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Big Data Value Creation by Example

Abstract

The Big Data Value contractual Public-Private Partnership between the European Commission and the Big Data Value Association (BDVA) was signed in October 2014. Since then, more than 50 projects and numerous BDVA members have explored how data can drive innovation across the data stack and how industries can transform business practices. Meanwhile, start-ups have been working at the confluence of new sources of data (e.g. IoT, DNA, HD pictures, satellite data) and new or revisited processing paradigms (e.g. Edge computing, blockchain, machine learning) to tackle new use cases and to provide disruptive solutions for known problems. This chapter details a collection of stories showing concrete examples of the value created thanks to a renewed usage of data.

Keywords

  • Big data
  • Best practice
  • Data-driven innovation
  • Digital transformation
  • Success story

1 Introduction

Since the signing of the Big Data Value contractual Public-Private Partnership (PPP) in October 2014, more than 50 projects and numerous BDVA members have explored how data can drive innovation across the data stack and how industries can transform business practices. They are working at the confluence of new sources of data (e.g. IoT, DNA, HD pictures, satellite data) and new or revisited processing paradigms (e.g. Edge computing, blockchain, machine learning) to tackle new use cases and to provide disruptive solution for known problems (Zillner et al. 2017). The dimensions of big data value are multiple: they embrace data; skills; legal and policy issues; technology leadership through research and innovation; transforming applications into new business opportunities; the acceleration of business ecosystems and business models, with a particular focus on SMEs; and successful solutions for the major societal challenges Europe is facing in areas such as health, energy, transport and the environment (Cavanillas et al. 2016).

With an initial indicative budget from the European Union of €534 million for the period 2016–2020 and €201 million allocated in total by the end of 2018, the BDV PPP has already mobilised €1570 million of private investments since the launch of the PPP (€467.47 million for 2018). Forty-two projects were running at the beginning of 2019 and the BDV PPP in only 2 years developed 132 innovations of exploitable value (106 delivered in 2018, 35% of which are significant innovations) including technologies, platforms, services, products, methods, systems, components and/or modules, frameworks/architectures, processes, tools/toolkits, spin-offs, datasets, ontologies, patents and knowledge. Ninety-three per cent of the innovations delivered in 2018 had an economic impact, and 48% had a societal impact. In 2018 alone, the BDV PPP organised 323 events (including own events, seminars and conferences) outreaching over 630,000 participants; and taking into account mass media, the Monitoring Report 2018 (Big Data Value PPP Monitoring Report 2018 2019) estimated the number of people outreached and engaged in dissemination activities as 7.8 million.

But how to make these numbers tangible? How to explain what the BDV PPP actors achieved? To answer these questions, in Spring 2019 the BDVA and the BDVe project launched the Best Success Story Award to identify and give visibility to success stories based on impact, developed in a way that can be easily explained to a broad audience. The first edition of the award enabled the five finalists to present their stories on stage at the BDV PPP Summit 2019 in Riga (Fig. 1).

Fig. 1
figure 1

BDV PPP 2019 Best Success Story Awards Ceremony

The first edition, won by the TransformingTransport project with DataBio/Wuudis as runner-up, had the chance to have Mrs. Dace Melbārde, Member of the European Parliament and former Minister for Culture for the Republic of Latvia, award the prize to Rodrigo Castiñeira González, the project coordinator. The 2020 edition introduced a new category – SMEs and start-ups – and the awards ceremony took place during EBDVF 2020 with the Data Pitch project and the start-up Orbem as winners in their respective categories, while Ubiwhere was distinguished for the quality of its promotional video (Table 1).

Table 1 Main characteristics of the stories

In this chapter, we decided to present a set of success stories representative of the BDV PPP activities amongst the 2019 and 2020 participants. Each section shows the collateral provided by the contenders, a summary of the story and contact details to enable the reader to investigate further.

2 How Can Big Data Transform Everyday Mobility and Logistics?

TransformingTransport (TT) is one of the first two lighthouse projects of the EU Big Data Value Public-Private Partnership. The project, coordinated by Indra, has involved 49 partners. During its 31 months of execution, TT has been able to demonstrate the transformation that big data could bring to the mobility and logistics industries, which represent 15% of the global GDP and employ over 11 million people in the EU-28 zone. TransformingTransport leverages big data to reinvent and optimise mobility and the transport value chain. Significant results from pilots showed increased traffic observation of 70% in the city of Tampere (Finland), accurate traffic and accident predictions up to 2 h in advance on the AUSOL highway in Spain, reduced overall turnaround times and increased gate capacity of up to 10% at Malpensa Airport, reduced truck driving and handling process of 17% at a critical central EU Corridor (Amsterdam to Frankfurt), and reduced delivery vehicle usage at Valladolid (Spain) of 30% (Fig. 2).

