innovation and the digital economy Empowering people, promoting SMES and flexicurity-Promoting entrepreneurship & SME development-Promoting employability & adaptability-Flexicurity:
and introduce more coherence in the gathering of data about the impacts on SMES which are underestimated often
driven primarily by the rapid expansion of the digital economy. Retail and wholesale are adapting at pace,
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,
DATA SIM (http://www. uhasselt. be aimed/datasim at providing an entirely new 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.
With the goal to forecast the nationwide consequences of a massive switch to electric vehicles, given the intertwined nature of mobility and power distribution networks,
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,
which analyses and discusses the managerial challenges of technological trends focusing on governance models, the transformation of work and collaboration as a consequence of the digitization of the work environment,
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...
70 4. 3. 1 Text mining and Conversation's Analysis...72 4. 3. 2 Classification and Analysis Methods and Solutions...
Maturity Model Integration COBIT Control Objectives for Information and related Technology COC Cross Organizational Collaboration Cop Community of Practice CRM Customer relationship management CSCW Computer
-Supported Cooperative Work CSFS Critical Success Factors Cxo C-level Manager DDS Digital data stream DMS Document management system ECM Enterprise
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
what concerns the availability and the volume of data archived, stored, and exchanged as a consequence of the V. Morabito, Trends and Challenges in Digital Business Innovation, DOI:
and potentially see the world as a big data repository to be exploited, adapted, and aggregated depending on their current needs.
what the Economist called a Data Deluge 4, and they are worth to be considered 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
when we refer to raw, unstructured facts that need to be stored and processed by an information system,
being processed the data, organized, structured, and presented. Thus, adopting the General Definition of Information (GDI) we could define information‘‘data?
meaning''35. It is worth noting that computer based information systems are a specific type of information system and not exhaustive 36.
with a consequent change in the volume and variety of data to be managed by banks and financial services providers.
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
driver for innovation of products and services and the increasing availability of unstructured data (images, video, audio, etc.)
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.
From 1. 3 billion RFID tags in 2005 to about 30 billion RFID today. Twitter processes 7 terabytes of data every day.
Facebook processes 10 terabytes of data every day. 220 Terabytes of Web Data. 9 Petabytes of dat-a Web. 2 billion Internet users by 2011 (worldwide.
4. 6 billion Mobile phones (worldwide. 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,
as the speed of their creation and use is often (nearly) real-time. As pointed out by IBM,
examples of value added exploitation of data streams concern the analysis of 5 million daily trade events created to identify potential fraud,
the third dimension concerns type of data actually available. Besides, structured data traditionally managed by information systems in organizations,
most of the new breed encompasses semi structured and even unstructured data, ranging from text, log files, audio, video,
and images posted, e g.,, on social networks to sensor data, click streams, e g.,, from internet of things. Accessibility:
the fourth dimension concerns the unmatched availability of channels a business may increase and extend its own data and information asset.
It is worth noting that at the state of the art another dimension is considered actually relevant to Big data characterization:
Veracity concerns quality of data and trust of the data actually available at an incomparable degree of volume, velocity, and variety.
Thus, this dimension is relevant to a strategic use of Big data by businesses, extending in terms of scale
and complexity the issues investigated by information quality scholars 9 11, for enterprise systems mostly relying on traditional relational data base management systems.
As for drivers, cloud computing is represented in Fig. 1. 1, besides the above mentioned social networks, mobile technologies, and internet of things.
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).
However, cloud computing faces IT managers and architects the choice of either relying on commercial solutions (mostly expensive) or move beyond relational database technology,
thus, identifying novel data management systems for cloud infrastructures 12,13. Accordingly, at the state of art Nosql (Not only SQL) 3 data storage systems have been emerging,
usually not requiring fixed table schemas and not fully complying nor satisfying the traditional ACID (Atomicity, Consistency, Isolation, e Durability) properties.
and analyzing large data sets, Mapreduce4 and 3 Several classifications of the Nosql databases have been proposed in literature 39.
Here we mention Key-/Value-Stores (a map/dictionary allows clients to insert and request values per key)
and Column-Oriented databases (data are stored and processed by column instead of row). An example of the former is Amazon's Dynamo;
and Cassandra represent Column-Oriented databases. For further details we refer the reader to 39,40. 4 Mapreduce exploit, on the one hand,(i) a map function,
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'';
''the latter concerning static data that can be mined for insight. Whereas digital data streams (DDSS) are‘‘dynamically evolving sources of data changing over time that have the potential to spur realtime action''15.
Thus, DDSS refer to streams of real-time information by mobile devices and internet of things, that have to be captured
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:
or a specific product/service or to exploit sensor data to detect the need for intervention on jet engines, data centers machines, extraction pump, etc.
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;
on the other hand, for accountability and transparency of government and administrative activities, due to i) the increasing relevance and diffusion of open data initiatives, making accessible and available large public administration data sets for further
elaboration by constituencies 16,17, and ii) participation of citizens to the policy making process, thanks to the shift of many government digital initiatives towards an open government perspective 18 21.
