O p i n i o n    S e c t i o n 
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Big Data, Tacit Knowledge and Organizational 

Competitiveness 

 
Nowshade Kabir and Elias Carayannis 

 

Grenoble Graduate School of Business, France  

Email: nowshade@gmail.com 

George Washington University School of Business, USA 

Email: caraye@gwu.edu 
 

 

Received October 10, accepted 19 December 2013 

 

ABSTRACT: In the process of conducting everyday business, organizations generate and gather a large number 

of information about their customers, suppliers, competitors, processes, operations, routines and procedures. 

They also capture communication data from mobile devices, instruments, tools, machines and transmissions. 

Much of this data possesses an enormous amount of valuable knowledge, exploitation of which could yield 

economic benefit. Many organizations are taking advantage of business analytics and intelligence solutions to 

help them find new insights in their business processes and performance. For companies, however, it is still a 

nascent area, and many of them understand that there are more knowledge and insights that can be 

extracted from available big data using creativity, recombination and innovative methods, apply it to new 

knowledge creation and produce substantial value. This has created a need for finding a suitable approach in 

the firm’s big data related strategy. In this paper, the authors concur that big data is indeed a source of 

firm’s competitive advantage and consider that it is essential to have the right combination of people, tool 

and data along with management support and data‐oriented culture to gain competitiveness from big data. 

However, the authors also argue that organizations should consider the knowledge hidden in the big data as 

tacit knowledge and they should take advantage of the cumulative experience garnered by the companies 

and studies done so far by the scholars in this sphere from knowledge management perspective. Based on this 

idea, a big data oriented framework of organizational knowledge‐based strategy is proposed here. 

KEYWORDS: Big data, tacit knowledge, big data strategy, knowledge management, knowledge strategies and 
organizational knowledge 

 

Available for free online at https://ojs.hh.se/ 

 

Journal of Intelligence Studies in Business 3 (2013) 54-62 

mailto:nowshade@gmail.com
mailto:caraye@gwu.edu
https://ojs.hh.se/


O p i n i o n    S e c t i o n 
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Introduction 

One of the key driving forces of knowledge 

economy is knowledge intensity of economic 

activities (Smith, 2002). In recent decades 

increasing dependence of economy on knowledge 

has bolstered by rapid pace of technological 

innovation and information technology revolution. 

This, in turn, propelled the emergence of new 

knowledge‐based industries and augmented share 

of knowledge as a resource in economic input in 

most traditional industries. Knowledge, now, is 

recognized as a pillar of innovation, a source of 

economic growth and a central element in 

organization's competitive advantage (Stehr, 1994). 

This heightened importance of knowledge, in part 

thanks to globalization and speedy technological 

advancement, obliges organizations to pay serious 

attention to their existing, potential and growing 

knowledge resources. 

Present phenomenal growth of knowledge resource 

can be attributed to several factors such as 

continuous advances in information technology 

related hardware, development of new algorithms 

and programs, ubiquitous access to information 

thanks to the Internet and steady decline of cost 

related to data creation, transmission and storage. In 

recent years, the combination of these factors has 

also prompted the appearance of a new knowledge 

resource, which is capable of further revolutionizing 

organizational knowledge landscape. This new 

knowledge resource is big data! 

Big data is a unique knowledge resource that is 

immensely valuable to any organization. It helps 

transforming many of the traditional methods of 

conducting business activities. Insights and 

knowledge from big data boost management’s 

ability to take well‐informed decisions (Provost and 

Fawcett, 2013). Efficient use of  data created and 

located within a firm and collection and analysis of 

critical data from external sources impact a firm's 

product, process and strategic innovation as well 

as marketing and operational capabilities. Current 

development shows that big data has already become 

a major catalyst in bringing sweeping changes to a 

range of business processes in many industries. As 

a result of this, organizations’ interest in big data 

initiatives has intensified significantly. A study 

done by Tata consulting (2013) shows that almost 

half of the companies surveyed have introduced 

some types of big data projects, and they are 

expecting a very high return from these initiatives. 

