Big Data

Summary

Big Data is one of the key economic assets of the future. Mastering the generation of Value from Big Data will create a significant competitive advantage for European industry, creating economic growth and jobs.  Value is considered one of the 5 Vs (currently) of Big Data with the others being Volume, Velocity, Variety and Veracity and so Big Data is not just ‘big’ as in, for example, exabytes of information, but also considers big as in high-speed, variety of types, or the ambiguity of the information. The volume of data however is rapidly growing: It is expected that by 2020 more than 16 zettabytes of useful data will exist (16 Trillion GB), which implies an equivalent growth of 236% per year from 2013 to 2020. This data explosion is a reality that Europe must both face and exploit in a structured and aggressive way to create value for society, its citizens, and its businesses in all sectors.  However, powerful tools have been developed to collect, store, analyse, process, and visualize huge amounts of data. Open data initiatives have been launched to provide broad access to data from the public sector, business and science.  Linked data initiatives have been established to help try to make sense of the different data sets. The following table has been presented by the recently established Big Data Value Public Private Partnership and the Big Data Value Association (BDVA) which represents the private side of the partnership along with the European Commission representing the public side.  It shows the impact that Big Data is having or will have. TEST Lucas Änderung hochgeladen

Sectors/Domains

Big Data Value

Source

Public administration

EUR 150 billion to EUR 300 billion in new value (Considering EU 23 larger governments)

OECD, 2013

Healthcare & Social Care

EUR 90 billion considering only the reduction of national healthcare expenditure in the EU

McKinsey Global Institute, 2011

Utilities

Reduce CO2 emissions by more than 2 gigatonnes, equivalent to EUR 79 billion (Global figure)

OECD, 2013

Transport and logistics

USD 500 billion in value worldwide in the form of time and fuel savings, or 380 megatonnes of CO2 emissions saved

OECD, 2013

Retail & Trade

60% potential increase in retailers’ operating margins possible with Big Data

McKinsey Global Institute, 2011

Geospatial

USD 800 billion in revenue to service providers and value to consumer and business end users

McKinsey Global Institute, 2011

Applications & Services

USD 51 billion worldwide directly associated to Big Data market (Services and applications)

Various

There are many challenges to establish a Big Data ecosystem and these can be represented in the following diagrams of the BDVA.  The hexagons represents the challenge areas which range from applications which can increasingly take advantage of Big Data to need to re-skill and educate the workforce of the future.  One of these, technical, is especially relevant to research and innovation projects such as CREMA, and further illustrated are 5 current technical fields which are seen as key to address to fulfil economic and business needs of the future,

The multiple dimensions of Big Data. Big Data and Big Data Value represents an extremely strategic and profitable opportunity for geographies and companies. But to drive innovation and competitiveness, it is necessary to foster the development and wide-scale adoption of Big Data Value technologies, successful use cases and data-driven business models. At the same time it requires dealing with many different aspects of an increasingly complex landscape:

  • Data: Availability of data and the access to data sources. There is a broad range of data types and data sources: structured and unstructured data, multi-lingual data sources, data generated from machines and sensors, data-at-rest and data–in-motion. Value is generated by acquiring data, combining data from different sources, and providing access to it while ensuring data integrity and preserving privacy. Value is added by pre-processing, validating, analysing augmenting and ensuring data integrity and accuracy
  • Skills: In order to leverage the potential of Big Data Value, a key challenge for Europe is to ensure the availability of highly and rightly skilled people who have an excellent grasp of the best practices and technologies for delivering Big Data Value within applications and solutions. There will be the need for data scientists and engineers who have expertise in analytics, statistics, machine learning, data mining and data management. These technical experts will need to be combined with domain experts with strong industrial knowledge and the ability to apply this know-how within organisations for value creation
  • Legal: The increased importance of data will intensify the debate on data ownership and usage, data protection and privacy, security, liability, cybercrime, Intellectual Property Rights (IPR) and the impact of insolvencies on data rights. These issues have to be resolved in order to remove adoption barriers and enable a favourable European regulatory environments that is needed to facilitate the development of a true Big Data market
  • Technical: Key aspects including real-time analytics, low latency and scalable data processing, new and rich user interfaces, data interaction and linking data, information and content, all have to be advanced to open up new opportunities and to sustain or develop competitive advantages. Interoperability of datasets and data-driven solutions are essential for a wide adoption within and across sectors. De facto standards are a primary mechanism to avoid any long negotiation process which could slow down Big Data market interoperability
  • Application: Business and market ready applications need to be a core target to allow activities to have market impact. Novel applications and solutions must be developed and validated based technologies and concepts in ecosystems that provide the basis for Europe to become world-leader in the creation of Big Data Value.
  • Business: A more efficient use of Big Data and understanding data as an economic asset carries great potential for the economy and society. The setup of Big Data Value ecosystems and the development of appropriate business models on top of a strong Big Data Value ecosystem must be supported in order to generate the desired positive impact on economy and employment
  • Social: Big Data will provide solutions for major societal challenges, such as the improved efficiency in healthcare information processing or reduced CO2 emissions through climate impact analysis. In parallel it is critical for an accelerated adoption of Big Data to increase awareness on the benefits and the Value that Big Data can create for business, the public sector, and the citizen