Fig. 2
figure 2

“How can big data transform everyday mobility and logistics?” Entry

3 Digitalizing Forestry by Harnessing the Power of Big Data

The importance of forests with carbon sink and wood as renewable materials to replace synthetic, oil-based materials is growing rapidly. For this, a digital forest management solution integrated with ‘data to decisions’ is essential as it makes the business value chain more efficient. The ‘forestry pilot’ implemented within the scope of the H2020 DataBio project introduced a new standard for a forest management plan to enable easy data sharing across the full range of forest stakeholders. Moving from the static paper-based forest management plan updated every 10 years, the Wuudis forest management platform was introduced to manage all of the forest business data in one place. The introduction of Laatumetsä (‘quality forest’ in English), a forestry-specific mobile solution for ‘fieldwork quality monitoring’ and ‘forest threat data collection’, enables both field workers and citizens to collect forest threat data leveraging AI for automatic image processing. This provides citizens with a unique e-tool to collect forest threat data, and it is the first ever tool in the EU where crowdsourced data has been utilised to control forest damage. Furthermore, the Wuudis platform standard interfaces are developed to integrate different forest data (e.g. data from drone monitoring, very high-resolution satellite data) to develop further services beneficial to the sector (Fig. 3).

Fig. 3
figure 3

“Digitalizing forestry by harnessing the power of big data” Entry

Since March 2018, the available amount of open forest data has increased from 0.36 TB to 0.38 TB, the amount of downloaded data has exceeded 10.5 TB, and the service has been visited and data downloaded over 3.5 million times. It is worth noting that the innovations for better forestry developed in DataBio have been tested in the real business environment through customer pilots in Finland, Spain (Galicia), Belgium (Wallonia) and the Czech Republic. This confirms the industry’s acceptance of the solutions (Fig. 3).

4 GATE: First Big Data Centre of Excellence in Bulgaria

The first Centre of Excellence (CoE) in Big Data and AI for Eastern Europe has been launched as ‘Big Data for Smart Society’ – GATE in Sofia, Bulgaria. The Centre is led by Sofia University ‘St Kliment Ohridski’, in partnership with Sweden’s Chalmers University of Technologies and Chalmers Industrial Technologies (Fig. 4).

Fig. 4
figure 4

“GATE: First Big Data Centre of Excellence in Bulgaria” Entry

Catching the momentum within the booming data and AI-driven EU economy, and supported by the EU’s Horizon 2020 Widespread programme, Regional Development Funds and industry, GATE creates a unique research environment and a globally competitive digital hub for big data and AI innovations in future cities, intelligent government, smart industry and digital health. The CoE also accumulates significant expertise and inspires and cultivates the next generation of AI and data scientists and professionals. Providing advanced infrastructure – platform, data, services, and testing and experimentation facilities – GATE City Living Lab, Digital Twin Lab and Visualisation Lab are the heart of a vibrant ecosystem where innovative ideas are generated, developed in projects and applied in effective collaboration with stakeholders. GATE pioneered the usage of the BDVe’s best practice guide for big data CoEs, leveraging the collective experience of 31 EU centres on strategy, governance, structure, funding, culture, research-industry collaboration and outreach practice. GATE succeeded in a severe competition, created trust in EC and in the Bulgarian government and industry, and attracted more than €30 million in public and private funding for its operation in the next 7 years.

GATE boosts Bulgarian organisations in target sectors to become, and remain, competitive, thus increasing research capacity and reducing innovation gaps with other EU regions, and also creating confidence amongst citizens and businesses that Bulgaria can efficiently contribute to their needs for a data-driven society and economy (Fig. 4).

5 Beyond Privacy: Ethical and Societal Implications of Data Science

Everywhere we go, from our homes and workplaces to holiday destinations and shopping trips, we generate huge amounts of data which are stored, analysed and used by companies, authorities and organisations. Big data is a feature of our everyday lives (Fig. 5).