No doubt that big data is considered as a 

valuable knowledge resource. If that is the case, 

what type of knowledge is found in big data? Can 

this knowledge be considered as tacit knowledge? 

What should be the right strategy for organizations 

to handle a knowledge resource as complex as big 

data? In the article we try to answer to these 

questions and offer a big data related strategy 

framework. The rest of the article is divided into 

several parts: a short discussion on the present 

interest in big data followed by a review of big 

data concept, analysis of knowledge and tacit 

knowledge in the context of big data, a holistic 

big data strategy model with explanation and finally, 

the closing remarks. 

Why now? 

The emergence of big data phenomenon is the result 

of a blending of several rising trends: the 

proliferation of social and business networks, the 

growth of mobile telecommunication, dramatic cost 

reduction in data collection, storage, processing and 

transportation and the increased deployment of 

sensors and machine to machine communication 

along with technological advancement in cloud 

computing, smart ICTs, data mining and analytics 

(OECD, 2011). 

LaValle et al. (2010) assert that companies that use 

business information and analytical tools in their 

differentiation strategy have twice as many chances 

to be in the group of top performers than lower 

performers of their industry. 

Big data can produce minimum two types of values 

to an organization. Firstly, it can be a source of 

innovation. Specially, it can enable development of 

new products, processes and services. Secondly, use 

of  various analytics on big data can generate  

knowledge and insights  that can support and 

improve  organizational decision making 

significantly (Provost and Fawcett, 2013). The 

present interest in big data grew mostly thanks to 

these new value creation possibilities that were 

unavailable to most companies even recently due to 

the high cost of data storage, processing and 

analyzing. 

Big Data – the concept 

Big data is a concept that means, firstly, the 

volume of the data is too large. Secondly, it is 

impossible to analyze it using conventional 

technologies, and thirdly, special tools and 

treatment are necessary to extract knowledge from 

it (Manyika et al., 2001). Another way of viewing 

big data is to regard it as a massive pool of data that 



O p i n i o n    S e c t i o n 
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allows creating insights and values that are not 

possible to generate from smaller scale of same data 

(Jacobs, 2009). 

Douglas Laney (2001) of Gartner, while explaining 

the challenges related to data growth noted that 

there are three dimensions to this problem: 

increasing amount of data – the growth of its 

volume, inflow and outflow speed of data – its 

velocity and heterogeneity of the data types and 

sources – data variety, the three Vs. This has 

become the industry standard in characterizing big 

data. However, many argues that along with this 

model, value, veracity and variability also should 

be included as they are more important than the 

attributes of 3Vs (Swoyer, 2012). 

The concept of knowledge 

Definition of knowledge in organizational science 

differs from the classical epistemological view of 

knowledge as "justified true belief" (Nonaka and 

Von Krogh, 2009). Despite its long history, the 

concept of knowledge is still subjective, complex 

and opaque. As a result, we see numerous 

variations of definitions of knowledge depending 

on discipline, context, approach and task at hand. In 

a broader sense, and for the purpose of this article, 

knowledge can be defined as information that is 

validated, contextual, relevant and actionable 

(Soliman and Youssef, 2003). Another similar 

definition is, knowledge is tested, validated and 

codified information (Earl, 1994). 

Scarbrough & Barrel (1996) propose the content 

theory of knowledge, where knowledge is deemed 

as an object that can be codified and stored. This 

approach of objectification of knowledge brings 

flexibility to the perception of knowledge. 

Knowledge as an object can be acquired, integrated, 

stored and disseminated much like a commodity and 

becomes a tradable product (Carlsson et al., 1996). 

In knowledge science knowledge is also considered 

as information with meaning, information is data 

with context and data is a basic element of analysis 

(Boisot, 1995). This concatenation of data, 

information and knowledge is the most popular 

model of their correlation in knowledge and 

information literature (Rowley, 2007).  

