Synopsis: Ict: Data:

We empirically test our model using a novel individual data set from the online gaming industry

behavior and product usage has been limited because of the lack of revealed preferences data on consumption; data collection mostly focuses on transactional information.

As an alternative, until recently, surveys or self-reported questionnaires have been used to study usage behavior, especially regarding technology products (Ram and Jung, 1990;

we make use of a unique data set-from the popular online video game World of Warcraft-that tracks product usage, content consumption choices,

-preferences data on product usage Our research builds on two streams of literature. The first stream is based on psychological or

data that mimics usage patterns closely, Hartmann and Viard (2008) and Koppalle et al. 2012 propose dynamic methods that investigate the eï €ect of rewards on consumer activity in the golf

and Yang (2013) use leisure activities data to investigate the relation between consumption usage decisions and consumer lifestyles.

-sions of drinks using intra-day data, with consumers deciding between managing short run needs

Section 3 provides details about the novel data set on post-purchase decisions used in the paper

2we opt to model the consumer decision to join any group, instead of a specific group, in part due to data

the one-day state dependence combined with content aging to explain the data well 9

data section The state variable pëoet denotes the index of the most recently introduced product update, pëoet 2

duration model and using historic data on update releases. The probability of an update occurrence

3 Industry and Data The proposed approach can be used to obtain insights about the relation between product usage

We use data from the online game World of Warcraft developed by Blizzard Entertainment, a divi

Our data is related to the second expansion of the game, which sold more than 4 million copies in

The game environment and related data are particularly suitable to the study of product usage

These data take the form of dates of first time completion of specific content consumption or a task

Several independent websites process this information into databases that allow cross-player comparisons and provide recommendations on how to progress in the game.

this paper, we use a publicly available data set on product usage collected from such a site called

Wowhead. 11 We complement these data with information about product updates, their content firm†s actions,

Our data set includes daily information about the game from November of 2008 to December of

player, we use the beginning periods in our data set to create a starting state for each player, which

the data includes only content related to the game main storyline. There are other unrelated tasks that we do not include in our

similar to the schedule observed in our data. Although there was not a predefined schedule, the time interval between updates varied by only a few weeks.

The product usage data include actions of 206 users from one of the game servers, for whom the

Our data set does not include new players for two reasons. First, the website used as a source of the

data provides information about experienced users only. Second, the content introduced by the firm during our analysis was dedicated almost entirely to increasing participation of experienced players

We use these data to create an empirical distribution used to define consumer expectations about the schedule of product updates

14we note that our data is more detailed than the patterns presented in the figure,

The data set contains the dates when an individual decides to join or leave a game community

zero badges all the way up to 2500 badges, with a clear break in the data, with a group of users

We use data from the website World of Logs18 about the success rates for diï €erent content.

we discuss the data patterns that identify the pa -rameters in our model. Starting with the content utility function, its intercept is identified by the

For the community membership parameters, the individual data on decisions of joining, re -maining, or leaving a group identify the overall costs of joining

and using the product, through the observed frequency in the data of remaining in groups

In the data, we observe consumers attempting higher level tasks more frequently when a product update is about to be released,

Our data show that users play more and at higher levels just before and once they are part

rate that is available as data. We note that for the community membership decision, the probability

the data Y is LL (Y â ¥, âoe, VÂ =NX i=1 0@GX

model explains the data well. Second, we analyze parameter estimates and discuss the implied importance of diverse motivations of product use.

given the parameter values and data for each time period. Figure 3 shows actual and estimated

pattern is observed if we disaggregate content consumption by product update in our data For our model, the hit-rate across all consumer choices

average experience level of the user population in our data set We observe that the evolution of experience is significantly diï €erent across segments,

our data, we observe most updates concentrated in the first half of the product lifecycle,

Using data from the popular online game â€oeworld of Warcraftâ€, we find that motivations for product

To collect data from interviewees a number of 730 companies were contacted by phone or email between January 2013 and June 2013

would the input of a cluster analysis The present analysis also had the aim to investigate the state of

data We certainly have many casualties among SMES due to the incorrect application of innovation process,

Data from 430 small and medium-sized enterprises were analyzed through hierarchical regression analysis and innovation was found to be a significant factor in both family and non-family

The cross-sectional nature of the data collection limits potential findings, and it is unclear if similar results would be found in a com

reliable data. While this research combined two samples from different countries, evi -dence of how this process can enhance the study was presented.

sets of data in this study is consistent with prior research, suggesting that the direct im

performance data that could be verified independently Though recent research on family firms has begun to yield findings about perform

samples varied from the findings of the combined data sets in terms of nationality and industry. In order to test for country effects, the data were broken into two sub

-sets:( (1) US family and non-family respondents and (2) Australian family and non -family respondents.

The Australian data had less explanation in the family sample, and the adjusted R2 was 0. 15 but significant

comparable results for each data set. For example, the hierarchical model with the innovation variable in the US family data set explained 42%of the variance

and was sig -nificant at the 1%level (Î=2. 91, t=3. 09, p<.01.