Fig. 5
figure 5

“Beyond privacy: ethical and societal implications of data science” Entry

Data-driven innovation is deeply transforming society and the economy. Although there are potentially enormous economic and social benefits, this innovation also brings new challenges for individual and collective privacy, security, and democracy and participation. Within this framework, the EU-funded e-SIDES project has provided legal, ethical and economic guidance for big data and AI projects. e-SIDES has shown how these issues can be addressed through the use of privacy-preserving technologies leveraged and implemented in their research and architectures at design time. In 3 years, e-SIDES involved more than 3500 stakeholders in 25 events and was selected as the Success Story Innovation Highlight for DG Connect (Fig. 5).

6 A Three-Year Journey to Insights and Investment

At Data Pitch, we understand that data has the potential to create huge value for businesses, that start-ups and entrepreneurs have the initiative and ideas to create solutions to sector challenges, and that large organisations can unlock hidden potential in their businesses by sharing data and collaborating with start-ups. We set a range of Data Pitch challenges relating to the industries that are identified in the SRIA as having shown or predicted significant gains from data innovation. As an example, the aim of the ‘Health and Wellness’ Challenge – featured in the 2019 Best Success Story – was to identify and analyse patterns in patients’ clinical pathways. This first cohort showed the importance for start-ups of working closely with medical data providers in order to manage the challenges surrounding sharing medical data. The result was an increase in client base and pilots’ outreach, securing more than €7 million worth of new funding. By end 2019 – the official closure of Data Pitch – we supported 47 data-driven start-ups from 13 different EU countries. Collectively to date, the start-ups have amassed a total of €22.4 million worth of impact through further investment, sales and efficiencies. Not only have we seen great success in terms of impact, but the programme is also estimated to see just a mere 6% (3) death rate of companies over the same period (2022). Data Pitch has not only helped businesses and public sector organisations to unlock value from data, but the partners have also enabled early-stage companies to create viable long-term solutions. By working closely with the Big Data Value Public-Private Partnership (BDV PPP), we aim to share these insights and learnings to support other EU-funded programmes to achieve similar success in helping to drive a positive impact within the European data economy (Fig. 6).

Fig. 6
figure 6

“A three-year journey to insights and investment” Entry

7 Scaling Up Data-Centric Start-Ups

Data Market Services is a consortium of accelerators, investors, consultants, lawyers, universities and corporations created in 2019 under the European Union’s Horizon 2020 research and innovation programme (Fig. 7).

Fig. 7
figure 7

“Scaling up data-centric start-ups” Entry

Its objective is to serve as a gateway for data-centric SMEs and start-ups in Europe to overcome market barriers through the provision of free services. The list of services provided includes a data science academy, entrepreneurial training, IP and GDPR awareness, standardisation and data workshops, storytelling packages, trust-building, fund-raising packages, and mentoring and venture match-making activities that are tailor-made to the needs and characteristics of their product and the company lifecycle. The selection of the portfolio of start-ups is based on a three-step scouting method. First, the businesses are shortlisted from EC-backed and private incubators and accelerators. Then, they are contacted, monitored and analysed to determine if they are an appropriate fit for the programme. Finally, they are categorised according to the lifecycle maturity of the company.

Over a year, Data Market Services recruited a portfolio of 50 start-ups, facilitated 40 meetings with investors and helped to secure €5 million in funding, with 60% of the start-ups increasing their teams (Fig. 7).

8 Campaign Booster

Digital marketing is evolving towards a content and message personalisation, adapting the services and products offered to the user’s likes and needs. This trend is also influenced by external factors like weather and events, which strongly affect user digital behaviour (interests) (Fig. 8).

Fig. 8
figure 8

“Campaign booster” Entry

In this scenario, JOT has combined internal predictive tools and the EW-Shopp toolkit aimed at deploying and hosting a platform to easily integrate multilingual consumer-related data with weather and event data to support analytics on top of the enriched data. The toolkit has processed 2 years of marketing data statistics from Spanish and German campaigns, which represents 100 Gb of data on weather and events. More than 3000 models per region were generated.

This has enabled JOT to predict (1)when the campaign has to be launched, (2) which is the best location, (3) which will be the most relevant category and (4) the expected impact.

Thanks to this new analytical system, by activating campaigns activated relevant keywords, JOT is now able to generate relevant traffic data in 1 day with 30–50% of impressions (Fig. 8).