Tacit knowledge 

Much of the theoretical understanding of tacit 

knowledge in knowledge science derives from 

Polanyi's concept of tacit and explicit knowing 

(Polanyi, 1962). Tacit and explicit knowledge are 

two sides of knowledge continuum (Nonaka and 

Takeuchi, 1995). Explicit knowledge is the type of 

knowledge, which can be expressed using common 

language and codes. It is fully transferable and easy 

to share (Nonaka, 1991). Tacit knowledge, on the 

other hand, is subjective and informal (Polanyi, 

1958; Nonaka, 1995). Taking after Polanyi's view, 

the concept of tacit knowledge and its place in 

organizational knowledge creation was largely 

popularized by Nonaka (1995) and defined as 

knowledge that indwells human mind and body 

(Nonaka and Von Krogh, 2009). Many subjectivist 

scholars believe that tacit knowledge cannot be 

articulated, captured or interpreted in any form as 

this type of knowledge gets developed and remains 

embodied only in the human mind (See Tsoukas 

2006). However, others conclude while some tacit 

knowledge is impossible to explicate, most tacit 

knowledge can be codified (Nonaka, 1995; Collins, 

2010). 

We believe that reality exists independently from 

the human mind and knowledge, including tacit 

knowledge, can reside in various other silos apart 

from the human cognizance (Searle, 1993). Many 

other scholars also support this notion. Walsh and 

Ungson (1991) posit that knowledge resides in five 

venues of an organization: people, roles and 

organizational structures, operating procedures and 

practices, culture, and the physical structure of the 

workplace. Hershbach (1995) believes 

Technological activities embody a larger portion of 

tacit knowledge than we normally recognize. Some 

researchers describe tacit knowledge as uncertain, 

unstructured, indeterminate, and indirect (See 

Kikoski and Kikoski, 2004) and others conclude 

tacit knowledge is the kind of knowledge which is 

implied but not yet documented (Junnarkar and 

Brown, 1988). These views support the idea that 

knowledge, insights, patterns, indicators and 

pointers embedded in big data and waiting to be 

extracted are a form of tacit knowledge. 

Knowledge management strategy 

Organizational knowledge management strategy 

refers to planning and deployment of methods, 

processes, procedures and guidelines of knowledge 

acquisition, organization,  utilization  and  

distribution  in order  to achieve business goals. 

Knowledge being a valued resource, knowledge 

management strategy must be always aligned with 

the organization's business strategy (Eisenhardt and 

Santos, 2002). For example, focus of knowledge 

management strategy can be the development of 

intellectual capital using both knowledge 



O p i n i o n    S e c t i o n 
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exploration and knowledge exploitation and as a 

result gain competitive advantage (Zack, 1999) 

Knowledge exploitation strategy builds upon 

existing knowledge and knowledge exploration on 

acquisition of new knowledge. Both of them are 

vital in organization's overall knowledge strategy 

(Ichijo, 2002). These knowledge strategies 

encompass knowledge processes that include 

knowledge creation, acquisition, integration, 

sharing, replication, storage, organization, 

measurement and identification (Grant, 2008) and 

require performing balancing act between external 

and internal factors relevant to organization’s goals. 

Big data strategy 

The sudden emergence of big data as a source for 

new knowledge, valuable insights, and innovation 

and, as a result, competitive advantage has caught 

many companies off‐guard. The fact that 

management can have a more holistic picture of 

their business and convert that knowledge to 

make more informed decision and improve overall 

company performance is forcing firms to adopt 

comprehensive big data related knowledge 

strategies. Mere adoption of a strategy based on 

industry experience is not good enough. Knowledge 

strategy, in this case, must be aligned with the 

expected insights and knowledge received from big 

data and correlated to the business strategy, so that 

this new knowledge can be implemented across 

the board. This means focusing on not just 

understanding how the insights and knowledge can 

be infused in the business processes but also take 

necessary actions to embed the new knowledge in 

the business processes of most critical areas starting 

from new product development to customer 

satisfaction and from manufacturing to logistics. 