As the data were collected from various industries, they were tested in further regres -sion analysis for possible industry effects.

gathered the data, and drafted the manuscript. MS contributed to the research design and performed the statistical analysis.

personally for data collection whenever possible. Last but not least, the surveyed SMES were given the possibility to answer the questionnaire in their native language (with

In the following sections we describe our data in relation to these topics 3 A Characterization of Hungarian and Romanian SMES

the primary data for our explorative research was acquired through collaboration with well-established institutions as well as individual

In collecting data on SME innovativeness in terms of their new product/service introductionsvi, we have followed the prescriptions of the Oslo Manualvii (2005

The remaining data has produced a realistic overview of SME innovativeness in our sample and is summarized in

and interpreting innovation data Publications de l'OCDE Fletcher, D.,Helienek, E. & Zafirova, Z. 2009.

data collection) have stimulated participating SMES from Hungary and Romania to consider three aspects before reporting new product/service introductions.

accurate data for further analysis. Participants were very positive about this †educational†aspect of the study, as

Government data is increasingly being made public, improving transparency and allowing software programmers to create extra value from underused data by,

for example, mapping out injuries and deaths to cyclists on London†s roads. vii The Young Foundation researches

Building upon the open data movement, www. Mydex. org-a new community interest company backed by the Young Foundation-aims to empower individuals by giving

them back control of their own data. The government holds data about citizens in hundreds of databases, with individuals having little control over it.

Mydex equips people with a platform for managing, sharing and realising the value of their

personal details and preferences Local Action Digital technologies are helping local communities organise local actions.

business models, laws and regulations, data and infrastructures, and entirely new ways of thinking and doing.

Data from the Johns Hopkins study also found astounding growth 35 rates within the nonprofit sector in all European countries where the sector†s

instead they collect data on the number of organisations with particular legal forms †that is, the number of social cooperatives, associations, social

In each case we have tried to focus on examples where there is some data on impact and reach.

appointment calendar, laboratory data, patient records, waiting list information from hospitals and so on Evaluations of the portal show that roughly one third of users seeking

-PESA application is installed on SIM CARDS and works on all handsets M-PESA has revolutionised money transfer in Kenya and significantly

Silicon valley can be attributed largely to the clustering of technology firms which enabled networks, alliances and collaborations to flourish

data •In the UK, the Department for Innovation, Universities and Skills has commissioned NESTA to develop a new †Innovation Index†to

However, data of this kind remains underdeveloped. There are nonetheless some interesting advancements across Europe: there are new perspectives on

of well-being requires both objective as well as subjective data. Some specific examples of a move beyond narrow economic indicators include the UNDP€ s

lxxvi http://ec. europa. eu/employment social/equal/data/document/etg2-suc6-synergia. pdf lxxvii http://www. wikipreneurship. eu/index. php5?

priority areas, clustering the regional efforts on achieving excellence in science and technology  Network of Technological Centres:

social movements, business models, laws and regulations, data and infrastructures, and entirely new ways of thinking

research, mapping and data collection are used to uncover problems, as a first step to identifying solutions

Many innovations are triggered by new data and research. In recent years there has been a rise in the use of mapping techniques to reveal hidden needs

much more interested in disaggregating data. There are also a range of tools for combining and mining data to reveal new needs and patterns

1 18 THE OPEN BOOK OF SOCIAL INNOVATION These sites show how to run competitions for †mash up†ideas from

citizens using government data, such as Sunlight Labs and Show Us a Better Way 9) Mapping physical assets.

of the research process †from design, recruitment, ethics and data collection to data analysis, writing up, and dissemination.

toward prescriptions emerging out of the data which can be employed for the improvement of future action

and analyse large quantities of data has been the basis for remarkable changes †for example: in flexible manufacturing, and

In Japanese factories data is collected by front 1 PROMPTS, INSPIRATIONS AND DIAGNOSES 21 line workers, and then discussed in quality circles that include technicians

311 complaint system, embedded with GPS data pinpointing the exact location of the problem. These complaints will then get forwarded to the

18) Integrated user-centred data such as Electronic Patient Records in the UK, which, when linked through grid and cloud computing models

19) Citizen-controlled data, such as the health records operated by Group Health in Seattle, and the ideas being developed by Mydex that adapt

service which provide a database that can be analysed for patterns of recurring problems and requests

The gathering and presentation of data requires a process of interpretation. This should ideally include those

In analysing an issue or a set of data, it is useful to have the

online repository of ideas and experiences †that has a database of 4, 000 ideas online, receives a quarter of a million visitors a year, and, of those

and research data to demonstrate effectiveness and value for money (see list of metrics below) as well as adapting models to reduce costs or improve

histories, databases, and manuals. One new initiative by Open Business is the creation of a database of open business models

199) Barefoot consultants. There is an important role for consultants and those with specialist knowledge †who can act as knowledge brokers and

provide funders or investors with data on impact; and to provide a tool for organisations to manage their own choices internally;

