32 2 Field of Action I-egovernment, Interoperability, Cyber security, Cloud computing, Open Data, Big data and Social media...
63 2. 6 Big data...64 2. 6. 1 Introduction...64 2. 6. 2 European context...
Field of action 1-egovernment, Interoperability, Cyber security, Cloud computing, Open Data, Big data and Social media increase efficiency
, Open Data, Big data and Social media 1. 1. Increasing the transparency of public administration acts through computerization of public services II-Interoperability and standards I-A vibrant digital single market
Big data and Social media Define the Informational Perimeter of Public services Ministry for Information Society (responsible) All Ministries offering public services (support) egovernment
at least 2 per county by 2020#of applications performed based on Big data. Unique Point of Contact or Single Sign on Ministry for Information Society (responsible) All Ministries offering public services Procure
Transparency, Participation and Collaboration Support for use of Big data in public administration Ministry for Information Society (responsible) All Ministries offering public services Elaboration of a legal frame related to the free
Ministry for Information Society (responsible) All Ministries offering public services Big data#of applications developed using Big data databases Target:
To be defined based on Appendix 5 Methodology#of application developed using Big data databases Target: To be defined based on Appendix 5 Methodology Educate teachers on ICT technologies Ministry of Education (responsible) Ministry for Information Society (support) Provide ICT specific training courses, directly related to the improvement of the quality of the learning
75%by 2020#of applications performed based on Big data. Target: At least 10 applications#of localities and medical centers benefiting from telemedicine services.
25%by 2020#of application developed using Big data databases Target: To be defined based on Appendix 5 Methodology#of digitized units of Achieve the minimum contribution to Europeana. eu (the European digital library) Ministry of Culture (responsible) Ministry for Information Society (support) Digitize the cultural content
75%by 2020#of application developed using Big data databases Target: To be defined based on Appendix 5 Methodology Field of action 3 ecommerce,
31 of 170 The Digital Agenda Roadmap for the implementation of strategic initiatives Field of Action Iegovernment, Interoperability, Cyber security, Cloud computing, Open Data, Big data and Social Mediafield of Action
BIG DATA AND SOCIAL MEDIA 2. 1 EGOVERNMENT AND INTEROPERABILITY 2. 1. 1 Introduction Preamble The combination of the use of advanced ICT, especially the Internet,
Transparency, Participation and Collaboration (Enabler) Support for use of Big data in public administration (Operational) Indicators: Number of public initiatives promoted through social media Number of ideas,
The Department for Online services and Design 2. 6 BIG DATA 2. 6. 1 Introduction Preamble Page 65 of 170 Big data is a concept
The Big data systems may provide information both to governmental organisations and to citizens from different sources which may be identified as follows:
Paper document (physical environment) Digital documents Points of access to governmental web Websites located on Internet Social media Operational systems available The information provided by Big data systems does not include personal information
Big data Definition Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on hand database management tools or traditional data processing applications.
There's nothing new about the notion of big data, which has been around since at least 2001. Big data is related with the information owned by your private organization
or public institution obtained and processed through new techniques to produce value in the best way possible. 2 6. 2 European context Data creation is occurring at an unexpected record rate.
Market research analysts believes that organizations that are best able to make real time business decisions using Big data solutions will thrive,
Big data, a general term for the massive amount of digital data being collected from all sorts of sources,
and business reports have proposed ways governments can use big data to help them serve their citizens
At the European level, the improvement of the analytics and data processing, especially Big data, will allow to:
has drafted a Strategic Research and Innovation Agenda (SRIA) on Big data Value for Europe. The objective of the SRIA is to describe the main research challenges
and needs for advancing Big data Value in Europe in the next 5 to 10 years. 2. 6. 3 National context Big data analytics can improve efficiency and effectiveness across the broad range of government responsibilities,
in reducing operational costs and improve collections. 2. 6. 4 Strategic Lines of Development Big data Approach in Romania Concepts Lines of Action Comments Big data-refers to an informational initiative
Strategic) Use the Big data concepts in order to optimize, reduce costs or bring value added services Example of fields where Big data project have proven feasible:
-Health (statistical analysis of cases, The Government is increasingly dependant on large variety of programs and services,
often unstructured and increasingly in real-time The benefits of leveraging Big data concepts include: -Reduced overpayments-Better fraud and abuse-Improve efficiency-Improved program integrity and preservation of limited budgets for eligible citizens Page 68 of 170 3 FIELD OF ACTION
and used for the management of the healthcare system's resources (Enabler) Using Big data to manage the information generated by the IT system will help increase transparency and flexibility of the medical system.
