degree or qualification of this or any other university or other institute of learning. Date: 18 September, 2015 Signature: Hafiz Usman Mohammed

5

Intellectual Property Statement

i. The author of this dissertation (including any appendices and/or schedules to this

dissertation) owns certain copyright or related rights in it (the “Copyright”) and he has given The University of Manchester certain rights to use such Copyright, including for administrative purposes.

ii. Copies of this dissertation, either in full or in extracts and whether in hard or electronic copy, may be made only in accordance with the Copyright, Designs and Patents Act 1988 (as amended) and regulations issued under it or, where appropriate, in accordance with licensing agreements which the University has entered into. This page must form part of any such copies made.

iii. The ownership of certain Copyright, patents, designs, trademarks and other intellectual property (the “Intellectual Property”) and any reproductions of copyright works in the dissertation, for example graphs and tables (“Reproductions”), which may be described in this dissertation, may not be owned by the author and may be owned by third parties. Such Intellectual Property and Reproductions cannot and must not be made available for use without the prior written permission of the owner(s) of the relevant Intellectual Property and/or Reproductions.

iv. Further information on the conditions under which disclosure, publication and commercialisation of this dissertation, the Copyright and any Intellectual Property and/or Reproductions described in it may take place is available in the University IP Policy (see http://documents.manchester.ac.uk/display.aspx?DocID=487), in any relevant Dissertation restriction declarations deposited in the University Library, The University Library’s regulations (see http://www.manchester.ac.uk/library/aboutus/regulations) and in The University’s Guidance for the Presentation of Dissertations.

6

Acknowledgement

I hereby express my sincere appreciation to my supervisor, Martin Henery (Dr.), for his priceless support and guidance on the preparation of this dissertation. Special thanks to mom parents for providing me with motivational support during the course of my study. Many thanks to my colleagues and other people whom have provided valuable advice that contributed to this work. Grand thanks to National Information Technology Development Agency (NITDA) for providing financial support and sponsoring my study.

7

List of Tables

Table 2.1: Some data format types and structural properties…………………………...… 20

Table 2.2: Overview of some open data benefits ………………………………………… 26

Table 2.3: Some of the barriers to adopting Open Data ……………………………..…… 28

Table A2.1: Examples of applications using open data in the transport and mobility sector 66

Table A2.2: Examples of applications using open data in education and sport sector …… 66

Table A2.3: Examples of applications using open data in the security and safety sector ... 67

Table A2.4: Examples of applications using open data health sector…………………...… 67

List of Figures

Figure 2.1: Closed and Open Data ……………………………………………………….. 16

Figure 2.2: Types of Open Data ………………………………………………………….. 17

Figure 2.3: Open Data Value Chain ……………………………………………………… 18

Figure 2.4: Tim Berners-Lee’s 5 star Open Data ………………………………………… 19

Figure 2.5: Open Data Ecosystem Taxonomy …………………………………………… 22

Figure 2.6: Visualisation of actors participating in open data ecosystem ………………… 23

Figure 2.7: Three Dimensions of Data Analytics …………………………………………. 31

Figure 2.8: The Data Visualisation Process …………………………………………......... 33

Figure 3.1. Exchange of information request in the application ……………………..…… 41

Figure 3.2. Screen shot of the browser application ……………………………………….. 43

Figure 4.1: A framework for social value creation ……………………………………….. 47

Figure 5.1: Workflow for the online tool ………………………………………………….. 51

Figure A1.1: A Snapshot of Open Data Index for different countries …………………….. 65

8

List of Abbreviations

AR Augmented Reality API Application Programming Interface BIC Business Integrity CEM Community Energy Manager CSV Comma Separated Value CSS Cascaded Style Sheet DEP Department of Environmental Protection DfE Department for Education ECO Energy Company Obligation ETL Extract, Transform and Load GCSE General Certificate of Secondary Education GD Green Deal GUI Graphic User Interface HMD Head-Mounted Displays HTML Hyper Text Mark-up Language IBM International Business Machine IT Information Technology JSON Java Script Open Notation LDIF Linked Data Integration Framework MGI Mckinsey Global Institute MIME Management Information Made Easy NHS National Health Services ODA Office of Data Analytics ODI Open Data Institute ONS Office for National Statistics OGWG Open Government Working Group POI Points of Interest RDF Resource Description Framework SAS Statistical Analysis System SROI Social Return on Investment, SQL Structured Query Language XML Extensible Mark-up Language VR Virtual Reality

9

Glossary of Terms

Basic and Standard Terminologies

It is crucial to understand some basic terminologies and their meanings because they serve the underlying context of open data even before it drifted into a technological movement. The Royal Society (2012) provided the following definitions:

API: An Application Programming Interface is an abstraction implemented in software that defines how others should make use of a software package such as a library or other reusable program. APIs are used to provide developers access to data and functionality from a given system

Copyright: Copyright enables creators/authors of original works to have authority over their work. Copyright is the means by which creators/authors prevent others from copying the expression of ideas in a work.

Copyleft: Copyleft is a means of using copyright law to create, copy, share, modify and extend creative work of authorship to be free as well (Copyleft.org, 2014).

Data: Data is qualitative or quantitative statements or numbers that are assumed to be factual. Data can be a recording (audio/visual), or document, but have not been a product of any sort of analysis or interpretation.

Dataset: a collection of data presented in an electronic or other formats that is presented in a structured or unstructured form. They contain factual information that is not the product of any analysis or interpretation.

Linked data: Linked data is described using a unique identifier and address in order to link it to other related data. The link between the data might not otherwise be connected, thus improving discoverability.

Open data: Open data is data that meets the criteria of intelligent openness. Intelligent openness means data must be accessible, useable, assessable and intelligible. An elaborative description is provided in section 2.2.

Meta data: Metadata is “data about data”, it defines the data itself and holds information about a dataset.

Open government/public data: Data that is acquired by the government or public services on public sector that has been made available to the public as open data (Cabinet Office, 2012).

Open publication of data: This is proactive publication of data/information, usually through the internet with no restriction on usage (with an exception when used for commercial gain) and information is machine readable using computers (Elena et. al, 2014).

Social Value: Social value is that which enables fulfilment of basic and long-standing needs citizens of a society.

Stakeholder: A person with an interest or concern in something, especially a business. In open data, a stakeholder is anybody who can affect or is affected by the publishing and consuming of open data and their indirect economic and social influences.

10

Abstract The aim of this dissertation is to create a framework that allow users who want to create social value through the use of open data. The framework will enable researchers and organisations to develop products and services based on open data that generates social value for their beneficiaries. Research has shown that open data has the potential to release both economic and social value through application of data technology solutions. Deploying open data-driven solutions in order to tackle public sector problems has resulted in remarkable achievements that is now transforming citizens’ well-being, cities, and governments for good. The findings from literature and investigation of successful case studies where open data was used to create to innovative products and services that lead to outcomes which generate value to stakeholders revealed solid insight into value creation process. The findings were used to develop a working framework that can be easily followed to create social value.

11

Chapter 1

Introduction

Citizens’ desire for transparency has led governments’ to reform their style of governance in an open way. Open governance means that it has to make available all of its data available and disseminate it to the public for free (O’Hara, 2015). Making government data open and available to the public allows citizens to know what is happening in the government and even influence what they want the government to start/stop doing . For example, we can know precisely how much of our tax money is spent on road construction, energy research or which part of our community has high crime rate, thus citizens and authorities can work together to reduce occurrence of these crimes there by making it safer for all to live in.(Open Knowledge Foundation, 2012; O’Hara, 2015).

Open Government Data has an essential charm. The notion of making data ‘open by default’ challenges the deep rooted cultures of national privacy and calls for data to be treated as a public resource: leads to participation of citizens in the decision-making processes, to improve the provision of public services and as a contribution to economic and social innovation and enterprise. Even with this, openness is a needed modern value that contributes to freedom and autonomy (Sen, 2001).

The concept of open data has spread rapidly around the globe. According to Mckinsey Global Institute (MGI) (2013) and Capgemini (2013), over 40 countries globally have established open data initiatives and strategies. Activists, sponsors and governments have hurried to replicate common approaches of building web data portals, enacting policies for open data and running hackathons events, competitions and capacity building training. Following this series of similar efforts has been “…an implicitly linear theory of change: data + intermediaries = impact “according to Davies (2014). See appendix A1 for the snapshot of Open Data Index across different countries.

MGI (2013) in their report also said governments and organisations are opening up all kinds of data and making it accessible, usually through internet platforms sponsored by the government and organisations. They claim that opening up datasets creates the opportunity for economic development valued more than $3 trillion annually across seven domains (Education, Transportation, Consumer Products, Electricity, Oil and Gas, Health Care and Consumer Finance).

In understanding the economic value, MGI (2013) identified the five most essential levers for unlocking value using open data and then estimated the annual value to economy each lever can help enable. The levers are 1) Creating transparency to discover information to make better personal, business, and governing decisions; 2) Exposing inconsistency and enabling experimentation to identify areas for improvement; 3) Segmenting populations to deliver targeted services such as creating custom marketing offers; 4) Making automated or augmented human decision making; and 5) Defining new products, services, and business models.

12

Using this, each lever works better by enabling better decision making. For example spurring energy efficiency by revealing relative consumption; by exposing anomalies in performance data that lead to better processes, such as using public budget data to find opportunities to save procurement etc. However, this values are from an economic dimension, there are other dimensions as well such as Political, Legal, Technical, Operational and Institutional (Zuiderwijk A. et al, 2014), which are could also serve as a source for creating social value.

From a social dimension, Social Value is explained to be a way of thinking about how scarce resources are allocated and used. It involves looking beyond the value of each individual resource and looking at what the collective benefit to a community is when a public body chooses to make a resource (e.g. data) open, accessible and easily usable (Social Enterprise UK, 2012). Values obtained from open data are diverse such that it can lead economic innovations by creating new products and services especially for small scale businesses. Small and medium companies with products and services based on Open Data, such as Global Positioning Systems, financial services and software applications, also generate new businesses and jobs (Capgemini, 2013). It equally creates social good and improve community development. Social benefits obtained from open/public data such as enhanced accountability, transparencies between citizens and their government, and also empowering citizens to participate more in social enterprises (Cabinet Office, 2012). 1.1. Research Motivation and Questions

Most governments and organisations are interested in open data mainly because of its economic or commercial value, ability to improve efficiency and effectiveness of processes; creating values for targeted consumers (MGI, 2013). While open data equally creates opportunity for social value creation, very few organisations with limited resources are making efforts to do this. Responsible governments provide little funding, support to this social capital projects (Zicari, 2012), and leave this job to interested individual citizens, researchers and developers to generate value from public data sets they have made available. The occurring challenge is how can one create value from open data? Is there a methodology that helps a researcher or organisations create products and services that deliver social value? These are questions that instigate this research and answering them is invaluable to organisations that seek to improve lives of citizens.

1.2. Aim And Objectives

The aim of this project is to create a framework that helps researchers and third sector organisations create products and services that generates social value for their beneficiaries as a result of using open data. To achieve this, the following objectives have been identified:

• To audit many forms of open data currently available within the UK. • To determine the nature of the data contained within these databases, how it may be

used, who can access it, how they could/would use it to create social value. • To develop a framework for creating social value from open data.

13

1.3. Deliverables

• An overview of open data, what it contains and how it could be used. • An investigation into datasets, tools, and case studies that have used open data to

create value. • A framework that researchers and organisations can follow in order to create social

value using open data.

1.4. Report Structure

The project has been introduced; the motivation and research question that drove the author to work on this research is briefly introduced as well. The aim, objectives and deliverables have been clearly stated. The remaining content of this report is structured as follows:

Chapter 2: Background

This section discusses relevant background materials with the aim of setting the up a foundation for understanding the project. The topics discussed in this section situates the project’s importance into a wider context. An explanation what open data is was addressed at length, criteria that makes data open or closed, formats in which open data can be collected. The Open Data ecosystem is also discussed to identify the stakeholders involved in open data scene, the role they play to ensure the supply and demand of open data is sustained. Furthermore, the benefits achieved as a result of using open data in different context, barriers that hinder users from obtaining value and how to overcome them. An explanation on how value can be created from data through the use of technology and tools that enable value creation.

Chapter 3: Social Value Creation

This section explains what social value is about, and the importance of creating it towards the thriving of citizens, societies and environment. It describes case studies where value has been created using open data describing what data and technology was used, how it was used to create value.

Chapter 4: Framework

This chapter presents the framework for social value creation. The phases of the framework are explained in great detail including the key activities to be considered in order derive value from data. It also highlights some key information discussed in chapter 2 and 3 that add value to the framework.

Chapter 5: Evaluation.

This chapter uses the framework developed in chapter 5 to explain the development of an application (“product”) that uses open data to create social value. It also explains what value is being created to the identified stakeholders of the product including the potential barriers that can arise over the course of developing the application.

14

Chapter 6: Conclusion

This chapter looks back at the main aim of this dissertation, highlighting how the questions raised have been addressed and providing concluding remarks on what has been achieved so far, limitations along the path of conducting this research and future research work that would be interesting to embark further upon.

15

Chapter 2 Background

This chapter explains the background and literature behind open data for this project. A glimpse into how open data started in its early history is stated, followed by a deep insight into what open data really is, its formats, benefits of using it, what makes it open and how it can be used to create value using enabling data technologies.