For example, a study of the operational data of public housing repairs found that the time taken to do repairs varied from a few minutes to 85

to gather chronic disease data in Sheffield and metrics geared to self -monitoring such as those used by Active Mobs in Kent

coaches, increasingly backed up by shared data services and networks Service design in the 1980s and 1990s often focused on disaggregating

Solutions & How-Tos Get the Data Economic Empowerment -A year-round conversation Forums, media spokespeople

•Girls Database/Scorecards •Girls Count Task force Reports •Partners & research initiatives measure girls more broadly

databases). ) With the increasing mixing of voluntary and professional roles (for example around care for the elderly,

244) Data infrastructures. A different, and controversial, infrastructure is the creation of a single database of children deemed †at risk†in the UK

This was seen as crucial to creating a holistic set of services to deal with children†s needs,

†for self-care for chronic disease, that combines rich data feedback with support structures which help patients understand

So while familiar data on income, employment, diseases or educational achievement continues to be gathered, there is growing interest in other types

public agencies to publish data on their balance sheets, or to show disaggregated spending patterns, or flows of costs, can then contribute

spending data for particular areas or groups of people. Too often, public accounting has been structured around the issues of targets, control

Wikiprogress, bringing together data and analysis on progress. The same year President Sarkozy commissioned Joseph Stiglitz to chair an inquiry

This includes file sharing services such as Napster, and open-source software such as the Linux operating system, the Mozilla Firefox browser,

transparent access to public financial and other data 342) Audit and inspection regimes which overtly assess and support

Guidestar†s services and databases in many countries worldwide, and New Philanthropy Capital in the UK

provides a useful platform for aggregating ultra local data Prosumption There has been marked a development of users becoming more engaged in

Data 17-18; 21-2; 101-105; 112; 114; 116 119-120; 204 De Bono, Edward 32

processes are customer call centers, Intranets that link business partners, data warehouses that improve customer relationships

books, blueprints, scientific journals, databases and the know-how of millions of individuals, is the ultimate source of all economic life. †15

ï€'Specific ontologies which describe the semantics of data, services, processes for that business sector ï€'Sector-specific education and training modules

indispensable that the protocols and the data format are open and not depending from a unique provider, to guarantee the independence from hardware and software

The large-scale research programmes in data mining funded by the Defence Advanced Research Projects Agency (DARPA) at Stanford and a few other universities provided the

Research limitations/implications †As the analysis was reported based on self data provided by the entrepreneurs of SMES, the authors had to rely on their judgment regarding the novelty of the

Section 3 introduces the data and the research methodology used in this study. In section 4 the results of statistical

which leads to missing data and possibly biased results When reviewing the existing literature on innovation-performance relationship

3. Data and research methodology 3. 1 Sample and data The primary data for this study were gathered in 2006 via a postal questionnaire

among the SMES located in the Northern Savo region in Eastern Finland approximately 400 kilometers away from the Finnish metropolitan area.

As a sample frame for constructing the database, we used the register of SMES in the region

that was offered by Suomen Asiakastieto, the leading business and credit information company in Finland. In this register, the latest financial statements data of 95 000

Finnish firms and groups are on one CD. The questionnaire was sent to 1, 282 entrepreneurs of SMES located in the Northern Savo region.

innovations during the four-year period (2002-2006) prior to the data collection, or whether there were only incremental modifications on the existing products, processes

with cross-sectional data we are unable to proof the existence of a causal relationship or its direction, even when

However, as our data do not allow a more detailed investigation of this issue, the propositions presented

self-reported data provided by the owner-managers of SMES, we have to rely on the

Second, on the basis of our data, we are unable to state whether the external

study the data were gathered from single informants †the owner-managers of the firms †only.

Collecting and Interpreting Innovation Data: Oslo Manual, 3rd ed.,OECD, Paris Pavitt, K. 2005), â€oeinnovation processesâ€, in Fagerberg, J.,Mowery, D c. and Nelson, R. R. Eds

ï Research, innovation and the digital economy ï Empowering people, promoting SMES and flexicurity -Promoting entrepreneurship & SME development

decision-making process and introduce more coherence in the gathering of data about the impacts on SMES which are underestimated often

the rapid expansion of the digital economy. Retail and wholesale are adapting at pace anticipating customers†changing needs and preferences.

Using big data for the future of personal transportation: DATASIM Published by Newsroom Editor(/digital-agenda/en/users/Newsroom) on 26/11/2014

DATA SIM (http://www. uhasselt. be aimed/datasim at providing an entirely new and highly detailed spatial-temporal

massive amounts of Big data of various types and from various sources, like GPS, mobile phones and social networking sites.

systems†trends (Part I), trying to examine technological issues such as Big data Cloud computing, Mobile services, etc.

-ration as a consequence of the digitization of the work environment, and finally dealing with what may be considered the real challenge to digital business:

one hand, of Big data, Cloud computing, and Mobile Services for business; on the other hand, it discusses the drivers and challenges of Social Listening and

Data and Cloud computing, through Mobile Services as platforms for socializing and ††touch points††for customer experience, to emerging paradigms that actually

1 Big data...3 1. 1 Introduction...3 1. 1. 1 Big data Drivers and Characteristics...5

1. 1. 2 Management Challenges and Opportunities...9 1. 2 Case studies...15 1. 3 Summary...

