4. Viewing the Vs through RBT and organizational learning

Reflecting upon the Vs customarily espoused within the big data literature, volume, variety, and velocity are seen as primary drivers. Access to more (and more diverse) data, generated at ever-increasing speeds, directly effects a firm's ability to make decisions and allows it to increase its competitiveness versus firms without access to similar data stocks. Firms actively employing a data-driven strategy require significant investment in data collection and storage capabilities, as well as the development of improved analytics to handle the large, diverse, and complex datasets. Due to the nature of volume, variety, and velocity aspects, an investment must be made in the development of necessary infrastructure. Such a commitment of resources (e.g., human capital, financial capital, technological capital), properly deployed, leads to a level of efficiency and greater predictive and analytic capabilities in order to exploit advantages relative to firms without similar investments.

Firms focused on the enhancement of existing capabilities (i.e., a resource-based orientation) build such infrastructure and capabilities as to seek improvement in solving existing problems. It is also reasonable that these firms may outsource some or all of the infrastructure or analytic capabilities to strategic partners who have greater strengths and/or efficiencies in big data-related tasks, still working to accomplish the same goals for the focal firm. Regardless, these big data initiatives, whether outsourced or in-house, are typically localized to functional areas, creating successes to definitive and specific challenges but not sharing them across business units or divisions.

In a similar fashion, firms with a learning-based orientation strive for gains in efficiency and traditional value chain improvements. However, this group of firms tend to stretch their commitments into human, financial, and technical (and, increasingly, social) capital to greater heights. Substantial investments in building immense data storage warehouses, intra- and inter-firm networks, computing power, and analytic capabilities are warranted, with a continuous push toward increasing and diversifying data inputs [28]. While there is value in focusing efforts and big data innovations within specific value chain activities, true strategic impact can only happen with management having a holistic view of the digital threats and opportunities as well as associates buying in to an overall vision for how big data can reshape the firm and its competitive landscape [48]. The learning mindset and big data aspirations embraced by these firms allow for increasing abilities to search for new product or market opportunities in non-traditional spaces. Rather than simply looking for greater volume, variety, or velocity with big data investments, decisions by learning organizations are based upon the belief that greater data flows will translate to increased veracity, variability, viability, visualization, and value of data stocks. Accordingly, an expanded view of the Vs becomes increasingly relevant as the resource-based and learning perspectives are contrasted. Because firms with a resource-based orientation are focused on exploiting advantages primarily in current markets, the remaining Vs are viewed in light of this limitation.

Data veracity is important for firms with a resource-based orientation due to the fact of working with traditional metrics and processes. They need to trust the quality of the data in order to follow through and make the gains in productivity and profitability they are seeking, but are limited by their own aspirations and through their ability to monitor, analyze, and control based upon their data collection and analysis. Because their chosen corporate- and business-level strategy predetermines the data and metrics of interest, resource-focused firms proceed without the benefit of viewing the potential of enhanced data stocks and data flows. In much the same way, data variability can often be overlooked by firms with a

understanding and application of big data analytics. Though the integration of knowledge across organizational lines still requires entrepreneurial thinking, visionary leadership, and organizational buy-in across groups, this is simplified if an

Complementary scholarship within this literature stream focuses on specific elements of organizational learning, such as knowledge creation/acquisition and knowledge transfer/distribution [40]. Organizational knowledge is created

through a continuous dialog between tacit and explicit knowledge [42], and amidst a balance between search and experimentation and the contrary activities of refinement and execution [39]. As such, knowledge acquisition occurs through a process of learning from experience and the recording or probing for information about the organization's environment or performance [40]. Learning is leveraged further when knowledge is transferred to more of an organization's components, who are also afforded mechanisms to enhance the ability, motivation, and opportunity to recognize that knowledge as potentially useful [43]. Hence, due to the complexities and difficulty in instituting or imitating, the creation and effective transfer of knowledge internally stand as a potential basis for competitive