2.1. Open Data: A Brief History

It is argued that the earliest form of Open Data movement began when it was released by the United State federal government. The basis for releasing open data then was founded on the Jeffersonian notions of the benefits of sharing data and the democratic importance of citizens having ready access to the information held by their government (Rhind, 2014). As long ago as 1817, the situation was different in the UK, as the government issued a warning in the London Gazette that anyone making unauthorised use of Ordnance Survey mapping, such as reproduction of the outline map, would be prosecuted. Much later there were various economic studies which suggested that significant economic benefits would accumulate to the state and to commerce from making access and reuse of such data free. (Rhind, 2014).

2.2. Open Data

A number of definitions have been attributed to define what open data means. Noting this, the author has selected the most commonly used, which is that of Open Definition:

“Data that can be freely used, re-used and redistributed by anyone - subject only, at most, to the requirement to attribute and share alike”. (Open Definition)

Open data has been said to be a philosophy and practice which requires that certain data are freely available to the public, with no restrictions from patents and any another control mechanism (Campbell & MacNeill, 2010). Open Data Dialogue (2012) also defined open data as a anonymised data produced in the course of an organization’s day-to-day business, which has been published openly with no license restriction.

According to Chernoff (2010), the data should be published in a format that is free of propriety and other intellectual property restrictions. Chernoff believes the problem is that a growing number of people are using the term open data to mean publicly available data but that open data does not mean that a government or other entity publish all of its data to the public. Rather, openness in this context means that whatever data is published is done in a particular manner to allow the public to access it without costing the individual much or being unfair in its usage restriction.

16

2.2.1. Criteria for Open Data

For data to become open and obtained in the form of a data set, as highlighted by (MGI, 2013), it needs to be:

• Accessible by a wide range of users. • Machine readable so that it can be processed and analysed by computers. • Acquired freely or should be accessed at a negligible cost. • Open licensed. Rights on the data in terms of use, re-use and sharing should be

unlimited.

2.2.2. Differentiating between Open or Closed Data

Data sets vary based on degree of openness from being completely open to completely close across the four measurements in the Figure 2.1. The figure shows how data are open or closed based on the four characteristics that define open data (MGI, 2013).

Figure 2.1: Closed and Open Data (MGI, 2013)

2.2.3. Types of Open Data

Omidyar Network (2014) explained the different types of data in open data initiative as shown in Figure 2.2

• Government data or public sector data is major, which includes a wide range of data collected or funded by government itself and government entities that needs collection (e.g. transportation, health, and other geographical data) or commissioned as part of the government initiative (e.g. spending records, court records).

• Research or science data is another major area, particularly those that are publicly funded.

17

• Private sector data is another type of data that can unlock both public and private values such as vehicle tracking information for traffic management.

Omidyar Network also stated that open data initiatives are broad, having very different levels of policy attention and implementation progress across these data types.

Figure 2.2: Types of Open Data. (Gurin, 2014)

2.3. Open Government Data

Open Government Data is an open data produced or commissioned by the government or an entity which the government controls. This data is generally accepted to be collected during the course of usual operations which do not identify individuals or breach commercial sensitivity (Open Government Working Group (OGWG), 2007). Open government data also adheres to the criteria for open data as stated in section 2.2.1. 2.3.1. Reasoning behind Opening Government Data A number of reasons are behind making government data open, the OGWG (2007) have identified the following three main reasons:

1. Transparency: Citizens’ desire to know what their government is doing has made governments to provide free access to government data and information and to share that information with other citizens. To understand this data, it needs to be analysed and visualised in order to infer knowledge from it.

2. Releasing social and commercial value: We live in a digital age where data is used to drive social and commercial activities. All of these activities require access to data, much of which the government created or holds. By opening up data, government can assist in stimulating innovative businesses and services that provide social and commercial value.

18

3. Participatory governance: Citizens can engage in governance. Opening up data makes citizens directly and better informed such that it enables them to participate in decision-making. This scales beyond transparency: it’s about encouraging a total “read/write” society, not just about having knowledge on what is going on in the governing process but equally being able to add to it.

Open government data are categorised as "service enablers", for example using real-time data of public transport or expenditure data, it can be used to develop targeted public service applications or products with a marketable business model.

Figure 2.3: Open Data Value Chain (cf. Rojas et al., 2013)

Figure 2.3 illustrates a simplified Open Data value chain. The data sets are transferred from Its source, the public sector, through infomediaries that process the data and thereby enhancing its value to the end-users. End-users can be individual citizens as well as enterprises, organisations or even the government itself. Section 2.5.1 provides an improved value chain model into a comprehensive ecosystem (Heimstädt, 2014). 2.3.2. Principles of Open Government Data

Government data shall be considered open if it is made public in a way that complies with the principles below. This set of principles were developed by thirty open government advocates OGWG (2007).

1. Data must be complete. 2. Data must be primary. 3. Data must be timely. 4. Data must be accessible. 5. Data must be machine readable. 6. Access to data must be non-discriminatory. 7. Data must be non-proprietary. 8. Data must be license-free.

The group also stated that by embracing the eight principles stated above, governments of the world can become more effective, transparent, and relevant to the lives of its citizens.

2.4. Formats of Open data.

The format of data matters. Data should be reasonably structured to allow readability and processing by machine. The machine readable principle is important because as the complexity or size of the data sets grow, most innovative applications of government data

19

does require the use of a computer to search, filter, or convert it into a new form. No matter how powerful computer applications are, they do not work well with uncertain data type (Tauberer, 2012).

Cole (2012, para.1) identified that “various types of data are apt to be more or less open, and the reasons for the degree of openness may vary from one situation to another, that is type of data, by country, by type of institution, etc.”, this diversity in format could lead to inability of open data to live to its promise, he stated.

Berners-Lee (2009), illustrates on the “5 star” deployment scheme for Open Data. The 5-star is visualised as steps in the reproduced below on figure 2.4.

Figure 2.4: Tim Berners-Lee’s 5 star Open Data. Adapted from Berners-Lee (2009)

Above categorisation by Berners-Lee is illustrated below.

1. ★: Data should be made available on the Web in whatever format under an open license.

2. ★★: Data should be made available in a structured format (e.g., Excel instead of image scan of a table)

3. ★★★: Data should use non-proprietary formats (e.g., CSV instead of Excel) 4. ★★★★: Data publishers should use URIs to identify things, so that people can link

to the data. 5. ★★★★★: Data should be linked to other data to provide context.

20

2.4.1. Overview of Data Formats

Table 2.1 below provides a brief description and structural properties in terms of openness of some common types of file formats used in open data.

Machine Readable (MR), Specifications Available (SA).

Format Description MR SA Java Script Open Notation (JSON)

JSON is a basic file format that any programming language can read easily. Its basicness simply means that it is generally easier for computers to process than others, such as XML.

Yes Yes

Extensible Mark-up Language (XML)

XML is a commonly used data exchange format because its allows the structure of the data to remain intact and the way files are built on, and enables developers to store pieces of the documentation in with the data with no interference while reading them.

Yes Yes

Comma Separated Value (CSV)

CSV files format is quite useful because it is compact and thus suitable for transfer of large data sets having similar structure. It is particularly important for CSV formats to have documentation of the individual fields for accuracy and understanding. Additionally, it is crucial that the structure of the file is maintained, as a single alteration of a filed may disturb the readability of remaining data with no repairing chance and no interpretation.

Yes Yes

Resource Description Framework (RDF)

RDF is a W3C-recommended format. It enables easy representation of data in a form that makes it easier to combine data from multiple sources. RDF data can be stored in XML and JSON, among other serializations. It also encourages the use of URLs as identifiers, making linking open data suitable.

Yes Yes

Hyper Text Mark-up Language (HTML)

HTML is the common format available on websites. It is better applicable to data that is very data is very stable and has limited scope. Typically, holding data using tables in the HTML document and providing IDs to various data field makes such data easy to find and manipulate.

Yes Yes

Scanned Image

Perhaps the least appropriate for most data, thus both TIFF and JPEG-2000 can at least mark them with documentation of what the picture entails.

No Yes

Proprietary formats

Some unique systems have their own data formats which data can be exported or saved in. situations which require the use of these propriety formats should be documented and reference. For example, giving additional information using a linked to a supplier’s website.

Yes Yes

Table 2.1: Some data format types and structural properties (Open Knowledge Foundation, 2012; Geiger & Von Lucke, 2011)

21

Some open data advocates provided their own recommendation on formats to use. Tauberer (2012) suggest that XML is best thought of as a type of data format, instead of a specific format. He however stated that the choice of file formats requires a consideration of both accessibility and machine processability. Additionally, the inventor of the Web, Berners-Lee (2009), proposed the use of linked RDF data as a goal to be sought for open data initiatives.

However, it is better to have multiple formats of the same data so as to satisfy the choices of the users of these data sets.

2.5. Open Data Ecosystem

Open data is described to be either “adaptable” or “inert” (Yu and Robinson, 2012). MGI (2013) in their report termed open data as “liquid” (i.e. open, widely available, and in shareable formats) due to its open definition and nature. Its liquid nature enables it to unlock large economic value by improving efficiency and effectiveness of existing processes in several domains between firms, researchers and entrepreneurs, and to citizens, and is adapted in the process. Van Schalkwyk (2014) compared the analogy adapted from the natural sciences, “the flow of data could result in a virtuous cycle, becoming a stable but dynamic part of an ecosystem. But equally possible, data could, despite being open, become inert and flow too slowly or not at all; it could be too viscous to contribute to the evolution of the ecosystem”.

2.5.1 Defining Open Data Ecosystem

Heimstädt (2014) in his work on studying the business ecosystems that evolved through the use of open data, he derived a working definition of an open data ecosystem as:

“a dynamic structure, which consists of an interconnected population of conscious agents, who either directly produce and process data sets that are technically and legally open, or indirectly enable these processes. These agents are exposed to various feedback processes and can be all forms of governmental bodies, small firms, large corporations, universities, research centres, public sector organisations, individuals or other parties, which influence the system. Open Data ecosystems are limited by the geographical and constitutional area, which the involved data sets refer to. Therefore Open Data ecosystems can exist on a municipal, state, federal, supranational and global level” (Heimstädt, 2014). 2.5.2 Stakeholders in the Open Data Ecosystem

The ecosystem usually involves and revolves three main stakeholders, that is – Suppliers, intermediaries and users. The value chain of data presented in section 2.3 is used to identify the actors in each stakeholder category, the identified actors also have a role to contribute to the ecosystem. The actors are structured taxonomically similarly to biological flora and fauna which are divided into different species (Heimstädt, 2014). Figure 2.5 illustrates the composition of the ecosystem.

22

Figure 2.5: Open Data Ecosystem Taxonomy (Heimstädt, 2014)

From Heimstädt’s (2014) and Immonen et al.’s (2014) description, Data suppliers are all public organisations that that produce valuable data sets for the other actors of the ecosystem. This includes institutions, which are (1) commissioned by the government, public entities, entirely tax-funded, like ministries. This institutions have lot of data but lack the capacity to use the data in the form of refined data or development of services with the data, and (2) commercially inclined to generate most of their revenue from selling data to the government or other users, like Trading Funds in the UK, providing guarantees of data availability to paying users only, limiting the frequency of access to the open data and requiring people to register in order to access the data (Stott, 2014). Intermediaries are the kind of organisations such as Data Publica and Open Corporates, that process raw data usually by means of aggregation in order to upgrade its value. Individual examples are Information Technology (IT) activists or data journalists, who visualise data sets and then make their information content available to the broad public. However, journalists are only one handy example for this group. Other intermediary examples are Non-Governmental Organisations, voluntary citizens and corporations. In general, most individuals and especially corporations that adds value to data by processing it and making it available for reuse, either for commercial purpose or for free, fit into the category of data brokers or intermediaries. A standard value proposition service by an intermediary to users might be to combine geo-enabled data with data sets from another category and offer the resulting map as a service. Application developers cooperate with partners to develop innovative applications or services that are usable to data consumers.

23

Figure 2.6: Visualisation of actors participating in open data ecosystem

(Ericson and Spraragen, 2012).

Data consumers are basically individuals (e.g. Application or service users) or organisations, which consume data to gain knowledge. This can be an individual using a geo-data tool, a local business which contracts an intermediary to combine its closed data with Open Data, or the government itself. Also indicated in the ecosystem is the case of feedback loop between suppliers and consumers. When data suppliers (e.g. government entities) open up their data sets, which get processed and enhanced by intermediaries for consumers, consumers might not be satisfied with the information or service they get, then they instigate further actions back to suppliers. The supplier is likely to learn and improve the kind of service it offers based on the feedback users provide. This feedback loop is best defined by Helbig et al. (2012: 22), “A feedback loop exists when information resulting from some action within the system (endogenous) travels through the system and eventually returns in some form to its point of origin and potentially influences future action”.

Through Freedom of Information requests or portals like data.gov.uk, users can access or request raw data directly. However, Heimstädt (2014) pointed that from a practical viewpoint only a fractional amount of UK citizens are able to derive insight from the raw data by themselves, due to the gap or lack of data skills.

24

Furthermore, the ecosystem enablers do not necessarily participate strongly in the data exchange activities, their role however influences the ability of the value chain to function effectively. Examples include 1) Socrata, which provides a Software-As-A-Service Open Data portal for governments and cities. 2) Cloudera, which provides data analytics services and platforms (Stott, 2014).

