New Horizons for a Data-Driven Economy

Front matter

José Maria Cavanillas - Edward Curry Wolfgang Wahlster Fditors f New Horizons for a Data-Driven Economy

A Roadmap for Usage and Exploitation of Big Data in Europe

A Springer Open

Foreword

Data has become a factor just as important to production as labor, capital, and land. For the new value creators in today’s technology start-ups, little capital and office space is required. Both can be almost free when a firm is growing 1 % per day, on any metric. But without talent, and without the right kind of data, such a takeoff is highly improbable.

We see the same forces at play in SAP’s Innovation Center Network. Attracting the right talent was critical to establish the first Innovation Center in Potsdam. And having large, real-world datasets from customers and co-innovation partners is critical to many of our innovations. To make a difference in cancer treatment and research with our Medical Research Insights app, we critically depended on data-driven collaboration with the National Center for Tumor Diseases. The same holds for incubating SAP’s new sports line of business by co-innovating with the German national soccer team based on real-time sensor feeds from their players. And it holds true for SAP’s many initiatives in the Internet of Things, like the predictive maintenance apps with John Deere and Kaeser.

The Big Data Value Association (BDVA) is poised to make a difference both for data availability and for talent. By bringing together businesses with leading researchers, software and hardware partners, and enabling co-innovation around large, real-world datasets, BDVA can help lower the data barrier. And helping educate the next generation of thought leaders, especially in data science, computer science, and related fields, BDVA can help increase the supply of talent. Both are critical so Europe can begin to lead, not follow, in creating value from big data.

By clearly defining the opportunity in big data, by examining the big data value chain, and by deep-diving into industry sector applications, this book charts a way forward to new value creation and new opportunities from big data. Decision makers, policy advisors, researchers, and practitioners on all levels can benefit from this.

Jiirgen Miiller Berlin, Germany Vice President, SAP Innovation Center Network Brussels, Belgium President, Big Data Value Association

Preface

Welcome to our humble contribution to the huge universe of big data literature. We could ironically say there are almost as many books, leaflets, conferences, and essays about the possibilities of big data as data itself to be collected, curated, stored, and analyzed, yet a single zettabyte of useful data is an amount of information we are currently incapable of writing, and as described in this book, 16 zettabytes of data are waiting for us in 2020.

However, according to many research and industrial organisations, this contribution is actually not that humble and is even unique in many senses.

First of all, this book is not just another approach made by a single player looking down from a corner of the world. It is the compendium of more than 2 years of work performed by a set of major European research centers and industries. It is the compilation and processed synthesis of what we all have done, prepared, foreseen, and anticipated in many aspects of this challenging technological context that is becoming the major axis of the new digitally transformed business environment.

But the most important part of the book is you, the reader. It is commonly said that “a map is useless for the one who does not know where to go.” This book is a map. An immediate goal of this book is to become a “User’s Manual” for those who want to blaze their own trail in the big data jungle. But it can also be used as a reference book for those experts who are sailing their own big data ship and want to clarify specific aspects on their journey.

You reader, either trailblazer or old sailor, have to make your own way through the book. In this map, you will not only find answers and discussions about legal aspects of big data but also about social impact and education needs and requirements. You will also find business perspectives, discussions, and estimations of big data actuations in the different sectors of the economy, ranging from the public sector to the retailing actors. And you will also find technological discussions about the different stages of data and how to address these emerging technologies.

We worked on all these matters within the context of a European Commission project called BIG (Big Data Public Private Forum), which was an enormous challenge and one that we reckon has been successfully achieved and accomplished.

ix x Preface

The book is divided into four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. Chapter | sets the scene for the value potential of big data and examines the legal, business, and social dimensions that need to be addressed to deliver on its promise. Next, Chap. 2 briefly introduces the European Commission’s BIG project and its remit to establish a big data research roadmap for Horizon 2020 to support and foster research and innovation in the European Research Area.

Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation, and storage to data usage and exploitation. Chapter 3 introduces the core concepts of the big data value chain. The next five chapters detail each stage of the data value chain, including a state-of-the-art summary, emerging use cases, and key open research questions. Chapter 4 provides comprehensive coverage of big data acquisition, which is the process of gathering, filtering, and cleaning data before it is put in a data warehouse or any other storage solution for further processing. Chapter 5 discusses big data analysis that focuses on transforming raw acquired data into a coherent, usable resource suitable for analysis to support decision-making and domain-specific usage scenarios. Chapter 6 investigates how the emerging big data landscape is defining new requirements for data curation infrastructures and how big data curation infrastructures are evolving to meet these challenges. Chapter 7 provides a concise overview of big data storage systems that are capable of dealing with high velocity, high volumes, and high varieties of data. Finally, Chap. 8 examines the business goals that need access to data and their analyses and integration into business decision-making in different sectors.

Part HI “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media, and public services. Chapter 9 provides the conceptual background and overview of big data-driven innovation in society, highlighting factors and challenges associated with the adequate diffusion, uptake, and sustainability of big data-driven initiatives. The remaining chapters describe the state of the art of big data in different sectors, examining enabling factors, industrial needs, and application scenarios and distilling the analysis into a comprehensive set of requirements across the entire big data value chain. Chapter 10 details the wide variety of opportunities for big data technologies to improve overall healthcare delivery. Chapter 11 investigates the potential value to be gained from big data by government organizations by boosting productivity in an environment with significant budgetary constraints. Chapter 12 explores the numerous advantages of big data for financial institutions. Chapter 13 examines the domain-specific big data technologies needed for cyber-physical energy and transport systems, where the focus needs to move beyond big data to smart data technologies. Chapter 14 discusses the media and entertainment sectors which are in many respects an early adopter of big data technologies because it enables them to drive digital transformation, exploiting more fully not only data which was already available but also new sources of data from both inside and outside the organization.

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Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research and outlines the most urgent and challenging technological, economic, political, and societal issues for big data in Europe. Chapter 15 details the process used to consolidate the big data requirements from different sectors into a single prioritized set of cross-sector requirements that were used to define the technology policy, business, and society roadmaps together with action recommendations. Chapter 16 describes the roadmaps in the areas of technology, business, policy, and society. The chapter introduces the Big Data Value Association (BDVA) and the Big Data Value contractual Public Private Partnership (BDV cPPP) which provide a framework for industrial leadership, investment, and commitment of both the private and public sides to build a data-driven economy across Europe.

We invite you to read this book at your convenience, and we wish that you will enjoy it as much as we have whilst preparing its contents.

Ciudad Real, Spain José María Cavanillas Galway, Ireland Edward Curry Saarbrücken, Germany Wolfgang Wahlster

October 2015

Book Acknowledgements

The authors would like to acknowledge the support, openness, and good humor of all their colleagues who contributed to the BIG project and to this book in ways both big and small. The editors would like to thank Meggan Gregory for her support in the preparation of the final manuscript. Thanks also go to Ralf Gerstner, Viktoria Meyer, and all at Springer for their professionalism and assistance throughout the journey of this book.

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Project Acknowledgements

The first person I would like to acknowledge is Edward Curry, the real alma mater of this book. He and his team at the National University of Ireland Galway have been working hard for the BIG project and fueling strongly this compilation from their rainy, green, and blue beautiful hometown where we met, under the sunshine, on that very first day. That day he promised us all that it was the only sunny day in Galway that year. He brought slides of the Galway streets under the rain to show he was telling the truth, and I was even accused of having brought the sun from Spain.

Many people have collaborated for this success as well in our company Atos. Our Atos colleagues, Alicia Garcia, Blanca Jordan, Nuria de Lama, Felicia Lobillo, Josep Martrat, Ricard Munné, Tomas Pariente, Ana Maria Pinuela, Pedro Soria, and too many others to be mentioned, have worked hard and participated in this success. Let me mention as well the initial leadership of Diana Pottecher, who spun off to carry out her own data business in the wilderness of the Internet.

In the case of the Deutsche Forschungszentrum fiir Ktinstliche Intelligenz, DFKI, the impulse coming from its CEO, Wolfgang Wahlster, was a leveraging factor for receiving the work of researchers such as Tilman Becker, Sabrina Hoppe, and Denise Paradowski as well as many others.

