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Generating competitive advantage from big data in the cloud: A case study Summary

The study aims to explore how cloud computing technologies shape the development and competitiveness of a big data business. Our research draws upon one rich case of a big data firm—Bluemetrix. We referred to two approaches—a narrative strategy and a temporal bracketing strategy—to theorize from process data available from this case. Our study makes a series of theoretical contributions to the research into effects of big data in the cloud on competitiveness of firms. It proposes an adapted version of 4V model of big data outside the cloud, and a model of business effects of big data in the cloud. The study has several methodological implications. It contributes to the discussion about key methods of theorising from process data and demonstrates application of the narrative strategy and the temporal bracketing strategy to analyses of competitiveness of Bluemetrix.

Track: E-Business and E-Government

Word Count – 6751

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Introduction Within the past decade, scholars and practitioners have been relentless in their

pursuit to understand the role of cloud computing in generating competitive advantage of user and provider organisations. The effects of cloud computing have been discussed in a number of areas including emergency management, healthcare sector, education, research and security issues (Lucus-McEwen, 2012; Sujith, 2008; Microsoft, 2012; Brodkin, 2008). Yet, very few sources examined the implications of cloud computing technologies on competitiveness of organisations in the big data industry (e.g. Scarpati, 2012). In attempt to address this literature gap, our study aims to explore how cloud computing technologies shape the development and competitiveness of a big data business.

Our discussion draws upon a broad research question. It focuses on how cloud computing enhances competitive advantages of big data firms. We enrich the discussion with an overview of existing theoretical insights and models dominating the area and with analysis of a case of Bluemetrix—an analytics provider firm working with big data.

The remainder of the paper is structured as follows. First, we centre on theoretical underpinnings of the study. For example, we discuss the concept and models of competitive advantage, outline taxonomy and benefits of cloud computing, define the term big data, and make an overview of characteristics and opportunities of big data in the cloud. This part also discusses challenges of big data outside and inside the cloud. Second, we look at the case of Bluemetrix. The paper ends with conclusions where we suggest directions for further research into the effects of cloud computing on competitiveness of firms.

Theoretical Underpinning This section aims to discuss central concepts of the study, namely, competitive

advantage, cloud computing and big data, and to suggest several theoretical models that will guide our analysis of Bluemetrix case. It contains three sections. The first defines the concept of competitive advantage and explains frameworks that add to a better understanding of sources of competitive advantage. In the second section, we speak about cloud computing. A particular emphasis is placed on taxonomy and benefits of cloud computing. The final section focuses on big data. It defines big data and its characteristics, discusses opportunities for big data in the cloud, how big data can help companies obtain a sustainable competitive advantage, and indicates several challenges of big data outside and inside the cloud.

Competitive Advantage

A competitive advantage is an integral part of strategic decisions in firms. Johnson et al (2008) define this term as an advantage for the organisation over competition. In line with Thompson and Martin (2006), competitive advantage refers to ‘the ability of an organisation to add more value for its customers than its rivals, and thus attain a position of relative advantage’ (p. 855).

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Competitive advantage is often conceived of as a source of superior performance and, therefore, an indicator of a competitive strength. Thus the business becomes a reference for other firms to imitate. Such imitation results in isomorphism, leading to a loss of competitive advantages of firms whose actions are imitated (Farndale and Paauwe, 2007). Typically, isomorphism and its effects are particularly acute among firms competing in turbulent environments, such as cloud computing firms.