targeting the utilisation of common standards for the performance of the pan-European communication of these systems Utilisation of certain Big data technologies for the review of data generated by healthcare informatics system
indirect direct direct#of applications performed based on Big data. direct direct direct indirect indirect indirect indirect indirect indirect direct direct#of public initiatives promoted by social media. direct indirect indirect direct indirect direct direct direct indirect indirect
indirect indirect#of applications developed using Big data databases direct direct direct direct indirect indirect indirect indirect indirect indirect indirect%individuals using the internet regularly. direct direct direct direct direct direct direct direct indirect indirect
#of application developed using Big data databases direct direct direct indirect indirect indirect indirect indirect indirect indirect indirect%of data registries identified
and implemented. direct direct direct indirect indirect indirect indirect indirect indirect indirect indirect#of applications performed based on Big data. direct direct direct indirect indirect indirect indirect indirect indirect indirect
or similar. direct indirect indirect direct indirect direct direct direct indirect indirect indirect#of application developed using Big data databases direct direct direct indirect indirect indirect indirect indirect indirect indirect
Web 2. 0 in education. direct direct direct direct direct direct direct direct indirect direct indirect#of application developed using Big data databases indirect indirect direct direct direct indirect indirect indirect indirect indirect
smart business, company, home smart city information security, security technology gamification, simulation and optimisation technology e-learning systems big data data mining software development remote monitoring
Yet on the level of services, the emerging cloud model of some services (proprietary social networks, big data providers, implementations of the Internet of things
while the value of big data is associated often only with efficiency and profitability, big data can also be used for social good,
to improve public services and stimulate inclusive innovation. 1. 3 DIGITAL SOCIAL INNOVATION IN THE CONTEXT OF FUTURE INTERNET IN EUROPE The world wide web became successful
Big data can also be used for social good, to improve public services and stimulate inclusive innovation. 18 Growing a Digital Social Innovation Ecosystem for Europe European SMES,
social media, crowdsourcing, crowdfunding, big data, machine learning, 3d printing, online learning and e-petitions. The main technological trends in DSI 0100 200 300 400 Arduino Smart Citizen Kit Fairphone Safecast OPEN NETWORKS Tor Confine Guifi. net Smart
what has been named as Industry 4. 019 This smart infrastructure is also increasingly getting to know people by aggregating personal and social data in massive data centres.
adapted from Sestini, F (Digital) Innovation Venture capital Big data and cloud computing COMPETITION, ECONOMIC ENTERESTS Innovation and innovation policy are not new to the European union.
A EU Big data strategy is becoming a priority for the competitiveness of European industries. In this framework the EC is promising to launch a multi-million euro Public Private Partnership on big data with industry.
The focus is driven business, with little attention to societal challenges or to the inclusion of civil society and bottom-up approaches.
Big data and cloud companies but also States have a lot of control over an individual's online identity.
Mckinsey Global Institute, Big data: The next frontier for innovation, competition and productivity, May 2011; available at:
http://www. mckinsey. com/mgi/publications/big data/index. asp. 151 Behavioural targeting or behavioural advertising is used a technique by online publishers
Open/Big data and fast mobile communications are all creating opportunities for major changes in business models, in societal behaviour and in value-creation models in general.
Big data in future cities Another‘hot topic'nowadays is‘big data'.'How can the use of big data create Future Cities?
Cities are immersed in huge amounts of data, which come from everywhere: buildings, phones, utilities, trains, etc.
big data can allow us to easily understand every level of city administration, users/citizen behaviour and market implications.
and Co-Creation Of value 95 Oulu Innovation Alliance an Open Innovation Ecosystem 105 Smart Fabric to Big data:
Big data, Youth Innovation, Smart Cities and two very special, but interesting, topics on Lawyers in Innovation as well as Drivers for Creativity Based on Humor!
Carrol interlinks existing youth unemployment solutions with modern approach of using data (and especially big data) as driver for future growth.
the extension of many big data projects to get more out of the datasets governed by financials.
and under the new‘Big data'phenomenon lays an opportunity to create value and benefits for society, business and citizens.
if US healthcare were to use big data creatively and effectively to drive efficiency and quality,
government administrators could save more than €100 billion ($149 billion) in operational efficiency improvements alone by using big data,
not including using big data to reduce fraud and errors and boost the collection of tax revenue.