Heimstädt (2014) subdivided the group of Enablers into five categories, such that a single stakeholder can execute more than one of the five characteristics. Stakeholders such as the Open Data Institute (ODI) or the Open Knowledge Foundation are active in making policies around information and transparency council and thereby significantly influencing other actors in the market. The ODI at the same time establishes capacity within the ecosystem, through training, seminars and professional courses, on open data for example. Philanthropic investors, such as Nesta, Omidyar Network or the Open Society Foundations, offer grant funding for open data projects within the UK and even the first for-profit investors have funded open data start-ups. The supply of funds is crucial for the survival and growth of the ecosystem. Working with open data in most situations means you will be dealing with large data sets. Therefore open enterprises like the Open Source Software projects (e.g. Apache Hadoop), are essential elements of the ecosystem, because they provide the technical infrastructure for processing and dealing with such large data sets. Finally, the knowledge and learning capacity development around the ecosystem enables its development as this makes it more accessible for potential participants. Universities, ODI are one of the main institutions to generate well-grounded capacity for training and knowledge.

2.6. People/Businesses using Open Data

2.6.1. Suppliers

These are organizations that produce and publish their data as Open Data to enable others to use and reuse it in the ecosystem. Suppliers are public bodies and some private sector companies, such as the Association of Train Operating Companies in the UK (where most of the railway operators are in the private sector). There is no income from the publication of data, but r e l e a s i n g the data may bring about improved levels of c i t i z e n / customer engagement and loyalty, with revenue gains in the case of private organizations. Additionally, there is the opportunity to sell value-added services depending on the supplier’s understanding of the data: An example of this is Statistics Germany, after it began to publish its data free of charge in October 2008, it recorded an income growth of 95% from premium and advisory services in two years (Stott, 2014). Some data suppliers in UK, such as Trading Funds and Weather Bureau (“The Met Office”), have two- tier models, where data is available for free on a “best endeavours” basis and for a premium service which guarantees availability, on the same data on with the same license terms (Heimstädt, 2014; Stott, 2014).

25

2.6.2. Aggregators

These kind of organizations collect and aggregate Open Data and, sometimes, other proprietary data for other users. Aggregators are usually on a sectorial or geographic theme. Examples include: Data Publica, which is a repository for all kinds of data about France; Placr/Transport Application Programming Interface (API) which aims to be the UK’s first comprehensive open platform for transport solutions; and Open Corporates, which holds data on 66,665,508 companies taken from 81 national or state company registers. Businesses use this model for generating revenue stream as remuneration for aggregation itself through added-value data access services such as APIs. Example of this kind of model is to charge users with re-selling purposes and make free for “share-alike” basis (Stott, 2014).

2.6.3. Developers

These are individual or citizen developers and organization who design, build web or smartphone applications to deliver government open data to customers (normally in the personal sector) in attractive and informative ways, such that it delivers value to its users. Sometimes these applications are offered for free or sold to customers (Stott, 2014). There are over 500 applications that use Transport for London’s 25 Open Data feeds, thus employing around 5000 people in development, marketing and support (Stott, 2014).

2.6.4. Enrichers

These are individuals and organizations that make use of open data to gain new or better insights that they can deliver in services or products to their customers - often completely new services which could not have existed before Open Data. For example, Zoopla in the UK and Zillow in the US. They use public data, from UK Land Registers and US Homes, by relating it to real estate sales information and then create forecast on home values in the market (Stott, 2014).

2.6.5. Enablers

These are organizations which provide training, platform and technological support that other businesses and individuals use. They are a vital part of the Open Data Ecosystem. They can have revenue generating capabilities themselves, equally providing cost-effective and easy-to-access services for both data suppliers and data consumers (Stott, 2014). Examples such as:

1) Socrata in the UK , which provides a Software-As-A-Service Open Data portal for governments and municipalities;

2) OpenDataSoft in France, which deliver same service as its competitor, Socrata; 3) MuSigma and ; 4) Cloudera which provide data analytics services and platforms.

26

2.7. Benefits of Creating Value using Open Data Indeed open data has the potential to release huge value and enable all stakeholders in the ecosystem to improve, better and strengthen the service they provide to other actors in the ecosystem. These benefits can be classified into economic, social, political and technical perspectives. Socially, social enterprises can use open data to provide targeted and suitable services to its beneficiaries. Economically, open data can stimulate innovation and creation of new products and services. Table 2.2 shows an overview of some benefits released by open data initiatives and they could be achieved according to MGI (2013), Beresford (2015) and Janssen et al. (2012) based on research, interviews and workshop they carried out separately. Category Benefits Achieved by Political and social

• Development of trust in government • More transparency and democratic accountability. • More citizen participation and empowerment. • Improvement of citizen services and satisfaction. • New (innovative) and targeted social services.

• Opening up public data.

• Using big data analytics to discover insights from data.

Economic • Creating new sector and companies that add value to the economy e.g. Zillow in US and Open Corporates in UK.

• Availability of information for investors\companies. • Development of new products and services. • Use of the wisdom of the crowds: tapping into the

collective intelligence. • An example is Zillow, a fast growing online real

estate marketplace, which couldn’t exist without public data. It uses public data in new ways, by relating public data to real estate sales information and creating forecast on home values. Zillow is now reported to be generating more that 50 million pounds in quarterly revenue and employing more than 500 workers (Newcombe, 2014).

• Analysing open data using advanced data analytical tools and techniques such as predictive analytics and visualisation.

• Creating applications driven by open data for informed decision making.

• Combining public and private data to discover insights.

Operational and technical

• Optimization of administrative capability. • Improving the efficiency and effectiveness of

existing processes and sustainability of data resources.

• External problem-solving capacity using citizen developers.

• Fair decision-making by comparison enablement. • Example of improved service efficiency and cost

saving is when the National Health Services (NHS) openly published infection rates of all hospitals in the UK. This publication, coupled with the sharing of league tables showing the worst hospitals, encouraged exchange of best practices amongst hospitals. It brought down infection rates from around 5,000 patients annually to fewer than 1,200 and leading to cost saving of £34 million (Capgemini, 2013).

• Benchmarking data from poor performing organisations against data from organisations performing with greater efficiency.

• Organizing contests, hackathons for crowdsourcing solutions and driving innovative use of existing data.

Table 2.2: Overview of open data benefits (MGI, 2013; Janssen et al., 2012; Beresford,2015).

27

2.8. Barriers to Adopting Open Data

Adopting the Open Data initiative has many successes and many countries have enjoyed the reward of the promises it holds. UK for example has many advantages which put it in a competitive spot to gain the benefits of the global data-related markets and data for good initiatives. However there are some barriers currently hindering its adoption and stops organisations from releasing its potential. In a white paper by Beresford (2015), she identified five key blocks that hamper the release of benefits that open data promises. The following list outlines her findings.

1. Infrastructure – Without building blocks such as data storage, management facilities, and connectivity, businesses and citizens cannot benefit from data enabled public resources and information. Hence discouraging economic and social investments that then unlock economic and social values.

2. Government release and use of data – there is a correlation between actions taken on public sector information release and growth of data economy. Developers can make products with free public data, as they begin to make money they will also begin to pay taxes which provides a revenue stream for the government. On the other hand, not releasing such data creates low barriers for entry to market for developers.

3. Skills – inadequate skills and expertise can hinder the ability to make productive and useful services needed by citizens.

4. Culture, trust, and openness - Some senior leadership players lack skill and understanding on data economy, as such, they tend see investments in data analytics and opening of data worthless, time consuming, and risky. Otherwise until mandated by the government or a more superior power.

5. Enabling standards and legislation - the big issue is when organisations refuse to share and link open data. Sharing identified data about service uses for public benefit. Policies will certainly play a big role in ensuring that this barrier is dissolved

Furthermore, Janssen et al. (2012) conducted a series of interviews and a workshop, they identified a number of barriers which they categorised into levels (Institutional, task complexity of handling data, use and participation in the open data process, legislation, information quality and technical) as shown in Table 2.3. Upon analysis, they identified that the barrier are linked to the ecosystem stated in section 2.5.2, precisely with either data suppliers ( i.e. wishing not to make data open) or data consumers (i.e. inability to use the data easily)

28

Category Barrier Ecosystem

Identity Institutional • Emphasis of barriers and neglect of opportunities

• Unclear trade-off between public values (transparency vs. privacy values)

• Risk-averse culture (no entrepreneurship) • No uniform policy for publicizing data

Data supplier

Task complexity • Lack of ability to discover the appropriate data. • No access to the original data (only processed data) • No explanation of the meaning of data.

Data consumer

Use and participation

• No incentives for the user involvement. • Public organizations do not provide requested feedback. • Having to pay a fee for the data. • Registration required before being able to download the data • Unexpected escalated costs. • Lack of knowledge to make use of or to make sense of data. • Lacking infrastructure to support users. • No statistical knowledge or understanding of the potential

and limitations of statistics

Data consumer

Legislation • Privacy violation • No license for using data. • Dispute and litigations. • Prior written permission required to gain access to and

reproduce data

Both Data supplier & consumer

Information Quality

• Data has missing values. • Lack of information. • Lack of accuracy or completeness of the information • Outdated and invalid data.

Both Data supplier & consumer

Technical • Absence of standards • No central portal or architecture • No support for making data available • Lack of meta standards • No standard software for processing open data

Both Data supplier & consumer

Table 2.3: Some of the barriers to adopting Open Data (Janssen et al., 2012). 2.9 Overcoming the Barriers to Adopting Open Data

In order to overcome these barriers that hinder the realization of values from Open Data, Government and Institutions play an active role in providing support and infrastructure essential to discovering those benefits. Some of the solutions here are to a large extent interconnected, and one solution category applies to several barriers in another category.

1. Political or Institutional Barriers - Government workers need a mentality of openness: reaping the benefits from big and open data, and empowering communities to use data to contribute to solving their own challenges, requires a mind-set where the public officials are comfortable as facilitators rather than simply providers (Beresford, 2015). Building trust among stakeholders in the open data ecosystem is often mentioned as a key element to overcoming political barriers open data.

29

Developing one-to-one professional relationships with stakeholders will strengthen the belief of individual parties in the effort to build on opportunities. Engaging politicians is another way of having the political buy-in from policy makers and enabling policies will certainly play a big role in ensuring that this barriers are dissolved (Sane and Edelstein, 2015).

2. Skills and Knowledge Capacity Barriers - Government and Institutions should focus on fostering digital and data capability skills amongst citizens of all backgrounds and sexes. Through the creation of positive environments and facilitating networks for training and learning, users can gain the required skills essential to making use or making sense of data for productivity. Government should provide the infrastructure to support development of talents and innovations. For example organising contests or sponsoring hackathon events while offering incentives for participation (Beresford, 2015).

3. Legal Barriers - Government and public institutions have a major role to play ensuring that released data complies with the criteria for openness. Data publishers should collect concerns of re-users and modify licenses that constrain the use or reuse of data. Data containing private data should be anonymised in order to protect the privacy of data owners (Sane and Edelstein, 2015).

4. Technical and Information Barriers - Barriers in this category can gradually be overcome by building automated systems and capacity building. Standardization and automation of data release would make sharing and access to data easier and cheaper for institutions producing open data. Building a central portal where all data sets can be requested and collected can address early challenges that can arise when using open data. Incorporating high quality production process would ensure that data made available is usable across the ecosystem without accuracy, quality issues. Publishing data sets in various formats and developing guidelines across government bodies producing the open data. Encouraging participation in the harmonization of metadata between open data catalogues and gathering metadata needs from re-users; implementing mechanisms to trace the provenance and use of data sets would eliminate metadata incompleteness (Sane and Edelstein, 2015).

2.10 How Open Data Leads to Value Creation

Open data enables creation of innovative products and services using datasets such as crimes data, budget data, transportation data, or data about medical treatments and their outcomes, that are generated in the course of providing public services or conducting research.

Value created from open data can be either commercial or social; however the underlying process remains the same. It is clear that value can be created from open data beyond the walls of the governments and institutions that make their data open. This data can not only be used to help increase the productivity of existing companies and institutions, it also can ignite

30

the creation of entrepreneurial businesses and improve the welfare of individual consumers and citizens (Michael et al., 2013).

MGI (2013) highlighted that effective use of open data can unlock significant amounts of value. This potential can be unlocked by applying technologies to a combination of open and proprietary data sets. Access to data sets can be obtained easily from government portals such as data.gov.uk in the UK and data.gov in the US, and with successful application of data manipulation tool, value can be unlocked. These technologies range from tools and techniques such as Data Analytics, Visualization and Augmented Reality. Using data analytics in the US health care, for example, Michael et al. (2013) found out that more than $300 billion a year in value potentially could be created through the use of more open data, i.e., through the analysis of open data to determine which therapies are both medically effective and cost-efficient.

2.11 Value from Data to Knowledge

The journey from raw open data to meaningful knowledge or novel insights which often provides value is realized by combining different data sets from multiple sources to those tools that allow one to apply knowledge enabling techniques in data analytics such as segmenting populations, predicting customer behaviour and exposing variability, which are commonly used in big data analytics, apply to open data analytics as well. This results to creating opportunities that organisations, public institutions and individual citizens could enjoy, and also giving rise to novel, data-driven innovations. Opportunities such as creating transparency to unearth information to make better personal, business, and governing decisions; exposing variability and enabling experimentation to identify areas for improvement; segmenting populations to tailor actions such as creating custom marketing offers; augmenting or automating human decision making; and defining new products, services, and business models (MGI, 2013). 2.12 Technologies Used to Provide Value

2.12.1 Data Analytics

Data analytics is the process of applying algorithms in order to analyse sets of data and extract meaningful and unknown patterns, relationships, and information (Adams, 2010). Furthermore, data analytics is used to extract previously unknown, useful, valid and hidden patterns and information from large data sets, as well as to detect important relationships among the stored variables. Therefore, analytics has a significant impact on research and technologies, since decision makers have become more and more interested in learning from historic data, thus gaining competitive advantage for businesses (Song & Kusiak, 2009) and enhance the quality and consistency of public services offered by the government (Deloitte, 2012). Along with some of the most common advanced data analytics methods, such as association rules, clustering, classification and decision trees, and regression some additional analyses have become common with open data.