In attempt to avoid negative consequences of isomorphism, more successful firms aim at transforming their competitive advantage into sustainable competitive advantage (SCA) (Thompson and Martin, 2006). This refers to a sustained ability of an organisation to add more value for its customers than its rivals, and thus attain a position of relative advantage. According to Thompson and Martin (2006), sustainable competitive advantage rests upon two elements. The first is associated with creating a valuable difference from competitors, or competitive advantage, whereas the second implies sustaining this difference by means of constant improvement and change. In this study our primary focus will be on the concept of sustainable competitive advantage

There exist various sources of SCA (Thompson and Martin, 2006). The framework of Ohmae (1982) suggests that managers generate SCA by focusing on three Cs: customers, competitors and corporations. Porter (1985) developed an alternative, yet very similar, framework. This is based on the idea that firms accrue their competitive advantages by using three different generic strategies labelled as ‘overall cost leadership’, ‘differentiation’, and ‘focus’. However, the above frameworks do not guarantee attainment of sustainable competitive advantage. The resource-based view of strategy (Penrose, 1959) puts forward that the achievement of SCA is moderated by unique strategic capabilities of a firm. These capabilities are made up of the firm’s unique resources and core competences. In addition, several authors stress that the achievement of SCA in firms performing in a turbulent environment (e.g. cloud computing field and big data industry) depends upon their dynamic capabilities—abilities to renew and recreate their strategic capabilities (Winter, 2003; Teece, 2007; Wang and Ahmed, 2007). The dynamic capabilities may also be considered as enablers of sustainable competitive advantage. That is, firms with dynamic capabilities are more likely to create and sustain their advantage over competitors. In section 3 of this part, we will continue the discussion as to how capabilities enable formation sustainable competitive advantage in firms with big data outside the cloud and in firms with big data in the cloud.

Cloud Computing

There are five essential characteristics that help to deine cloud computing, these are on-demand self-service, broad network access, resource pooling, rapid elasticity and measured service (Carstensen et al 2012). The on-demand self-service feature implies that a consumer can unilaterally provision computing capabilities, (e.g. server time and network storage) as needed automatically without requiring human interaction with each service’s provider. The broad network access characteristic suggests that capabilities are available over the network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms such as mobile phones, tablets, laptops and workstations. The resource pooling implies means that computing resources of a provider are pooled to serve multiple

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consumers via a multi-tenant model where different physical and virtual resources are dynamically assigned and reassigned according to consumer demand.

The rapid elasticity characteristic entails that capabilities can be elastically provisioned and released. This enables a greater flexibility of firms in their responses to workload changes. The measured service feature implies that cloud systems control and optimise resource use automatically by leveraging a metering capability at some level appropriate to the type of service (e.g. storage, processing and bandwidth). The characteristics of cloud computing translate into several benefits for organisations, which are addressed in the next section.

Big Data

Srinivasan (2011) maintains that big data are represented by massive amounts of data which is unstructured. The unstructured aspect of big data is particularly important. It suggests that rather than storing data in a structured format for use of a defined set of queries the philosophy is to store as much data as possible in an unstructured format without attempting to predict what analytics we might wish to run in the future. According to Jonas (2012), Manyika (2011) and Microsoft (2012), big data refers to datasets whose sizes are beyond the ability of traditional software tools.

According to 3V model of IBM (2012), big data has three essential characteristics: volume, velocity and variety. The volume feature describes the amount of data generated by organisations and individuals (Schmidt, 2012). The velocity characteristic describes the frequency at which data is generated, captured and shared (Schmidt, 2012). The variety characteristic refers to a proliferation of various data types such as audio, video and image data, and data sources such as retail transactions, text messages and genetic codes (Ferguson, 2012).

Meanwhile, some academics and practitioners agree that the original 3V model of big data may be extended to a 4V model to consider value (Dunn, 2013; MacVittie, 2012). This feature suggests how fast data can be analysed and acted on to provide value to its organisation. 4V model suggests that the first three characteristics of big data may be used to generate value superior to their competitors, which, may translate into a sustainable competitive advantage. However, as mentioned above, the sustainable competitive advantage achievement in such cases will depend upon unique strategic capabilities of firms.

Figure 1 shows an adapted version of 4V model. This shows a causal link from the first three Vs (volume, velocity and variety) to the fourth V (value). It also contains a moderating effect of enablers. These take form of unique strategic capabilities that allow attaining a value superior to that of competitors.