With this understanding of knowledge management capabilities, it is easy to infer that RBT is encompassed within an organizational learning framework. While new knowledge is developed by individuals (and, increasingly, through technology), organizations (and their strategic leaders) play a critical role in articulating and amplifying that knowledge [42]. Advanced technical capabilities, ever-expanding data stocks, and excessively large financial coffers serve as

resources that allow learning organizations to eschew established competencies and circumvent traditional industry boundaries and barriers to entry [27]. For example, Alphabet (nee Google) continues to explore and diversify into new markets, expanding well beyond web search and advertising as they seek to capture new data and knowledge [45]. Apple and Amazon are other well-recognized companies also focused on advancing ecosystems, new markets, and the development of analytical and learning capabilities [46]. Leaders at these organizations cultivate a growth mindset and entrepreneurial culture, embracing new technologies and tolerating risk in the pursuit of new knowledge that can push the organization forward in new and unforeseen ways. Exploration and exploitation decisions in these organizations are not solely predicated on profitability; instead, these firms are concerned with enhancing data flows, with the intent to develop innovative service modules that can be easily combined with existing platforms to execute increasing levels of

In essence, the focus on data flows presents opportunities for learning organizations to build dynamic capabilities through the extension of digital ecosystems, finding new ways to digitize and monetize evolving products and services. Strategic decisions on new product and service offerings are made based upon the potential for human capital development, multiplicative and exponential learning, and an expanding ecosystem of consumer influence. Organizations embracing a learning perspective view data not only as an available resource to be exploited for improving existing value chains, but also anticipate the untapped value of data, seeing unique sources from which to collect new data. They envision how that data can be used to gain novel and original knowledge and explore new markets and opportunities for future business endeavors [47]. From this synopsis, we now move on to an exploration for how the characteristics of big data can be interpreted through the theoretical lenses offered by RBT and organizational

analytical mindset is embraced within the firm.

Strategy and Behaviors in the Digital Economy

advantage [44].

service [8].

learning.

58

resource-based orientation, due to discretionary bias and dominant logics confining the firm to rigidities in traditional proprietary thinking. These firms are focused on existing value chain processes and metrics, and are therefore looking for and expecting consistency in their data to be measured and tracked over time to gauge improvements.

Awareness of data quality and data evolution is critically important to learning organizations, as it affords these firms unique perspective on opportunities to engage in ever-expanding data flows. As learning organizations are open to new opportunities and strategic renewal based upon their understanding of the data, it is incredibly important that they develop a level of comfort with the data and its sources because of the time-conscious decisions and indelible investments that follow. Even though these firms are more willing to take data-related risks, because of the sheer volume of data amassed through their ever-increasing data stocks and data flows, they are able to quickly make assessments in data quality. Their quest to increase data flows also comes with an expectation that the data will change over time, both in its source and its meaning, and so the firm develops capabilities around adapting and learning from these changes in order to parlay them into new business opportunities. Heightened alertness and responsiveness to the quality and changing nature of data contribute to the development of better organizational capabilities that identify trends in a broad array of markets, progressively monetizing data resources via entry into new markets as they extend analytic and predictive capabilities often ignored or underdeveloped by traditional firms [8].

The viability and visualization of data is also limited in firms with resourcebased orientations due to contextual factors, as situational analyses are hindered by conventional views of the organization and their markets. In reality, this is restricted by the abilities of senior leadership to see its importance [49] and to break down data silos within the firm [48, 50]. The data is discernably relevant for decision-making purposes because prior decisions on strategy dictate what is to be captured, collected, and analyzed. While tools to help visualize trends in the data prove helpful, it is only for the function of addressing previously determined metrics.

Conversely, in learning-oriented firms, leaders direct their resources to collect data from many data flows, making it more challenging to determine relevance. However, capabilities are developed within these firms to help identify, interpret, and predict new opportunities, even those potentially outside traditional markets. Such efforts may require the learning or development of novel or unfamiliar metrics. It should not be construed that these learning organizations are blindly looking for data and opportunities anywhere and everywhere; there are still likely to be well-defined social, industrial, or organizational challenges that are being pursued. It is simply that the learning orientation of these firms allows them to capture and look at far-reaching data to find the most accurate and data-supported solutions, even if it means developing new and diverse perspectives and taking risks in diversifying to new markets that offer the potential for tremendous pay off [51]. It is in this way that deft visualization actually helps management see the viability of certain data, and organizations are not left to stand solely on the instinctual decision-making of organizational leaders.