31

The following figure illustrates the dimensions of analytics that could be applied to open data analytics.

Figure 2.7: Three Dimensions of data analytics (Davenport, 2013)

According to Davenport (2013), if analytics is to retain its real definition, then it requires some sub definitions. For example, including some forms of reporting such as standard reports, ad hoc reports, queries, scorecards or alerts in analytics, these reporting are referred to as Descriptive Analytics. Descriptive analytics helps describe what has happened in the past. It may also be used to classify customers or other business entities into segments or groups that are similar on certain dimensions.

Beyond descriptive analytics is Predictive Analytics, which uses models of the events or operations that happened in the past to predict the future. They typically use multiple variables to predict a particular dependent variable. Examples include using open data about demographic and employment shifts within the community and area, school officials can predict future enrolment and estimate operating costs (for example, heating and electricity bills, maintenance costs) as well as the potential impact of school closings, such as disruption to families who will need to switch schools (MGI, 2013). Predictive analytics models are very popular in predicting the behaviour of criminals based on past crime history and perhaps some demographic variables (Davenport, 2013), this can help reduce criminals roaming freely in the community. Prescriptive Analytics are less popular, but they are prescriptive because, in effect, they tell you what to do. They try to quantify the consequence of future decisions so that they can to advise about possible outcomes before the decisions are actually taken. At best, prescriptive analytics not only predicts what will happen, but also why it will happen providing recommendations regarding measures that will take benefit from the predictions (Major, 2014).

Sophistication of Intelligence

Com

petit

ive

Adv

anta

ge

32

2.12.2 Data Visualisation

Data visualization is a pictorial or graphical format representation of data. Using visual presentation such as charts and maps, people can understand information more easily and quickly (SAS, n.d.). A good visualisation according to Oxford Consultants for Social Inclusion (2009) is one that can convey exploration and understanding of data to users, and also allow those users communicate that understanding to others by:

• Exploring and analysing data: Visualisation is a central tool in carrying out analysis, enabling researchers and other users to explore datasets to identify patterns, links, trends and so on;

• Presenting and communicating data: Good data visualisations can help users make robust decisions based on the data being presented. They should provide an effective representation of the underlying data, to help answer a particular question at hand. Communicating data in this way can support senior decision-makers engaged in strategic planning, service managers needing to understand where delivery could be improved, and managers wanting to monitor performance.

Due to complexities associated with data, visual analytics is employed to enable effective understanding of data. Visual Analytics integrates “...automated analysis techniques with interactive visualisations for an effective understanding, reasoning and decision making on the basis of very large and complex data sets.” (Daniel et al., 2010). Daniel et al. (2010) also stated that visual analytics can enable people to:

• Synthesise information and derive insight from massive, dynamic, ambiguous, and often conflicting data.

• Detect the expected and discover the unexpected. • Provide timely, defensible, and understandable assessments. • Communicate these assessments effectively for action.

Visual analytics is essential in application areas where large data sets have to be processed and analysed in order to extract meaningful information. With common data analysis techniques researchers can separate relevant data from noise, analyse patterns, and gain useful knowledge and insight from the data. But the visual analytics approach can significantly support the process of identifying unforeseen phenomena inside the big and complex data sets that would be difficult to identify or not found entirely by using standard algorithms. The Data Visualisation Process

The data visualisation process integrates automatic visual analysis methods with user interaction in order to gain insight from data (Daniel et al., 2010). Figure 2.8 shows overview of stages involved and their transition into the visual analytics process.

33

Figure 2.8: The Data Visualisation Process (Daniel et al., 2010)

The process above is featured through interaction between stages (data, visualisation, models and knowledge) and their flow along the transition. User interaction is also required for knowledge discovery. Before any visual analysis can begin, data has to be made available usually from integrating many data sources. Therefore the first step is often to pre-process and transform the data to build different representations for further exploration as indicated by the Transformation arrow in Figure 2.8. However, other applicable pre-processing tasks include data cleaning, extraction, transformation and loading.

After the transformation, the researcher may decide to apply automatic or visual analysis methods. Automatic analysis method requires that data mining technique(s) are applied to build models of the original data. The built model is then evaluated and refined to ensure that it best meets the desired result which is done by parameter refinement and interacting with the data. Visualisations allow the researcher to interact with the automatic methods by parameters refining or selecting other alternative algorithms for analysis. Model visualisation can then be applied to evaluate the results of the built models. Interchanging between visual and automatic methods is characteristic for the visual analytics process and leads to a continuous refinement and verification of intermediate results. Misrepresentative outcome from the intermediate result stage can thus be identified at an early stage, leading to better results with higher support and confidence.

When the visual data exploration is performed first, the user has to confirm the established assumptions by an automated analysis. User interaction with the visualisation output is needed to reveal insightful information, for example, by changing the data that is being

34

viewed or zooming in on a different data view (Daniel et al., 2010). In summary, in the visual analytics visual findings process, knowledge can be gained from visualisation, automated exploration, and also previous/continuous interactions between visualisations, models, and the researcher or decision maker.

2.12.3 Augmented Reality

Augmented reality (AR) provides a real world view where elements are superimposed by computer generated files such as graphics, sounds, videos, or digital information (Rankohi & Waugh, 2013). The term AR is used to describe a combination of technologies that enable real-time mixing of computer-generated content with live video display. AR is based on techniques developed in Virtual Reality (VR) and interacts not only with a virtual world but has a degree of interdependence with the real world. While VR is aiming at engaging the user entirely into a computer-generated virtual world, AR took the opposite approach, in which virtual objects generated by computer are added to the real physical world (Sielhorst, Feuerstein & Navab, 2008). In the so-called “virtuality continuum”, Milgram and Kishino (1994) described AR as a mixture of VR and the real world in which the real part is more dominant than the virtual one. Azuma described AR by its properties of aligning virtual and real objects, and running interactively and in real-time (Sielhorst, Feuerstein & Navab, 2008). Recent advances in the field of AR has opened perspectives for several opportunities to use AR in various application domains. AR technologies have been used in various disciplines and arenas, e.g. engineering, entertainment, aerospace, medicine, military, and automotive industry, visualisation and open data (Reynolds et al., 2010; Rankohi & Waugh, 2013).

Application of AR technologies might vary in different application domains, many experts agree to define an AR technology as one that requires the use of Head-Mounted Displays (HMDs) (Janin et al., 1993). However, a more generic characteristic definition of AR systems was then proposed by Mekni & Lemieux (2014), they highlighted that a system using AR technology should have the following features:

1. A combination of real and virtual; 2. Interactive in real-time; and 3. Registered in 3-D.

Mekni & Lemieux’s (2014) definition is aimed at allowing other technologies besides HMDs, such as mobile technology, maintain the essential components of an AR system.

35

2.13 Tools for Each Technology.

Having described technologies that can be used to create value from using open data in section 2.12, this section briefly explains examples of tools that have been applied in each of the technologies above.

2.13.1 Tools for Open Data Analytics

There are many data analytical tools for analysing both open data and big data, including amateur and professional software, free open source software and lavished commercial software. In this section, the author provides a brief review of the top commonly used software according to a survey made by KDNuggets (2014) on “What Analytics, Data mining, Big Data software you used in the past 12 months for a real project” of 3000 professionals.

Rapid-I RapidMiner: Rapidminer is an open source software used for data mining, machine learning, and predictive analysis. According to KDnuggets’s (2014) research, it is the most frequently used with 44.2% popularity earning it the top one rank in the share of users. Data mining and machine learning features provided by RapidMiner include Extract, Transform and Load (ETL), data pre-processing and visualization, modelling, evaluation, and deployment. The data mining flow is described in XML and displayed through a graphic user interface (GUI). RapidMiner is written in Java. What makes it much powerful is that it integrates and provides leaning schemes and evaluation method of other tools (e.g. Weka), and works with R. Rapidminer’s functionalities are implemented with connection of processes of operators. The entire flow can be deemed as a production line of a factory, with original data input and model results output. The operators can be regarded as specific functions and feature different input and output characteristics (Goopta, 2014; Chen et al., 2014).

R: R is an open source programming language and software environment, it is designed for data mining/analysis and visualization. While compute-intensive tasks are executed, code programmed with C, C++, and FORTRAN may be in under the R environment. Furthermore, a user who is skilled may directly call R objects in C. R is a realization of the S language. S is an interpreted language developed by AT&T Bell Labs and used for data exploration, statistical analysis, and drawing plots. Initially, S was mainly implemented in S-PLUS, but S-PLUS is a commercial software. Compared to S, R is more popular since it is open source. Popularity of R has made database manufacturers such as Oracle and Teradata release products that supports R (Chen et al., 2014).

Excel: Excel is a commercial software and one of the essential component of Microsoft Office. It provides powerful data processing and statistical analysis capability, and aids decision making. When Excel is installed, it offers a broad range of built-in plugins and statistical functions. Some advanced plug-ins such as statistical functions, Analysis ToolPak and Solver Add-in, with powerful functions for manipulation tasks and data analysis are also integrated but such plug-ins can only be used when the user enables them. Excel.

36

2.13.2 Tools for Open Data Visualisation.

Data visualization tools allow anyone to organize and present information intuitively.

Tableau: Tableau is a visualisation software for databases that allows users to visually explore, analyse and create reports. Using Tableau, the user can easily connect to data, then visualize and create interactive, sharable dashboards. It is integrated with unique Business Intelligence tools designed to deliver pervasive Business Intelligence capability throughout the organization. Tableau has three range of products that deliver sets of unique features to users namely (Butler, 2013);

1) Tableau Public, which is available as a free version, or in a Premium version with fewer restrictions. It is primarily targeted at the creation of graphics, heat maps, bubble charts, geo maps and many others.

2) Tableau Desktop, which supports the visualization of data on the desktop and connects to a bewildering array of data sources, either individually or in concert. The Tableau Data Engine sits on a Personal Computer and calls upon the relevant data sources when needed. And;

3) Tableau Server supports web based tools for data visualization and hence opening up business intelligence up to a very wide audience. It provides the very wide range of visualizations and dashboards which Tableau supports, and also make them available on portable mobile devices (Butler, 2013).

Many Eyes: Many Eyes is a web-based visualization application by IBM, which allows users to upload datasets create visualizations of that data, and leave comments on both visualizations and data sets on a web browser. Many Eyes provides several types of charts, as well as word clouds and tree maps. Many eye’s strong feature is the possibility of reusing datasets uploaded by other people, which is an effort to “democratize” the visualization application (Viegas et al., 2007). Infogram: Infogram is also a web-based tool that allows users to create visualizations based on data uploaded by them. It provides users with multiple chart templates which makes it easy for users to create interactive graphics such as pie charts, line charts, and bubble plots. The tool is aimed at users with basic data visualisation skills like journalists and bloggers (Graves & Hendler, 2013).

Spotfire: Spotfire is a commercial visualisation tool by Tibco, which provides an easy, usable interface for creating data visualisation, dashboards and analytics. Most of the work in Spotfire can be done through drag and drop and a multitude of intelligent functions make visualisation tasks easy and light. It has advanced features that allows it to execute fast with large data sets, integrate with R, and has support for SQL Server, Oracle and Teradata (Tibco, 2015).

37

2.13.3 Tools for Augmented Reality with Open Data

Since the emergence of AR technologies, numerous tools have been developed to allow researchers and engineers develop specialist technologies for industry related applications. Having demonstrated successful applications in industries such as Military, Health, and Entertainments, AR has emerged as an interactive medium for exploring information in the real world for non-special purposes (Vert et al., 2014), through its integration with linked open data, tools have been created that allow users to browse the reality around them based on open data (Nixon et al., 2012).

ARToolKit: ARToolKit is an open source software library that allows users to create augmented reality applications. It uses computer vision algorithm to overlay 3D virtual objects on real markers, this feature allows it to eliminate the problem of tracking users view point. ARToolKit is widely used in augmented reality related projects (Lamb, 2011).

Argon: Argon is an augmented reality browser by Georgia Tech's ‘Graphic Visualisation and Usability’ Centre, which uses a mix of Keyhole Markup Language and HTML/JavaScript/CSS to allow users develop AR applications. Using Argon makes it possible for any web content with appropriate meta-data and proper formatting to be converted into an AR content (Blair, 2012).

DroidAR: DroidAR is an open-source framework that adds location-based AR functionality to applications that use android operating system. DroidAR provides functionalities such as gesture detection, support for static and animated 3-D objects that the user can interact with, and marker detection (Roukounaki, 2015).

2.14 Summary

This chapter has presented the background work carried out to understand the project in the wider context open data and open data ecosystem. The basic and standard terms relevant for understanding the discussion were defined. Open data and closed data were explained from a variety of sources in the existing academic and business literatures. The open data ecosystem is presented extensively, describing the roles of actors and their contribution to the ecosystem. Benefits and barriers of adopting open data was also explained to identify those challenges hindering its use and how to overcome them. The chapter also explained how value can be created from data through the use of technology and tools that enable value creation, while providing examples of each specific technology.