Big data, Bigger Digital Shadows, and Biggest Growth in the Far east; IDC; December 2012; Available from:
http://www. emc. com/collateral/analyst-reports/idc-the-digitaluniverse-in-2020. pdf (8) Big data Big Impact:
http://www. weforum. org/reports/big data-bigimpact-new-possibilities-international-development http://www3. weforum. org/docs/WEF TC MFS Bigdatabigimpact briefing 2012. pdf (9
) Talbot D. Big data from Cheap Phones (Internet. 2013; Available from: http://www. technologyreview. com/lists/breakthrough-technologies/2013/10) Manyika J, Chui M.,Brown B.,Bughin J.,Dobbs R.,Roxburgh C
.,Hung Byers A. Big data: The next frontier for innovation, competition, and productivity. Mckinsey Global Institute; 2011.
to Big data: from One Innovation to Two Promising Businesses Introduction The Internet of things is now a reality.
Our geo time series technology does not address the final usage of Big data but the way we manage the data itself.
2) versatile sensors and actuators integrated in electronic textiles 3) platform, architecture, big data analysis and visualisation solutions for novel sport and health solutions,
If you are part of the big data movement, you would say that brainstorming is unreliable. With data-driven innovation, innovators generate ideas by exploiting existing
other than being perceived as entangled in the big data game. Thanks to the crisis and existing management techniques
and service delivery and business processes, requires companies to respond to global megatrends such as the cloud, web based delivery, big data, mobile commerce, cost of energy, technology pace and globalisation/localisation.
and service delivery and business processes, requires companies to respond to global megatrends such as the cloud, web based delivery, big data, mobile commerce, cost of energy, technology pace and globalisation/localisation.
, medical education, analises for sets of medical data, telemedicine, nano-electronics, opto-electronics, industrial software, Big data, GPS, ERP data systems, cloud computing, intelligent wireless
program fiche (ICT/Big data) in the online real-time Delphi consultations. Exploration and discovery Extended online consultation, Aug-Sept 2013 Critical mass 02004006008001000120014001600180020002014201520162017201820192020precondition:
ICT Analysis, management and security of big data Future internet Software development technologies, instruments, and methods High performance computing and new computational models A3.
and R&i programs include/assume KETS Biotech, ICT (Big data, future internet etc.),Advanced and nano-materials The R&i program fiches in the smart specializations fields provide where pertinent arguments pro/against the relevant KETS.
such as mobile applications and technology, cyber security, Big data, Internet of the Future, Cloud computing, all of which are crosscutting technologies for any economic activity
crowdsourcing, utilising big data; but much of this is still in experimental mode13 and European cities tend to lag those in the US14.
to harvest the potential of Big data? To meet these challenges, innovation is no longer a simple strategic option,
Big data is a goldmine for companies...p. 6 Boosting e-skills in European higher education requires political will at national level...
Experts believe a new wave of big data and smartphone applications has the highest potential in terms of job creation.
Big data is a goldmine for companies Computer algorithms are better at diagnosing severe cancer than humans,
and big data can predict crimes before they are committed and earn businesses money. Kenneth Cukier is data editor at The Economist
and co-author with Viktor Mayer-Schönberger of Big data: A Revolution That Will Transform How We Live Work
He spoke to Euractiv's James Crisp about what big data can teach us. What is big data?
Well there's no single definition, which is probably a good thing, because to define it is to constrain it.
which is a great example of how big data can be commodified. So big data can be sold?
Absolutely. In fact big data is a potential gold mine. There are a few forward-thinking companies who have realised they can sell the data they collect as they go about their everyday work.
It will be a revenue generator. In the future I expect to see companies employing data or chief information officers,
as people realise the enormous potential of big data. Will there be an impact on how people work?
Both white colour and blue collar jobs will be replaced by big data, but that destruction will also create jobs.
But it is hard to predict the impact of the big data revolution. What can policymakers do to ensure that the power of big data can be exploited?
The issue of data privacy and protection has been deservedly getting a lot of attention recently. What needs to happen is a change in law to reflect the reality of this type of statistical collection
That isn't really feasible with big data. Continued on Page 7 Euractiv ESKILLS FOR GROWTH SPECIAL REPORT 5-9 may 2014 7 Boosting e-skills in European higher education requires political will at national level With 25%of adults in the European union
Big data is like a mosh pit or jazz-improv. No one knows what's coming next.
