and introduce more coherence in the gathering of data about the impacts on SMES which are underestimated often
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.
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:
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
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.
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
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.
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,
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.
for the growing size of available data requires scalable database management systems (DBMS). However, cloud computing faces IT managers
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,
and analyzing large data sets, Mapreduce4 and 3 Several classifications of the Nosql databases have been proposed in literature 39.
and Column-Oriented databases (data are stored and processed by column instead of row). An example of the former is Amazon's Dynamo;
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.
''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
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.
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
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.
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
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
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,
the Technical-Data-Utilization classification addresses computational complexity issues related to both provision and use actions.
Whereas the Functional-Data-Provisioning one, mainly concerns approaches for the management of the data‘‘deluge''4,
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
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.
(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,
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,
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.
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.
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
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:
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.
, IT portfolio and the data asset of an organization, actually are determined also by external data, applications,
As for decisions, integration orientation seems to be required for satisfying the needs for optimization and effective data management of Big data.
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,
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 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.
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,
including data warehouse systems aimed at the collection and subsequent analysis of data from various corporate activities.
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,
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.
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.
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.
characterized as‘‘hardcore data scientists), ''located in San ramon and around the globe, as well (Bangalore, New york,
Harv Educ Lett 27 (5) 4. The Economist (2010) Data, data everywhere. Special report on information management 5. Davenport TH, Patil DJ (2012) Data scientist:
the sexiest job of the 21st century data scientist. 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.
what data quality means to data consumers. J Manag Inf Syst 12:5 33 10. Madnick SE, Wang RY, Lee YW, Zhu H (2009) Overview and framework for data and information quality research.
J Data Inf Qual 1: 1 22. doi: 10.1145/1515693.1516680 11. Huang KT, Lee Y, Wang RY (1999) Quality, information and knowledge.
Prentice-hall Inc, New jersey 1. 3 Summary 19 12. Agrawal D, Das S, El Abbadi A (2010) Big data and cloud computing:
Piccoli G, Pigni F (2013) Harvesting external data: the potential of digital data streams. MIS Q Exec 12: 143 154 16.
Zuiderwijk A, Janssen M, Choenni S (2012) Open data policies: impediments and challenges. In: 12th European conference on e-government (ECEG 2012.
Cabinet Office UK (2012) Open data white paper Unleashing the potential 18. Nam T (2012) Citizens'attitudes toward open government and government 2. 0. Int Rev Adm Sci 78: 346 368. doi:
Weber K, Otto B, Österle H (2009) One size does not fit all a contingency approach to data governance.
J Data Inf Qual 1 (1): 1 27, Article 4. doi: 10.1145/1515693.1515696 26. Vom Brocke J, Rosemann M (2010) Handbook on business process management 1. Springer, Heidelberg 27.
Awargal R, Weill P (2012) The benefits of combining data with empathy. SMR 54:35 41 28.
analytics for enterprise class hadoop and streaming data, 1st edn. 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),
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,.
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 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,
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
or offline) and the adaptive scheduling in dynamic conditions form an important challenge in cloud computing. 8. Storage technologies and data management.
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,
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.
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.,
, 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
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
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:
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,
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,
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.
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?''
in order to translate potential risks into quantified data, further efforts are required to design and develop frameworks
and web technologies to infer data about people preferences, activities, and their social environments 35.
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,
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,
and data management capabilities across the overall company functions and business processes is rising as one of the key factors
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.
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:
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.
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.
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.
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,
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.
Examples of these technologies comprise data conferencing which lets a set of PCS that are connected together to share
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,
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.
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.
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.
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,
because the network is not only a tremendous source of access to data and information in a digital format,
in the early stages of design and production of highly customized goods and services Digital resources Information and data in a digital form,
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.
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.
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
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.
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,
204 Customer experience management (CEM), 67 Customer relationship management (CRM), 67 Customer support, 34 Cybercrime, 134 136,143 DDATA, 4 Data deluge, 4
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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