38

Chapter 3 Social Value Creation This chapter explains what social value is about, and the importance of creating it towards the thriving of citizens, societies and environments. The chapter then closes with investigating the case studies where value has been created using open data, describing what data and technology was used, how it was used to create value. 3.1 Social Value

Social values have attracted the interdisciplinary attention of scholars and organisations for a very long period now and a great deal has been written regarding its origin and importance (Tsirogianni & Gaskell, 2011). By social values, the author refers to socially collective beliefs and systems of beliefs that operate as guiding principles in life. What is value? Value is a concept of worth which is “linked to the use of a product or service and perceived by customers rather than objectively determined” (Dumond, 2000, p. 1062). Bowman and Ambrosini (2000, p. 4) stated two dimensions to value. First, use of value which is “subjected by customers, based upon their perception of usefulness of the product on offer”, and second, exchange value, which is “the amount paid by the buyer to the producer for the perceived use value”. Lepak et al. (2007, p. 182) suggest on the above mentioned definition, that “value creation depends on the subjective value realization of a user - whether individual, organization, or society – and translates into the user’s willingness to exchange a monetary amount for the value received”. So basically, when customers are willing to pay a higher monetary amount for a commodity than its cost of production, while taking factors such as capital, equipment, logistics, and labour into consideration, value is being created. What exactly is social value about? Certo and Miller (2008, p. 267) explained that “social value has little to do with wealth creation but instead with the fulfilment of basic and long-standing needs such as providing food, water, shelter, education, and medical services to those citizens of society who are in need”. Social values are means by which citizens of society define the social order—what is acceptable and what is not acceptable. Those who reject such social values cannot be members of the society. Social values therefore impinge on individual choices but are collectively generated, sustained and changed. Tsirogianni & Gaskell (2011) indicated that those familiar with the theory of social representations will recognise that their conceptualisation of social values places them in the family of social representations. They are part of the common sense of a society—the common sense about morality and ethics that constitute the fabric of existence.

39

3.2. Importance of Social Value Creation

Creation of social value has been proven to deliver a large scale benefits from non-financial impacts of programmes, organisations and interventions that involve wellbeing of individuals and communities, social enterprise and the environment at large (DEMOS, 2010). Its significance has led to the development of management tool called Social Return on Investment (SROI), a framework for measuring the impact of value in social and economic outcomes. The Cabinet Office (2009) guide highlights that SROI is not about money, but value. Money is merely a common unit and because of that, it is a useful and widely accepted way of conveying value. Social benefits are outcomes of the process of creating social value. Although these benefits can be perceived differently to different people or societies. Because benefits can take almost any form, ranging from tangible, such as jobs for the jobless, to smaller but also important benefits such as engagement with citizens or communities who might otherwise feel entirely disengaged (Croydon Council, 2015). The following are outcomes that can be achieved from creating social values (Social Enterprise UK, 2012; Croydon Council, 2015; SITA UK, n.d.).

• Creating avenues for job creating organisations to identify opportunities for new job streams through seminars and workshops.

• Creation of skills and employment opportunities for the long term unemployed or those not in education, employment or training (NEETs).

• Providing additional opportunities for individuals or groups facing greater social or economic barriers.

• Encouraging community engagement especially with hard to reach communities. • Providing career advice and information for young people on specific careers, such as

construction, architecture or engineering. • Offering curriculum support to schools, with contractors sharing knowledge and

expertise about their discipline. • Community cohesion through volunteering. • Creating opportunities to develop third sector organisations.

40

3.3 Case Studies: Analysis of Products and Services Driven by Open Data

In this section, the author has investigated four case studies where open data has been used to create social value in different domains. The author’s analysis covers the intricacies on how products and services have been created by highlighting a brief background, approach employed, tools, technologies, datasets and sources used, resulting outcome and thus, the value created for the stakeholder(s).

3.3.1 Case Study: Using Open Data Analytics to Identify Restaurants Dumping Waste Illegally. (Feuer, 2013; GCN, 2013; Gilbride et al., 2012).

Brief Background

New York City’s Business Integrity Commission (BIC) and Department of Environmental Protection (DEP) witnessed an increased illegal hauling of grease, fats and oil in the city’s sewer system. Inspectors identified that disposal of grease at restaurants is the cause of the discovery, and they routinely find sewer pipes clogged with hardened grease. This restricts the normal flow of wastewater from businesses and homes and can lead to flooding and sewer backups (Gilbride et al., 2012). In an effort to crackdown on restaurants that are illegally dumping cooking oil into sewers and contributing to blocked drains in their neighbourhoods. The Mayor’s Office of Data Analytics (ODA) used a data driven approach to choose the target restaurants more wisely and enforce the law effectively by using data analytics to identify the targets (GCN, 2013).

Approach

The first step the ODA took was to collect and combine data from the three different agencies (BIC, DEP, and Office of Policy and Strategic Planning) which are in charge of regulation and compliance of business across the city. Using their analytical tools with other data technologies, the ODA analysed of cross-agency data and mapped this onto other geo-located data such as the location of drains and blockages. Then ODA were able to give inspectors a list of statistically likely suspects so that their inspections were targeted (GCN, 2013). Datasets and source.

• Sewer data from the DEP. • Restaurant license data from the Department of Health and Mental Hygiene. • Data on local restaurants with grease hauling service. Data is obtained from BIC,

which collects data on whether restaurants have a service to haul away their grease.

Tools and technology used.

• SAS Analytics was used for data analysis and mining. • Oracle database was used as the foundation for the data warehouse. • Palantir’s data fusion software, which was used to explore and connect the data,

technologies, and environments from many sources.

41

Result

As a result of the service created by the ODA, the inspectors recorded a 95 percent success rate in tracking down the dumpers which were dumping there grease waste illegally. With the use of public data, the problem of the Grease-Clogged Sewers was solved.

Value Created

• The city experienced reduction in cost of allocating valuable enforcement resources to problematic areas.

• The integrity of the market for licensed haulers and their customers is protected. Especially in an environment where waste has increasing value, particularly yellow grease, they can operate in a market where they can deliver safe and reliable disposal services to City businesses (Gilbride et al., 2012).

• The risk of the city being exposed to flood is eliminated as a result of clogged sewers. 3.3.2 Case Study: Exploration of Linked Open Data through a Mobile Augmented Reality

Application (Vert and Vasiu, 2015). Brief Background This case study looks into challenges faced by tourists when exploring new environments and unfamiliar points of attractions. These Tourists expect to be able to use dynamic, adaptive and personalized technology applications to move around an unknown place. Using Mobile augmented reality technology and open data, a mobile augmented reality touristic application was developed to help tourists get a sense of the unfamiliar surroundings based on popular linked open data content sources that are integrated for this purpose (Vert and Vasiu, 2015). Approach Vert and Vasiu (2015) built an AR mobile application that works in a browser of a mobile device and queries the triple store from an API and shows the information to the user in an overlaid AR experience. Figure 3.1 shows the steps used to build the AR application which uses data from multiple sources.

Figure 3.1. Exchange of information request in the application. (Adapted from: Vert and

Vasiu, 2015)

42

The first step was identifying the most relevant data sets to be used for the application. Data extracted from DBpedia, LinkedGeoData and Romanian Government Open Data portal was used. Selection was based on the quantity and quality of the data, data sets containing information regarding places with geographical tagging, labels and descriptions, mapping links and specific categories were used. Then the next step, Linked Data Integration Framework (LDIF) was used to collect, map and integrate the chosen data sets. From DBpedia, information relevant to a specific city was collected using SPARQL import module from LDIF. Similarly from LinkedGeoData, data was loaded in LDIF using Triple/Quad Dump import module because queries cannot be limited to a geographic area. Data obtained from Romanian Government Open Data portal was in CSV format, so it had to be cleaned and converted to RDF format using Open Refine tool (Sesame RDF Store) and then loaded in LDIF using Triple/Quad Dump import module. The last step involved, was to build an API for extracting data from the store and deliver it to the mobile augmented reality browser application built on top of awe.js library, which builds an augmented reality layer on top of the three.js library, which is a 3D library working in the browser (Vert and Vasiu, 2015).

Given the detected geolocation of the user, the application displays a set of Points of Interest (POIs) in the immediate vicinity of the user. Datasets and sources.

• Data about a specific location or POIs visited by tourist, e.g. Timisoara in Romania, extracted from DBpedia.org.

• Data containing geographical mapping points of the POIs obtained from LinkedGeoData.org.

• Datasets on POI museums collected from Romanian Government open data portal (data.gov.ro) in CSV format.

Tools and technologies used

• LDIF, a powerful tool for linked data integration. • Sesame OpenRDF, an open source framework for querying and analysing RDF data. • awe.js, an augmented reality web library that uses technologies such as HTML5, CSS

and JavaScript. It also supports access to latest capabilities of mobile devices, such as geolocation, photo/video camera, WebRTC or WebGL, to show 2D or 3D augmented reality objects on top of the surroundings.

• three.js, a java script library that allows developers to design 3D experiences in a web browser.

Result

The result produced in this case study is the mobile augmented reality browser which tourists can use to identify POIs around them as depicted in figure 3.2 below.

43

Figure 3.2. Screen shot of the browser application. (Vert and Vasiu, 2015)

On the left screen, as the tourist opens the web application in his mobile browser, real POIs are overlaid on the screen so it is easy for the tourist to understand what is around him immediately. The middle screen shows the selected POI by the tourist with a short information showing the name of the POI and distance to the POI with a link for further information. On the right screen, as the tourist selects the link, a window opens showing complete information on the POI covering name, description, image, source and link to the webpage. Value created

• Provides both citizens and tourists with easy and free access to information about the city’s cultural heritage. Tourists can create self-guided walking tours based on the information presented on selected POI.

• Opportunity for building personalized services on top of the information. As a tourist, he or she has the ability to customize his trip according to this preferences given various kinds of information such as maps, proximity between points of interest, accommodation and restaurants.

44

3.3.3 Case Study: Check That Bike! – A service that tackles bike theft crimes. (ODI, 2014; Moss, 2014; Parkes & Philips, 2014)

Brief Background

Bike theft remains a persistent problem in the UK. There are over 376,000 bikes reported to be stolen every year, this figure covering reported to the police and unreported according to ODI (2014). Bike theft is worth £100m and it occurs on a huge scale because it is easy to profit from it. To address this challenge of personal crime, “Check That Bike”, a search engine service, which uses open data and crowdsourcing to help prevent cyclists from buying stolen second-hand bikes. Additionally, the service aims to break the market for bikes which are reported stolen by making them far more tough to sell and easy for police forces to tackle bicycle theft (Nesta, 2014). Approach Since bikes manufactured in the UK have a unique serial(frame) number or asset tag printed on them, and using Check That Bike’s search engine, users can search using bikes frame number against reported stolen bikes database so that cyclists can make better decisions about buying second-hand bikes at the time they’re actually buying them. The search engine accesses a number of databases containing stolen bikes such as, 1) Ad hoc police data; 2) National Registers including Stolen Bikes UK and Bikesecure.co.uk; 3) Local Registers including Stolen Bristol Bikes; 4) Manufacturers that run mini stolen bike registers for warranty purposes; 5) Insurers whose databases help identify stolen bikes when a claim is raised; and 6) The Bing search engine (Parkes & Philips, 2014). Datasets and source.

• Databases containing stolen bike details such as police data, national and local registers, manufacturers, insurers and the Bing search engine to identify crime hot spots.

• Freedom of information requests and an ongoing quest to encourage police forces to open data on bike unique frame numbers that have been registered as stolen.

• A free-to-use API allowing other developers to harness the power of Check That Bike! In their own projects.

Tools and technologies

• A custom developed web-based search engine that integrates with database sources containing stolen bikes.

45

Result

The service helped identified about 2,257 stolen bikes in 2014 with a total value of about £760,609. This number could improve with more release of open data on stolen bikes which will be incorporated into the service.

Value created

• The service helps reduce crime, and the number of victims of crime. • The service has tackled one of the main reasons that people don’t take up cycling –

the fear of getting their bike stolen – potentially leading to more people taking up cycling and experiencing its health benefits.

3.3.4 Case Study: Community Energy Manager (CEM) – A service for community energy groups. (Epstein, 2014; Wood & Corbin, 2014)

Brief Background

The UK faces big challenges in the area of energy with social, environmental and economic consequences at individual, city and national levels. The problem in the energy sector is described as the energy trilemma: long term security of supply; the rising cost of energy; and tackling carbon emissions. Discussions with over 50 stakeholders suggested that community energy provides a huge opportunity to tackle some of the problems that being experienced in the energy sector (Epstein, 2014). Wood and Corbin (2014) developed the service, CEM, which seeks to addresses this problem. Specifically, by addressing the barriers that individual householders face when taking advantage of the energy saving interventions available through the Energy Company Obligation (ECO) and the Green Deal (GD), as well as organise bulk buying initiatives.

Approach.

CEM aggregates these savings and initiatives and enable individuals and communities to select those interventions that are best suited to their situation. They developed CEM as an online tool, a service that provides open data on energy consumption information at ward level or lower, using annual consumption data on energy combined with datasets from the Office for National Statistics (ONS) and local councils. CEM uses this information to advise local community energy groups on the areas in most need of support, to cut their emissions and energy costs.

Then the platform allows community groups to record the data and intelligence they’ve gained from discussions with householders. Using this new source of data, CEM provides an aggregate picture of energy needs and utilization in a given area. The CEM development team then use this information to negotiate retrofit and renewable energy projects with energy companies, retail outlets and renewable energy installers.

