Synopsis: Ict: Data: 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

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

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

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

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?

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

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

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

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

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

†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

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

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

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

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

In this register, the latest financial statements data of 95 000 Finnish firms and groups are on one CD.

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

decision-making process 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

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

Data, one of the IT trends actually emerging as strategic for companies competing in current digital global market.

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

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.,

and the increasing availability of unstructured data (images, video, audio, etc from sensors, cameras, digital devices for monitoring supply chains and stocking

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

the first dimension concerns the unmatched quantity of data actually available and storable by businesses (terabytes or even petabytes), through the

world's data is unstructured From 1. 3 billion RFID tags in 2005 to about 30

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

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

the growing size of available data requires scalable database management systems DBMS). ) However, cloud computing faces IT managers and architects the choice

thus, identifying novel data management systems for cloud infrastructures 12,13. Accordingly, at the state of art Nosql (Not only

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;

as encompassing both data ††stocks††and ††flows††14. For example, at the state of

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.

-ments towards a brand or a specific product/service or to exploit sensor data to

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

Mapreduce has been used to rewrite the production indexing system that produces the data structures used for the Google web search service 41

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

data-driven decisions are better decisions, relying on evidence of (an unmatched amount of) facts rather than intuition by experts or individuals.

Sensor Data Fusion †Fig. 1. 2 Big data applications 8 1 Big data 1. 1. 2 Management Challenges and Opportunities

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.

Considering, the technological type of perspective, the Technical-Data-Provi -sioning classification mainly concerns storage related actions with regards to

On the contrary, the Technical-Data-Utilization classification addresses computational complexity issues related to both provision and use

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

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

Fig. 1. 3 structure a lifecycle, starting from the (continuous) storage of data from the outer and inner flood involving today†s organizations.

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.

volume of data does not imply that it provides valuable information to an orga -nization†s business processes or to decision making.

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

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.

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

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

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.

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:

28 pointed out that among the impediments to becoming data driven, companies answer the following main issues

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.

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

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

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

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

and store relevant data from all information sources (internal/external Relevance Management Technology Adopt analytics appropriate to the

and velocity of data real-time Timeliness and accuracy Management Industry /Organization Establish clear goals and articulate a

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

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

-lection and subsequent analysis of data from various corporate activities. The production and use of information reports were introduced gradually in the

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

IT Portfolio and data asset, for identifying relevant data from all information sources (internal/external) to be stored,

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

and investments, in particular, on human resources to be focused on data scientist capabilities. Furthermore, talent management and employees reten

††hardcore data scientists†â€), located in San ramon and around the globe, as well Bangalore, New york,

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

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

J Data Inf Qual 1: 1†22. doi: 10.1145/1515693.1516680 11. Huang KT, Lee Y, Wang RY (1999) Quality, information and knowledge.

Piccoli G, Pigni F (2013) Harvesting external data: the potential of digital data streams. MIS Q Exec 12:

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

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

hadoop and streaming data, 1st edn. Mcgraw-hill Osborne Media, New york References 21 Chapter 2 Cloud computing

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

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 management and programming models respectively 5 2. 2 Strategic and Managerial Challenges and Opportunities

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

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

8. Storage technologies and data management. The concerns that can arise here come from the compatibility issues between the Internet-scale file systems that

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

-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.,

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

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

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

-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

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

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

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

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:

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.

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

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

and defining data ownership. 3 Implementing mobile strategy includes enabling mobile device management infrastructure such as, for example, System Center Configuration Manager 2012

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.

Examples of these technologies comprise data conferencing which lets a set of PCS that are connected together to share

-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

•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.

-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

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

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

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

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

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

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

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

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

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