Big data strategy framework 

Rubenstein‐Montano et al. (2001) asserts that a 

holistic framework of knowledge management that 

covers general requirements and can be followed 

by any knowledge management initiative 

independent of methodologies and tools is essential. 

Following this suggestion in this paper we propose 

a universal strategy framework suitable for any 

organization in relation to big data initiatives from 

knowledge  management strategy perspective. 

Prerequisites 

An organization must possess or develop several 

critical preconditions in order to implement an 

initiative successfully, to execute the processes 

smoothly and to ensure having expected outcome. 

Management support 

For any transformation oriented knowledge project 

to become successful, it requires strong support 

from management (Davenport et al., 1997). 

Management support should include: giving clear 

motivational message to the organization about the 

importance of the big data project and its 

benefits in company's success, participating in 

identifying objectives and domain of the big data 

projects, allocating finance  and  other required 

resources and monitoring success. Success of a big 

data project depends among others on having a 

clear understanding of what types of knowledge and 

insights are necessary in a decision making process. 

Often, this requires knowledge way beyond data 

engineering skills of a data specialist. On the 

other hand, the business decision makers also 

need to have knowledge about what type of big 

data can provide needed insights. This means 

people involved in the big data project either have 

to have the necessary knowledge and education or 

they have to develop needed skills and core 

competencies. 

Senior management's commitment and involvement 

in facilitating learning are crucial in building an 

adequately knowledgeable team capable of 

accomplishing big data project related assignments. 

 

Infrastructure 

Organizational Infrastructure includes people, 

process, technology, structure and their correlation. 

Big data related infrastructure needs to be focused 

on innovation and knowledge creation and, as a 

result, should have a high degree of flexibility and 

freedom. To achieve set strategic objectives 

organizational structure and roles should ensure a 

seamless flow of best practices throughout the 

firm. Strategic goals setting and decision making 

in relation to the big data project should come 

from top management. If the big data initiative 

envisioned to be a large project, it makes sense to 

appoint a chief data officer who can oversee all data 

related projects. 

People 

Big data projects need to have different skills set 

than organizations are normally accustomed to. This 

is one of the added reasons why it is necessary to 

pay special attention to the key success factor of a 

big data project ‐ people. Depending on the kind of 

technologies the company is planning to implement, 



O p i n i o n    S e c t i o n 
58 

 

 

it would require at the IT level specialists in cloud 

architecture, Hadoop, MapReduce, Semantic Webs 

and number of other key areas. Vital to big data 

project are the holders of a new job title called data 

scientist. Data scientists are necessary for making 

sense from big data. Business intelligence 

professional understands the business decisions 

needs and capable of analyzing the big data in order 

to divulge correlations, knowledge and insights. 

 

Figure 1: Big data strategy framework 

Data‐driven culture 

Organizational culture is the collective 

programming that includes vision, norms, values, 

symbols,  rituals, beliefs, habits and attitudes of the 

members that work as a normative glue in unifying 

the organization and influence the behavior of an 

individual member (Hofstede, 1996).Having a 

data‐driven culture that fosters implementation of 

big data projects is imperative for a firm that is 

striving to have competitive edge using data‐based 

decision‐making and business analytics. Data‐

driven culture means having a clear understanding 

among the employees that data is everybody's 

business not just IT departments and data has to be 

taken in consideration in almost all decision 

making. A study by Economist Intelligence Unit 

shows that there is a strong positive link between 

data‐driven decision‐making and organizational 

performance. Moreover, data driven companies 

with superior performance regard data sharing as a 

valuable process. They also consider that shared 

data needed to be used across the board and all 

units should  collect data proactively  (The 

Economist, 2013). 