46

Datasets and sources.

• UK census data from the ONS database. • Annual energy consumption data from the Department for Energy and Climate

Change data base. • Data from the local councils. • Ordnance Survey open data. Open data sources enable community energy groups to

understand the feasibility of different projects in their area with things like housing stock and energy usage in mind.

Tools and technologies

• An online data explorer that presents open data on data consumption. • Visualisation of energy needs and consumptions of a chosen area.

Value created

• The community benefits through reduction in energy costs and savings. • The platform stimulates more community energy projects, more energy efficient

homes and reduced greenhouse gas emissions to the environment. More examples of products using open data in the sector of transport and mobility, education and sports, security and safety and health from different cities are highlighted in appendix A2.

3.4 Summary

This chapter has defined what social value is, the importance of why it should be created. The chapter provides examples of outcomes achieved from creating social and how the community benefits from different value being created. Four case studies were explained by highlighting motivation of the case study, approach employed, data and technology used and the value created in each of the cases. All the cases studied have used open data to create services that generate social value. Using Analytics and Augmented Reality technologies in the first and second cases have yielded significant and impacting results which further proves that using open data can lead to generating social value. Developing web applications that allow open data to be searched and presented informatively as done in the third and fourth cases studied have led to tackling crime and better decision making. The framework developed in the next chapter is based on insights learned from these successes and activities that lead to impactful value creation.

47

Chapter 4 Framework This chapter presents the framework for social value creation. The elements of the framework describes the key variables to be considered in order to create products or services that bring about social value.

4.1 Developing a Framework for Creating Social Value

A framework identifies key factors and shows how the factors are structured and linked in relation to an issue. Miles and Huberman (1994) defined a framework as a visual or written presentation, one that “explains, either graphically or in narrative form, the main things to be studied - the key factors, concepts, or variables—and the presumed relationships among them” (p. 18). Frameworks offer a tool to help a researcher answer questions. A framework may help a researcher find a way into a complex situation by breaking it down into components, providing a structure for the key variables and mapping out how these variables may be connected (Nunan, 2015).

The framework presented here is the outcome of the investigation into cases where open data has been used to create social value such as the ones studied in section 3. The framework is designed to offer help to individuals or researchers, as an approach to consider while trying to create value from using open data, more specifically on creating social value. The framework has four functional phases each of which includes a list of activities that are to be considered in order to reach the desired output or goal.

Figure 4.1: A framework for social value creation.

OPPORTUNITY• Identify social & environmental challenges

• Ask crunchy questions• Define strategic social priorities

OPEN DATA• Select open datasets with social\environmental context

• Asses the data landscape e.g. source, quality, accuracy etc.

• Combine multiple data sources for richness

TECHNOLIGICAL PROCESS • Analytics• Descriptive • Exploratory• Predictive

• Visualization• Develop applications

OUTPUT• Outcome from previous activities.

• Outcome leading social good. e.g.;• Accountability.• Fighting Crime.• Social Inclusion.

Feedback loop

48

4.2 Explaining the Framework

Opportunity

Identifying opportunities begins with the researcher investigating social challenges being faced in the society and environment, asking crunchy questions which will lead to opportunities that create suggestions for addressing identified challenge. Defining strategic social priorities based on ‘crunchy questions’ that concern the well-being of citizens, environment and society at large. For example,

(1) How can we increase community involvement with the criminal justice system? (2) How might we assist the provision of public services or how we can make the current

service delivery more relevant? (3) How does the budget expenditures of a department compare with actual spending? (4) How might we empower citizens to enhance their neighbourhood? (5) How can we use open data to help people eat more healthily, eat more sustainably

and/or have a more secure food chain?

Similar to challenging problems identified in the brief background of the cases studied, e.g. clogging of sewers, difficulty in exploring unfamiliar point of attraction, or theft of bikes, the crunchy questions could be raised to address these problems. Having successfully arrived at a definite challenge question, like the ones highlighted above will enable the researcher to have a glimpse of the kind of value that is to be created which will further link to the specific open data sets required for exploration or manipulation.

Open Data

The open data element referred to in this framework is the type of data that satisfies the criteria for openness as defined in section 2.2 and based on the crunchy question. It is important to select data sets generated by government, public institution or social enterprises as a result of delivering services to their citizens and customers, the reason for this is to ensure that type of data collected has social or environment context to it, thereby leading to a social benefit. Access to various datasets is obtainable from government and local council portals such as data.gov.uk and open.manchester.gov.uk. In cases when needed public data is not available on the portal, through Freedom of Information Act, researchers can request for access to the relevant data essential to addressing the problem. However, the researcher should assess various data landscapes including determining data sources, format, quality and accuracy of open data to be used. Knowledge of these data landscapes and their variations in format, openness or category as captured in section 2.4, how to overcome data barriers, like the ones mentioned in section 2.9, would be beneficial to the researcher. For instance, in the third case studied, Vert and Vasiu (2015) obtained museums data from the government portal (data.gov.ro) in CSV format, the data had to be cleaned and converted to RDF format because the LDIF only processes RDF data. In some cases, data collected from a single source limits the richness of data which makes it insufficient to carry out analysis, therefore, combining data sets from multiple sources could add more value and uncover new insights or pattern to the data assets to be used.

49

Technological Processes

In the framework, the technological processes are the tools and techniques to be employed in order to address the challenging problem, using analytics or visualisation for instance, as explained in section 2.12. This is how value will be derived from the datasets. Open data could be used for different purposes, depending on the skill, position and intention of the researcher accessing the data. For instance, analytical methods can be employed for discovering deep insights in to the data, the data can be visualized in an interactive way that could lead to reduction in time for decision making or developing an application that delivers new products and services driven by open data. Developed applications driven by open data can be as simple as presenting data informatively to allow one to make better decisions quickly and as insightful as using predictive/prescriptive analytics to recommend the best course of action in a situation or even try to quantify the consequence of decisions that can lead to more effectiveness and cost efficiency.

Output

Output element in the framework is the resulting outcome of the activity(s) carried out after implementing the technological process (s). The effect of the produced output will tackle the identified challenge thereby creating social impact needed in the community. The output is assessed with an eye towards how it has helped create a social values such as ones that lead to accountability, efficiency on how services are delivered, innovation in public service, environmental sustainability and social inclusion. The SROI framework can then be used to measure the impact of the social outcome. Social outcomes can be similar to the ones highlighted in section 3.2. The value creation process uses feedback loop mechanism to allow the researcher improve the activity in each phase of the framework based on feedback received from stakeholders in the ecosystem and value chain. New insights garnered are then used to improve the output produced along the value creation chain.

4.3 Summary

This chapter has presented a framework for creating social value. Explanation has been given on the elements and processes to be followed in order to identify the challenging problem and how it links to the type of data sets to collect or request for, then applying appropriate technology to arrive at a solution. The impact of the solution leads to outcome that create social value. The next chapter presents how the framework is evaluated using an application that creates social value to young people.

50

Chapter 5 Evaluation This chapter uses the framework developed in the previous chapter to explain the development of a product that uses open data to create value.

5.1 Evaluating the Framework

The framework presented in the previous chapter will be evaluated by using it to illustrate the development of a tool that creates social value by following the processes explained in chapter 3.

Skills Route (MIME Consulting, 2014)

Skills route offers a personalised service to help young people see how well they could perform on courses they might take at local schools or colleges, and weigh up their choices into higher education and future career. It is an online tool that uses open data on the post-16 performance of social institutions like schools and colleges in different subjects to help young people and their families identify providers offering their chosen subjects and their personalized expected grades at each, by projecting the performance on A-level or vocational courses. The tool also shows the list of potential universities and career progression routes open to them, with average salaries they can expect. Opportunity

Identifying an opportunity begins with recognising the problems being faced in the education sector. A lot young people and their families are not aware of the full range of educational options which are available after completing their GCSEs, so they end up making wrong decisions due to insufficient or misguided information. Recent Statistics show that over 5170 young people dropout permanently from school and over 304,370 of them drop out temporarily (DfE, 2013), the numbers of dropout extends well beyond the figures, if taken into account young people who “haven’t withdrawn schools but are not fully taking part either because they have given up any trying or because they resist to do so” (Lumby, 2013). Over 40,000 Year 12 students drop out of school each year, often because they start a course that isn’t appropriate for them (Preston, 2014).

These facts instigates questions that seek to address this challenge. Specifically, how can open data be used to solve drop out challenges in the society?, Hence the final challenge question is how to use open data to help parents make better and informed decisions about their children's education in the following key areas (Nesta, 2014b):

a. Expressing a preference for a school. b. Choosing a subject or other learning priorities.

These questions help narrow down the problem thereby suggesting the type of data sets to be requested/used and the potential sources where the data can be acquired.

51

Open Data

Data required for manipulation has to contain information about subjects, schools, colleges, other educational institutions, student academic performance, occupation and salaries. Obviously it will be difficult to obtain all the datasets from one single source because the institutions producing these data are functionally different and diverse. The Department for Education (DfE) collects pupil level data on performance, and other relevant information regarding educational establishments which it has made open satisfying the criteria for openness and available is multiple formats (CSV + XLS Excel) with open license. Data containing jobs and salary information can be accessed from the ONS. The data from DfE is a complex dataset showing not only the overall progress of students, but also the impact of schools and teachers in the educational journey, therefore it requires specific data analytical expertise for people to use it in meaningful ways, and this could be a barrier for users without sufficient data knowledge.

All the datasets from the different sources are combined together, linking subjects, courses, jobs and salaries provide insights into the performance of pupils, subjects and schools.

Technological process

The activity in this phase is to develop an online tool that benchmarks the student’s GCSE subject and selected postcode and then provide personalised expected grade at each subject in the local post-16 providers offering courses in the subjects. The grades they are likely to achieve is based on the performance of former students with similar prior grades at those institutions. The tool then shows the student higher education institutions open to them and employment prospects in the future with average salary expectations for each prospect. The figure below shows a workflow of the online tool.

Figure 5.1: Workflow for the online tool (MIME Consulting, 2014)

52

Output

Skills Route establishes a direct link between the quality of services an educational institution provides and a student’s likely post-16 qualification results and future earning expectation. It also offers a personalised recommendation on the higher education and employment options that enables the young person to achieve a good result, hence adding value to the person’s academic performance.

5.2 Value Created for Stakeholders

1. Young People and their families: They can now make the effective choices that are best suited to their educational needs and that are linked to their future aspirations. Also, they can make decisions base factual data instead of word-of-mouth or ill-advices from schools.

2. Schools: Schools can also use this tool to offer careers advice to young people. This will also encourage accountability amongst educational providers and has the potential to drive greater competition in education which could lead to better services in the future.

5.3 Barriers to Developing Skill Route and How to Overcome.

1. Privacy of pupil level data and schools admission data is serious issue. Overcoming this involves anonymising the data to an extent that it protects the identity element in the records.

2. Data set from DfE is largely complicated because it contains many information such

as overall progress of students, impact of schools and teachers in an educational journey and therefore requires skilled expertise to make productive use of it. However, acquiring data analysis training can help overcome the skill gap that can block making productive use of it.

5.4 Summary

This chapter has presented an evaluation of the framework developed in the previous chapter, using the Skill Route as an example, a service that helps young people find out how well they can perform on courses at schools and also provide advice on future aspirations. A detailed illustration has been presented using the phases in the framework to explain how the opportunity of the service is identified, what open data drives it, the landscape of the data, the kind of technology used to drive the open data and the impact it produces. Stakeholders and value created for them has been explained, barriers to creating the value and how to overcome them was also highlighted.

53

The following are checklists of some of the best practices in a standard software development process which could be helpful for developing the software application (Perks, 2006).

1. Development process: Choosing an agile based software development method (e.g. Scrum, Kanban) for developing the application would be appropriate mainly because agile processes enable users to be involved early for capturing requirements and delivering a minimum viable product to user for test earlier rather than later in the development process.

2. Design: Choosing good design is key to the usability success of the application. Two basic principles to note are ‘keeping it simple’ and ‘Protecting information’. Good practice is performing object oriented analysis and design using Unified Modelling language.

3. Code Construction: Best practice for constructing code includes daily build and smoke test. Using continuous integration which integrates unit tests and self-testing code.

4. Testing: Testing needs to be planned carefully and done proactively. Planning test cases before coding starts, and developing test cases while the application is being designed and coded to ensure a robust test pattern is planned.

5. Deployment: Deployment is the final stage of releasing the application to users. However things can still go wrong. Planning for deployment will ensure that risks of failure are minimised and mitigated.

54

Chapter 6 Conclusion, Limitations and Future Work

This chapter looks into the main aim of this dissertation by providing concluding remarks on what has been achieved so far, limitations along the path of conducting this research and future research work that would be interesting to embark further upon.

6.1 Conclusion

Making public data open is still an ongoing process. The UK is leading in the global open data initiative, while most countries are beginning to release the potential of open data. The open data movement is still evolving, with more potential yet to be unlocked. In this dissertation, the author has captured the relevant literature with regards to open data. Open data has demonstrated the potential to create value that which can improve governance, economies, communities, and social well-being of citizens. The value creation chain largely depends on a sustainable open data ecosystem which ensures the supply and demand of open data is efficient. All stakeholders play an active role in the ecosystem, having regular feedback on the services offered will drive improvement and ensure that the open data that is in supply is accurate, comprehensive, and of good quality. Effective application of data technologies with the support of the government has demonstrated the potential to drive growth in many domains through creation of innovative products and services that generate value. Creating value has a number of potential stumbling blocks that needs to be addressed, ways to overcome this blocks have been highlighted.