So regulators need to support this new reality, not least because of the huge potential of big data.
What are the dangers of big data? Of course there are risks, and there will be challenging questions for us to answer as we enter this new reality,
Big data could be used to predict which people are most likely to commit murder. That throws up interesting questions.
There is an argument to suggest that the 2008 Financial crisis was in a way a crisis of big data.
But despite that I am convinced big data will change the world for the better. Continued from Page 6 Continued on Page 8 8 5-9 may 2014 SPECIAL REPORT ESKILLS FOR GROWTH Euractiv between member states
Need is the mother of innovation Chatzidakis noted that the revenues from big data are expected to amount to €16 billion on a global level,
Using big data for the future of personal transportation: DATASIM Published by Newsroom Editor(/digital-agenda/en/users/Newsroom) on 26/11/2014 Many scientists point out that the goal of social sciences is not simply to understand how people behave in large groups,
and highly detailed spatial-temporal microsimulation methodology for human mobility, grounded on massive amounts of Big data of various types and from various sources, like GPS, mobile phones and social networking sites.
trying to examine technological issues such as Big data, Cloud computing, Mobile services, etc.,from a managerial perspective, aiming to reach a wide spectrum of executives,
In particular, Part I first considers Digital Systems Trends issues related to the growing relevance, on the one hand, of Big data, Cloud computing,
Focusing on systems evolution trends from a technology push perspective, the analysis will move from information and service infrastructure topics such as Big data and Cloud computing,
Vincenzo Morabito Acknowledgments xiii Contents Part I Digital Systems Trends 1 Big data...3 1. 1 Introduction...
3 1. 1. 1 Big data Drivers and Characteristics...5 1. 1. 2 Management Challenges and Opportunities...
Chapter 1 Big data Abstract The role of this Chapter is to introduce the reader to the area of Big data,
one of the IT trends actually emerging as strategic for companies competing in current digital global market.
The Chapter aims to clarify the main drivers and characteristics of Big data, both at technical and managerial level.
Furthermore, the Chapter aims at investigating management challenges and opportunities, identifying the main phases and actions of a Big data lifecycle.
suitable to support Big data-driven decision making and operational performance. 1. 1 Introduction‘‘Try to imagine your life without secrets''claimed the incipit of an article by Niv Ahituv appeared on the Communications of the ACM in 2001 1. The author preconized the advent of an Open
and potentially see the world as a big data repository to be exploited, adapted, and aggregated depending on their current needs.
in order to clearly understand actual and future business challenges of the phenomenon called Big data, a core component of the information infrastructure upon which our society is building its own open environment. 2 1 In the following we use data
Senior vice president of Wintrust Financial in an article appeared in July 2013 on Bloomberg Businessweek 38.4 1 Big data 1. 1. 1 Big data Drivers and Characteristics The spread of social media as a main
As a consequence of the above scenario, the term‘‘Big data''is dubbed to indicate the challenges associated with the emergence of data sets
Furthermore, Big data require new capabilities 5 to control external and internal information flows transforming them in strategic resources to define strategies for products and services that meet customers'needs, increasingly informed and demanding.
Thus, Big data call for a radical change to business models and human resources in terms of information orientation and a unique valorization of a company information asset for investments and support for strategic decisions.
Nevertheless, as usual with new concepts, also Big data ask for a clarification of their characteristics and drivers.
At the state of the art the following four dimensions are recognized as characterizing Big data 6 8: Volume: the first dimension concerns the unmatched quantity of data actually available and storable by businesses (terabytes or even petabytes), through the internet:
for example, 12 terabytes of Tweets are created every day into improved product sentiment analysis 6. BIG DATA Cloud computing Social networks Internet of things Mobile 80%of the world's data is unstructured.