4. 3. 1 Text mining and Conversation†s Analysis...72 4. 3. 2 Classification and Analysis Methods

DDS Digital data stream DMS Document management system ECM Enterprise content management HR Human resources ICT Information and Communication Technology

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.

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. Finally, the discussion of case studies concludes the Chapter,

on factors and strategic points of attention, suitable to support Big data-driven decision making and operational performance

and the volume of data archived, stored, and exchanged as a consequence of the V. Morabito, Trends and Challenges in Digital Business Innovation

information flows in social networks and potentially see the world as a big data repository to be exploited,

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

information systems, 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

in the volume and variety of data to be managed by banks and financial services providers Furthermore, it shows how, e g.,

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 from sensors, cameras, digital devices for monitoring supply chains and stocking

in warehouses (i e.,, what is called actually internet of things), video conferencing systems and voice over ip (VOIP) systems, have contributed to an unmatched

challenges associated with the emergence of data sets whose size and complexity require companies adopt new tools,

Furthermore, Big data require new capabilities 5 to control external and internal information flows, transforming them in strategic resources to define

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

Nevertheless, as usual with new concepts, also Big data ask for a clarification of their characteristics and drivers.

-acterizing 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 data -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,

or 500 million daily call detail records in real -time to predict customer switch Variety:

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 net -works to sensor data, click streams, e g.,, from internet of things

Accessibility: the fourth dimension concerns the unmatched availability of chan -nels 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 actually considered 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. It is worth noting that a priority number is associated to each driver,

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. Among the programming para -digms for processing, generating, 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; whereas HBASE, Google†s Bigtable, 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, specified by the user to process a key /value pair and to generate a set of intermediate key/value pairs;

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†â€; 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 real

-time action††15. Thus, DDSS refer to streams of real-time information by mobile devices and internet of things, that have to be ††captured††and analyzed real-time

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: e g.,, to identify variations in costumer senti

-ments towards a brand 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 •Explore network relationships on, e g.,

, Linkedin, Facebook, and Twitter to identify potential threats or opportunities related to human resources, customers competitors, etc

As a consequence, we believe that the distinction between DDSS 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 Indeed, while DDSS may be suitable to be used for marketing and operations

Data refer to the information asset an organization is actually able to archive manage and exploit for decision making, strategy definition and business inno

focus on Big data applications As shown in Fig. 1. 2 they cover many industries, spanning from finance (banks

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, opinion mining,

As for public sector, 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 account -ability 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 constit

-uencies 16,17, and ii) participation of citizens to the policy making process thanks to the shift of many government digital initiatives towards an open gov

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.

believe that management challenges and opportunities of Big data need for further discussion and analyses, the state of the art currently privileging their technical

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

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. However, they do not allow yet a clear understanding of the specific actions required for exploiting their research and

business value with regard to traditional information management problems. Indeed on the one hand, as pointed out by Pospiech and Felden 7,

Big data seems to be yet another brick in the wall in the long discussion in the information systems field on

hand, Big data change the rules of the game, asking to change the overall infor -mation 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, moving information

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, 8, 14.

Therefore, Big data challenges can actually be addressed by actions asking a technological/functional or else a business perspective, depending

fields, contributions to Big data research. In Table 1. 1 we classify these per -spectives 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-Provi

-sioning 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 and actions that technological

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,

or information system oriented), and the manage -ment challenges they have to provide answers and solutions.

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. Here, the challenge concerns the fact that executives often argue that they have to make decisions

based on information they do not trust or they do not have. As pointed out by

Tallon 23, 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 orga

-nization†s business processes or to decision making. Besides storage, 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 Processesinformation systems 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

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 tech

-nology level, showing the enabling role of alignment, centralization, standardi -zation, and strategic use of IT orientation.

Nevertheless, these enablers being quite recognized in theory and practice as a good management of information systems

target, on the other hand, they look as by far challenging, due to the distributed nature of Big data and the unpredictable dynamics of the digital environment

producing them. Furthermore, they often require business process management and optimization to get the target performance levels 26

business process optimization issues, organization may fail to exploit Big data Indeed, optimization often leads to rigidity and inflexibility of business processes

Data is what Awargal and Weill 27 called softscaling, requiring three core capabilities for companies and their IT units to act as enabling factors for an

rely and exploit Big data to develop flexible strategy and business models, thus anticipating and responding to volatility of market and customer needs, while

Table 1. 2 Data governance enablers and inhibitors Factors Enablers Inhibitors Organization Highly focused business strategy Complex mix of products and services

Predictable rate of data growth Absence of industry-wide data standards Technology Culture of promoting strategic use of

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,

further cultivating emotional connections 27. An example, is described the case by Awargal and Weill 27 of the use of demographics made by Hero Motocorp.

