big data concepts theories and applications pdf

Big Data Concepts Theories And Applications Pdf

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This book covers three major parts of Big Data: concepts, theories and applications. Written by world-renowned leaders in Big Data, this book explores the problems, possible solutions and directions for Big Data in research and practice.

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The digitization of products, processes, and business models—and the corresponding explosion of big data—has led to an evolution within business organizations. As different organizational approaches have developed toward big data, we use resource-based theory and organizational learning as anchoring perspectives to link this phenomenon with traditional strategic management.

We also identify four avenues for future scholarship as the nature of business moves increasingly digital. Strategy and Behaviors in the Digital Economy. The global digitization of products, processes, and business models is reshaping the very nature of business. Entire industries are rapidly evolving as more firms take advantage of increases in clicks, sensors, and technological innovation.

With storage costs becoming increasingly affordable and the lure of new or fear of missed opportunities, more and more firms are integrating information technology IT planning into their strategic thinking. Given these advances, firms are increasingly aware that every person or device is a potential data generator. Consumers leave an extensive digital trail as they go about their daily lives. Connected devices are also doing more to communicate with one another, including the tracking and transfer of data to value chain partners.

In a similar manner, organizations have become information processors. They are making considerable investments into analytic capabilities and data science talent to exploit opportunities presented by digitization, seeking to create or capture value and develop competitive advantage. In secrecy or in plain sight, organizations are working diligently to obtain consumer data and attempting to interpret and apply it to their strategic decision making [ 1 ].

Executives across a multitude of industries are plunging resources into big data projects with aims to better monitor, measure, and manage their businesses. These strategic leaders are leveraging information to exploit current markets with incremental innovations that influence marketing efforts, product selection, and operational processes. Yet a small number of organizations employ a different role for data within their strategic approach. These firms recognize that information is at the core of most modern radical innovations [ 4 ]; their approach is resulting in the unforeseen entry into existing market spaces using innovative business models, the creation of new markets, and the invalidation of long-standing assumptions in traditional strategic thinking.

Scholars in the field of strategic management have an opportunity to play a major role in developing an understanding of how the emergence of big data is changing the nature of competition. Though the conversation has begun, management scholars have yet to build theory around the role of big data in the world of modern day corporate- and business-level strategy. As noted in recent research, big data has the promise of bringing new theories and practices to the organizational sciences, and is likely to play a central role in the development of new strategic approaches to firm governance and leadership [ 5 ].

We add to this promising literature through an integrative perspective of familiar organizational theories while triggering broader discussions for management research. We identify theoretical foundations necessary for an examination of the emergence of big data in strategic decision making through the lenses of resource-based theory RBT and organizational learning.

The result of such an approach is the realization that the field of strategy needs to be flexible enough to accommodate a new understanding of the interplay among data, technology, and strategy.

As the economy turns increasingly digital, scholarship must adapt to better explain new and unique phenomena of interest. The primary objective for this work is to stimulate the research agenda surrounding the integration of big data and corporate strategy. We aim to engage a broad variety of management scholars via our contributions, spurring on new theories and models to describe the disruption of value chains, supporting the development and reconceptualization of successful outcomes in business, and orchestrating linkages between business analytics methodologies and strategy scholarship methods.

While setting forth a theoretically grounded framework that will allow strategy researchers to begin tackling important questions in the field, we introduce components of the discussion that are heretofore absent in the management literature and offer numerous avenues for future scholarship. Big data applies to huge troves of raw data structured, semi-structured, and unstructured that cannot be processed or analyzed using traditional methods or tools, leading to increasing challenges in how value is to be extracted [ 7 ].

Though the origination of the term is still muddled and under debate, the concept of big data has become a topic of great interest, often under the assumption that it serves as a potential source for competitive advantage in many industries [ 8 ]. To understand the evolution leading to the current era of big data, the foundation lies in the development of database management and warehousing [ 9 ].

Collecting and storing mostly structured data in relational database management systems was increasingly employed by organizations in the s, with data mining techniques and basic statistical analyses applied as a means to gain insight into growing volumes of information. As the Internet gained prominence and widespread use, more data collection and analytical research and development opportunities were created, with new challenges of text and web analytics for unstructured web content moving to the forefront [ 3 , 9 ].

The increasing number of mobile connected devices and other sensor-based, Internet-enabled gadgets are pushing analytical capabilities even further, trapping organizations in a race to adapt to the challenges in collecting, processing, analyzing, and visualizing such large-scale and fluid mobile and sensor data [ 9 ]. The compilation and advancement of these technological innovations are increasing organizational competencies, defining new sources of competitive advantage, transforming business models, and opening new windows of entrepreneurial opportunity.

Under the promise of innovation and operational efficiency, big data investments have exploded at major corporations. With McKinsey Global Institute [ 11 ] predicting significant benefits to individual industries e. Despite noted challenges facing firms with regard to technological advances [ 12 ]—or perhaps because of them—a thriving industry has emerged that specializes in the capture, storage, analysis, and interpretation of big data.