1 2 3 4 Veracity Fig. 1. 1 Big data drivers and characteristics 1. 1 Introduction 5 Velocity:
the second dimension concerns the dynamics of the volume of data, namely the time-sensitive nature of Big data,
It is worth noting that at the state of the art another dimension is considered actually relevant to Big data characterization:
Thus, this dimension is relevant to a strategic use of Big data by businesses, extending in terms of scale
depending on its impact on one of the Big data characteristics. As pointed out by Pospiech and Felden 7, at the state of the art,
cloud computing is considered a key driver of Big data, for the growing size of available data requires scalable database management systems (DBMS).
on the other hand,(ii) a reduce function that merges all intermediate values associated with the same intermediate key 41.6 1 Big data the open source computing framework Hadoop have received a growing interest
and adoption in both industry and academia. 5 Considering velocity, there is a debate in academia about considering Big data as encompassing both data‘‘stocks''and‘‘flows''14.
For example, at the state of the art Piccoli and Pigni 15 propose to distinguish the elements of digital data streams (DDSS) from‘‘big data'';
provided or not they are stored as‘‘Big data''.''The types of use of‘‘big''DDSS may be classified according to the ones Davenport et al. 14 have pointed out for Big data applications to information flows:
Support customer-facing processes: e g.,, to identify fraud or medical patients health risk. Continuous process monitoring:
and Big data is useful to point out a difference in scope and target of decision making, and analytic activities, depending on the business goals and the type of action required.
Big data refer to the information asset an organization is actually able to archive, manage and exploit for decision making,
we now focus on Big data applications. As shown in Fig. 1. 2 they cover many industries,
and energy (Footnote 4 continued) Mapreduce has been used to rewrite the production indexing system that produces the data structures used for the Google web search service 41.5 See for example how IBM has exploited/integrated Hadoop 42.1.1 Introduction 7 management.
Moreover, marketing and service may exploit Big data for increasing customer experience, through the adoption of social media analytics focused on sentiment analysis,
Big data represent an opportunity, on the one hand, e g.,, for improving fraud detection as tax evasion control through the integration of a large number of public administration databases;
Thus, Big data seem to have a strategic value for organizations in many industries, confirming the claim by Andrew Mcafee
Nevertheless, we believe that management challenges and opportunities of Big data need for further discussion and analyses,
In the following Section, we actually would try to provide some arguments for understanding Big data value from a business and management point of view.
BIG DATA Applications Public sector Banks/Insurances Marketing/Services Utilities/Manufacturing Sentiment Analysis Opinion Mining Social media Analytics Recommender systems Riskanalysis Fraud detection
and control Sensor Data Fusion Fig. 1. 2 Big data applications 8 1 Big data 1 1. 2 Management Challenges and Opportunities In the Sect. 1
. 1. 1 we have provided a set of drivers and characteristics actually identifying Big data and their target applications.
Big data seems to be yet another brick in the wall in the long discussion in the information systems field on information supply to decision makers and operations in enterprise.
Big data change the rules of the game, asking to change the overall information orientation 22 of an organization (from the separation of stocks and flows,
Thus, Big data are different because they actually prompt a rethinking of assumptions about relationships and roles of business and IT,
Accordingly, Big data change decision making and human resources with regard to capabilities satisfying it, integrating programming, mathematical, statistical skills along with business acumen, creativity in interpreting data and effective communication of the results 5
Therefore, Big data challenges can actually be addressed by actions asking a technological/functional or else a business perspective, depending on the skills required by the specific task to be held.
, information systems and computer science, among other fields, contributions to Big data research. In Table 1. 1 we classify these perspectives with regard to their type
and we associate actions they may be suitable to support in Big data value exploitation. Considering, the technological type of perspective, the Technical-Data-Provisioning classification mainly concerns storage related actions with regards to database management systems performance, in particular,
leading to an advanced information demand analysis and improved information supply 7. Table 1. 1 Big data perspectives
this may be seen as a management of information systems perspective, governing the overall lifecycle from Big data storage to use.
and experience in the usage of Big data from state of the art in various disciplines such as, e g.,, social sciences, finance, bioinformatics,
and climate science, among others 7. Considering now the actions required for exploiting Big data value,
companies need actions for Big data management for (i) valuing information asset,(ii) understanding costs, (iii) improving data governance practices to extract the right data 23,(iv) providing useful information to demanding business processes and decision making.
Volume of data Value of information BIG DATA Business Information systems Processes High High Technological perspective Business perspective Management Executives often have to make decisions based on information they do not trust,
Low Low Fig. 1. 3 Big data management challenges. Adapted from 7 10 1 Big data As for data governance
several approaches have been proposed in the literature for Data Quality Management (DQM) to face strategic and operational challenges with quality of corporate data 25.