-tionship 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 chal -lenge. As for their use,

as seen above, companies has to rely on new data man -agement technologies and analytics to get evidence of facts rather than intuition by

manage data Operational excellence Focus on customers Decision making optimization IT enabler Value from information Fig. 1. 4 Empathic use of information for value creation:

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 •lack of understanding of how to use analytics to improve the business

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 sum -marize 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

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.,

the data asset of an organization, actually are determined also by external data applications, and services due to the growing relevance of social networks, mobile

represent the main Big data sources, alimenting in a volatile and dynamic way the digital asset of an organization,

seems to be required for satisfying the needs for optimization and effective data management of Big data.

Indeed, the greater the integration of a company†s information system, the faster the overall planning and control cycles 29

Applying to Big data issues the SIGMA model, that we have proposed in a previous work to improve strategic information governance modeling and

determined by two variables, application integration and data integration (see also 29,30. Accordingly, integration orientation constitutes a fundamental lever of

-plete 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

Data Integration Integration Orientation IS Organizational Absorptive Capacity Process Orientation Change Orientation Analytics Orientation Information

DATA ASSET C O M PE TI TI V E EN V IR O NM

Î (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 and challenge has to be considered as the background to the agenda

how strategy points for Big data lifecycle phases in Table 1. 3 have been addressed in practice, emphasizing point of attention and insights for managers

and strategy points for big data lifecycle phases Lifecycle phase Factors Recommendations Strategy points Storage Technology Consolidate corporate databases (internal

and integrate new information sources internal/external Completeness Storage Technology Identify 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

manipulate data with real-time tools Timeliness /Simplification Use Organization Ensure access to information and an

aligned with IS strategy for Big data exploitation from social media. The case has been discussed by Moses et al. 31

Twitter, and the ability of these platforms to provide data that can translate user choices in demographic information valuable to achieve marketing and commu

external data sources through appropriate storage and data warehouse technolo -gies. Bahrti Airtel operates in the Indian mobile market characterized by constant

Thus, data are the main asset for evidence-driven decision making The claim ††Our objective is to have one version of the truth!

Goel, CIO of Bharti Airtel Limited, summarize the need for a single set of data that

Using Big data should be enhanced and sup -ported by a business strategy focused and shared by the overall company

16 1 Big data As a consequence, Baharti Airtel, to manage the evolution of the market,

including data warehouse systems aimed at the col -lection and subsequent analysis of data from various corporate activities.

The production and use of information reports were introduced gradually in the company, up to in-house solutions aimed at the production of ad hoc reports for

from Big data, with a specific attention to data base technologies. The case analyzes how Nokia, the Finland based global telecommunications company, has

Indeed, effective collection and use of data is strategic to Nokia for understanding and improvement of users†experiences with their

terabyte-scale streams of unstructured data from phones in use, services, log files, and other sources.

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, Cali -fornia. GE charged William Ruh from Cisco systems to lead the center, devel

-oping software and data science capabilities for GE€ s Big data domain of interest †the industrial Internetâ€

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 reten -tion 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†â€), located in San ramon and around the globe, as well Bangalore, New york,

and Cambridge), reporting into the center. The center organizes employees into reference disciplines, such as, e g.,

, machine learning statistics, and operations research, among others. Furthermore, centralization of the staff is motivated by three factors:

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.

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†â€

Big data lifecycle. As for these issues, the Chapter has pointed out the relevance of ††softscaling††approaches, balancing optimization issues, such as, e g.,

experience and contextual needs for an empathic exploitation of Big data as a digital asset Finally, the Chapter has discussed a set of case studies,

importance of a clear and shared Big data strategy together with investments and focus on human resources for capabilities,

suitable to support Big data-driven decision making and operational performance References 1. Ahituv N (2001) The open information society.

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. Harv Bus Rev

90 (10: 61†68 9. Wang RY, Strong DM (1996) Beyond accuracy: 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:

new wine or just new bottles? Proc VLDB Endow 3: 1647†1648 13. Agrawal D, Das S, Abbadi A (2011) Big data and cloud computing:

current state and future opportunities. In: Proceedings of extending database technology (EDBT), ACM. March 22†24, Sweden, pp 530†533

14. Davenport TH, Barth P, Bean R (2012) How ††Big Data††is different.

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. Barcelona, Spain, pp 794†802 17.

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

Tallon PP (2013) Corporate governance of big data: perspectives on value, risk, and cost IEEE Comput 46:

Weber K, Otto B, à sterle H (2009) One size does not fit all†a contingency approach to data

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

Awargal R, Weill P (2012) The benefits of combining data with empathy. SMR 54: 35†41

Lavalle S, Lesser E, Shockley R, Hopkins MS, Kruschwitz N (2011) Big data analytics and the path from insights to value.

Morabito V (2013) Business technology organization†managing digital information technology for value creation†the SIGMA approach.

using big data to bridge the virtual & physical worlds 34. Consultancy T (2013) Big data case study:

how GE is building big data, software and analytics capabilities for an ††Industrial Internet. ††http://sites. tcs. com/big data-study

/ge-big data-case-study/./Accessed 20 jul 2013 35. Floridi L (2010) Information: a very short introduction.