Opportunities for Big Data in the Cloud

Academics and practitioners agree that a cloud computing model is a perfect match for big data since it provides unlimited information storage and processing resources on demand (Chong, 2012). This translates into three major business benefits for users of big data (Linthicum, 2012). ). The first benefit is related to cost efficiency (Linthicum, 2012; Yasin, 2012). One of the sources of cost efficiency is that most information coming from big data in the cloud is substantially cheaper due to the on-

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demand access. Regarding the second benefit, big data in the cloud allows businesses obtaining the most recent information in a real time regime (Linthicum, 2012). Having access to the latest information helps develop more accurate forecasts of future trends and may form basis for first-mover advantages in the earlier stages, while guaranteeing survival of businesses in the later stages of trends (Johnson et al, 2008; Anderson et al, 2012). The third benefit is associated with the ability of businesses to quickly analyze big data when it is in the cloud (Linthicum, 2012).

Figure 1: An adapted version of 4V model of big data outside the cloud

The three benefits of big data in the cloud reveal a number of opportunities for businesses. For example, being a source of cost efficiency, big data in the cloud allows saving scarce resources and rechanneling them into new undertakings such as launching new products, entering new markets and investing into quality. It also helps organisations to respond faster to emerging opportunities in order to generate customer value superior to that of competitors what, in turn, forms basis for sustainable competitive advantage. Yet, the achievement of positive effects from having big data in the cloud is moderated by strategic capabilities of firms. These capabilities may also be affected by business benefits, business opportunities and value when big data is in the cloud.

We propose a model that summarises the above ideas (Figure 2). It illustrates how big data in the cloud enhances business benefits which, in turn, open up new business opportunities that allow attaining a value superior to that of competitors and reaching a sustainable competitive advantage. These are main effects of the model which are shown as cause-and-effect links from big data in the cloud to business benefits; from business benefits to business opportunities; from business opportunities to value superior to that of competitors; and from the value to sustainable competitive advantage.

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Figure 2: Business Effects of Big Data in the Cloud

This model may be used as a managerial tool that visualises a process of generating business value, or the fourth element in the 4V model of big data (IBM, 2012). This model assumes that sustainable competitive advantage may be achieved only by those firms that possess a unique combination of strategic capabilities for generating superior value from big data in the cloud. Such capabilities incorporate both tangible and intangible resources.

The model also suggests that enablers may be influenced by business benefits, opportunities and value generated thanks to having big data in the cloud. For example, a provider may improve its customer relationship management while having its client data in the cloud. These effects are shown as arrows leading from business benefits, opportunities and value to enablers. It is worth noting that the model may be a helpful tool for managers pursuing any of Porter’s (1985) generic strategies for attaining sustainable competitive advantage. That is, it may be applied to visualise formation of sustainable competitive advantage via a cost leadership strategy. Indeed, a manager may use business benefits from big data in the cloud in order to avail of business opportunities that allow minimizing costs and gaining cost leadership. Likewise, the model may assist in understanding of how sustainable competitive advantage comes from differentiation and focus strategies. For instance, a manager following a differentiation strategy may refer to the model in order to see how business benefits of big data in the cloud add a value of high importance to customers who are ready to pay premium prices. In a similar manner, a manager implementing a focus strategy may employ the model to explore how benefits of big data in the cloud help to deliver a better service to a narrower segment of customers.

Challenges of Big Data outside and inside the Cloud

Nonetheless, advantages of big data sometimes turn into its challenges (Customer Service, 2012; Oaks, 2012), the biggest challenge would appear to be when one considers big data and the cloud. Some academics and practitioners contend that it is difficult to move big data into the cloud, manipulate it and bring it back to one’s own infrastructure securely, reliably and economically (Guess, 2012; GigaOm, 2012). That

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is, transportation of big data to and from the cloud is time consuming and, therefore, costly because the data becomes out-dated by the end of the import-export process. This problem is particularly acute in the social media sector (Oaks, 2012). As a consequence, many firms have refrained from using a cloud computing model for big data. Those who have adopted the cloud rely mainly on traditional means of moving data (Aspera, 2012). These methods options impose costs on the business stemming from delayed access to data, risks of loss and damage and the need to invest resources into maintaining an infrastructure that can support it.