In Table 1. 2 we show the ones highlighted by Tallon 23 for implementing data governance practices suitable to support valuable Big data management.
due to the distributed nature of Big data and the unpredictable dynamics of the digital environment producing them.
organization may fail to exploit Big data. Indeed, optimization often leads to rigidity and inflexibility of business processes,
and use of Big data is what Awargal and Weill 27 called softscaling, requiring three core capabilities for companies
and exploit Big data to develop flexible strategy and business models, thus, anticipating and responding to volatility of market and customer needs,
Furthermore, companies aiming to exploit the opportunities offered by Big data have to connect business agility to information value (axes in Fig. 1. 4), through informed empathy.
The above arguments and cases lead us to the third Big data lifecycle challenge. As for their use
actions and targets of IT as enabling factor 12 1 Big data management, and workforce planning and allocation.
it is worth noting that data were considered not by interviewees among the main impediments to a full exploitation of Big data opportunities to business value.
and IT actions of Big data for business value as follows: Convergence of information sources: IT in the organization must enable the construction of a‘‘data asset''from internal and external sources, unique, integrated and of quality.
and evolution of Big data as key part of the digital asset of today's organizations. To this end, Fig. 1. 5 shows how digital asset components, i e.,
1. 1 Introduction 13 As a consequence, the competitive environment and the outer context both represent the main Big data sources,
As for decisions, integration orientation seems to be required for satisfying the needs for optimization and effective data management of Big data.
Applying to Big data issues the SIGMA model, that we have proposed in a previous work to improve strategic information governance modeling and assessment 29,
Indeed, absorptive capacity measures the ability of an organization to complete a learning process as coping with IT complexity or in our case with Big data management and use by businesses.
which aim to exploit the opportunities of Big data for business performance and value. Decisions Actions Application integration Data Integration Integration Orientation IS Organizational Absorptive Capacity Process Orientation Change orientation Analytics Orientation Information Orientation IT PORTFOLIO DATA ASSET
COMPETITIVE ENVIRONMENT (Outer Context)( Services)( Data) DIGITAL ASSET Fig. 1. 5 A framework for managing digital asset 14 1 Big data Taking all the
and recommendations for managerial actions in building what we call a Big data intelligence agenda. It is worth noting that a relevant factor
which illustrate at a glance how strategy points for Big data lifecycle phases in Table 1. 3 have been addressed in practice,
and strategy points for big data lifecycle phases Lifecycle phase Factors Recommendations Strategy points Storage Technology Consolidate corporate databases (internal)
and an appropriate level of decision-making autonomy at all levels of the company Accountability 1. 1 Introduction 15 The first case study shows the relevance of having a clear business strategy aligned with IS strategy for Big data exploitation from social media.
Using Big data should be enhanced and supported by a business strategy focused and shared by the overall company functions and processes.
and should not be bound by formal standards that might reduce its effectiveness in the short and long term. 16 1 Big data As a consequence, Baharti Airtel,
The third case study, based on a Cloudera case history 33, focuses again on the relevance of consolidation and integration for retrieving valuable information from Big data, with a specific attention to data base technologies.
and Hadoop at the core of Nokia's infrastructure. POINT OF ATTENTION: Big data ask for a clear understanding of both IT Portfolio and data asset,
for identifying relevant data from all information sources (internal/external) to be stored, and for a savvy and sustainable choice of the right mix of technologies to consolidate corporate databases (internal)
As reported by Cloudera 33 the centralized Hadoop cluster actually contains 0. 5 PB of data.
and efficiently moving of data from, for example, servers in Singapore to a Hadoop cluster in the UK data center.
The solution has been found in Cloudera's Distribution that includes Apache Hadoop (CDH bundling the most popular open source projects 1. 2 Case studies 17 in the Apache Hadoop stack into a single, integrated package.
In 2011, Nokia put its central CDH cluster into production to serve as the company's information core.
Finally, we present a case study that shows how a Big data strategy can be implemented in a specific industry.
the US based utility corporation, is building Big data and analytics capabilities for an‘‘Industrial Internet''.
''In 2011, GE announced $1 billion investment to build software and expertise on Big data analytics, launching a global software center in San ramon, California.
developing software and data science capabilities for GE's Big data domain of interest(‘the industrial Internet').
Big data require top management commitment and investments, in particular, on human resources to be focused on data scientist capabilities.
Furthermore, talent management and employees retention have to be considered as a core target for the success of a Big data strategy.