The participation of Siemens has been especially welcome, namely from researchers Stephan Guido, Yi Huang, Johannes Riedl, Sebnem Rusitschka, and Sonja Zillner, as well as from the large team at Siemens Research.

The French company Exalead has been one of the main fuellers of the project and its contents, mainly from a great professional, Amar-Djalil Mezaour.

The German company AGT has as well contributed strongly, namely, from researchers Herman Ravkin, Martin Strohbach, and Panayotis Kikiras.

The Anglo-Austrian team, consisting of John Domingue, Dieter Fensel, Nelia Lasierra, Sabrina Neurerer, Serge Tymaniuk, Iker Larizgoitia, Michael Rogger, Ioannis Stavrakantonakis, Amy Strub, and Ioan Toma from STI International and University of Innsbruck, also performed really memorable work in the project.

XV xvi Project Acknowledgements

Thanks as well to Helen Lippell from Press Association; to Axel Ngonga, Sebastian Hellman, and Sandra Praetor from the University of Leipzig; as well as to Daniel Dietrich, Pablo Mendes, and Walter Palmetshofer from Open Knowledge Foundation.

Others within the National University of Ireland Galway who helped immensely are Andre Freitas, Umair ul Hassan, Aftab Iqbal, Nur Aini Rakhmawati, and Stefan Decker. The BIG project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 318062. The work has been supported in part by Science Foundation Ireland under grant SFI/12/RC/2289.

Finally, the Technical Working Groups and Sectorial Forums gratefully acknowledge the debt we owe to all our interviewees and subject matter experts who gave up their valuable time and provided extremely useful input for us. In particular, we would like to thank the following people for participating in public interviews: Matt Asay, Kevin Ashley, Sören Auer, Ricardo Baeza-Yates, François Bancilhon, Richard Benjamins, Helen Berman, Lukas Biewald, James Cheney, Hjalmar Gislason, Carole Goble, Paul Groth, Alon Halvey, Usman Haque, Steve Harris, Jim Hendler, Alek Kołcz, Prasanna Lal Das, Helen Lippell, Nick Lynch, Peter Mika, Andy Palmer, Andreas Ribbrock, Joe Sewash, John Sheridan, Kelly Stirman, Jeni Tennison, Bill Thompson, Andraž Tori, Frank van Harmelen, Marco Viceconti, Jim Webber, and Antony Williams. We would also like to thank the many CEOs, CTOs, VPs, Directors, Founders, Professors, Researchers, and Technical Experts who provided inputs to the project from organizations including AGT International, Associated Press, Atos, BBC, CGIA, Chemspider/Royal Society of Chemistry, Cosm (Pachube), CrowdFlower, Data Publica, Data Tamer, DataMarket Inc., Digital Curation Centre, European Commission, Exalead, Experian, Fraunhofer, Garlik, Geomapp, German Research Centre for Artificial Intelligence, Google, Insight, Legislation.gov.uk, MapR, MongoDB, National University of Ireland Galway, Neo, Open Data Institute, Open Knowledge Foundation, Pistoia Alliance, Press Association, Rensselaer Polytechnic Institute, Rutgers University (Protein Data Bank), SAP, Siemens, Software AG, STI International, Telefoncica, Teradata GmbH, Twitter, University of Amsterdam, University of Edinburgh, University of Innsbruck, University of Leipzig, University of Manchester, University of Sheffield, VPH Institute, VU University Amsterdam, World Bank, Yahoo!, and Zemanta. Without your support this work would not have been possible.