However, there are various solutions to the problem of big data transportation in the cloud. This means that cloud services may become a viable option for big data provided that there exist a high-speed transport mechanism (e.g. Aspera on-demand application) that addresses two bottlenecks: the degradation in WAN transfer speeds and the slowdown inside the cloud data centre.

Methodological Framework This study draws upon process data. According to Langley (1999), process data

has four major characteristics. First, it deals mainly with sequences of events, or conceptual entities that researchers are less familiar with. Second, it often involves multiple levels and units of analysis whose boundaries are ambiguous. Third, temporal embeddedness of process data varies in terms of precision, duration and relevance. Fourth, process data draws on changing relationships.

As this study is based on a single source case study it should be noted that its purpose is to develop theory and not to test it so theoretical sampling is appropriate (Eisenhardt and Graenbner, 2007). Theory building from case is the best way to bridge from rich qualitative evidence to mainstream deductive research (Eisenhardt and Graenbner, 2007). To overcome the bias associated with interviewees, highly knowledgeable informants who have diverse perspectives where chosen for the study (Eisenhardt and Graenbner, 2007).

Process data is a valuable source for developing process theories. In line with Weick (1979), strategies for theorizing may be viewed via lens of three dimensions: accuracy, generality and simplicity. Accuracy takes place when strategies focus closely on the original data. Generality of strategies refers to a range of situations to which a theory may be applicable. Simplicity concerns the number of elements and relationships in theories.

According to Langley (1999), there exist several major strategies for theorising of process data. They include narrative strategy, quantification strategy, alternative templates strategy, grounded theory strategy, visual mapping strategy, temporal bracketing strategy and synthetic strategy. We intend to refer to the narrative strategy to prepare a chronology of events for subsequent analysis. Then, we will proceed with the temporal bracketing strategy. Both approaches require one rich case. It is expected that the narrative strategy will form the basis for further use of the temporal bracketing strategy for theorizing from data. Also, we believe that the two strategies will enhance each other. The narrative strategy is to strengthen the accuracy dimension in theorising whereas the temporal bracketing strategy is to guarantee greater levels of generality and simplicity of theoretical insights.

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Strategies for theorizing from process data have implications on research strategies and methods. As mentioned earlier, both the narrative and temporal bracketing strategies for theorizing rely on data from one case only. Given this, we intend to employ a single case study research strategy. This, in turn, relies mainly on qualitative techniques for collection and analysis of primary data (Saunders et al, 2009). For example, it makes use of semi-structured and unstructured interviews. This study employed a semi-structured interview with two managers from Bluemetrix. The interview questions were expected to address the research question presented in the introduction to this paper.

The case study: Bluemetrix

Bluemetrix as a Web Analytics Software Reseller

Bluemetrix was started in 2001 by Liam English and his partner Keiko Tanaka. Both had come from working with the Irish Development Authority (IDA) in Japan and had previously formed Bia Ltd, a technology trading company in Tokyo. In late 2000, Bia acquired the distribution rights to LiveStat’s web analytics services in Japan and in early 2001, English and Tanaka establish Bluemetrix to develop this opportunity.

Web analytics is the measurement, collection, analysis and reporting of internet data for purposes of understanding and optimizing web usage (Web Analytics Association, 2008). LiveStat, a subsidiary of LiveTechnology, Inc., was a South African software provider that developed and marketed Internet audience measurement solutions. In 2000, Bia’s research uncovered that despite a high level of demand from enterprise clients, the Japanese web analytics market was underdeveloped with one company serving the market. Identifying the opportunity, English and Tanaka formed Bluemetrix to resell LiveStat in Japan. The service was a subscription service billed monthly at an average fee of Stg£2,500 per month. Bluemetrix quickly won accounts with major Japanese brands including ACOM, Sony Music and Ricoh.