Beyond all of the other Vs, it is paramount, of course, that firms actively engaged in data-driven decision making are also seeking and receiving value from their investments in big data initiatives. Resource-centric firms create value from big data through better business decisions that improve and exploit traditional capabilities. They learn to increase the efficiency of employees, improve inventory logistics for suppliers and distribution, and better service their customers. To some degree then, the value through a resource-oriented approach is concentrated on the

Table 2.

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How the eight Vs of big data impact digital business strategy based upon firm orientation.

Big Data and Strategy: Theoretical Foundations and New Opportunities

DOI: http://dx.doi.org/10.5772/intechopen.84819


Table 2.

How the eight Vs of big data impact digital business strategy based upon firm orientation.

resource-based orientation, due to discretionary bias and dominant logics confining the firm to rigidities in traditional proprietary thinking. These firms are focused on existing value chain processes and metrics, and are therefore looking for and expecting consistency in their data to be measured and tracked over time to gauge

Awareness of data quality and data evolution is critically important to learning

organizations, as it affords these firms unique perspective on opportunities to engage in ever-expanding data flows. As learning organizations are open to new opportunities and strategic renewal based upon their understanding of the data, it is incredibly important that they develop a level of comfort with the data and its sources because of the time-conscious decisions and indelible investments that follow. Even though these firms are more willing to take data-related risks, because of the sheer volume of data amassed through their ever-increasing data stocks and data flows, they are able to quickly make assessments in data quality. Their quest to increase data flows also comes with an expectation that the data will change over time, both in its source and its meaning, and so the firm develops capabilities around adapting and learning from these changes in order to parlay them into new business opportunities. Heightened alertness and responsiveness to the quality and changing nature of data contribute to the development of better organizational capabilities that identify trends in a broad array of markets, progressively monetizing data resources via entry into new markets as they extend analytic and predictive

capabilities often ignored or underdeveloped by traditional firms [8].

data prove helpful, it is only for the function of addressing previously

The viability and visualization of data is also limited in firms with resourcebased orientations due to contextual factors, as situational analyses are hindered by conventional views of the organization and their markets. In reality, this is restricted by the abilities of senior leadership to see its importance [49] and to break down data silos within the firm [48, 50]. The data is discernably relevant for decision-making purposes because prior decisions on strategy dictate what is to be captured, collected, and analyzed. While tools to help visualize trends in the

Conversely, in learning-oriented firms, leaders direct their resources to collect data from many data flows, making it more challenging to determine relevance. However, capabilities are developed within these firms to help identify, interpret, and predict new opportunities, even those potentially outside traditional markets. Such efforts may require the learning or development of novel or unfamiliar metrics. It should not be construed that these learning organizations are blindly looking for data and opportunities anywhere and everywhere; there are still likely to be well-defined social, industrial, or organizational challenges that are being pursued. It is simply that the learning orientation of these firms allows them to capture and look at far-reaching data to find the most accurate and data-supported solutions, even if it means developing new and diverse perspectives and taking risks in diversifying to new markets that offer the potential for tremendous pay off [51]. It is in this way that deft visualization actually helps management see the viability of certain data, and organizations are not left to stand solely on the instinctual

Beyond all of the other Vs, it is paramount, of course, that firms actively engaged in data-driven decision making are also seeking and receiving value from their investments in big data initiatives. Resource-centric firms create value from big data through better business decisions that improve and exploit traditional capabilities. They learn to increase the efficiency of employees, improve inventory logistics for suppliers and distribution, and better service their customers. To some degree then, the value through a resource-oriented approach is concentrated on the

improvements.

Strategy and Behaviors in the Digital Economy

determined metrics.