9 AI Technology Meets Animal Welfare to Sustainably Feed the World

Every year, the global poultry industry wastes 9 billion edible infertile eggs and kills 7 billion 1-day-old male layers. This is unethical, unsustainable and very expensive. Orbem – a start-up that made it to the final stage of the European Data Incubator (EDI) – is developing AI-powered imaging technology to address these problems (Fig. 9).

Fig. 9
figure 9

“AI technology meets animal welfare to sustainably feed the world” Entry

Orbem’s AI technology combines non-invasive sensor technology with AI algorithms to automatically screen eggs. Specifically, we are developing the Genus: AI-powered magnetic resonance imaging (MRI) technology that predicts the fertility status of eggs before incubation and the sex of embryos in ovo. Throughout the EDI, Orbem adopted novel big data tools to improve AI model performance and to handle the large data streams demanded by the high-volume poultry industry. As a result, the technical solution evolved from proof of concept results to a minimal viable product operating on an industrial-scale computational unit. With these technical results at hand, they were able to confirm the impact of our technology across multiple dimensions, making a difference to the triple bottom line: people, planet and profit, creating a €2.3 billion yearly market opportunity and the introduction of 9 billion infertile eggs into the food market that would be the equivalent of one egg per day for 50% of 49.5 million children under 5 years of age who are malnourished (Fig. 9).

10 Creating the Next Generation of Smart Manufacturing with Federated Learning

The emerging data economy holds the promise of bringing innovation and huge efficiency gains to many established industries. However, confidentiality and the proprietary nature of data are often barriers as companies are simply not ready to give up their sovereignty. Musketeer offers the capacity to tackle these two dimensions by bringing efficiency while respecting the sovereignty of data providers in industrial assembly lines. Welding quality assessment can be improved using machine learning algorithms, but a single factory might offer too little data to create such algorithms. This requires accessing larger datasets from robots (Comau) located in different places to boost the robustness and quality of the machine learning model. Collecting manual ultrasound testing data and combining it with the welding data from the robot enables the algorithm to be trained locally. In parallel, this machine learning model is trained on different datasets from other factories. Trained models are eventually merged on the Musketeer platform (in a different location) to provide a robust model. Once the model is trained and has a satisfactory accuracy, thanks to this federated approach it becomes possible to provide the classification of the welding spot directly from the welding data. Massimo Ippolito, Head of Digital Innovation and Infrastructure at Comau, states that ‘Using federated and collaborative Machine Learning techniques, Comau will be able to provide innovative maintenance services to their customers providing them more robust and more accurate predictive models, using data coming from different customers plants, while at the same time preserving privacy issues related to Company data’ (Fig. 10).

Fig. 10
figure 10

“Creating the next generation of smart manufacturing with federated learning” Entry

11 Towards Open and Agile Big Data Analytics in Financial Sector

With more than 5000 branches, 40,000 employees and 14 million customers, CaixaBank is one of the largest financial institutions in Spain. Its consolidated big data models use more than 300 different data sources, and more than 700 internal and external active users are enriching its data every day, which is translated into a data warehouse with more than 4 petabytes that increases by 1 petabyte per year. Much of this information is already utilised by means of big data analytics techniques, for example to generate security alerts and prevent potential fraud. CaixaBank receives around 2000 attacks per month. Agility is key in this context, and CaixaBank needed to find ways to bypass rigid processes without compromising security or privacy. The GDPR limits the usage of customer data, even if used for fraud detection and prevention or for enhancing the security of customer accounts. The I-BiDaaS CaixaBank roadmap was a turning point for CaixaBank, and completely changed its approach from non-sharing real data at all positions to looking for the best possible way to share real data and perform big data analytics outside its facilities. I-BiDaaS helped to push for internal changes in policies and procedures and evaluate tokenisation processes as an enterprise standard to extract data outside their premises, breaking both internal and external data silos. This enabled a reduction of 75% of the time to access data by external stakeholders thanks to the use of synthetic data, breaking of data silos, external processing in a compliant way, and evaluation of external big data analytics tools in a much more agile manner (Fig. 11).

Fig. 11
figure 11

“Towards open and agile big data analytics in financial” Entry

12 Electric Vehicles for Humans

Are electric vehicles (EVs) a viable solution for everybody? Within the Track & Know H2020 project, solutions are being developed and tested that, through a mix of mobility data analytics, trip planning and simulation, can analyse the current fuel-based mobility of a user and quantitatively describe the expected impact of switching to EVs on their mobility lifestyle. Electric mobility is frequently addressed as one of the future ways to make cities more sustainable and to improve the quality of life in urban environments.