By 2001, the so called “dotcom bubble” was deflating rapidly. In September 2001, Livestat licensed their technology and services to Jupiter Media Metrix, an international market research company specialising in web measurement and analytics. . Bluemetrix continued to market and sell the new service and by the end of 2001 had 5 staff and over 25 customers generating approx. Stg£0.5m in revenue. Bluemetrix attribute their success in acquiring these enterprise clients based on three factors – low competition in the Japanese web analytics market, internalised Japanese values providing a competitive advantage and finally their extensive network of contacts from their time with the IDA and Bia.

In February 2002, Bluemetrix took the decision to move from a reseller business to a product development business. . With English’ background in software developer and two contractors, Bluemetrix started developing their own enterprise analytics system. With Bluemetrix trying to migrate their customer base for the second time in less than 12 months, the migration was less successful with only half of customers choosing the new service due to quality issues.

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Bluemetrix as Proprietary Web Analytics Software Service Provider

With Bluemetrix’ business in Japan stabilised, Bluemetrix made the decision to locate its software development team in Ireland and expand its business in the UK. This proved difficult, as Bluemetrix did not have the same cultural advantage in the UK market. The market had competitors from across Europe and the US resulting in significant price pressures for Bluemetrix. UK companies were paying one quarter the price compared to the Japanese market.

English began reassessing the European opportunities with a number of advisors. One such advisor, Tommy Lync of NPD Group informed English of a crisis in TNS Scandinavia with their web analytics provider being acquired by Nielsen, TNS’s largest competitor. This ultimately led to a multimillion euro contract between TNS and Bluemetrix.

For political reasons, the ICT infrastructure to support TNS would need to be located in locally in TNS facilities in Denmark resulting in a €0.5m investment from Bluemetrix in dedicated TNS infrastructure. TNS prohibited parallel running and expected a seamless migration from the previous provider to Bluemetrix without any live scaling testing. In February 2005, Bluemetrix deployed the TNS service overnight in Denmark, Finland, Norway and Israel. Throughout 2005, Bluemetrix encountered problems with the system in Denmark. While Bluemetrix were able to deal quickly with collection of data, they could not process it on time. Two major factors impact data processing. Firstly, the data was very different to what the team was used to dealing with and presented problems in their database design. Secondly, Bluemetrix suffered from resource availability, both human and physical. TNS required Bluemetrix to be able to scale its ICT infrastructure with increased data volumes exacerbated by the increased use of the Internet during the period. Managing the capacity of the ICT infrastructure required manual monitoring and intervention to make capacity when traffic peaked and slowdowns occurred. The company only had two engineers supporting and maintaining the scalability of this infrastructure, with an estimated five employees required working on a 24/7 basis. Bluemetrix renegotiated their contract with TNS to fund additional engineers, however, the scalability problem refused to go away.

Throughout 2006, Bluemetrix gained some stability in the Scandinavian market and in 2007, TNS introduced Bluemetrix into their Russian operation. This was a new venture and unlike TNS Scandinavia the data volumes were not based on past performance with previous known volumes, they were purely indicative. In late 2007, Bluemetrix went live in Russia however TNS Russia totally underestimated the data volume by a factor of five. Bluemetrix’ scalability issues were back and at an unprecedented level. Bluemetrix quickly realised that it could not deliver the TNS Russia project profitably and by 2009, Bluemetrix agreed to sell their Russian infrastructure to TNS Russia with a license for their software as product.

The contract with TNS shaped the business portfolio of Bluemetrix. Leveraging the solution initially developed for TNS, BlueMetrix transformed into a web analytics company with three pillars as shown in Table 1.