Ciudad Real, Spain José María Cavanillas October 2015

Contents

PartI The Big Data Opportunity

José Maria Cavanillas, Edward Curry, and Wolfgang Wahlster

2 “The BIG Project. oree een cn ca eee be ea ee ea eee hs Edward Curry, Tilman Becker, Ricard Munné, Nuria De Lama, and Sonja Zillner

Part II The Big Data Value Chain: Enabling and Value Creating Technologies

3 The Big Data Value Chain: Definitions, Concepts, and Theoretical Approaches fe 23 390s eS Ee ee SG Smale RN os Edward Curry

Klaus Lyko, Marcus Nitzschke, and Axel-Cyrille Ngonga Ngomo

John Domingue, Nelia Lasierra, Anna Fensel, Tim van Kasteren, Martin Strohbach, and Andreas Thalhammer

André Freitas and Edward Curry

Martin Strohbach, Jorg Daubert, Herman Ravkin, and Mario Lischka

8 -Big Data Usage. goron co na ele ee bal ce be hee a eons Tilman Becker xvii xviii Contents

Part III Usage and Exploitation of Big Data

Sonja Zillner, Tilman Becker, Ricard Munné, Kazim Hussain, Sebnem Rusitschka, Helen Lippell, Edward Curry, and Adegboyega Ojo

Sonja Zillner and Sabrina Neururer

Ricard Munné

Kazim Hussain and Elsa Prieto

Sebnem Rusitschka and Edward Curry

Helen Lippell

Part IV A Roadmap for Big Data Research

15 Cross-sectorial Requirements Analysis for Big Data Research. ... 263 Tilman Becker, Edward Curry, Anja Jentzsch, and Walter Palmetshofer

16 New Horizons for a Data-Driven Economy: Roadmaps and Action Tilman Becker, Edward Curry, Anja Jentzsch, and Walter Palmetshofer

List of Contributors

Tilman Becker German Research Centre for Artificial Intelligence (DFKD, Saarbriicken, Germany

José Maria Cavanillas Atos Spain, S.A., Madrid, Spain

Edward Curry Insight Centre for Data Analytics, National University of Ireland Galway, Lower Dangan, Galway, Ireland

Jorg Daubert AGT International, Darmstadt, Germany Nuria De Lama Atos Spain, S.A., Madrid, Spain

John Domingue STI International, Vienna, Austria; and Knowledge Media Institute, The Open University, Walton Hall, Milton Keynes, UK

Anna Fensel University of Innsbruck, Innsbruck, Austria

André Freitas Insight Centre for Data Analytics, National University of Ireland Galway, Lower Dangan, Galway, Ireland

Kazim Hussain Atos Spain, S.A., Madrid, Spain

Anja Jentzsch Open Knowledge Foundation (OKF), Berlin, Germany Nelia Lasierra University of Innsbruck, Innsbruck, Austria

Helen Lippell Press Association, London, UK

Mario Lischka acentrix GmbH, Munich, Germany

Klaus Lyko University of Leipzig, Leipzig, Germany

Ricard Munné Atos Spain, S.A., Barcelona, Spain

Sabrina Neururer Department of Medical Statistics, Informatics and Health Economics, Innsbruck Medical University, Innsbruck, Austria; and Semantic Technology Institute Innsbruck, University of Innsbruck, Innsbruck, Austria

XX List of Contributors

Marcus Nitzschke University of Leipzig, Leipzig, Germany Axel-Cyrille Ngonga Ngomo University of Leipzig, Leipzig, Germany

Adegboyega Ojo Insight Centre for Data Analytics, National University of Ireland Galway, Lower Dangan, Galway, Ireland

Walter Palmetshofer Open Knowledge Foundation (OKF), Berlin, Germany Elsa Prieto Atos Spain, S.A., Madrid, Spain

Herman Ravkin Department of Industrial Engineering, Tel-Aviv University, Ramat-Aviv, Tel-Aviv, Israel

Sebnem Rusitschka Corporate Technology, Siemens AG, Munich, Germany Martin Strohbach AGT International, Darmstadt, Germany

Andreas Thalhammer Institute for Applied Informatics and Formal Description Methods, Karlsruhe Institute of Technology, Karlsruhe, Germany

Tim van Kasteren AGT International, Darmstadt, Germany

Wolfgang Wahlster German Research Centre for Artificial Intelligence (DFKI), Saarbriicken, Germany

Sonja Zillner Corporate Technology, Siemens AG, Munich, Germany; and School of International Business and Entrepreneurship, Steinbeis University, Berlin, Germany

Chapter 1 The Big Data Value Opportunity José Maria Cavanillas, Edward Curry, and Wolfgang Wahlster

The volume of data is growing exponentially, and it is expected that by 2020 there will be more than 16 zettabytes (16 Trillion GB) of useful data (Turner et al. 2014). We are on the verge of an era where every device is online, where sensors are ubiquitous in our world generating continuous streams of data, where the sheer volume of data offered and consumed on the Internet will increase by orders of magnitude, where the Internet of Things will produce a digital fingerprint of our world.