60

decision-making of organizational leaders.

organizational research—and even how such research is conducted [5]—needs to change. What is more, it seems imperative to reflect and examine whether existing frameworks, variables, and measurements are still relevant in today's digitalized business environment. Through such reflection, the evidence of earlier paradigms specifically the important role that RBT plays—in today's digital era and the relationship with organizational learning is apparent. Yet scholars are now presented with an opportunity to conduct a renewed examination of how technology interacts with strategy. Although leaders of the field have iterated a call for research and theory development in this area, significant movement has lagged. To assist in the advancement toward this end, we present a number of avenues to begin addressing

Through this paper, we extend theoretical development by looking at how the big data phenomenon is interacting with RBT and organizational learning in new and novel ways. These are overtly and perceptibly not the only theoretical underpinnings found within the big data phenomenon, nor are they the only ones that may be challenged by the changing competitive landscape. As such, scholars have an exceptional opportunity to identify unique applications for existing theories, create new proposed boundary conditions across the field of management, or develop novel theoretical frameworks and extensions, such as in the domains of

We make the case to further develop theory around existing streams of research in the extant knowledge creation, knowledge management, and exploration/exploitation literatures. The likelihood that management theories are universally true across all periods of time, contextual situations, and especially after radical innovations have been brought forth to the market is highly unlikely. It is our contention that when the assumptions used in developing theory are challenged by existing realities of the world, the management field should reconsider "what it knows" and look at its theories to drive forward more relevant understandings of the world. This is not to suggest that traditional theories of management will be invalidated. Rather, it is necessary to revisit paradigms, challenge assumptions, and explore alternative explanations. The digitization of business models, fueled by the big data phenomenon, is a massive economic transformation; therefore, a new and concerted effort to look at the underlying theories of our respective fields should be considered at this

5.2 Avenue B: investigating antecedents to data- and analytic-related

sion making in the era of big data, it is imperative to explore the context and antecedents that allowed these organizations to leverage data and analytics for competitive advantage. What is it that allows for firms to transition into data-savvy organizations? Are there characteristics or nuances that propel firms and allow for the transformation into learning organizations? Externally, are their environmental

mental hostility. Yet scholarly examination should better explore the true characteristics and environmental stressors that elicit impactful organizational change that increases data and analytic capabilities. Tracking firms globally and

Beyond applying theory to better understand the nature of organizational deci-

Undoubtedly, we see application for traditional explanations such as visionary leadership, organizational culture, strategic resource heterogeneity, and environ-

these gaps, not only in strategy, but across the field of management.

5.1 Avenue A: theoretical development across management

Big Data and Strategy: Theoretical Foundations and New Opportunities

DOI: http://dx.doi.org/10.5772/intechopen.84819

strategy, entrepreneurship, or human resources.

factors that specifically trigger such adaptation?

time.

63

capabilities

#### Figure 1.

Visualizing RBT and OL in Digital Strategy.

improved relationships with key stakeholders. Such relationships can be recognized and measured through a variety of financial and operational metrics likely already in service throughout an industry.

For the learning-oriented firm, value is captured through the development of knowledge and dynamic capabilities to recognize, learn from, and act on large socio-cultural patterns, often with such scale as to offset traditional competitive forces in the creation, entrance, and/or development of new markets or industries with innovative business models. They are able to effectively integrate and disseminate new knowledge across organizational silos to drive further innovation and entrepreneurship. Their business models look to expand existing lines of business, building an increasing ecosystem of services that benefit customers and build brand loyalty [30]. Accordingly, these learning organizations follow a pattern that not only builds what they know into their business models, but also incorporates a means to facilitate learning while relentlessly increasing the data gap over competitors. Despite these benefits, it is still evident that most industries still have not even scratched the surface of realizing the potential value of big data and analytics [11, 52].

Table 2 summarizes how the Vs commonly attributed to big data influence firms resource- and learning-based orientations when employing digital business strategies. Figure 1 offers a visual to further describe how these orientations are staged across organizations.