Niche firms are building platforms and proprietary software to serve clients in both public and private sectors, offering analytic tools and capabilities unable to be matched in-house. Also of note, data-related research centers are springing up at universities across the globe. Nine figure investments in data science programs are becoming commonplace as universities seek new knowledge and aim to produce students with skills sought by an increasing number of organizations. Early conceptualizations of big data were built around three central characteristics: volume, velocity, and variety [ 13 ].

The sheer volume of data is exploding, with some organizations collecting as much as a terabyte of data each and every hour, every single day [ 7 ].

With societal trends toward social media and remarkable advancements in technology, partnered with decreasing storage costs that have made it more economically feasible to manage, data volume is likely to continue rising. The second core element, velocity , deals with the rate at which the data arrives, is stored, and retrieved for processing. With more sensors available, the growing introduction of connected devices, and an ever-rising number of codified transactions occurring globally, we are seeing increasing speed in data flow [ 14 ].

With technological advancements allowing for the tracking of data in a multitude of mediums, we are also seeing changes in the variety of the data.

Beyond traditional numeric data, we are now seeing raw, semi-structured, and unstructured data sourced from web pages, web log files, search indexes, social media forums, email, documents, sensor data, images, video footage, GPS signals, and many other outlets [ 7 , 15 ]. There is growing consensus to include the veracity of data as a relevant characteristic. Veracity relates to data quality [ 6 , 8 ], with some segregating data quality into separate dimensions for timeliness, accuracy, consistency, and completeness [ 16 ].

Others have distinguished consistency as its own characteristic, choosing to deal with the changing nature of data as an issue of variability [ 17 ]. In this light, the definition or meaning of data is changing, as evolving forms of media e. In a similar notion, some have argued that the relevance of data is another important factor. Such relevance, or viability , concerns the possibility of the data to be analyzed in a manner to make it decision-relevant for the firm, i.

Similarly, visualization also has been brought forth and defined as a potentially significant characteristic.

Visualization refers to making data comprehensible in a manner that is easily understandable [ 17 ]. A final element that is receiving increasing attention, and proves most interesting from a strategic perspective, is the value of big data [ 6 , 17 , 20 ].

In essence, this factor is about how data can be leveraged for benefit in the form of financial gain or some other outcome of organizational import, such as operational efficiency or knowledge creation. The propensity of certain data to be used in solving operational challenges and increasing effectiveness of an organization significantly impacts its value to the focal firm. Though, while proprietary data may in and of itself provide value for consumption or to be sold , the interaction with analytical tools and capabilities allows data to become increasingly useful and valuable [ 21 ].

Table 1 summarizes these eight common Vs described in the big data literature. Though these individual elements are still being disputed as to their specific validity, there is little debate as to the growing influence big data is having on and within organizations today.

Ubiquitous conversation and escalating investments signal the current and future importance of big data in shaping strategic thought and direction in organizations [ 22 ]. Nearly every industry has made or is making substantial investments in big data. Despite this increasing emphasis, corporate decision makers are often left disconnected from the exact value proposition from big data investments in their strategic decision making. As such, the role of technology, data, information, and knowledge officers in corporate strategic decisions continues to evolve [ 23 , 24 ].

Standards and best practices have not yet been formed, leading decision-makers to seek guidance wherever possible. Though data-driven business models are still evolving and somewhat unproven, research suggests that IT capabilities positively influence firm performance [ 25 ].

More specific to our line of inquiry, a recent study found that organizations that claim to have achieved a competitive advantage through their data analytic capabilities are over two times more likely to substantially outperform their industry peers [ 26 ]. This same study determined that top performing firms were twice as likely to use insights gleaned from big data analytics to guide day-to-day operations and twice as likely to use analytics to guide future strategies.

Such findings would suggest that firms might take different approaches to their big data strategies and seek value through different means and ends [ 27 ]. Many, likely most, firms in the new digital economy are currently focused on solving traditional problems in traditional markets with new and creative solutions using big data analytics.

These firms are seeking innovations to improve day-to-day decision-making, drawing technology resources out of a centralized IT department and distributing them throughout other value chain functions [ 28 ]. Marketing, procurement, inventory management, operations, and customer service operate more efficiently and effectively through various product and process innovations, all driven by information generated by big data investments.

As an example, retail companies are utilizing digitized marketing analytics to deliver more effective advertising, incremental product improvements, and increasing rates of customer acquisition and retention. Similar improvements are being made in nearly every industry. Scholarly works, practitioner manuscripts, and private sector whitepapers describe an evolving competitive landscape and would also suggest that another subset of firms has emerged [ 11 , 27 , 29 , 30 ]. These organizations have adopted a data-driven, information-centric focus that subsumes all aspects and decisions for their firm, including measuring how successful certain projects are beyond profitability.

Such emphasis has allowed these firms to build extraordinary data stocks and data flows. Access to inordinate amounts of data increases opportunities for learning, transforming new knowledge and ideas into fresh opportunities for exploration, often outside traditional markets. These learning organizations build ecosystems with constantly increasing data flows, developing advanced technical and analytic capabilities and tools along the way, which can be leveraged as they compete with traditional competitors and diversify into new markets.