Relationship to CREMA

CREMA states that ‘for any collaborative situation it is critical to know and take advantage of the data and information flowing between the different parties.  Particularly in a multi-party environment it is key that maximum knowledge is captured and shared”. Thus CREMA task “Manufacturing Big Data Knowledge and Analytics” purpose is to take the data of CREMA, from sensors and applications and other input devices, and then to filter, digest and analyse it to project it to parties than can benefit from it.  This could be human operators through Visual analytics or to automated consumers of information to help with optimisation algorithms. This thus ties in with the ‘Value’ aspects mentioned above – ie the manufacturing users of CREMA will be able to take these feeds, information and knowledge regardless of whether they are executives, operators or machines.  Most probably the value will be generated from the consideration on the velocity (sensors), and variety of information (eg from different machines, partners etc) but also the veracity (ambiguity) of information may also need to be considered particularly in the optimisation algorithms.  From a technical perspective the technical fields of most interest will be Data Management, Data Analytics and Data Visualisation to the end users. 

General References


Articles

  1. Gartner Research, "Magic Quadrant for Business Intelligence and Analytics Platforms" 4 February 2016 (G00275847) Link
    The BI and analytics platform market's multiyear shift from IT-led enterprise reporting to business-led self-service analytics has passed the tipping point. Most new buying is of modern, business-user-centric platforms forcing a new market perspective, significantly reordering the vendor landscape.
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  2. Gartner Research, "Magic Quadrant for Business Intelligence and Analytics Platforms," 23 February 2015 (G00270380)
    Traditional BI market share leaders are being disrupted by platforms that expand access to analytics and deliver higher business value. BI leaders should track how traditionalists translate their forward-looking product investments into renewed momentum and an improved customer experience.
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  3. X. L. Dong and D. Srivastava, “BDI: Data Fusion” in Big Data Integration, 1st ed., Waterloo, USA: Morgan & Claypool Publishers, Synthesis Lectures on Data Management, 2015, pp. 107-136
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  4. G. Jifaa, Z. Linglingb, "Data, DIKW, Big Data and Data Science", 2nd International Conference on Information Technology and Quantitative Management (ITQM), Moscow, Russia, 2014.
    In this paper we discuss the relationship between data and DIKW, that the data only evolves to knowledge, which may have some value, but if without the wisdom we still could let the knowledge be really useful to people. Now the big data occupies much attention in some extent for his volume, velocity, and variety. But in practical use the value plays more important role. Finally to judge the value for data not necessary for big, in some cases the small data also may lead to big value. So we appreciate the data science, which may consider more inherent value from data.
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  5. P. Agarwal et. al., “Approximate Incremental Big-Data Harmonization”, IEEE International Congress on Big Data, Santa Clara Marriott, CA, USA, 2013, pp. 118-125
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  6. S. LaValle et. al. “Big Data, analytics and the path from insights to value”, MIT Sloan Management Review, 2013, vol. 21
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  7. A. Cuzzocrea et. al., “Analytics over large-scale multidimensional data: the big data revolution!” in ACM 14th International Workshop on Data Warehousing and OLAP, New York, USA, 2011, pp. 101-103
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Software

  1. Pentaho Link
    Pentaho Big Data: Within a single platform, this solution provides big data tools to extract, prepare and blend data, plus the visualizations and analytics that will change the way to run business. From Hadoop and NoSQL to analytic databases, Pentaho allows to turn big data into big insights.
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  2. Tableau Desktop Link
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  3. BIRST Link
    Birst’s Networked BI is a new and disruptive approach to enterprise BI
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  4. Apache (TM) Hadoop Link
    The Apache™ Hadoop® project develops open-source software for reliable, scalable, distributed computing. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.
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  5. MongoDB Link
    MongoDB 3.2 is a giant leap forward that helps organizations standardize on a single, modern database for their new, mission-critical applications.
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Projects