GE envisions Big data as a $30 trillion opportunity by 2030, using a conservative 1%savings in five sectors that buy its machinery (aviation, power, healthcare, rail,
Big data is strategic for a growing percentage of GE's business related to services, such as, e g.,
, airlines, electric utilities, hospitals to exploit GE's Big data expertise, generating big savings, likewise. Thus, human resources and talent management are key issues to GE Big data strategy.
The center has a staff of about 300 employees (most of them characterized as‘‘hardcore data scientists),
''Ruh, reported by Consultancy (2013). 18 1 Big data 1. 3 Summary In this Chapter, we have discussed the business challenges of Big data as a core component of the information infrastructure upon which our society is building its own open environment.
Often referred as an IT trend, the Chapter has clarified the main drivers and characteristics of Big data,
both at technical and managerial level, emphasizing their differences with regards to, e g.,, digital data streams (DDSS;
provided or not they are stored as‘‘Big data''.''Furthermore, we have investigated management challenges and opportunities, identifying the main phases and actions of a Big data lifecycle.
As for these issues, the Chapter has pointed out the relevance of‘‘softscaling''approaches, balancing optimization issues, such as, e g.,
and an attention to experience and contextual needs for an empathic exploitation of Big data as a digital asset.
confirming the importance of a clear and shared Big data strategy together with investments and focus on human resources for capabilities,
suitable to support Big data-driven decision making and operational performance. References 1. Ahituv N (2001) The open information society.
Harv Bus Rev 90 (10): 70 76 6. IBM (2013) What is big data? http://www-01. ibm. com/software/data/bigdata/.
/Accessed 9 jul 2013 7. Pospiech M, Felden C (2012) Big data a state-of-the-art. In: Americas conference on information systems (AMCIS 2012) 8. Mcafee A, Brynjolfsson E (2012) Big data:
the management revolution. Harv Bus Rev 90 (10): 61 68 9. Wang RY, Strong DM (1996) Beyond accuracy:
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how GE is building big data, software and analytics capabilities for an‘‘Industrial Internet.''''http://sites. tcs. com/big data-study/ge-big data-case-study/.
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Avison DE, Fitzgerald G (1999) Information systems development. In: Currie WL, Galliers RD (eds) Rethinking management information systems:
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Mcgraw-hill Osborne Media, New york References 21 Chapter 2 Cloud computing Abstract During the last decade, the Information and Communication Technology (ICT) industry has been transformed by innovations that fundamentally changed the way
and Big data. 26 2 Cloud computing 2. 2. 1 Challenges Accompanying Cloud computing Businesses across industries have come to a consensus about the inherent business value of cloud computing
These applications use the Mapreduce frameworks such as Hadoop for scalable and fault-tolerant data processing. However
modeling the performance of Hadoop tasks (either online or offline) and the adaptive scheduling in dynamic conditions form an important challenge in cloud computing. 8. Storage technologies and data management.
Big data Amazon's Dynamo, HBASE, Google's Bigtable, Cassandra, Hadoop, etc. Ultra-fast, low latency switches Cisco Networks, etc.
or through the internet (see Chap. 1 of this book on Big data issues). As already seen in previous Sections, new instruments such as, e g.,
in forms of audio or video streams) Increased storage Hyperscale storagea for big data Fast stream processing platforms Platforms such as, e g.,
and minimize the cost, focusing on a high degree of automation (see also Chap. 1 of this book for storage issues for Big data) 4. 5 Social Sensing 79 mobile phones and tablets or ipad.
compression techniques, supporting the efficient process of large amounts of data or Big data; data quality techniques, enabling, e g.,
and priorities for IT executives as well as for other Cxos (as also early emphasized in Chap. 1 on Big data).
Baeza-Yates R (2013) Big data or Right Data? In: Proceedings 7th Alberto Mendelzon International Work Foundation Data Management Puebla/Cholula, Mex May 21 23 30.
it is worth noting that the potential evolution trends are going to concern a further focus on convergence of mobile services and social sensing, that is an increased exploitation of advanced analytics for behavioral analysis from intensive data streams as well as from Big data.
As for the digital trends we have considered the business challenges of Big data as a core component of the information infrastructure upon which our society is building its own open environment.
34 Bring your own device (BYOD), 90,97, 103,108, 109,134 Big data, 5 CCALLED technology steward (TS),
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