As major corporations, hedge funds, and entrepreneurs are struggling with the emergence of big data, academicians continue working to understand its role in business, the inputs and outputs of big data, and how big data projects are best executed.

While active research streams have developed in the information systems e. Most contributions to date have been through consulting white papers e. Hence, there is a need for strategy scholars to develop theoretical approaches to better comprehend how big data is shaping strategic decision making and at the core of novel business models that challenge traditional strategic conceptualizations.

Drawing upon the influential Vs of big data vernacular, we move to ground the big data phenomenon in accepted strategic management theory. Recognizing current practices by a wide variety of firms, we arrive at two long-standing theoretical lenses: RBT and organizational learning. Witnessing a vast majority of organizations employing analytics within functional areas of their firm in an effort to achieve sustainable competitive advantage, we draw upon RBT. Noting the organizational philosophies adopted by the minority of firms with truly advanced analytic capabilities, we also recognize the contributions of the organizational learning perspective.

Much of the practitioner-based literature focuses on increasing efficiency and effectiveness in existing markets, and is therefore best viewed through the lens of RBT. More compelling to RBT arguments are the ability of firms to bundle data resources with analytic capabilities and strategic decision making.

The significance of data increases immensely when combined with the dynamic capabilities of a firm that maximize its ability to extract and apply knowledge and insight from the data to the exploitation of business models [ 34 ].

Consistent with traditional tenets of RBT, mounting research into opportunities presented through big data initiatives in most every sector would seem to imply considerable value potential [ 11 ]. Market conditions exist for the buying and selling of data as well as analytical services, signaling a more definitive value [ 35 ]. Further, the proprietary nature of any data stocks or capabilities would suggest a level of rareness. Firms without similar capabilities or infrastructure might also find it extremely costly and difficult to imitate.

Finally, empirical research into the linkage between big data, IT capabilities, and firm performance e. Given the above characteristics, big data and the complementary capabilities associated with handling, analyzing, and applying massive amounts of data can serve as a means for achieving and sustaining competitive advantage. Managers at a significant number of firms are making investments in capabilities that allow them to use big data to generate deeper business insights and optimize existing processes.

These firms are typically focused on creating and exploiting advantages in current markets, seeking resolution to traditional problems that have plagued their operations in the quest for profitability.

Big Data Concepts, Theories, and Applications

All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Big data is one of the hottest research topics in science and technology communities, and it possesses a great potential in every sector for our society, such as climate, economy, health, social science, and so on. Big data is currently treated as data sets with sizes beyond the ability of commonly used software tools to capture, curate, and manage.

Written by world-renowned leaders in Big Data, this book explores the problems, possible solutions and directions for Big Data in research and practice. The theoretical research ranges from Big Data representation, modeling and topology to distribution and dimension reducing. Chapters also investigate the many disciplines that involve Big Data, such as statistics, data mining, machine learning, networking, algorithms, security and differential geometry. The last section of this book introduces Big Data applications from different communities, such as business, engineering and science. Shui Yu received the B.

The digital age has presented an exponential growth in the amount of data available to individuals looking to draw conclusions based on given or collected information across industries. Challenges associated with the analysis, security, sharing, storage, and visualization of large and complex data sets continue to plague data scientists and analysts alike as traditional data processing applications struggle to adequately manage big data. Big Data: Concepts, Methodologies, Tools, and Applications is a multi-volume compendium of research-based perspectives and solutions within the realm of large-scale and complex data sets. Taking a multidisciplinary approach, this publication presents exhaustive coverage of crucial topics in the field of big data including diverse applications, storage solutions, analysis techniques, and methods for searching and transferring large data sets, in addition to security issues. Emphasizing essential research in the field of data science, this publication is an ideal reference source for data analysts, IT professionals, researchers, and academics.

Yu S., Guo S. - Big Data Concepts, Theories and Applications - 2016.pdf

The digitization of products, processes, and business models—and the corresponding explosion of big data—has led to an evolution within business organizations. As different organizational approaches have developed toward big data, we use resource-based theory and organizational learning as anchoring perspectives to link this phenomenon with traditional strategic management. We also identify four avenues for future scholarship as the nature of business moves increasingly digital. Strategy and Behaviors in the Digital Economy. The global digitization of products, processes, and business models is reshaping the very nature of business.

Written by world-renowned leaders in Big Data, this book explores the problems, possible solutions and directions for Big Data in research and practice. The theoretical research ranges from Big Data representation, modeling and topology to distribution and dimension reducing.

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Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Big data is one of the hottest research topics in science and technology communities, and it possesses a great potential in every sector for our society, such as climate, economy, health, social science, and so on. Big data is currently treated as data sets with sizes beyond the ability of commonly used software tools to capture, curate, and manage. We have tasted the power of big data in various applications, such as finance, business, health, and so on. View on Springer.

It seems that you're in Germany. We have a dedicated site for Germany. Written by world-renowned leaders in Big Data, this book explores the problems, possible solutions and directions for Big Data in research and practice. The theoretical research ranges from Big Data representation, modeling and topology to distribution and dimension reducing. Chapters also investigate the many disciplines that involve Big Data, such as statistics, data mining, machine learning, networking, algorithms, security and differential geometry.

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