Big data is the emerging field where innovative technology offers new ways of extracting value from the tsunami of new information. The ability to effectively manage information and extract knowledge is now seen as a key competitive advantage. Many organizations are building their core business on their ability to collect and analyse information to extract business knowledge and insight. Big data technology adoption within industrial sectors is not a luxury but an imperative need for most organizations to survive and gain competitive advantage.

This chapter explores the value potential of big data with a particular focus on the European context and identifies the positive transformational potential of big

J.M. Cavanillas Atos Spain, S.A., Albarracin, 25, 28037 Madrid, Spain e-mail: jose-maria.cavanillas@atos.net

Insight Centre for Data Analytics, National University of Ireland Galway, Lower Dangan, Galway, Ireland

W. Wahlster German Research Centre for Artificial Intelligence (DFKI), Saarbrücken, Germany e-mail: wahlster@dfki.de

© The Author(s) 2016 3 J.M. Cavanillas et al. (eds.), New Horizons for a Data-Driven Economy, DOI 10.1007/978-3-319-21569-3_1

4 J.M. Cavanillas et al.

data within a number of key sectors. It discusses the need for a clear strategy to increase the competitiveness of European industries in order to drive innovation and competitiveness. Finally the chapter describes the key dimensions, including skills, legal, business, and social, that need to be addressed in a European Big Data Ecosystem.

1.2 Harnessing Big Data

The impacts of big data go beyond the commercial world; within the scientific community, the explosion of available data is producing what is called Data Science (Hey et al. 2009), a new data-intensive approach to scientific discovery. The capability of telescopes or particle accelerators to generate several petabytes of data per day is producing different problems in terms of storage and processing. Scientists do not have off-the-shelf solutions ready to analyse and properly compare disperse and huge datasets. Enabling this vision will require innovative big data technologies for data management, processing, analytics, discovery, and usage (Hey et al. 2009).

Data has become a new factor of production, in the same way as hard assets and human capital. Having the right technological basis and organizational structure to exploit data is essential. Europe must exploit the potential of big data to create value for society, citizens, and businesses. However, from an industry adoption point of view, Europe is lagging behind the USA in big data technologies and is not taking advantage of the potential benefits of big data across its industrial sectors. A clear strategy is needed to increase the competitiveness of European industries through big data. While US-based companies are widely recognized for their works in big data, very few European organizations are known for their works in the field. This currently makes Europe dependent on technologies coming from outside and may prevent European stakeholders from taking full advantage of big data technology. Being competitive in big data technologies and solutions will give Europe a new source of competitiveness and the potential to foster a new data-related industry that will generate new jobs.

Addressing the current problems requires a holistic approach, where technical activities work jointly with business, policy, and society aspects. Europe needs to define actions that support faster deployment and adoption of the technology in real cases. Support is needed not only to “build” the technology but also to “grow” the ecosystem that makes innovation possible. There are many technical challenges that will require further research, but this work has to be accompanied by a continuous understanding of how big data technologies support both business and societal challenges. How can data-driven innovation be integrated into an organization’s processes, cultural values, and business strategy? Europe has a track record in joint research efforts, as well as strength in converging policies or eliminating adoption barriers. There is an opportunity to build upon these and other European

1 The Big Data Value Opportunity 5 strengths in order to enable a vision where big data contributes to making Europe the most competitive economy in the world in 2020.