  1. datACRON (2016-2018): Big Data Analytics for Time Critical Mobility Forecasting. H2020-ICT-2015 Programme of the European Commission Link
    addresses core challenges related to the European Big Data Vision towards increasing our abilities to acquire, integrate, process, analyse and visualize data-in-motion and data-at-rest in integrated manners. will address requirements from the air-traffic management and maritime domains by developing advanced tools for detecting and visualizing threats, abnormal activity, increasing the safety and efficiency of operations related to vessels and airplanes, and further reducing the impact of these operations on the environment.
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  2. BigData4ATM Project (2016-2018) "Passenger-centric Big Data Sources for Socio-economic and Behavioural Research in ATM". SESAR 2020 Exploratory Research. Link
    BigData4ATM (Passenger-centric Big Data Sources for Socio-economic and Behavioural Research in ATM) is a research project within SESAR 2020 Exploratory Research which aims to investigate how new sources of passenger-centric data coming from smart personal devices can be analysed to extract relevant information about passengers’ behaviour and how this information can be used to inform ATM decision making processes. The project is conducted by a consortium composed by Nommon (Project Coordinator), IFISC, Fraunhofer-IAIS, the Hebrew University of Jerusalem, and ISDEFE. The project started on 9 May 2016 and will run for 24 months.
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  3. Big Data Europe (2015-2017) Link
    The growing digitization and networking process within our society has a large influence on all aspects of everyday life. Large amounts of data are being produced permanently, and when these are analyzed and interlinked they have the potential to create new knowledge and intelligent solutions for economy and society. Big Data can make important contributions to the technical progress in our societal key sectors and help shape business. What is needed are innovative technologies, strategies and competencies for the beneficial use of Big Data to address societal needs. Climate, Energy, Food, Health, Transport, Security, and Social Sciences – are the most important societal challenges tackled by the European Union within the new research and innovation framework program “Horizon 2020”. In every one of these fields, the processing, analysis and integration of large amounts of data plays a growing role – such as the analysis of medical data, the decentralized supply with renewable energies or the optimization of traffic flow in large cities.
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  4. BISON Project (2015-2018): Big speech data analytics for contact centers. H2020 Programme of the European Commission. Link
    Contact centers (CC) are an important business for Europe: 35,000 contact centers generate 3.2 Million jobs (~1% of Europe’s active population). A typical CC produces a wealth of multilingual spoken data that is nowadays mined by humans (CC agents and supervisors) or by rudimentary technical means.

    BISON consortium plans to bring significant innovations in three areas: (1) basic speech data mining technologies (systems quickly adaptable to new languages, domains and CC campaigns), (2) business outcome mining from speech (translated into improvement of CCs’ Key Performance Indicators) and (3) CC support systems integrating both speech and business outcome mining in user-friendly way.

    The project will produce two prototypes: smallBison (end of the 1st year) will be a functioning system for real, though limited, deployment and user feedback collection. bigBison (end of the project) will include full range of capabilities and be fully integrated with CC hardware and software infrastructure. Generation of business outputs will be demonstrated on real data.

    Business indicators and values for the market were instrumental for the definition of the project and will be crucial for project execution.

    BISON consortium is composed of eight players with complementary skills. Two end users running large CC operations (EBOS, ComCzech) are generating user requirements and are ready to deploy the prototypes immediately in real scenarios. Phonexia (the coordinator), Brno University of Technology and Telefónica I+D are experts in speech data mining - from R&D, data processing to developing products placed on the market. Telefónica Móviles is an expert in business outcome mining and MyForce is a skilled Contact Center hardware and software integrator. CC data involve a number of legal issues, therefore, the University of Bologna (with significant experience in regulatory and legal aspects) complements the consortium.
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  5. BYTE Project (2014-2017): The Big data roadmap and cross-disciplinarY community for addressing socieTal Externalities Link
    The BYTE project will assist European science and industry in capturing the positive externalities and diminishing the negative externalities associated with big data in order to gain a greater share of the big data market by 2020.

    BYTE will accomplish this by leveraging the BYTE advisory board and additional network contacts to conduct a series of big data case studies in actual big data practices across a range of disciplinary and industrial sectors to gain an understanding of the economic, legal, social, ethical and political externalities that are in evidence. BYTE will supplement these case studies with a horizontal analysis that identifies how positive externalities can be amplified and negative externalities can be diminished.

    BYTE moves beyond current practices to consider how big data will develop to the year 2020 using foresight tools to identify future practices, applications and positive and negative externalities. This will allow BYTE to develop, in collaboration with expert stakeholders, a vision for big data in 2020 that includes meeting the relevant goals of the Digital Agenda for Europe. In collaboration with expert stakeholders, the consortium will then devise a research and policy roadmap that will provide incremental steps necessary to achieve the BYTE vision and guidelines to assist industry and scientists to address externalities in order to improve innovation and competitiveness.

    BYTE will culminate in the launch of the big data community, a sustainable, cross-disciplinary platform that will implement the roadmap and assist stakeholders in identifying and meeting big data challenges. Furthermore, BYTE will disseminate project findings and recommendations and publicise the big data community to a large population of stakeholders to encourage further innovation and economic competitiveness in Europe’s engagement with big data.
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This page was last changed on 4 May 2017, at 11:19.


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