Absorptive capacity 

The concept of "Absorptive Capacity" is defined as 

"ability of a firm to identify, assimilate and exploit 

knowledge from the environment" (Cohen and 

Levinthal, 1989: p. 569). Absorptive capacity is 

considered as part of dynamic capabilities of the 

firm and are divided as potential absorptive 

capacity, which derives from knowledge acquisition 

and integration abilities and realized absorptive 

capacity, which encompasses transformation and 

exploitation of knowledge (Zahra and George, 

2002). Absorptive capacity is the firm's capability 

of developing skills related to tacit knowledge 

(Mowery and Oxley, 1995).  Kim (1997) deems that 

it is the firm's learning and problem solving ability 

and Kedia and Bhagat (1988) view that absorptive 

capacity is firm’s ability to transform in accordance 

with technological shift. 

Processes 

A key reason for paying attention to processes in 

strategy is the need for the organization to grasp 

how technologies, people, and processes in 

combination influence its business performance. 

Goal setting 

The first and foremost goal for a company that is 

seriously investing in big data should be to depict 

a clear vision that emphasizes on the expected 

strategic outcome from the realization of the big 

data projects. In setting goals and developing 

roadmaps, all relevant departments and units need to 

participate. Setting achievable and measurable goals 

is vital for the success of a big data project as half 



59 
 

 

of the big data projects initiated never get completed 

(LaValle et al., 2011). 

Team building 

Because of the complex knowledge and skill set that 

are required for receiving effective results from a big 

data project, it is necessary to organize the team 

according to organization's business objectives. 

The two most needed members of such a team are 

a data scientist and a business analytics 

professional. Other members may include IT 

specialists and workers from the business 

department most relevant to the data project. For 

example, if the big data team is working on 

finding a solution related to marketing, for best 

result it has to incorporate people from the 

marketing department as well (Ohlhorst, 2013). 

Mistake will be to assign the team to IT 

department. Analyzing information from a number 

of large corporations, researchers found that while 

IT departments are highly efficient in data storage 

and protection, they are unable to offer solutions that 

can convert data into business value (Beath et al., 

2012). More over, organizations that are endowed 

with a large amount of big data, they have 70 

percent more chances of having business 

intelligence projects initiated by the business 

community rather than IT people (Rowe and White, 

2012). 

Technology selection 

Big data projects are complex systems requiring 

various types of information technologies that 

encompasses from storage to applications and 

include data warehouse solutions, information and 

data management, virtualization and visualization, 

different analytical tools to name a few. These 

elements can be divided into three categories: 

Warehouse infrastructure, big data analytics 

platforms and big data applications. Big data 

analytics is not a recent phenomenon. Business 

intelligence tools are getting used in business 

decision making for more than several decades. 

What is new now is the explosive growth of data 

and capacity to store that data. The sudden 

popularity of big data can be attributed to the 

new technological platforms that haveemerged 

recently. They are capable of processing and 

analyzing data in various structures outperforming 

traditional database technologies in massive scale. 

Selection of needed technologies will depend on 

the followings: data amount, speed of data flow, 

structure of data expected to be used, integration 

requirement of the data, expected outcomes from the 

data analysis, users' need, costs, etc. 

Metrics selection 

The criticism of financial performance based 

management style that does not accommodate 

knowledge as one of the most valuable assets has 

been well documented (Meyer and Gupta, 1994). 

Efforts have been made to develop performance 

measurement frameworks that are more 

encompassing and comprehensive in relation to 

intangible assets (Epstein and Manzoni, 1997) and 

which in various degrees encapsulate knowledge 

assets measurements (See for example: Edvinsson 

and Malone, 1997). Since, big data analytics don't 

impact on the revenue generation directly, the ROI 

analysis metrics should include indirect benefits 

that emanate from the big data initiative. 

Plan implementation 

In line with the strategic goals and expected 

outcome, a firm needs to create and deploy a 

roadmap of big data initiative. Along with setting 

objectives and milestones, selecting teams 

members and developing proof of concept one 

more important issue is to identify and obviate 

stifles related to the specificity of big data 

initiative environment. Big data fundamentally 

differ from any other technology related projects. At 

one side, the team members work with the high 

velocity, high volume, high intensity and complex 

data in a real‐time environment of discovery and 

innovation, but the insights and knowledge garnered 

in this environment ultimately need to be aligned 

with traditional technology based environment of 

data compliance, governance, security and 

perfunctory decision making. Organizations should 

be aware that this coupling of the two different 

environments might not go smoothly and may have 

a negative impact on the implementation of a well 

developed plan. 