Thus, Big data seem to have a strategic value for organizations in many industries, confirming the claim by Andrew Mcafee
and Erik Brynjolfsson 8 that data-driven decisions are better decisions, relying on evidence of (an unmatched amount of) facts rather than intuition by experts or individuals.
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,
as for scalability and query performance. On the contrary the Technical-Data-Utilization classification addresses computational complexity issues related to both provision and use actions.
As for the business type of perspectives, it is worth noting that they provide the management complement to challenges
Whereas the Functional-Data-Provisioning one, mainly concerns approaches for the management of the data‘‘deluge''4,
leading to an advanced information demand analysis and improved information supply 7. Table 1. 1 Big data perspectives
and related actions Perspectives Types Actions Technical-data-provisioning Technological Storage Technical-data-utilization Technological Use Functional-data-provisioning Business Management Functional
-data-utilization Business Use Elaboration from 7 1. 1 Introduction 9 Thus, this may be seen as a management of information systems perspective, governing the overall lifecycle from Big data storage to use.
Nevertheless, the latter is suitable to be addressed with a Functional-Data-Utilization perspective, exploiting lessons learned
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,
Fig. 1. 3 provides a summary of the priority ones together with the related perspective (being technological, business,
Priority actions in Fig. 1. 3 structure a lifecycle, starting from the (continuous) storage of data from the outer and inner flood involving today's organizations.
managers have insights on value of data for their organization from profits, revenues, recovery costs derived by critical data loss or inaccessibility.
As a consequence they have to assess their information asset to decide about retaining searching, acquiring new data
and to invest on storage technology. Indeed, the value of data and information they allow to produce in the information lifecycle curve,
change depending on its currency and the usefulness in business processes and decision making 23,24. As shown in Fig. 1. 3,
In this case, the volume of data is reduced to a limited view on the asset actually stored in databases.
Thus, having a very large volume of data does not imply that it provides valuable information to an organization's business processes or to decision making.
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,
or they do not have. 50%of managers say they do not have access to the information they need to perform their jobs.
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.
Accordingly, scholars in the research areas of information systems and information quality have identified a set of enabling and inhibiting factors for effective data governance.
In Table 1. 2 we show the ones highlighted by Tallon 23 for implementing data governance practices suitable to support valuable Big data management.
The factors considered in Table 1. 2 act at organization, industry, and technology level, showing the enabling role of alignment, centralization, standardization,
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,
Table 1. 2 Data governance enablers and inhibitors Factors Enablers Inhibitors Organization Highly focused business strategy Complex mix of products and services IT/Strategy alignment
Predictable rate of data growth Absence of industry-wide data standards Technology Culture of promoting strategic use of IT Data hoarding Standardization Weak integration of legacy IT systems
Adapted from 23 1. 1 Introduction 11 managing effectively data, supporting time-to market and evidence-driven decision making.
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 latter meaning to contextualize data sources, improving data access to customers, employees, and value-chain partners,
This New Dehli based manufacturer of motorcycles and scooters integrated its Customer relationship management (CRM) with contextual data on young women customer experience entering India's workforce.
The above arguments and cases lead us to the third Big data lifecycle challenge. As for their use
as seen above, companies has to rely on new data management technologies and analytics to get evidence of facts rather than intuition by experts or individuals.
customer experience management, brand Create emotional ties Empathic use of information Business Agility Optimize Business processes Effectively manage data Operational excellence Focus on customers Decision making optimization IT enabler Value from information
actions and targets of IT as enabling factor 12 1 Big data management, and workforce planning and allocation.
Furthermore, Lavalle et al. 28 pointed out that among the impediments to becoming data driven, companies answer the following main issues:
and a consequent internal diffusion of data scientists among the employees 5. In addition, 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.
However, managers considered as a priority or mandatory premise for their organization to have their data asset characterized by high degree of integration, consistency, standardization and trustworthiness.
Thus, we can summarize the main challenges 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.
Data architecture: IT must support the storage and enable the extraction of valuable information from structured, semi-structured as well as unstructured data (images, recordings, etc.).
Information infrastructure: IT must define models and adopt techniques for allowing modular and flexible access to information and analysis of data across the enterprise.
Furthermore, organizations must commit human resources in recruiting and empowering data scientist skills and capabilities across business lines and management.
Investments: The IT and the business executives must share decisions on the budget for the management and innovation of information assets.
Taking these issues into account, we can now provide a comprehensive representation of the factors
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.,
, IT portfolio and the data asset of an organization, actually are determined also by external data, applications,
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,
and is determined by two variables, application integration and data integration (see also 29,30). Accordingly, integration orientation constitutes a fundamental lever of both analytic, information,
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
contributions discussed in this section into account Table 1. 3 summarizes a set of strategy points
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 store relevant data from all information sources (internal/external) Relevance Management Technology Adopt analytics appropriate to the volume, variety,
and velocity of data (real-time) Timeliness and accuracy Management Industry/Organization Establish clear goals and articulate a vision coherent with market opportunities, effectively engaging customers, employees and other relevant stakeholders Leadership Management Organization Investments in human resources with a mix of new analytical skills and business
and manipulate data with real-time tools Timeliness/Simplification Use Organization Ensure access to information 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.