1.3 A Vision for Big Data in 2020

The Information and Communications Technology (ICT) sector is directly responsible for 5 % of European GDP, with a market value of 660 billion euros annually; it also contributes significantly to overall productivity growth (20 % directly from the ICT sector and 30 % from ICT investments). Big data solutions can contribute to increase European competitiveness by delivering value adding tools, applications, and services. One estimate for 2020 puts the potential of big and open data to improve the European GDP by 1.9 %, an equivalent of one full year of economic growth in the EU (Buchholtz et al. 2014). International Data Corporation (IDC) forecasts that the big data technology and services market will grow at a 27 % compound annual growth rate (CAGR) to $32.4 billion through 2017 (Vesset et al. 2013).

The European Commission launched in March 2010 the Europe 2020 Strategy (European Commission 2010) to exit the crisis and prepare the EU economy for the next challenges in terms of productivity, economy, and social cohesion. The Digital Agenda for Europe is one of the seven flagship initiatives of the Europe 2020 Strategy; it defines the key enabling role that the use of ICT will have to play if Europe wants to succeed in its ambitions for 2020. The paramount importance of big data was recognized by including a specific topic in the Digital Agenda to get maximum benefit from existing data and specifically the need to open up public data resources for re-use. As then EU Commissioner Kroes stated, “Big Data is the new Oil” that can be managed, manipulated, and used like never before thanks to high-performance digital tools, making big data the fuel for innovation.

1.3.1 Transformation of Industry Sectors

The potential for big data is expected to impact all sectors, from healthcare to media, from energy to retail (Manyika et al. 2011). The positive transformational potential has already been identified in a number of key sectors.

¢ Healthcare: In the early twenty-first century, Europe is an ageing society that places significant demands on its healthcare infrastructure. There is an urgent need for improvement in efficiency of the current healthcare system to make it more sustainable. The application of big data has significant potential in the sector with estimated savings in expenditure at 90 billion euros from national healthcare budgets in the EU (Manyika et al. 2011). Clinical applications of big data range from comparative effectiveness research where the clinical and financial effectiveness of interventions is compared to the next generation of

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clinical decision support systems that make use of comprehensive heterogeneous health datasets as well as advanced analytics of clinical operations. Healthcare R&D applications include predictive modelling, statistical tools, and algorithms to improve clinical trial design, personalized medicine, and analysing disease patterns.

Public Sector: Europe’s public sector accounts for almost half of GDP and can benefit significantly from big data to gain efficiency in administrative processes. Big data could reduce the costs of administrative activities by 15-20 %, creating the equivalent of 150 billion euros to 300 billion euros in new value (OECD 2013). Potential benefits in the public sector include improved transparency via open government and open data, improved public procurement, enhanced allocation of funding into programmes, higher quality services, increased public sector accountability, and a better-informed citizen. Crucial to the future is the definition of policies to share data across government agencies and to inform citizens about the trade-offs between the privacy and security risks of sharing data and the benefits they can gain. Big data will also change the relationship between citizens and government by empowering citizens to understand political and social issues in new transparent ways, enabling them to engage with local, regional, national, and global issues through participation.

Finance and Insurance: There are a number of ways for financial service companies to achieve business advantages by mining and analysing data. These include enhanced retail customer service, detection of fraud, and improvement of operational efficiencies. Big data can be used to identify exposure in real time across a range of sophisticated financial instruments like derivatives. Predictive analysis of both internal and external data results in better, proactive management of a wide range of issues from credit and operational risk (e.g. fraud and reputational risk) to customer loyalty and profitability. A challenge for the financial sector is how to use the breadth and depth of data available to satisfy more demanding regulators while also providing personalized services for their customers.