However, when it comes to private vehicles, the switch has to face the practical difficulties that it might introduce in the lives of travellers, and this is currently a big deterrent for mass conversions to electric vehicles. Single users need to evaluate how their mobility lifestyle is going to change when their fuel-based vehicle is replaced by an electric one, given the various constraints it introduces – the foremost being less independence and (at present) lower availability of recharge points – and in most cases, their lack of means. Our approach includes two answers: 1) numerical Key Performance Indicator (KPI), in particular ‘How often would I recharge?’, ‘How much time would I waste?’, ‘How much battery/how many euros would I spend?’ and ‘How much CO2 would I conserve?’; 2) impact on lifestyle, we place the (expected) recharge activities on the Individual Mobility Network (IMN), in order to understand which moments of a user’s life will be affected: the home-to-work routine? Trips to occasional destinations?

A mass analysis of several users can help to identify those who easily convert to using EVs and those who have difficulties. Put on a map, this will help to shape market strategies that address different geographical areas in different ways (Fig. 12).

Fig. 12
figure 12

“Electric vehicles for humans” Entry

13 Enabling 5G in Europe

Rui Costa and Nuno Ribeiro were two young(er) researchers developing software for the telecom sector when they decided to take a chance and create their own business. The year was 2007, and Ubiwhere was born in the lovely city of Aveiro, on the sunny and windy coast of Portugal. With a team of three inspired and motivated people, the start-up was created to do precisely what the founders did best: research projects for the telecom sector. Building on its know-how, Ubiwhere focused on the research and development of innovative user-centred software solutions, with expertise in Internet-of-things (IoT) and machine-to-machine (M2M) solutions, data management and analysis, open data, and cloud-based services, targeting the future through innovation. In 2015, the company succeeded in taking the first steps into the next-generation network world. Having shown the SME’s data analysis skills and ambition, Ubiwhere was invited to participate in two research projects funded by the European Commission, under the first phase of the 5G-PPP programme. This opened the doors to the creation of future-proof concepts and solution. All experts were present to propose an integrated approach for smart cities and city service providers and to combine multiple vertical domains into a unified ecosystem (mobility, environment and energy), allowing service providers to enhance their operational efficiency and cities to make better decisions based on data collected from diverse sources (Fig. 13).

Fig. 13
figure 13

“Enabling 5G in Europe” Entry

Ubiwhere is now almost 13 years old, with around 70 employees, building solutions to connect people with everything and leveraging an infinite number of possibilities for services in several sectors that can have a real impact on people’s lives. This motivation has led Ubiwhere to continually seek partners that can provide strategic value to both its research activities and commercial endeavours. Today, Ubiwhere is enhancing the future of 50 cities around the world (Fig. 13).

14 Summary

Ranging from industry transformation to promising start-ups, from agriculture to the retail industry, from the adoption of electric vehicles to ethical and societal policies, we hope that these brief descriptions of the stories give the reader the wish to know more about them. These 13 success stories are only the tip of the iceberg of all the work that is ongoing in the projects and companies from the BDV PPP ecosystem. Exploiting big data requires adding processing capabilities and smart algorithms: in addition to classical analytics tools, we have to highlight that AI technology, especially data-driven AI, is used in the majority of these success stories or the start-ups followed by our different incubators.

The know-how of our members is an extremely valuable asset for Europe, and it is no surprise that several BDV PPP members were instrumental in developing solutions to fight COVID-19 and that INRIA (FR), Orange (FR), INDRA (ES) and SAP(DE) were on the front line in the development of the tracing applications embedded in the privacy by design approach that conforms to the EU’s fundamental values.

Choosing amongst all the stories was not an easy task, but we hope that this chapter encourages the reader to learn more about the featured stories and the other stories that we cannot feature due to space limitations. If the reader wants to know more details about these stories and all of the participants in the 2020 contest, they can visit the BDV PPP website at the following URL: https://www.big-data-value.eu/best-success-story-award-2020/.

References

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Pazzaglia, JC., Alonso, D. (2021). Big Data Value Creation by Example. In: Curry, E., Metzger, A., Zillner, S., Pazzaglia, JC., García Robles, A. (eds) The Elements of Big Data Value. Springer, Cham. https://doi.org/10.1007/978-3-030-68176-0_10

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