The case has been discussed by Moses et al. 31 and concerns The Minnesota Wild, an ice hockey team based in St paul,
and the ability of these platforms to provide data that can translate user choices in demographic information valuable to achieve marketing and communications initiatives,
and integrate internal and external data sources through appropriate storage and data warehouse technologies. Bahrti Airtel operates in the Indian mobile market characterized by constant growth.
Thus, data are the main asset for evidence-driven decision making. The claim‘‘Our objective is to have one version of the truth!''
''by Rupinder Goel, CIO of Bharti Airtel Limited, summarize the need for a single set of data that include finance, marketing, customer service,
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,
including data warehouse systems aimed at the collection and subsequent analysis of data from various corporate activities.
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.
Indeed, effective collection and use of data is strategic to Nokia for understanding and improvement of users'experiences with their phones and other location products/services.
terabyte-scale streams of unstructured data from phones in use, services, log files, and other sources.
enabling a comprehensive version of truth from data captured at global level. Furthermore, Nokia had to face the cost of capturing petabyte-scale data using relational databases.
As a consequence the choice has been to build an information infrastructure based on a technology ecosystem,
including a Teradata enterprise data warehouse, Oracle and Mysql data marts, visualization 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)
and integrate new information sources (internal/external). As reported by Cloudera 33 the centralized Hadoop cluster actually contains 0. 5 PB of data.
The resulting infrastructure allows data access to Nokia's employees (more than 60,000), and efficiently moving of data from, for example, servers in Singapore to a Hadoop cluster in the UK data center.
Nevertheless, Nokia faced also the problem of fitting unstructured data into a relational schema before it can be loaded into the system,
requiring extra data processing step that slows ingestion, creates latency and may eliminates important elements of the data.
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 case is based on a Consultancy case history 34 and shows how General electric Co. GE), 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.
GE charged William Ruh from Cisco systems to lead the center, developing software and data science capabilities for GE's Big data domain of interest(‘the industrial Internet').
'POINT OF ATTENTION: 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.
As argued by Consultancy 34, 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,
and oil and gas), estimating the savings from an industrial Internet for these sectors alone could be nearly $300 billion in the next 15 years.
In particular Big data is strategic for a growing percentage of GE's business related to services, such as, e g.,
, supporting its industrial products and helping customers use GE's machines more effectively and efficiently.
Indeed, the GE assesses the success of software and analytics by their enabling a new portfolio of compelling service offerings, helping, 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),
''located in San ramon and around the globe, as well (Bangalore, New york, and Cambridge), reporting into the center.
, machine learning, statistics, and operations research, among others. Furthermore, centralization of the staff is motivated by three factors:
''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;
the latter referring to streams of real-time information by mobile devices and internet of things, that have to be captured
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,
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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
which enables better stock controlling by using Electronic Point of Sales (EPOS), Electronic Data Interchange (EDI) and Electronic Funds Transfer at Point of Sale (EFTPOS),
or pool of services over the network through virtualized IT servers such as data centers and specialized software applications 2. It is the latest development in the computing models that performs computing functions on multilevel virtualization and abstraction by integrating many IT resources.
such as server using time and data storage, automatically and without human interaction. 2. Broad network access,
and manage server instances in data centers using APIS. The second one is Microsoft windows Azure platform,
The first component which is Windows Azure provides Windows based environment to enable users to run applications and store data in data centers.
The second component which is SQL Azure provides data services in the cloud using SQL SERVER.
reliable access to data using large clusters of Application Layer---Business Applications, Web Services Multimedia Platforms---Software Framework (Java, Python,.
Google Apps, Facebook Youtube Microsoft Azure, Google Appengine, Amazon Simple DB/S3 Amazon EC2, Gogrid, Flexiscale Data centres Fig. 2. 2
which is simplified a model large-scale distributed database. Finally, the last one is the Map Reduce1 programming model that can be modified according to the characteristics of the applications that Google is running on its servers.
The previous three systems represent data storage data management and programming models respectively 5. 2. 2 Strategic and Managerial Challenges
and Opportunities The decision of whether to implement the cloud computing project by the organization itself
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
Virtualization can be important for cloud computing by enabling virtual machine migration to balance the load throughout the data center.
Virtual machine (VM) migration enables robust and highly responsive provisioning in data centers. As a result, it can be concluded that the major benefit of VM migration is to avoid hotspots;
and Opportunities 27 5. Traffic management and analysis. The importance of the analysis of the data traffic is faced by many challenges in cloud computing. These challenges stem from the difficulties in calculating,
measuring and predicting the traffic to the data centers, especially when they are composed of several hundreds of servers. 6. Data security.
for secure data access and transfer, and (2) auditability, for attesting whether the security settings of the application have been altered or not.
and run large-scale data intensive applications. 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.
