The Elements of Big Data Value


This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts:

Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders.

Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value.

Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society.

Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value.

Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation. 


Yvo Volman
DG Communications Networks, Content and Technology, European Commission, Brussels, Belgium

The global health crisis, growing concerns about the environment and mounting threats in the digital environment are changing our priorities. These threats and problems also come with opportunities and, very often, an important part of the solution to global problems lies in the digital transition, a better sharing of data and responsible, data-driven Artificial Intelligence (AI). Digital platforms have allowed us to keep society functioning in times of confinement. Data-driven AI helps to track infection chains, model disease-spreading patterns and assess the efficiency of alternative disease management options by means of simulation rather than by heavy, slow and expensive trial and error.

Although we have come a long way in terms of increasing the availability of data (especially for open data), there are still many obstacles to the sharing of personal, commercial and industrial data. Common European data spaces are a way to systematically eliminate obstacles to data sharing and enable a vibrant economy based on digitalisation and a safe and controlled flow of different kinds of data. Data spaces play a key role in making the world safer, more resilient towards threats and more friendly to the environment. For example, a data space in healthcare will allow an easy, yet safe and compliant, sharing of clinical and patient data to better track and combat diseases, as well as to develop better medicines and vaccines at a faster pace. An environmental data space will allow better models of climate, pollution and other environmental threats to be built. An energy data space will allow us to produce cleaner power efficiently, deliver it when and where it is needed, and reduce energy wastages.

The European Union is supporting the digital transition through its new 7-year framework programmes, Horizon Europe and Digital Europe. They will help create a greener society and economy, more resilience towards threats, and new opportunities for building businesses and prosperity. The Horizon Europe programme will support enabling technologies for secure data spaces, responsible AI and the green transition. The Digital Europe programme will support the actual building, operations and deployment of data spaces, gradually making large-scale, safe data sharing a reality.

Making data work for the economy and society is not only about technology. In order to progressively eliminate the legal, institutional and societal obstacles to data sharing, the European Commission recently proposed a data governance framework to allow the safe, fair and easy sharing of data – in compliance with all applicable legal and ethical requirements. The development of technology and the framework conditions need to be tightly coupled: one is not effective without the other. A broad involvement and constant interaction of businesses, academia, administrations and civil society is necessary to build a data economy that leads to prosperity, growth and jobs. Finally, it is of utmost importance that the whole value chain and computing continuum (cloud-fog-edge-IoT) is addressed when designing data-sharing infrastructures and facilities. This prerequisite is also clearly outlined in the European Strategy for Data, which was published by the European Commission on 19 February 2020.

To respond to these challenges, a structured and broad-based action is required. Until 2020 when it reached the end of its contractual term, the Big Data Value Public-Private Partnership (PPP) was a key instrument in supporting this response. This book and the upcoming PPP Monitoring Report 2019–20 will document an important milestone on the road to the data economy and will set the scene for the new Public-Private Partnership on AI, Data and Robotics, which is currently under preparation. The achievement of a thriving data economy – an ambitious goal set in 2014 when the first partnership was signed – is still a valid goal, and we are a big step closer to it. In the coming years, a much broader involvement of technology areas, research disciplines as well as sectors of business and society will be needed. As the Big Data Value PPP has in its past years of activity excelled in creating bridges to other relevant technology areas – high-performance computing, IoT, cybersecurity, Artificial Intelligence – the future looks particularly promising for the new endeavour, as many paths have already been opened.

Peter Körte
Siemens, Berlin, Germany

Artificial Intelligence (AI) is on everyone’s lips. Many countries and companies have launched an AI action plan and have undertaken activities for the adoption of AI, from research to deployment. Almost everyone and every sector now realises the huge business potential of AI – a fact underscored by official forecasts, such as the IDC AI Worldwide Spending Guide.

As with any truly disruptive technology, AI also raises concerns. Some of them belong to the realm of science fiction; we are nowhere near having AI algorithms that could mimic “general intelligence”. But even with the current state of the art, AI is a transformational technology that is bound to have a few unwanted side effects. Some of them are already well known, such as AI algorithms with a bias against certain individuals due to the way they have been trained, while others are yet to emerge. In his recent book AI Superpowers Kai-Fu Lee, former head of Google in China, rightfully acknowledges in his conclusion: “As both the creative and disruptive force of AI is felt across the world, we need to look to each other for support and inspiration”.