Oxford university Press, Oxford pp 1†43 36. Avison DE, Fitzgerald G (1999) Information systems development. In:

Currie WL, Galliers RD (eds) Rethinking management information systems: an interdisciplinary perspective Oxford university Press, Oxford, pp 250†278

20 1 Big data 38. Kharif O (2013) ATMS that look like ipads. Bloom Businessweek, pp 38†39

Han J, Haihong E, Le G, Du J (2011) Survey on Nosql database. In: Proceedings of the 6th

Strauch C (2010) Nosql databases. Lecture notes on Stuttgart Media, Stuttgart, pp 1†8 41.

IBM, Zikopoulos P, Eaton C (2011) Understanding big data: 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 Technol

Electronic Point of Sales (EPOS), Electronic Data Interchange (EDI) and Elec -tronic Funds Transfer at Point of Sale (EFTPOS),

such as data centers and specialized software applications 2. It is the latest development in the computing models that performs computing functions on

computing capabilities, such as server using time and data storage, automati -cally and without human interaction 2. Broad network access,

instances in data centers using APIS. The second one is Microsoft windows Azure platform, which is composed of three components.

applications and store data in data centers. The second component which is SQL Azure provides data services in the cloud using SQL SERVER.

The last component which is. NET services facilitates the creation of distributed applications. The last

reliable access to data using large clusters of Application Layer ---Business Applications, Web Services Multimedia

Data centres Fig. 2. 2 Cloud computing architecture. Adapted from 5 Fig. 2. 1 The three layers of cloud computing. Adapted from 4

large-scale distributed database. Finally, the last one is the Mapreduce1 pro -gramming model that can be modified according to the characteristics of the

-resent 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 orga -nization itself or to outsource it to a third party depends on the abilities and

1 See also Chap. 1 of this book for details on Mapreduce and Big data 26 2 Cloud computing

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; however, this is not straightforward. Currently, detecting workload hotspots and initiating a

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

data centers, especially when they are composed of several hundreds of servers 6. Data security. The infrastructure provider tries to achieve best data security by

meeting the following two objectives (1) confidentiality, for secure data access and transfer, and (2) auditability, for attesting whether the security settings of

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 condi

-tions 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

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

•storage areas that are specified for customers†data •hypervisors •cloud mapping services 30 2 Cloud computing

Data (and Application) migration, which will happen during the change to the cloud computing model and comprises risks related to data security and

another 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 min

•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

Other examples of the risks include the low controllability over the service, 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 previous mentioned risks

which needs to be able to deal with them by having backup plans in case of a disaster.

-porate data and security and system performance. However, private cloud is usually not as large-scale as public cloud,

concerns about security and data sovereignty. In contrast to previous model, the public cloud is open for use by the general public i e.,

SLAS), security standards, backup strategies, customer support, downtime history and pricing policy. Thus, this choice has to be built upon a careful decision and a

Backup system System update Maintain service Education training Facility Reliability Specialisation Compatibility Link/Connection Flexible

backup plans and satisfying the overall strategic intent without making compromises 6. Negotiate. To agree on all issues

supplier, having clear and solid backup plans in disaster situations and having clear contract get out clauses

transforming an in-house data center to Amazon EC2, benefiting from the (Iaas that Amazon has.

its entire IT infrastructure and data storage. In response for this situation, the Vice President of Information technology suggested using cloud computing to move

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 and department, including the Ministry of Education science and Technology, the

networks, fostering digital communication, processing, and storage of diverse types of information as a service. Accordingly, Tilson et al. define digitalization as

Analytics •Mining of event-stream data •Real-time execution of business rules Adapted from 4

Mobility is currently one the main characteristics of today digital information infrastructures. However the diffusion of mobile devices, such as, e g.,

Big data •Amazon†s Dynamo, HBASE, Google†s Bigtable Cassandra, Hadoop, etc Ultra-fast, low latency switches •Cisco Networks, etc

High density, low cost chips •IBM, Intel, AMD chips 50 3 Mobile Services As pointed out by Bagozzi 20,

to mobile data services having an ubiquitous nature and an impact on individuals†lifestyle. In particular, they focus on devices

data, video, and pictures, thus providing a suite of utilitarian and hedonic func -tions. Apart from traditional core constructs of TAM (such as perceived ease of

information systems, at software as well as data level •Performance, encompassing the need for monitoring and control of applications

of guarantying the privacy of data and the trustworthiness of mobile applications and services interactions

software and data ï§Device management data privacy and security Software solutions to support applications management on devices

often interfacing different preexisting platforms and heterogeneous databases To solve the above issues, managers can follow an integrated or else a best-of

device management, monitoring of applications and systems integration of the data 3. 3 Digital Management Solutions 57

Finally, ensuring the security of the data (see Fig. 3. 3) is by far one of the most

-viders, ensuring the safety of sensitive data access, user profiling as well as compliance with corporate policies.