The main contribution of this dissertation is the framework that captures the phases involved towards creating products and services that can deliver outcomes that has significant social value. To develop the framework, the author investigated evidence from literature and case studies where open data was used to deliver value. The framework serves as a helping tool to researchers and organisations that wish to create products and services that enable value creation using open data. The framework can save researchers considerable amount of time while wondering what steps, pathways to take to kick start the value creation process.

6.2 Limitations

A lot of time was spent understanding current social challenges being faced in the community. Partly due to the author’s unfamiliarity with the challenges facing the community. A close collaboration with local organisations providing social support to the community would have provided the author with a real comprehensive insight into these problems that open data can address. Hence, the dissertation is lacking the existence of a tangible product or service that creates an actual social value. This is considered as future work.

55

6.3 Future Work

Further work would include using the framework to develop an innovative application driven by a combination of open data and private data that delivers commercial value. Another interesting work would be to investigate the suitability of the framework towards adapting it for open data projects in third world developing countries.

56

References

Adams N. M. (2010). ”Perspectives on data mining”. International Journal of Market Research Vol. 52, No. 1. [Online] Available at: http://www.warc.com/Content/ContentViewer.aspx?MasterContentRef=98138b89-c7a5-4fe7-95c6-9ab19bcd6145&q=AID%3a91205&CID=A91205&PUB=JMRS (Accessed 28 August 2015) Beresford M. (2015). ‘Demystifying Data: The data revolution and what it means for local government’, New Local Government Network. [Online]. Available at: http://www.nlgn.org.uk/public/wp-content/uploads/DEMYSTIFYING-DATA2.pdf (Accessed: 28 April 2015). Berners-Lee T. (2009). ‘Five star Open Data’. [Online]. Available at: http://5stardata.info/ (Accessed: 03 May, 2015).

Blair M. (2012). Argon and the AROS (AR in Open Spaces) project for ISEA2012 Blair's Research Blog. [Online] Available at: http://blairmacintyre.me/2012/09/21/argon-and-the-aros-ar-in-open-spaces-project-for-isea2012/ (Accessed 20 August, 2015). Bowman, C., & Ambrosini, V. (2000). Value Creation Versus Value Capture: Towards a Coherent Definition of Value in Strategy. British Journal of Management, 11(1), 1–15. doi:10.1111/1467-8551.00147 Bright J, Neuschmid J. & Atz U. (2015). “Social and Open Data Visualisation Methods and Data Sources Report”. UrbanData2Decide: Integrated Data Visualisation and Decision Making. [Online] Available at: http://www.urbandata2decide.eu/wp-content/uploads/deliverables/UrbanData2Decide-D2.1-Data-Sources-and-Visualisation-Methods-Report.pdf (Accessed 20 August, 2015). Butler (2013). “Spotfire, Tableau and QlikView – in a Nutshell”. [Online] Available at: http://www.itcentralstation.com/product_reviews/tibco-spotfire-review-28541-by-martin-butler . Accessed 20 August, 2015. Cabinet Office (2009). “A Guide to Social Return on Investment”.

Cabinet Office (2012).’Open Data White Paper: Unleashing the Potential’ [Online]. Available at: http://data.gov.uk/sites/default/files/Open_data_White_Paper.pdf (Accessed: 13 March, 2015).

Campbell L.M. and MacNeill S. (2010). ‘The Semantic Web, Linked and Open Data’, JISC Certis, U.K.

57

Capgemini Consulting (2013). ‘The Open Data Economy Unlocking Economic Value by Opening Government and Public Data’ [Online]. Available at: http://www.uk.capgemini.com/ (Accessed: 10 March 2015). Certo, S. T., & Miller, T. (2008). Social entrepreneurship: Key issues and concepts. Business Horizons, 51(4), 267–271. doi:10.1016/j.bushor.2008.02.009 Chen M., Mao S., Zhang Y. & Leung V. C.M. (2014). “Big Data Analysis”. Big Data: Related Technologies, Challenges and Future Prospects”, Springer Briefs in Computer Science, Pages 51-58, and DOI: 10.1007/978-3-319-06245-7 . [Online]. Available at: http://link.springer.com/book/10.1007/978-3-319-06245-7 Accessed 20 August, 2015.

Chernoff M. (2010). ‘What “open data” means and what it doesn’t’. [Online] Available at: http://opensource.com/government/10/12/what-%22open-data%22-means-%E2%80%93-and-what-it-doesn%E2%80%99t (Accessed: 13 March, 2015). CopyLeft.org (2014) ‘What is Copyleft?’[Online]. Available at: https://copyleft.org/ Accessed (05 May 2015).

Cole, R.J. (2012), ‘Some observations on the Practice of ‘Open Data’ as Opposed to its Promise’, The Journal of Community Informatics, Vol. 8(2), pp. 1-6 [online] Available at: http://www.mendeley.com/research/some-observations-practice-open-dataopposed-promise/ (Accessed: 04 May 2015)

Croydon Council (2015). “Inspiring and Creating Social Value in Croydon: A Social Value Toolkit for Commissioners“. [Online] Available at: https://www.croydon.gov.uk/sites/default/files/articles/downloads/socialvalue.pdf (Accessed 13 August, 2015) Daniel A. K., Jorn K., Florian. M., Thorsten M. & Franz W. (2010). “Mastering the Information Age: Solving Problems with Visual Analytics”. Eurographics Association. Davenport T. H. (2013). “What do we talk about when we talk about analytics?” Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data. Pearson Education Inc. Davies T. (2014). ‘Open Data in Developing Countries: Emerging Insights from Phase 1’. ODDC, WWW Foundation. [Online]. Available at: http://www.opendataresearch.org/ (Accessed: 04 May 2015)

Deloitte (2012). “Driving growth, ingenuity and innovation: Open data“. [Online] Available at: http://www2.deloitte.com/uk/en/pages/deloitte-analytics/articles/open-data-driving-growth-ingenuity-and-innovation.html (Accessed: 04 August 2015)

58

DEMOS (2010), Wood C. & Leighton D. “Measuring Social Value: The Gap between Policy and Practice”. [Online] Available at: http://www.demos.co.uk/files/Measuring_social_value_-_web.pdf Department for Education (2013). “Permanent and Fixed Period Exclusions from Schools in England: 2011 to 2012 Academic Year”, London: DfE

Dumond, E. (2000). Value management: an underlying framework. International Journal of Operations & Production Management, 20(9/10), 1062-1077-1077. Elena et. al. (2014). “Emerging Impacts in Open Data in the Judiciary Branches in Argentina, Chile and Uruguay“. CIPPEEC. [Online]. Available at: http://www.opendataresearch.org/ (Accessed: 05 May 2015).

Ericson T. & Spraragen S. (2012). ‘Visualisation and Open Data in Helsinki’. [Online] Available at: http://citizenibm.com/2012/01/visualization-and-open-data-in-helsinki.html (Accessed: 05 May 2015).

Epstein D. (2014). “Open Data Innovation and Creation”. [Online] Available at: http://usefulsimpleprojects.co.uk/open-data-innovation-creation/ (Accessed 20 August 2015). Feuer A. (2013). “The Mayor’s Geek Squad”. The New York Times. [Online] Available at: http://www.nytimes.com/2013/03/24/nyregion/mayor-bloombergs-geek-squad.html?pagewanted=all&_r=0 (Accessed 20 August 2015).

GCN (2013). “How analytics is making NYC’s streets and buildings safer”. [Online] Available at: http://gcn.com/Articles/2013/10/04/GCN-Award-NYC-DataBridge.aspx?Page=2 (Accessed 20 August 2015). Geiger, C. P., & von Lucke, J. (2011). ‘Open Government Data’. In Conference for E-Democracy and Open Government, (p. 183)

Gilbride C., Chambliss C. & Kairam J. (2012). “New York City Business Integrity Commission, Department Of Environmental Protection, And Mayor’s Office of Policy and Strategic Planning Launch Comprehensive Strategy to Help Businesses Comply with Grease Disposal Regulations”. New York City Government. [Online] Available at: http://www.nyc.gov/html/bic/downloads/pdf/pr/nyc_bic_dep_mayoroff_policy_10_18_12.pdf (Accessed 20 August 2015).

Goopta C. (2014). Six of the Best Open Source Data Mining Tools”. [Online] Available at: http://thenewstack.io/six-of-the-best-open-source-data-mining-tools/ . Accessed 20 August, 2015.

Graves H. & Hendler J., (2013). “Visualisation tools for Open Government Data”. Proceedings of the 14th Annual International Conference on Digital Government Research, pages 136 – 145, DOI: 10.1145/2479724.2479746. [Online] Available at:

59

http://dl.acm.org/citation.cfm?id=2479746&CFID=540166928&CFTOKEN=23090903 . Accessed 20 August, 2015.

Gurin, J. (2014). ‘Open Data Now’, McGraw Hill. [Online] Available at: http://www.mcgrawhillprofessionalbusinessblog.com/2014/02/18/an-infographic-big-data-is-big-open-data-is-revolutionary/ (Accessed: 05 May 2015).

Omidyar Network (2014). ‘Open for Business: How Open Data Can Help Achieve the G20 Growth Target’. [Online] Available at: https://www.omidyar.com/sites/default/files/file_archive/insights/ON%20Report_061114_FNL.pdf (Accessed: 05 May 2015).

Heimstädt M. (2014). ‘The British Open Data Ecosystem’. [Online] Available at: www.heimstaedt.com/british-open-data-ecosystem/ (Accessed: 06 May 2015). Helbig N., Cresswell A.M., Burke G.B. & Luna-reyes L. (2012). ‘The Dynamics of Opening Government Data’. New York, Centre for Technology in Government. [Online] Available at: http://www.ctg.albany.edu/publications/reports/opendata/opendata.pdf (Accessed: 06 May 2015).

Immonen A., Palviainen M. & Ovaska E. (2014). ‘Requirements of an Open Data Based Business Ecosystem’. IEEE Xplore, DOI: 10.1109/ACCESS.2014.2302872. [Online] Available at: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6730652 (Accessed: 06 May 2015).

Janin, A. L., D. W. Mizell, et al. (1993). “Calibration of head-mounted displays for augmented reality applications”. Proceedings IEEE Virtual Reality Annual International Symposium ‘93. Seattle, WA: 246-255 Janssen M., Charalabidis Y. & Zuiderwijk A. (2012). ‘Benefits, Adoption Barriers and Myths of Open Data and Open Government’. Information Systems Management, 29:258–268, 2012, DOI: 10.1080/10580530.2012.716740. [Online] Available at: http://www.tandfonline.com/doi/abs/10.1080/10580530.2012.716740 (Accessed: 06 May 2015). KDNuggets (2014). “What analytics, data mining, big data software you used in the past 12 months for a real project?” [Online] Available at: http://www.kdnuggets.com/polls/2014/analytics-data-mining-data-science-software-used.html . Accessed 20 August, 2015. Kieron O’Hara (2015). ‘Government open data and transparency: Oakeshott, civil association and the general will’, Global Discourse: An Interdisciplinary Journal of Current Affairs and Applied Contemporary Thought, 5:1, 135-152. Lamb P. (2011). “ARToolKit”. [Online] Available at: http://artoolkit.org/ (Accessed 20 August, 2015).

60

Lepak, D. P., Smith, K. G., & Taylor, S. (2007). Value creation and value capture: a multilevel perspective. Academy of Management Review, (Vol. 32, No. 1), 180–194. Lumby . J. (2013). Education Isn’t Working for Us – Listening to Disengaged Young People, BERA Insights, (5):1-4.

Major R. (2014). “Descriptive, Predictive, and Prescriptive Analytics Explained”. [Online] Available at: https://halobi.com/2014/10/descriptive-predictive-and-prescriptive-analytics-explained/ (Accessed 28 August 2015) Mekni M. & Lemieux A. (2014). “Augmented Reality: Applications, Challenges and Future Trends”. Proceedings of the 13th International Conference on Applied Computer and Applied Computational Science ’14. Pp 205 – 214. [Online] Available at: http://www.wseas.us/e-library/conferences/2014/Malaysia/ACACOS/ACACOS-29.pdf (Accessed: 19 August, 2015) McLean, M. (2006). Social entrepreneurship: A critical review of the concept. Journal of World Business, 41(1), 56-65-65. doi:10.1016/j.jwb.2005.10.007 Mckinsey Global Institute, (2013). ‘Open data: Unlocking innovation and performance with liquid information’ [Online]. Available at: http://www.mckinsey.com/ (Accessed: 03 March 2015). Michael C., Diana F. & Steve V. K. (2013). “Beyond Transparency: Generating Economic Value through open data”. Code for America Press. [Online] Available at: http://beyondtransparency.org/chapters/part-3/generating-economic-value-through-open-data/ (Accessed 19 August 2015).

Miles, M. B., & Huberman, M. A. (1994): “Qualitative Data Analysis: An Expanded Sourcebook” (2nd edition). Beverley Hills, Sage. Milgram K. & Kishino K. (1994). “A Taxonomy of Mixed Reality Visual Displays,” IEICE Trans. Inf. Syst., pp. 1321–1329. MIME Consulting (2014). ‘About Skill Route’. [Online]. Available at: http://www.skillsroute.com/#about (Accessed: 10 September 2015). Moss, J. (2014). “Check That Bike!” [Online] Available at: https://checkthatbike.co.uk / (Accessed 19 August 2015). Newcombe T. (2014). “How Government Can Unlock Economic Benefits from Open Data”. [Online] Available at: http://www.govtech.com/data/How-Government-Can-Unlock-Economic-Benefits-from-Open-Data-Part-I.html (Accessed 24 August, 2015).