The concerns that can arise here come from the compatibility issues between the Internet-scale file systems that host the software frameworks such as Mapreduce on one hand,
and the legacy applications that are required to run on these file systems from the other hand. These issues are based on the differences in storage structure,
access pattern and application programming interface. 2. 2. 2 Advantages and Risks in Cloud computing Outsourcing Projects Cloud computing is like any other new development in IT,
which could result in private data exposure. In such a case, the Cloud Service Provider CSP should ensure proper data isolation to handle such sensitive situation. 2. Identity management As the traditional identity
and access management is still facing challenges from different aspects such as security, privacy, provisioning of service as well as VMS, etc.,
storage areas that are specified for customers'data; hypervisors; cloud mapping services. 30 2 Cloud computing B. Process and regulatory-related aspects,
cost-effective. 7. Insecure APIS The Applications Programming interface (APIS) are the software interfaces that document how to communicate with the concerned software.
The CSP publishes those API to allow users to discover the available features of the cloud computing. However,
insecure APIS would invite attackers'attention to know the architecture of the CSP and internal design details which would lead to major security concerns for CSP as well as customers like cyberattacks
Data (and Application) migration, which will happen during the change to the cloud computing model and comprises risks related to data security and portability.
and comprises risks about data migration security and about making sure that the old CSP, will delete customer's data on its cloud servers. 9. Service Level Agreement
and trust management gaps Service Level Agreement (SLA) is the document that details the agreed minimum performance provided by the cloud provider.
logical segregation of customer data; accessibility and auditability of the customer and CSP; guaranteed data deletion when customer is no longer with the CSP;
24/7 availability of the service; 2. 2 Strategic and Managerial Challenges and Opportunities 31 agreements on security related issues;
data ownership and loss of data since it is provided by a third party service provider, which can result in weak auditing ability of the service 12.
which needs to be able to deal with them by having backup plans in case of a disaster.
The chief advantage of this model is that the enterprise retains full control over corporate data and security and system performance.
when there are concerns about security and data sovereignty. In contrast to previous model, the public cloud is open for use by the general public i e.,
flexibility, Service Legal Agreements (SLAS), security standards, backup strategies, customer support, downtime history and pricing policy.
FDM is a combination of the Fuzzy logic, which is an approach for computing that is based on degrees of truth rather than the usual true
Therefore, the fuzzy logic is used to provide more accuracy in making judgments and solves the problems exist in AHP 22.
and how the transformation to the cloud computing model would help to meet those Outsourcing Provider Evaluation System Function Service Quality Integration Economics Professionalism Usefulness Ease of use Accuracy Tangibles Reliability Security
/Privacy Backup system System update Maintain service Education training Facility Reliability Specialisation Compatibility Link/Connection Flexible Interaction Customization Setup cost Maintain cost Price Reputation
backup plans and satisfying the overall strategic intent without making compromises. 6. Negotiate. To agree on all issues
having clear and solid backup plans in disaster situations and having clear contract get out clauses
In this case study, Khajeh-hosseini et al. 8 argues that the costs will be 37%less over the 5 years period resulting of transforming an in-house data center to Amazon EC2,
a hurricane that hit and destroyed the company's operations including its entire IT infrastructure and data storage.
This project will be achieved by leveraging cloud computing technologies in the government data centers of the National Computing and Information Agency (NCIA.
This agency is consisted of two huge data centers that contain thousands of computer systems for about 47 organizations
On the other hand digitalization requires the convergence of digital devices and networks, fostering digital communication, processing, and storage of diverse types of information as a service.
, the Business Process Execution Language (BPEL) 10 Usage models based service invocation Analytics Mining of event-stream data Real-time execution of business rules Adapted from 4
and opportunities. 3. 2 Mobile Services Drivers and Challenges Mobility is currently one the main characteristics of today digital information infrastructures.
Big data Amazon's Dynamo, HBASE, Google's Bigtable, Cassandra, Hadoop, etc. Ultra-fast, low latency switches Cisco Networks, etc.
to mobile data services having an ubiquitous nature and an impact on individuals'lifestyle. In particular, they focus on devices designed to provide the users heterogeneous types of information, such as, e g.,
, data, video, and pictures, thus providing a suite of utilitarian and hedonic functions. Apart from traditional core constructs of TAM (such as perceived ease of Table 3. 3 Constructs and related anchors for perceived ease of use
Integration, facing the issue of the alignment and adaptation to enterprise information systems, at software as well as data level.
but focused on the challenges of guarantying the privacy of data and the trustworthiness of mobile applications and services interactions.