Outcome 

The big data generated by the organization's 

business processes and operational activities, 

which include innovation and knowledge related 

activities, as well as employee's skill development, 

have all the potential to become instrumental to 

developing competitive advantage. The big data 

base innovations are still in its infancy! Early 

signs from various large corporations, however, 

demonstrate the immense possibilities that are hold 

in the tacit knowledge hidden in big data. 

Improved human capital 

One of the fundamental elements of organizational 

intellectual capital is human capital (Edvinsson and 



60 
 

 

Sullivan, 1996). Stewart (1999) defines intellectual 

capital as a combination of intellectual elements that 

include knowledge, information, intellectual 

properties and experience that are applied to  

generate wealth. The execution of big data projects 

requires hiring  new talents and developing new 

professional skills among existing workers. The 

experiences of the professionals developed in the 

process of big data project are indubitably valuable 

assets. Their contribution to the creation of new 

knowledge and innovative products, services and 

processes has a positive influence on the top and 

bottom lines of an organization. 

Innovation 

Most organizations understand that key to 

sustainable competitive advantage in today's 

globalized and wired world is innovation. In fact, 

Innovation capabilities, arguably, are the most 

important determinant of firm's performance (Mone 

et. al., 1998). Big data is an enabler, a driver and a 

source of new products, processes, services, 

strategies and business models (Manyika et al., 

2011). Through big data capturing, aggregating, 

storing and analyzing companies from every 

industry and sector have the potential to reap 

benefits of innovation. Innovations originated and 

spawned from big data can be divided into three 

categories: 

Big data‐driven innovation: Innovation where big 

data is the primary material in the development of a 

product, service, process or model. One example is 

high speed trading. 

Big data enabled innovation: In an innovation 

where big data works as a catalyst. Examples are: 

Determining marketing campaign effectiveness, 

using sensors data to predict machinery failure, 

monitoring customer's experience of a product and 

finding design and manufacturing problems.  

Big data related innovation: Technology, process 

and service innovation that opens new possibilities 

in handling big data. Example could be a new in‐

house business analytics technique. 

New knowledge base 

Knowledge acquired from diverse sources is crucial 

for creating new knowledge. Organizations pursue 

externally sourced knowledge actively as the more 

knowledge absorbed from external sources the 

better the chances of new types of knowledge 

recombination and generation (Cohen and 

Levinthal, 1989). Developing dynamic capabilities 

that help recognizing new possibilities and 

capturing new business opportunities thanks to 

aggressive acquisition of external knowledge, 

which in turn leads to better innovation, is a key 

to firm's competitiveness (Zhou and Uhlaner, 

2009). Big data initiative develops a kind of 

dynamic capability that contributes significantly to 

organizations knowledge base in respect to 

knowledge repositories, employees' knowledge 

foundation and absorptive capacity. 

Conclusion 

In this paper, we have explored the idea that 

knowledge residing in the big data is indeed tacit 

and in most of the cases open to explicability. Once 

extracted this new knowledge can be transferred, 

used and shared much like any other explicit 

knowledge. This new and unique knowledge has all 

the potential of creating economic value for an 

organization and bolster innovation, productivity 

and growth. Thus, It is also a possible major 

source of competitive advantage. 

We then proposed a big data centric knowledge 

strategy framework that outlines requirements, 

processes and outcomes of a big data initiative that 

aims at creating competitive advantage. We 

recommend consulting and adopting the strategy 

framework prior to engaging in a big data project. 

The main limitation of this paper is, although, in 

this paper we have delineated a strategy model that 

can be implemented in any organization, the concept 

is not validated by any empirical research. We 

suppose that study covering multiple industries on 

the impact of this strategic framework is necessary to 

identify its strength and weakness. 

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