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

, 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, which illustrate at a glance

•data on previous purchases of a given customer •the types of orders typically performed by a given customer

-tiveness of the sales (historical data on the customer, complete catalog, real time inventory, etc. 31,32.

reduction of the time of data entry, increased personal productivity, and a reduction of errors at order entry.

chain activities and sales provide increased productivity, improved data quality, and knowledge on customers†behavior and history

because the comparison with data from the company†s management system took place only in the final balance, without the ability to

workers, also offering real-time production data with a higher precision To solve these issues, Habasit has implemented a solution based on the

the data on the availability of the product in a purchase order system by SAPÏ , the Warehouse Management Software (WM) SAPÏ¿

obtaining increased productivity, improved data quality, knowledge on customers†behavior, and consequent substantial savings from enhanced customer experience

the case of mobile data services. Inf Syst Res 17: 162†179 24. Là pez-Nicolã¡

•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

Data issues. As already seen in previous Sections, new instruments such as, e g blogs, wikis, microblogging,

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

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, sentiments and emotions expressed through a text, although at different level of analysis, as seen above 14.

-structured 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. Once achieved this goal, it is easier to reach shorter information, such as

issues, 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

public data base (as for gender, age, and location, see also 30). Other researchers has shown the risk to privacy related to vanity queries, in

into quantified data, further efforts are required to design and develop frameworks and applications to recognize potential threats into a text.

data about people preferences, activities, and their social environments 35 According to this perspective, social sensing is an intelligence activity acting on

empowered analytics for large volumes of real-time information or digital data streams (as outlined in Chap. 1,

data. This may definitely shift the meaning of what businesses mean time-to -market towards the capability of interpreting individual customer experience

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.,, IBM System S, storing 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,

see also Chap. 1 of this book for storage issues for Big data 4. 5 Social Sensing 79

interactions and data streams Table 4. 5 Social sensing domains and applications Domains Applications (sample Crowdsourcing for user

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 not verified 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

consequent strategic focus on analytics and data management capabilities across the overall company functions and business processes is rising as one of the key

emphasized in Chap. 1 on Big data References 1. Weill P, Vitale M (2002) What IT infrastructure capabilities are needed to implement

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

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:

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

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

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

to business data and transactions, and communication facilities. Cost cuts can be achieved by lower spending in hardware and other types of infrastructures, as

•Data Management opportunities: successful implementation of the IT consu -merization requires strong architecture that could result in better data man

-agement 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 accu -racy, 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 pre

-senting a challenge for the managers 10. This problem is among the other issues related to the consumerization of technology devices 11.

device and the data on it are under question Nevertheless, the ISF€ s report 12 offers guidance that is related to organiza

-nificant amounts of sensitive corporate data. According to Mcafee, a third of such devices losses resulted in a financial impact on the organization.

3. It is hard to discriminate between user and company data on the employee -owned 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

risks related to data loss 94 5 IT Consumerization Moreover, more cost oriented businesses might also be interested in legal

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 from possible fines and

devices, firms have to concentrate on protecting the corporate data that will be accessed by a range of devices 14

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

IT consumerization by moving the desktop and/or applications into a data center This strategy makes it easier to provide new desktops,

and configure sensitive user†s data and resources 16 5. 5. 4 Bring Your Own Device BYOD Strategy

the owner of the data on a privately owned device, accessing the cor -porate data remotely from a personal device,

and legal issues related to privacy laws and contracts permits Financial and tax considerations. 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

Data synchronization Data synchronization VDI VDI Adapted from 16 5. 7 Considerations Related to IT Consumerization 105

corporate data Finally, the Chapter has discussed case studies, confirming the importance benefits and issues associated with the consumerization of the IT.

messages, files, data or documents to each other. This category includes e-mail instant messaging, fax machines, voice mail and web publishing tools.

methods of sharing data and information. This type typically includes telecon -ferencing 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

•keeping databases that are being used by different users in different locations synchronized and up to date

-tribution by Koan 10, a group of data collection methods that include interviews short questionnaires and online survey were used to evaluate the value of such

perspectives, 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, standards that govern the way these systems work, user

-tains many useful applications such as Google doc for file sharing among team members, Google Calendar for scheduling meeting at times that works for

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

it provides users with features related to cloud storage, file sharing and documents editing in a collaborative manner.

•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

podcasts, finding contact information or labeling data to prepare it for the use in machine learning 21.

for text digitization 22 Nevertheless, despite its powerful features, many research areas related to Crowdsourcing need to be covered.

internet as a two-way of information and data sharing, which would lead to higher effectiveness and involvement by the participants.

utilizing digital communication and collaboration platforms, it is important as well to consider the challenges that accompany this usage such as the

-oration is basically about using digital devices, open source data and cloud technology to share knowledge, manage information

the basis of digital business, increasing the volume of data stored, information production as well as the flexibility and capacity of sourcing activities (often

and defining data ownership. However, this is only a part of the current challenges that a company has to face

of privacy of data and security of its own information infrastructure. Apart from IT consumerization, other phenomena such as the diffusion and pervasivity of social

enacted accomplishment††14, 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 and accounts in

data and reliable transactions for the target customers As for the above mentioned inner perspective,