Development Platforms for Mobile Applications (App) based on a reference framework Application integration with enterprise information systems (software and data) Device management (data privacy and security) Software solutions
often interfacing different preexisting platforms and heterogeneous databases. To solve the above issues, managers can follow an integrated
considering tools for application development, device management, monitoring of applications and systems integration of the data).
ensuring the security of the data (see Fig. 3. 3) is by far one of the most critical issues in the field of development and management of mobile applications and services.
or service providers, ensuring the safety of sensitive data access, user profiling as well as compliance with corporate policies.
so the IT department of a company has to define policies to regulate the practices of users Application streaming The data is stored not directly on the device as well as a part of the application code,
and business data as well as operating systems (the former managed by the mobile operators, the latter by the IT department of the company where the user is actually working).
, to the access and unauthorized disclosure of corporate data as well as the sharing of personal sensitive data. 3. 4 Case studies In this Section we discuss fact-sheets of case studies,
data on previous purchases of a given customer; the types of orders typically performed by a given customer;
The goal of the solution was to provide more useful information to sellers to increase the effectiveness of the sales (historical data on the customer, complete catalog, real time inventory, etc.
The solution resulted in a reduction of the time of data entry, increased personal productivity,
and sales provide increased productivity, improved data quality, and knowledge on customers'behavior and history. 3. 4 Case studies 61 The following case is based on a Datalogic success story 33
because the comparison with data from the company's management system took place only in the final balance,
also offering real-time production data with a higher precision. To solve these issues, Habasit has implemented a solution based on the development of a mobile application and the purchase of rugged devices10 that,
thus, synchronizing the data on the availability of the product in a purchase order system by SAP.
among the key issues for obtaining increased productivity, improved data quality, knowledge on customers'behavior,
the case of mobile data services. Inf Syst Res 17: 162 179 24. López-Nicolás C, Molina-Castillo FJ, Bouwman H (2008) An assessment of advanced mobile services acceptance:
As a consequence, companies face unprecedented challenges in terms of marketing perspectives and Customer relationship management (CRM) vision, actually redefined in terms of Customer Experience Management (CEM.
are data centric; are designed to collect and share data among users; enable the user access
and intervention to add and modify the data; have improved an usefulness through different devices. Thus, there are several situations in which these solutions,
based on new digital business models, have been implemented supporting decision making processes in different fields such as, e g.,, R&d, market research and analysis,
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.,
1 According to 22 data were gathered through telephone interviews conducted by Princeton Survey Research Associates between August 3, 2007 and September 5, 2007, among a sample of 2,
clear and userfriendly systems able to really help consumers in their online decision making processes. 4. 3. 1 Text mining
and Conversation's Analysis The developing potential about text mining for sentiment analysis and opinion mining represents an extension
and evolution of the traditional researches about text mining. In particular sentiment analysis means the computerized analysis of opinions,
In simple terms, the aim is to transform notstructured and qualitative data, coming from, e g.,
, online comments, posts or tweets, into a well-structured data set through which quantitative analysis can be done.
Finally, the precision and the accuracy of the classification can be influenced by the domain of the elements in
considering, on the one hand, data interception; on the other hand, the possibility of losing the device by the user/costumer.
''Organize a series of data‘‘Are reviews classified on the basis of the positivity degree expressed?''
,{ZIP CODE, date of birth, gender} allow to identify 87%of US citizens using public data base (as for gender, age,
in order to translate potential risks into quantified data, further efforts are required to design and develop frameworks
/Finally, statistical approaches are used for machine learning such as Support vector machines (SVM) and Elastic-net Logistic Regressions (ENETS.
and web technologies to infer data about people preferences, activities, and their social environments 35.
or digital data streams (as outlined in Chap. 1, which we refer the reader for further details).
integrating sensors with social networks data. This may definitely shift the meaning of what businesses mean time-tomarket towards the capability of interpreting individual customer experience through real-time offerings.
, GPS, Bluetooth and Near Field Communication (NFC) functions Increased bandwidth Large wireless bandwidth required to transmit large amount of data in real time (for example,
in forms of audio or video streams) Increased storage Hyperscale storagea for big data Fast stream processing platforms Platforms such as, e g.,
and processing large volume of data streams in real time Stream synopsis algorithms and software Histograms and sketches for data stream computation problems (see 37 for a survey) a According to 42 Hyperscale storage is measured a storage space in terms of petabytes,
serve millions of users with often one or a limited set of applications, may lack redundancy,
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.
privacy sensitive techniques, protecting personal data involved in real-time interactions and data streams; Table 4. 5 Social sensing domains and applications Domains Applications (sample) Crowdsourcing for user centered activities Location trends Google Latitude Google Public
compression techniques, supporting the efficient process of large amounts of data or Big data; data quality techniques, enabling, e g.,
, the trustworthiness, accuracy, and completeness of data collected through sensors which most of the time are verified not for their provenance;
dynamic and real-time response for multiple and large volume of sensors data tracked at a given application transaction time.
The above discussion on the domain, application, and challenges for the use of social sensing technology can act as well as a bridge to the following section where case studies are further detailed for social listening as mainly focused on market signals. 4. 6 Case studies As seen in previous Sections,
the online social interaction has contributed in empowering marketing intelligence, thus, opening new opportunities in terms of market monitoring.
and data management capabilities across the overall company functions and business processes is rising as one of the key factors
and priorities for IT executives as well as for other Cxos (as also early emphasized in Chap. 1 on Big data).