For all these reasons we should ask ourselves how we will handle this technology – how can we get the most out of it, how can we mitigate risks? Having clear answers to these questions is crucial because the huge potential of AI can only be realised if society not only understands the potential of AI, but also trusts that those who design and implement AI algorithms are fully aware of the risks and know what they do. The difficult adoption of biotechnology in countries like Germany is a painful reminder that this trust is by no means a given and needs to be earned.

The development of AI in Europe thus depends on several critical success factors. One is the obvious need to focus AI-related efforts on domains such as manufacturing, infrastructure, mobility or healthcare, where Europe is already strong and can make a real difference – for Europe’s competitiveness, but also in the fight against climate change and other societal challenges. The other is to strongly focus on responsible AI – the art of creating trustworthy AI solutions which are designed against transparent objectives in accordance with European values and implemented to reliably deliver on these objectives. This dual focus on industrial domain knowhow and European values is key to making “AI made in Europe” a success story.

In this endeavour, speed is essential. AI can shift the balance of power from incumbents to newcomers almost overnight. In the race for industrial AI, Europe’s strong domain know-how, embedded in world-class universities and research institutes, in a strong network of innovative small and medium-sized enterprises (SMEs), in world-leading suppliers of electrical and industrial equipment as well as industrial software, gives Europe a considerable head start. However, this head start is only temporary, and Europe is well-advised not to squander it. Fast-track programmes to exploit the opportunities offered by industrial AI are needed, the sooner the better. Europe also needs to get serious with the “better regulation” initiative and take bold steps to create a regulatory environment for AI-driven innovations to take root. Responsible AI is best developed and proven in practical projects, not in ethics councils. If needed, regulatory sandboxes, which have yet to be introduced at EU level, can be used to strike the right balance between innovative spirit and regulatory caution.

Last but not least, collaboration in ecosystems is indispensable in making Europe the pacemaker for industrial AI. Efforts by the European Public-Private Partnership on Big Data Value to establish a Data Innovation Ecosystem in Europe are exactly the right approach. Only through the sharing and joint exploitation of data, but without disregard for companies’ obligation to return a profit to their shareholders, can we power a value-focused data-driven transformation of Europe’s business and society. Most importantly, the Partnership acts as a hub for the European data community – researchers, entrepreneurs, businesses and citizens – to collaborate with one another across all the member states. Europe’s wellbeing depends on a productive and effective data innovation ecosystem which positions Europe as a front runner in artificial intelligence.

Noel O’Connor
Insight SFI Research Centre for Data Analytics, Dublin, Ireland

Data is the defining characteristic of the twenty-first century, its importance such that it is often referred to as the “new oil”. The ability to refine this resource, i.e. the ability to extract value from raw data through data analytics and artificial intelligence, is having a transformative effect on society, driving scientific breakthroughs and empowering citizens to create a smarter, better world.

Collaboration between researchers, industry and society to derive value from big data through data-driven innovations that enable better decision-making has been the driving force behind this transformation. Europe has been a leader in value-driven transformation through the Big Data Value PPP and the Big Data Value Association. This community has acted as the nucleus of the European data community to bring together businesses with leading researchers from across Europe to harness the value of data to benefit society, business, science and industry. As one of the largest research centres of its kind in Europe, the Insight SFI Research Centre for Data Analytics is proud to be at the heart of this community. In turn, we as a centre have significantly benefited from the openness of the European ecosystem and are committed to continue to invest in its collective endeavour to transform European society.

The book you are holding describes in detail the foundational “elements” needed to deliver value from big data. It clearly defines the enablers needed to grow data ecosystems, including technical research and innovation, business, skills, policy and societal elements. The book charts pathways to new value creation and new opportunities from big data. Decision-makers, policy advisors, researchers and practitioners at every level will benefit.


Book Highlights and Key Related Concepts

Periodic Table of the Elements of Big Data Value

To foster, strengthen and support the development and wide adoption of big data value technologies within an increasingly complex landscape requires an interdisciplinary approach that addresses the multiple elements of big data value. This section captures the early discoveries of the big data value community as an initial set of Elements of Big Data Value. 

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Roadmap for Adoption of Big Data Value

The roadmap ensured and guided the development of the ecosystem in distinct phases, each with a primary theme. The three phases, are discussed in detail.