Telecom, Media, and Entertainment: Big data analysis and visualization techniques can enable the effective discovery and delivery of media content enabling users to dynamically interact with new media and content across multiple platforms. The domain of personal location data offers the potential for new value creation with applications, including location-based content delivery for individuals, smart personalized content routing, automotive telematics, mobile location-based services, and geo-targeted advertising. Retail: Significant opportunities for using big data technologies reside in the interactions between retailers and consumers. Data is playing an increasing role as consumers search, research, compare, buy, and obtain support online and the products sold by retailers increasingly generate their own data footprints. Big data can increase productivity and efficiency resulting in a potential 60 % increase in retailers’ operating margins (Manyika et al. 2011). Big data can impact retail in areas such as marketing: cross-selling, location-based marketing, in-store behaviour analysis, customer micro-segmentation, customer sentiment

1 The Big Data Value Opportunity 7 analysis, enhancement of multi-channel consumer experience; merchandizing: assortment optimization, pricing optimization, placement and design optimization; operations: performance transparency, labour inputs optimization; supply chain: inventory management, distribution and logistics optimization, informing supplier negotiations; new business models: price comparison services, web-based markets.

e Manufacturing: The manufacturing sector was an early adopter of IT to design, build, and distribute products. The next-generation of smart factories with intelligent and networked machinery (i.e. Internet of Things, Industry 4.0) will see further efficiency improvement in design, production, and product quality. Big data will enable fulfilment of customer needs through precisely targeted products and effective distribution. In addition to efficiency gains and predictive maintenance, big data will enable entirely new business models in the area of mass production of individualized products.

¢ Energy and Transport: Big data will open up new opportunities for innovative ways to monitor and control transportation and logistics networks using a variety of data sources and the Internet of Things. The potential for big data in the transport sector is estimated at USD 500 billion worldwide in the form of time and fuel savings, with the avoidance of 380 megatonnes of CO, emissions (OECD 2013). The digitization of energy systems enables the acquisition of real-time, high-resolution data via smart metres that can be leveraged within advanced analytics to improve the levels of efficiency within both the demand and supply sides of energy networks. Smart buildings and smart cities will be key drivers of enhanced efficiency in the energy sectors. Big data technology in the utilities sectors has the potential to reduce CO, emissions by more than 2 gigatonnes, equivalent to 79 billion euros (OECD 2013).

A successful data ecosystem would “bring together data owners, data analytics companies, skilled data professionals, cloud service providers, companies from the user industries, venture capitalists, entrepreneurs, research institutes and universities” (DG Connect 2013). A successful data ecosystem, which is a prominent feature of the data-driven economy, would see these stakeholders interact seamlessly within a Digital Single Market, leading to business opportunities, easier access to knowledge, and capital (European Commission 2014). “The Commission can contribute to this by bringing the relevant players together and by steering the available financial resources that facilitate collaboration among the various stakeholders in the European data economy” (DG Connect 2013).

Big data offers tremendous untapped potential value for many sectors; however, there is no coherent data ecosystem in Europe. As Commissioner Kroes explained, “The fragmentation concerns sectors, languages, as well as differences in laws and policy practices between EU countries” (European Commission 2013; Kroes 2013). During the ICT 2013 Conference, Commissioner Kroes called for a European public—private partnership on big data to create a coherent European data ecosystem that stimulates research and innovation around data, as well as the uptake of cross-sector, cross-lingual, and cross-border data services and products. She also

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noted the need for ensuring privacy “Mastering big data means mastering privacy too” (Kroes 2013). In order for this to occur, an interdisciplinary approach is required to create an optimal business environment for big data that will accelerate adoption within Europe.

1.4 A Big Data Innovation Ecosystem

In order to drive innovation and competitiveness, Europe needs to foster the development and wide adoption of big data technologies, value adding use cases, and sustainable business models. While no coherent data ecosystem exists at the European level (DG Connect 2013), the benefits of sharing and linking data across domains and industry are becoming obvious. An ecosystem approach allows organizations to create new value that no single organization could achieve by itself (Adner 2006). A European Big Data Ecosystem is an important factor for commercialization and commoditization of big data services, products, and platforms. Within a healthy business ecosystem, companies can work together in a complex business web where they can easily exchange and share vital resources (Kim et al. 2010). If a Big Data Ecosystem is to emerge in Europe, it is important that the different actors within the ecosystem “define a shared vision and jointly identify gaps in the current data landscape” (DG Connect 2013). A successful big data ecosystem would see all “stakeholders interact seamlessly within a Digital Single Market, leading to business opportunities, easier access to knowledge, and capital” (European Commission 2014).