Tsytsarau M, Palpanas T (2011) Survey on mining subjective data on the web. Data Min Knowl Discov 24: 478 514. doi:
10.1007/s10618-011-0238-6 14. Feldman R (2013) Techniques and applications for sentiment analysis. Commun ACM 56:82 89 15.
In the 21st national conference on Artificial intelligence (AAAI2006) Volume 2 (pp 1331 1336. AAAI Press 28.
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.
Jones R, Kumar R, Pang B, Tomkins A (2007) I know what you did last summer:
Lu H-M, Chen H, Chen T-J, Hung M-W, Li S-H (2010) Financial text mining:
Proceedings 2008 International Conference Web Search Data Min ACM, New york, pp 219 230 34. Schuster D, Rosi A, Mamei M, Springer T, Endler M, Zambonelli F (2013) Pervasive social context:
Aggarwal CC (ed) Manag Min Sens Data, Springer, US, pp 237 297 37. Aggarwal C, Yu P (2007) A survey of synopsis construction in data streams.
In: Aggarwal C (ed) Data streams SE 9. Springer, US, pp 169 207 38. Antoniou G, van Harmelen F (2008) A semantic web primer, 2nd edn.
MIT Press, Cambridge 39. GIA (2010) Delivering Maximum Strategic Value with Market Monitoring GIA Best Practices White paper (vol 2) 40.
both personal and business data can reside in the cloud on large servers run by giant technology firms such as Amazon and Google,
where staggering amounts of data are stored for retrieval from almost anywhere in the world. Combine this with the cloud-based social networks like Facebook (over 1 billion users), Twitter (over 500 million) and a host of smaller firms and the use of portable,
Employees can be more productive due to permanent access to business data and transactions, and communication facilities.
a trend that is irreversible and will lead in the middle term to the falling of traditional security models. 2 Data Management opportunities:
successful implementation of the IT consumerization requires strong architecture that could result in better data management practices and results.
For example, Cloud storage can enhance the availability of data, which would help employees to increase online interaction and online data access,
while using approved applications deployed via the company's own app server. Frequent data interactions can increase data accuracy,
and at the same time the degree of data sharing will be increased 8. Moreover, such storage architectures can allow for a better control of data flow within the organization 7. 5. 2. 2 Challenges
and Risks of the Consumerization of IT The increasing number of employees'private devices used in workplace is presenting a challenge for the managers 10.
This problem is among the other issues related to the consumerization of technology devices 11. Also, the Information security Forum (ISF), 12, has analyzed the challenges, trends and solutions for IT consumerization:
Also, besides the misuse of the personal device legal matters that concern the ownership of device and the data on it are under question.
since the lost smartphone can contain significant amounts of sensitive corporate data. According to Mcafee, a third of such devices losses resulted in a financial impact on the organization.
and claims of ownership on intellectual property. 3. It is hard to discriminate between user and company data on the employeeowned devices,
Risks Affecting Data (Confidentiality, Integrity and Privacy) The risks under this category are: 1. the possibility of losing corporate data because of unauthorized sharing and usage of information on employees'devices by the services running on them;
2. the possibility of losing corporate data as a result of access by unknown users and unmanaged devices to enterprise networks;
3. the risk of losing corporate data as a result of difficulty in applying security measures and policies on application-rich mobile devices,
especially when the device is owned by the employee; 4. increased risk of the corporate data being hacked due to external attack.
The following table (Table 5. 1) summarizes and classifies the previous mentioned risks into primary and secondary categories.
might also be interested in risks related to data loss. 94 5 IT Consumerization Moreover, more cost oriented businesses might also be interested in legalrelated risks.
and secondary classification/dependencies of identified risks Category (cat) & risk (R) Category Comment Costs Legal and regularity Data Cat (1) R (1) X
(X)( X) Secondary categories due to effects on compliance and data loss Cat (1) R (2) X (X)( X) Secondary categories due to effects
on compliance and data loss Cat (1) R (3) X (X)( X) Secondary categories due to effects on compliance and data loss Cat (1) R
(4) X (X) Secondary categories due to effects on compliance and data loss Cat (2) R (1)( X) X (X) Secondary categories costs
firms have to concentrate on protecting the corporate data that will be accessed by a range of devices 14.3.