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Innovations Emerging from Projects

In its second year of operation, the BDV PPP’s 32 running projects reported 106 innovations of exploitable value as delivered in 2018: 63% have a medium impact and 37% are considered innovations of…

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The first three steps of the methodology produced a set of consolidated crosssectorial technical research requirements. The result of this process was the identification of five key technical research priorities discussed in detail.

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A Reference Model for Big Data Technologies

An overview of the BDV Reference Model is discussed. It distinguishes between two different elements. On the one hand, it describes the elements that are at the core of the BDVA; on the other, it outlines the features that are developed in strong collaboration with related European activities.

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Best Practice Framework for Big Data and Artificial Intelligence Centre of Excellence

The objective of the framework is to develop a best practice guide for use in promoting value generation and sharing of ideas within the big data and AI innovation ecosystem.

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Role of an i-Space and its Alignment with Other Initiatives

The concept of Data Innovation Space was initially coined in 2014 by the BDVA and identified as a key instrument to foster data-driven innovation based on experimentation, testing and benchmarking. Since then, many other instruments have appeared in Europe, aimed at bringing innovation closer to industry and society, and more specifically to those actors with no capacity to benefit from the latest European digital innovations.

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Data-Driven Innovation Framework and Success Stories

The economics of data has a strong impact on the development of data-driven business opportunities. For instance, data can be consumed an unlimited number of times without losing its value, and it can be reused as input for the production of different goods and services. However, its value still depends on complementary assets related to the capability to extract information out of the data.

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BDV Data Science Badges for Formal Education

The recognition strategy proposed by the BDVe for formal education science is based on the use of Open Badges. Open Badges are images that can be included in a curriculum, uploaded to platforms like LinkedIn and shared on social media.

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Towards an AI, Data and Robotics Ecosystem

The Big Data Value Association (BDVA) and the European Robotics Association (euRobotics) have developed a joint Strategic Research, Innovation and Deployment Agenda (SRIDA) for an…

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A Common European Data Space

For European data economy to develop further and meet expectations, large volumes of cross-sectoral, unbiased, high-quality and trustworthy data need to be made available. The exploration of ethical, secure and trustworthy legal, regulatory and governance frameworks is needed.

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Edward Curry


Edward Curry is a Principal Investigator at the Insight SFI Research Centre for Data Analytics at NUI Galway and leads a research unit on Open Distributed Systems. Edward has made substantial contributions to semantic technologies, incremental data management (dataspaces), event processing, software engineering, as well as distributed systems and data ecosystems. He is co-founder and elected Vice President of the Big Data Value Association, an industry-led European big data community.

Andreas Metzger

University of Duisburg-Essen, Germany

Andreas Metzger is senior academic councilor at the University of Duisburg-Essen, Germany, and head of Adaptive Systems and Big Data Applications at paluno, the Ruhr Institute for Software Technology. His background and research interests are software engineering and machine learning for self-adaptive systems. He serves as steering committee vice chair of NESSI, the European Technology Platform dedicated to Software, Services and Data, and as deputy secretary general of the Big Data Value Association.

Sonja Zillner

Siemens AG

Sonja Zillner works at Siemens AG, Technology as principal research scientist focusing on the definition, acquisition and management of global innovation and research projects in the domain of semantics and artificial intelligence. Since 2020 she is Lead of the Core Company Technology Module “Trustworthy AI” at Siemens Corporate Technology. Before that, from 2016 to 2019 she was invited to consult the Siemens Advisory Board in strategic decisions regarding artificial intelligence.

Jean-Christophe Pazzaglia


Jean-Christophe Pazzaglia is Chief Support Architect Higher Education & Research at SAP France and supporting SAP’s involvement in BDVA by managing the Big Data Value ecosystem project while also leading the pilot AI4Citizen in the AI4EU project. Complementary, within SAP University Alliance, he gives lectures on SAP Technologies and Design Thinking workshops. Prior to this, he was Director of the SAP Research Center Sophia Antipolis, and the principal investigator for SAP in several European and French research projects.

Ana García Robles

General Secretary, BDVA

Ana Garcia Robles is currently Secretary General of the Big Data Value Association (BDVA). She has a strong ICT industrial background in the Telecommunications sector, with over 10 years’ experience in the design, implementation and configuration of large-scale telecom networks and services, and, in the research and techno-economical assessment of new technologies and solutions for large-scale implementation. Ana has participated in multiple research and innovation projects in the areas of Open and Big Data, IoT, Open Platforms, Digital social innovation, and many more.