61

Nesta, (2013). ’Crime and Justice Open Data Challenge.’[Online]. Available at: http://www.nesta.org.uk/closed-crime-and-justice-open-data-challenge (Accessed: 12 March 2015). Nesta, (2013). ‘Innovation in the UK’ [Online]. Available at: http://www.nesta.org.uk/ . (Accessed: 14 March 2015). Nesta (2014b). ‘Open Data Challenge Series’. [Online]. Available at: http://www.nesta.org.uk/open-data-challenge-series (Accessed: 10 September 2015). Parkers, E. & Philips, B. (2015). ‘Open Data Challenge Series Handbook’ [Online]. Available at: http://www.nesta.org.uk/sites/default/files/nesta-odi-odcs-handbook.pdf (Accessed: 14 August 2015). Preston, S. (2014). “Skills Route gives students data-driven careers advice”. Open Data Institute. [Online]. Available at: http://theodi.org/news/skills-route-allows-students-a-glimpse-into-the-future-thanks-to-open-data-innovation (Accessed: 10 September 2015). Nixon L., Grubert J., Reitmayr G. & Scicluna J. (2012). “SmartReality: Integrating the Web into Augmented Reality”. Proceedings of the I-SEMANTICS 2012 Posters & Demonstrations Track, pp. 48-54. [Online] Available at: http://ceur-ws.org/Vol-932/paper10.pdf . Accessed 20 August, 2015. Nunan F. (2015). “Understanding Poverty and the Environment: Analytical Frameworks and Approaches”. [Online] Available at: https://books.google.co.uk (Accessed 20 August, 2015).

Open Data Dialogue (2012), ‘Open Data Dialogue Final Report’. [Online] Available at: http://www.rcuk.ac.uk/documents/documents/TNSBMRBRCUKOpendatareport.pdf Open Data Institute, (2013).’Open Data Institute’ [Online]. Available at: http://opendatainstitute.org/ (Accessed: 12 March 2015).

Open Data Institute, (2014).’ How open data is putting the brakes on £100m worth of bike thefts’ [Online]. Available at: https://theodi.org/news/check-that-bike-tackles-over-100m-worth-of-bike-theft-in-england-and-wales-each-year-with-open-data (Accessed: 12 August 2015).

Open Definition (no date). ‘The Open Definition’ [Online]. Available at: opendefinition.org/ (Accessed: 14 March, 2015). Open Government Working Group (2007). ‘8 Principles of Open Government Data’. [Online] Available at: http://www.opengovdata.org/home/8principles (Accessed 05 May 2015).

62

Open Government Working Group (2007). ‘What is Open Government Data?’[Online] Available at: http://opengovernmentdata.org/ (Accessed 05 May 2015). Open Knowledge Foundation (2012). ‘Open Data Handbook Documentation’. [Online]. Available at: http://opendatahandbook.org/pdf/OpenDataHandbook.pdf (Accessed: 09 March 2015). Open Knowledge, (2012). ’What is open?’ [Online]. Available at::okfn.org/opendata/ (Accessed: 15 March, 2015). Perks M. (2006).”Best practices for software development projects”. IBM Developer Works. [Online] Available at: http://www.ibm.com/developerworks/websphere/library/techarticles/0306_perks/perks2.html Accessed: 20 August 2015. Rankohi S. & Waugh L. (2013). “Review and analysis of augmented reality literature for construction industry”. Visualization in Engineering ‘13, 1:9. [Online] Available at: http://www.viejournal.com/content/pdf/2213-7459-1-9.pdf Accessed: 20 August 2015. Reynolds V., Hausenblas M., Polleres A., Hauswirth M. and Hegde V. (2010). “Exploiting Linked Open Data for Mobile Augmented Reality”. Proceedings of WC3 Workshop: Augmented Reality on the Web.

Roukounaki K. (2015). “Top 5 Tools for Augmented Reality in Mobile Apps”. [Online] Available at: http://www.developereconomics.com/top-5-tools-for-augmented-reality-in-mobile-apps/ (Accessed 20 August, 2015). Rojas, L. A., Lovelle, J. M., Tarazona, G. M., & Bermúdez, C. E. (2013). ‘Open Data as a key factor for developing expert systems: A perspective from Spain’. International Journal of Interactive Multimedia and Artificial Intelligence, 2.

Royal Society (2012). ‘Science as an open enterprise’. The Royal Society Science Policy Centre. [Online] Available at: https://royalsociety.org/~/media/Royal_Society_Content/policy/projects/sape/2012-06-20-SAOE.pdf (Accessed: 05 May 2015).

Rhind D. (2014). “What is the Value of Open Data”? The Advisory Panel on Public Sector Information. [Online] Available at: https://www.nationalarchives.gov.uk/documents/meetings/20140128-appsi-what-is-the-value-of-open-data.pdf (Accessed 24 August, 2015). Sane J. and Edelstein M. (2015). “Overcoming Barriers to Data Sharing in Public health: A Global Perspective”. Chatham House: The Royal Institute of International Affairs. [Online] Available at: https://www.chathamhouse.org/sites/files/chathamhouse/field/field_document/20150417OvercomingBarriersDataSharingPublicHealthSaneEdelstein.pdf (Accessed 20 August, 2015).

63

Santos, F. M. (2009). “A positive theory of social entrepreneurship”. INSEAD.

Sen A. K. (2001). “Development as Freedom”. Oxford: Oxford University Press.

Song, Z. & Kusiak, A. (2009). “Optimizing Product Configurations with a Data Mining Approach”. International Journal of Production Research 47(7), 1733–1751 SITA UK, n.d. “Creating social value: The role of the resource and waste management industry”. [Online] Available at: http://www.sita.co.uk/downloads/CreatingSocialValue-1210-web.pdf (Accessed 20 August, 2015). Sielhorst T, Feuerstein M, Navab N. (2008). “Advanced Medical Displays: A Literature Review of Augmented Reality”. Journal of Display Technology, volume 4, No. 4:451–467 Stott, A. (2014). “Open Data for Economic Growth”. The World Bank. [Online] Available at: http://www.worldbank.org/content/dam/Worldbank/document/Open-Data-for-Economic-Growth.pdf (Accessed 20 August, 2015). Sullivan Mort, G., Weerawardena, J., & Carnegie, K. (2003). Social entrepreneurship: towards conceptualisation. International Journal of Nonprofit and Voluntary Sector Marketing, 8(1), 76–88. doi:10.1002/nvsm.202 Tauberer J. (2012). ‘Open Government Data: The Book’. [Online] Available at: https://opengovdata.io/2012/#chapter_14 (Accessed: 07 May 2015). Thake, S., & Zadek, S. (1997). Practical people, noble causes: How to support community-based social entrepreneurs. London: New Economics Foundation. Tibco (2015). “Tibco: Spotfire”. [Online] Available at: http://spotfire.tibco.com/ . Accessed 20 August, 2015. Tsirogianni, S. & Gaskell, G. (2011). “The Role of Plurality and Context in Social Values”, Journal for the Theory of Social Behaviour, Volume 41, Issue 4, pages 441–465, December 2011. Online. Available at http://dx.doi.org/10.1111/j.1468-5914.2011.00470.x Van Schalkwyk F. (2014). ‘Notes on Open Data Ecosystems’. Open Data Research Network. [Online] Available at: http://www.opendataresearch.org/content/2014/719/notes-open-data-ecosystems (Accessed: 09 March 2015). Vert S., Dragulescu B & Vasiu R. (2014). “LOD4AR: Exploring Linked Open Data with a Mobile Augmented Reality Web Application”. Proceedings of the ISWC 2014 Posters & Demonstrations Track, Vol-1272, Page 185-188. [Online] Available at: http://ceur-ws.org/Vol-1272/paper_142.pdf (Accessed 20 August, 2015).

64

Vert S. & Vasiu R. (2015). “Integrating linked open data in mobile augmented reality applications - a case study”. TEM Journal, Volume 4(1), 35 - 43. [Online] Available at: http://www.temjournal.com/content/41/04/temjournal4104.pdf (Accessed 20 August, 2015). Viegas F., Wattenberg M., Van Ham F., Kriss J., & McKeon M. (2007).”Manyeyes: a site for visualization at internet scale. IEEE Transactions on Visualization and Computer Graphics, Vol. 13, no. 6, page 1121 - 1128.

Wood M. and Corbin M. (2014). “Community Energy Manager”. [Online] Available at: http://www.communityenergymanager.org/pages/about (Accessed: 12 August 2015). Yoskovitz B. & Croll A (2013). ‘Lean Analytics’. O’Reilly. [Online] Available at: http://www.slideshare.net/Leananalytics (Accessed: 03 May 2015). Yu H. & Robinson D.G. (2012). ‘The New Ambiguity of “Open Government”’, UCLA Law Review Discourse 178: 178-208. Zicari R. V., (2012). ‘Big Data for Good. A distinguished panel of experts discuss how Big Data can be used to create Social Capital’ [Online]. Available at: http://www.odbms.org/blog/2012/06/big-data-for-good/ (Accessed: 09 March, 2015).

Zuiderwik A. et al. (2014).’Special Issue on Innovation through Open Data - A Review of the State-of-the-Art and an Emerging Research Agenda: Guest Editors’ Introduction’, Journal of Theoretical and Applied Electronic Commerce Research [Online]. Available at: www.jtaer.com (Accessed: 09 March 2015).

65

Appendix A1: Snapshot of Open Data Index for Different Countries

Figure A1.1: A Snapshot of Open Data Index for different countries (Bright et al., 2015)

Figure A1.1 demonstrates a snapshot of the countries with top scores in Open Data Index, by the Open Knowledge Foundation, a mechanism to assess the state of open data around the world. From the snapshot shows that that five European countries are in the top ten of the index. Furthermore, the Open Data Index also provides a detailed breakdown score for each country in each category.

66

A2: Examples of applications using open data in the sector of transport and mobility, education and sports, security and safety and health.

City Application Description Reference Berlin wheelmap.org Open map to search, find

and mark accessible places for wheelchair

http://wheelmap.org/

London London JamCams Shows all traffic jams through live feed

http://data.gov.uk/apps/london-jamcams-iphone- app

London Roadwork Database

Find all current road works around you and plan your trip carefully

http://data.gov.uk/apps/roadworks-database

Manchester PayByPhone Pay a parking fee through the app

http://www.manchester.gov.uk/info/471/parking_ in_public_areas/5897/pay_by_phone

Rotterdam SuperB Shows in real-time boats on the Maas river. In this game, the user is the captain

http://www.rotterdamopendata.org/es/web/gues t/app;jsessionid=223BE819894197CA53102AAAAB B18F61/?state=getAppSlider

Stockholm STHLM Traveling (SL)

Journey planner for Stockholm public

http://markupartist.com/sthlmtraveling/

Stockholm Taxi Stockholm Find taxis, check prices or book a time

http://www.slowtravelstockholm.com/2014/10/20

Table A2.1: Examples of applications using open data in the transport and mobility sector (Bright et al., 2015)

City Application Description Reference Berlin Kindergarten-

Suche Search for kindergarten http://www.tursics.de/kindergarten

/de/ London School Atlas Map out patterns of demand of

schools in an interactive map http://data.london.gov.uk/case- studies/school-atlas/

London Intelligent London

Analyse and visualize the skills of young Londoners

http://data.gov.uk/apps/intelligent-london

Vienna Büchereien in Wien

Library finder https://open.wien.gv.at/site/buechereien-in- wien/

Manchester NH connect Find a league or facility where you can play your favorite sport

http://manchesterinklink.com/city-launches- manchester-nh-connect-mobile-app/

Table A2.2: Examples of applications using open data in education and sport sector (Bright et al., 2015)

67

City Application Description Reference Berlin Berliner

Fahrradunfälle Visualization of bicycle accidents http://daten.berlin.de/anwendunge

n/berliner-fahrradunf%C3%A4lle

London Crime in London

Find out about crimes and hotspots in your neighborhood

http://data.gov.uk/apps/crime-in-london

Manchester NH connect Report local issues http://manchesterinklink.com/city-launches- manchester-nh-connect-mobile-app/

Stockholm Resledaren Helps people with a cognitive handicap to get from point A to B safely

http://www.openstockholmaward.se/

Vienna Fundboxen in Wien

For Handing in found items https://open.wien.gv.at/site/fundboxen-in- wien/

Table A2.3: Examples of applications using open data in the Security and Safety sector (Bright et al., 2015)

City Application Description Reference Berlin Ozon Sonar Visualization of ozone data http://ozon.sonar1.mobi/berlin/ London AirText Information about air quality for

people who suffer from asthma, emphysema, bronchitis, heart disease or angina

http://data.gov.uk/apps/airtex

Manchester Light Raider Motivation app, 'collect' street lamps while jogging, try to collect as much as possible to beat other joggers.

http://www.manchestereveningnews.co.uk/busi ness/business-news/10-mobile-apps-created- greater-8123152

Stockholm Cykeland Triggers people to use the bike, game based and real-time info about routes etc.

http://www.openstockholmaward.se/

Vienna Familienges undheit

Health planner (find POIs, diary, health checklist, etc.)

https://open.wien.gv.at/site/familiengesundheit

Table A2.4: Examples of applications using open data health sector (Bright et al., 2015)