For example, the media tablet can connect to the corporate data center and can be used to answer all the emails that are related to work
and defining data ownership. 3 Implementing mobile strategy includes enabling mobile device management infrastructure such as, for example, System Center Configuration Manager 2012 and Windows Intune,
and associated application software from the physical client device that is used to access it 5. 5. 3 Virtualization Strategy This strategy enables enterprises to quickly achieve business benefits gained from IT consumerization by moving the desktop and/or applications into a data center.
manage and configure sensitive user's data and resources 16.5.5.4 Bring Your Own Device BYOD Strategy This strategy encourages talented employees and contractors,
the owner of the data on a privately owned device, accessing the corporate data remotely from a personal device,
This category includes licensing costs, data plans, and support. Enterprises have to establish baseline needs at the beginning of consumerization of IT strategy planning in order for them to be able to determine the financial impact
Apps virtualization VDI Roaming environment Roaming environment Data synchronization Data synchronization VDI VDI Adapted from 16 5. 7 Considerations Related to IT Consumerization 105 Therefore,
and user support have to be considered thoroughly in order not to face situations that the company has no control over devices that access important corporate data.
the purpose of these instruments is to facilitate information sharing by giving the people the ability to send messages, files, data or documents to each other.
they provide more interactive methods of sharing data and information. This type typically includes teleconferencing and videoconferencing tools.
in order to have more interactive techniques for conferencing and data sharing. Examples of these technologies comprise data conferencing
which lets a set of PCS that are connected together to share and view a common whiteboard which users can add to its content
keeping databases that are being used by different users in different locations synchronized and up to date; reducing the costs of transportation, phone calls,
For example, in a contribution by Koan 10, a group of data collection methods that include interviews,
which data showed that they valued the knowledge sharing and collaboration and they believe that the time
The issues that accompany the use of such systems are the security, data integrity and quality,
The latter one contains many useful applications such as Google doc for file sharing among team members, Google Calendar for scheduling meeting at times that works for everyone with features such as reminders,
and to have built-in or easy to use backup. In what follows, we are going to list and explain some of the most adopted collaborative tools in the market such as Skype, Dropbox, and others.
file sharing and documents editing in a collaborative manner. In this service, the files shared publicly on Google Drive can be searched with web search engines.
It supports geo-location data for photos uploaded onto the service. Microsoft Onenote Onenote is a software from Microsoft that enables a free-form information gathering
finding contact information or labeling data to prepare it for the use in machine learning 21.
Moreover, many other websites such as Google books and the New york times, use this kind of service for text digitization 22.
and collaborate over separated geographical locations by using the internet as a two-way of information and data sharing,
Despite the benefits that can be gained through utilizing digital communication and collaboration platforms, it is important as well to consider the challenges that accompany this usage such as the need for high end infrastructure,
open source data and cloud technology to share knowledge, manage information and to have generated the user work shared
increasing the volume of data stored, information production as well as the flexibility and capacity of sourcing activities (often involving costumers and final users, likewise).
and defining data ownership. However, this is only a part of the current challenges that a company has to face
in order to protect its identity at internal level, in terms of privacy of data and security of its own information infrastructure.
Hyman pointed out also that the following identity-related motivations security experts see as constraints and causes for a limited accuracy of costs estimations 8: 134 7 Digital Business Identity
p. 4 from the data stored about us and the information flows we are involved in.
, on the basis of historic or benchmark data; while for the security/risk/and compliance perspective they can be the mapping of users
the latter is essential to guarantee a suitable infrastructure preserving privacy of data and reliable transactions for the target customers.
As for this issue, according to an independent study commissioned by Talktalk Business, a United kingdom (UK)' s leading provider of B2b data Offer and communicate a clear,
The research data collection was made through 35 case studies and the hypothesis was tested for the IT governance of the 34 IT processes of the COBIT framework,
and a serious security issue as various consultants and subcontractors were working with highly confidential citizens'data.
This has produced also confusion with regard to data access, accounting, auditing and usage statistics over a multiyear period, further complicating the integration process.
In summary, there was lack of analytics and reporting systems on the use of data and its implications across the enterprise.
As a result, the data collected by analytics could be used to monitor the IT performance
That is due, in particular, to the exposure of business's data and systems to external environment.
but it has become particularly relevant for companies that today have to operate in the new digital economy,
keeping in mind that the advent of new technologies has resulted in the enhancement of the capacity of processing data associated with a significant reduction in costs,
One of the major complexities of the digital economy also lies in the fact that the traditional value chain centered on the offer system has turned into a complex value network in
Conceptualizations The advent of the digital economy can be conceived really as a new industrial revolution both in terms of magnitude
and implement a business model able to deal with and exploit such characteristics of the digital economy.
Table 9. 7 compares the essential features of the processes of transformation of the traditional industrial economy on one side and that of the digital economy on the other.
In the transition from a traditional industrial digital economy the whole process of value creation is transformed entirely.
while, as already discussed, the digital economy is driven fundamentally by customer demand. In the digital economy, the essential input of the value creation process is information itself, for example,
customer profiles and preferences that companies need to collect, organize, select, synthesize and distribute 37 in the transformation process to be able to provide customers with customized solutions.
information, in the digital economy, is an essential source of value and every business is an information business 38.
Information and knowledge play a crucial role both in the traditional and the digital economy. However, in the industrial economy knowledge generation and application processes are aimed essentially at making production more efficient through cost reductions
while in the digital economy they are directed mainly to intercepting the customer's preferences and expand his choice.