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

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. Consequently, the vulnerability assessment considered the four major security issues mentioned below

data and its implications across the enterprise. The above mentioned problems called for a robust IT governance framework, delivering measurable value to the

As a result, the data collected by analytics could be used to monitor the IT performance

particular, to the exposure of business†s data and systems to external environment The risk of an enterprise not knowing the identity of its business partners is

digital economy, which is characterized by the rapid and continuous interaction of innovative applications and services. In fact, especially where the company†s

capacity of processing data associated with a significant reduction in costs, making it possible to manufacture

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

The advent of the digital economy can be conceived really as a new industrial revolution both in terms of magnitude and extension of the economic transfor

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.

industrial digital economy the whole process of value creation is entirely trans -formed. In the industrial economy a process of value creation starts from raw

digital economy is driven fundamentally by customer demand. In the digital economy, the essential input of the value creation process is information itself, for

the digital economy, is an essential source of value and every business is an information business 38.

digital economy. However, in the industrial economy knowledge generation and application processes are aimed essentially at making production more efficient

while in the digital economy they are directed mainly to intercepting the customer†s preferences and expand his choice.

-mendous source of access to data and information in a digital format, but it also

Table 9. 7 Comparison between the industrial 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

•Digital information Output •Intermediate or finished products or services •Products or services with a high

are vital to meet the challenges of the digital economy, which requires a paradigm shift. Companies are called to deal with the Internet and the opportunities of

-nizations operating in the digital economy must identify and exploit economically these specific attributes of the Internet and of electronic commerce and their success

In the context of the digital economy, the innovation of the business model can be defined as the creation and utilization of new knowledge

Digital resources Information and data in a digital form, duly selected, organized and summarized, become a source of essential value that

In the digital economy companies need to continually adapt to changes, which are extremely fast and frequent

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

Ind Manag Data Syst 104: 78†87 27. Haaker T, Faber E, Bouwman H (2006) Balancing customer and network value in business

-tion data, collected through mobile phones, GPS, Wifi, cell tower triangulation RFID and other sensors. Using powerful machine learning algorithms,

This tool allows to transform existing data into predictive behavioral data, leading to a better understanding of customers without requiring

any change in behavior. It is also a key element for effective real-time marketing campaigns, for prediction of specific group 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

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 One of the founders is Alex Pentland, Toshiba Professor at MIT, serial entre

while, at the same time, the large amount of location data was fundamental for testing hypotheses about human behavior

according to data about stream of people, weather forecast, specific news, traffic Table 10.3 Company competitiveness indicators

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 mer -chant lists to their banks and to on-line communities

team is composed of data scientists, mathematicians, security experts and industry specialist, supported by the investments of some of the founders and CEOS of

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

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

The reports†data can be exported easily and the software itself is developed for easy integration with other systems

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,

better data collection, and higher customer retention As shown in Table 10.14, the User Value is quite high, with positive feedback

side gathering meaningful data about their customers. Around 10%of all trans -actions are completed currently through the mobile app

-ioral analysis from intensive data streams as well as from Big data References 1. Christensen CM (1997) The innovator†s dilemma:

trends we have considered the business challenges of Big data as a core com -ponent of the information infrastructure upon which our society is building its own

with consequences ON IT policies as for security, disclosure of data, and privacy Taking the digital trends challenges into account, Fig. 11.1 summarizes the

, policies for privacy and security of data and infor -mation flows; on the other hand, promoting it in terms of brand in an

digital information infrastructure, leading to a change of perspective on its stra -tegic role as the ††guardian††of a company digital business assets and ††heritage†â€

Backup strategies, 34 Bring your own device (BYOD), 90,97, 103 108,109, 134 Big data, 5 C

Called technology steward (TS), 118 Campus connect Initiative, 129 Capabilities, 5 Chronological age, 55 Closeness centrality, 69

Column-oriented databases, 6 Community cloud, 34 Community coordinator (CC), 118 Community of practice, 117

Data, 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 Digital enablers, 48,49 Digital governance, 145,146, 149, 151†153

158,159 Digital infrastructures, 49,50 Digitalization, 48 Digital natives, 4 Digitizing, 48 Distance effects, 55 Document-level sentiment analysis, 70

Hadoop, 7, 28 Hybrid cloud, 34 Hyperscale storage, 80 I Information, 4 Information aggregation markets (IAMS), 146

Open data, 8 Open government, 8 Open Information Society, 3 Open innovation, 182,183 Opinion identification, 73,74

1 Big data Abstract 1. 1†Introduction 1. 1. 1 Big data Drivers and Characteristics 1. 1. 2 Management Challenges and Opportunities

1. 2†Case studies 1. 3†Summary References 2 Cloud computing Abstract 2. 1†Introduction 2. 1. 1 Cloud computing:

4. 3. 1 Text mining and Conversation†s Analysis 4. 3. 2 Classification and Analysis Methods and Solutions

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