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Table 1: Bluemetrix business portfolio

By 2008, the dynamics in the web analytics market started to change. Firstly, the Internet was increasingly pervasive with dramatic growth in smartphone usage. Secondly, digital marketing channels and specifically online advertising was continuing to grow dramatically. Thirdly, Google Analytics was available to all users from August 2006 and was an instant success with huge adoption. In 2008, over 50% of the Top 500 sites worldwide used Google Analytics as their solution. Against this backdrop, TNS began phasing out its reliance on Bluemetrix as its sole analytics supplier and began using Spring Technology, a European provider of online measurement, analysis and research.

Bluemetrix as a Market Research Data Provider

From 2004 to 2008, Bluemetrix had transformed itself from a reseller of US software in the Japanese markets with no ICT infrastructure to a company managing 100 servers outside Japan with major blue chip market research organisations. While the TNS relationship occupied much of the new product development activity, there was rapid growth in the Japanese market for web analytics software services in 2008. Identifying this trend, Bluemetrix launched its suite of software services in Japan. Despite the economic decline, Bluemetrix survived and subsequently recovered by 2011 through introducing SiteMetrix, StreamMetrix and Mobile Metrix to their existing customer base and supplementing these with related consulting services such as online advertising and search engine optimisation.

Having reflected on their relationship with TNS, Bluemetrix management wanted to diversify their risk in Europe as in Japan. One such way was for Bluemetrix to establish direct relationships with media sites, advertising agencies and media buying companies and sell independently verified data to these stakeholders. In 2008, Bluemetrix looked to expand its services in Ireland. It involved two significant

SiteMetrix StreamMetrix MobileMetrix SiteMetrix was a service that provides clients with access to their online reports at all times. For example, it provided real time third-party independently verified statistics in relation to online activity including flash based websites. This allowed clients evaluate online advertising potential and provided transparency in the marketplace to advertisers.

StreamMetrix measures streaming media on websites and integrates the information with the SiteMetrix data. Using StreamMedia, clients may track unique visitors across the individual streams on a site. It also helps to measure how many times each unique visitor enters each stream, how much of a stream is viewed, and how many times each stream is visited per a specific period of time. Furthermore, it gives critical third party independent figures that clients may use to sell different types of streaming media such as looping streams, individual streams, live streams and advertisements with streams.

MobileMetrix emerged as a result of a rapid increase of usage of internet on mobile phones. MobileMetrix allows measuring the online activity on mobile content websites. It shows how users behave surfing on their mobile phone. For example, it provides information on page views, sessions, distribution, page reports and referring domains. The information on online behaviour of mobile phone users provides clients with information needed to make informed strategic decisions and to identify new revenue streams.

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strategic challenges, one technical and one business. On the technical side, Bluemetrix would have to address its scalability issues and its ability to process and analyse big data efficiently. On the business side, Bluemetrix would be providing a multi-sided platform offering web measurement services and data to advertisers, media sites and corporate marketers using a common infrastructure and rules.

Impressed by the success of Google in processing and scaling big data, Bluemetrix found itself at the crossroads of two emerging technologies in trying to solve their scalability issue– big data and cloud computing. Bluemetrix looked at adopting Apache Hadoop, an open-source software framework that supports data-intensive distributed applications, i.e. big data and is based on research by Google on MapReduce1. Reports on cloud computing suggested that a cloud solution might reduce or eliminate scalability issues, reduce cost and most importantly lower the work pressure on Bluemetrix’ technical team. In addition, Hadoop was designed with the cloud in mind so this was also a good fit.

Bluemetrix began experimenting with a public cloud, Amazon EC2. While it was true that data storage in the cloud was less costly, transporting the data to the cloud was highly expensive. In addition, some of Bluemetrix’ clients, and in particular TNS, were uncomfortable with data security issues in the public cloud. Bluemetrix had a substantial investment in local infrastructure. As such the technical team changed their focus to executing cloud technologies on Bluemetrix’ infrastructure to replicate a cloud, a “private cloud”. It would take a further two years before Bluemetrix successfully deployed Hadoop on its own ICT infrastructure benefitting from the deployment of cloud technologies. In 2010, Bluemetrix was ready.