The digital economy offers companies a variety of tools (e g.,, web-based supply chain management 176 9 Reinventing Business models systems, online commerce,
because the network is not only a tremendous source of access to data and information in a digital format,
and the digital economy Industrial economy Digital economy Business process orientation Guided by offer Guided by demand Economic focus Cost minimizing Value maximizing Product policy Offer standardization Offer personalization
intermediate products Digital information Output Intermediate or finished products or services Products or services with a high information/knowledge content The role of information A supporting and connecting element during the phases of production
The formulation and implementation of an appropriate business model are vital to meet the challenges of the digital economy,
Organizations operating in the digital economy must identify and exploit economically these specific attributes of the Internet
In the context of the digital economy, the innovation of the business model can be defined as the creation
in the early stages of design and production of highly customized goods and services Digital resources Information and data in a digital form,
and frequency of changes In the digital economy companies need to continually adapt to changes, which are extremely fast and frequent Virtual capacity The recent progress in networking and storage
but their effects are particularly significant and pervasive in the digital economy. When the costs associated with the transition from one provider to another are so high as to reduce any benefit from switching
Ind Manag Data Syst 104: 78 87 27. Haaker T Faber E, Bouwman H (2006) Balancing customer and network value in business models for mobile services.
and analyzes large amounts of real-time mobile location data, collected through mobile phones, GPS, Wifi, cell tower triangulation, RFID and other sensors.
Using powerful machine learning algorithms, it provides extremely accurate profiling and segmentation of consumers based on habits and spending preferences.
This tool allows to transform existing data into predictive behavioral data leading to a better understanding of customers without requiring any change in behavior.
Models based on thorough analysis and observation of large quantities of data on geo-location of specific individuals provide a significant insight into human behavior.
who were fascinated by the prospect to understand human behavior through the analysis of location data over time.
Back then location data was increasing thanks to the diffusion of mobile phones, which soon became smarter and smarter
and started generating even richer data (e g. Foursquare voluntary check in, automatic collection of location by different apps, Wi-fi recognition.
while, at the same time, the large amount of location data was fundamental for testing hypotheses about human behavior.
tastes and behaviors, improve the planning of public or private transport supply according to data about stream of people,
In addition to the fraud data possessed by the system, Billguard automatically scouts the web, using crowdsourced data to harness the collective knowledge of millions of consumers reporting every day about billing complaints
and suspicious merchant lists to their banks and to on-line communities. The underlying concept is that single users seldom take the time to check their balance sheets at the end of the month,
The management team is composed of data scientists, mathematicians, security experts and industry specialist, supported by the investments of some of the founders and CEOS of Google, Paypal, Verisign and Sun microsystems.
Digitization of physical content is also possible by scanning paper documents with a smartphone camera.
and compare data, increasing engagement and allowing parallel working and synchronous data visualization. The new technology challenges the traditional linear view of meetings
increasing the explanatory power of the affective data (Fig. 10.1). ) The system has been tested extensively with scientific research accuracy
and the underlying science of emotions is the result of years of studies The advantages,
The reports'data can be exported easily and the software itself is developed for easy integration with other systems. 10.7.1 Developer Noldus Information technology was established in 1989,
Cogito helps companies gain valuable data about their clients'behavior and increase the quality of interaction.
All data are combined with traditional performance indicators, in order to create detailed predictive models. 10.8.1 Developer Founded in 2006,
Feedback from clients report benefits as increase in productivity, improved sales performance, better data collection
on the other hand, it allows the business side gathering meaningful data about their customers. Around 10%of all transactions are completed currently through the mobile app.
However, among the discussed digital innovation practices we have found also a coverage of digital work and collaboration (Mezzanine) as well as digital business identity (Tycoon) issues.
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.
and this can be their own private personal one, with consequences ON IT policies as for security, disclosure of data, and privacy.
, policies for privacy and security of data and information flows; on the other hand, promoting it in terms of brand in an ever-changing and dynamic digital market (see Chap. 7). Thus,
The above directions require that IT must be able to generate value from current digital information infrastructure, leading to a change of perspective on its strategic role as the‘‘guardian''of a company digital business assets and‘‘heritage''.
34 Bring your own device (BYOD), 90,97, 103,108, 109,134 Big data, 5 CCALLED technology steward (TS),
, 51 Collaboration, 194 Collaborative management tools, 113,114 Collaborative software, 123 Collaborative working environment (CWE), 123 Collective Intelligence, 68 Column-oriented databases, 6
204 Customer experience management (CEM), 67 Customer relationship management (CRM), 67 Customer support, 34 Cybercrime, 134 136,143 DDATA, 4 Data deluge, 4
Decision 2. 0, 67 Degree centrality, 69 Degree of positivity, 74 Delphi method, 35 Digital artifacts, 4 Digital data streams, 7, 19
49 Multi-sided markets, 49 NNOSQL, 6 OOBJECT buzz, 72 Offer to customers, 169 Ontologies, 70 Open data, 8 Open government, 8
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