In 2008, Bluemetrix managed to persuade over 60 sites to participate in a national web measurement service trial including Eircom, the largest Irish telecommunications company, Thomas Crosbie Holdings, a major Irish media publisher and the Irish Times, one of the leading Irish national newspapers. While Bluemetrix began collecting data on these sites in 2008, timing and lack of knowledge resulted in Bluemetrix missing a major tender to supply its data services for the Joint National Internet Research (JNIR) survey. Like the Joint National Readership Survey (JNRS) for traditional media, this survey provided the opportunity to become the de facto point of reference for Irish online media.

Realising that while Bluemetrix understood web measurement, it did not have the networks and knowledge of the market research and advertising industry, Bluemetrix approached Behaviour & Attitudes (B&A), the largest independent market research company in Ireland, to collaborate on a joint venture. While recognising the impact of the Internet and social media, B&A neither had the knowledge nor resources to exploit these opportunities. As such, a collaboration with Bluemetrix was viewed positively and they agreed to launch Acumen—a joint venture to inform Irish and international companies of consumer behaviour online. Joining survey data on profiles of recruits with their online behaviour patterns provided invaluable insights into the motives underlying consumer activities across websites. It was expected that

1 MapReduce is a programming model for processing large data sets. See Dean, J.

and Ghemawat, S. (2004). "MapReduce: Simplified Data Processing on Large Clusters” http://research.google.com/archive/mapreduce.html

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such information would have demand from large companies, advertising professionals and media planners. The recruitment of the panel began in the summer of 2010. The mechanism of Acumen consisted of two major elements. The first was formation of a panel. Bluemetrix decided to recruit participants of the panel from a pool of 40,000 people it had been tracking across 100 Irish websites. It sent emails inviting consumers to join the panel to around 20,000 potential recruits. In total, 6,000 people were recruited as members of the panel. The recruitment contracts were to last 5 years. However, recruitment rates were significantly slower than expected and as a result recruitment costs higher. In 2011, Bluemetrix and B&A terminated their joint venture. Bluemetrix licensed the rights to the panel to B&A and agreed to provide expertise in tracking online behaviour of the panel for a monthly fee.

Discussion

The Importance of capabilities

Bluemetrix develops four important capabilities during this case, networking capability, dynamic capability, business intelligence and cultural intelligence. Due to its networking skills and long-term personal relationships Bluemetrix managed to attract its first clients such as ACOM, Sony Music and Ricoh and, most importantly, to retain its core clients during its acquisition by Jupiter Media Metrix and its transition to the development business. Likewise, the networking capability helped the firm to enter new markets in Europe through the contract with TNS in Scandinavia and then again in Russia.

Cultural intelligence capability was shown in the successful internalisation of Japanese values. Bluemetrix was able to enhance its position in the Japanese market to gain an advantage over its American and European competitors whose style remained alien to the Japanese tradition. In Phase 2 and 3, the impact of cultural intelligence lessened due to increasingly homogenous business mores in Europe.

Dynamic capability manifested in Bluemetrix’ ability to to respond quickly to market changes, to withstand threats to their business, and to pursue opportunities in a foreign country settings. Business intelligence capability of Bluemetrix formed the basis for its clear understanding of the importance of timing in the introduction of new products and services. This coupled with cultural intelligence proved invaluable to Bluemetrix in supporting their initial entry in to the web analytics market and the subsequent transition to the product development.

Bluemetrix is most successful in Japan where it can bring all of these capabilities together to give it a distinctive competitive advantage over non-Japanese rivals. In the Irish and UK markets, Bluemetrix cannot exploit these capabilities. In Japan, Bluemetrix’ capabilities were valuable, rare, inimitable and Bluemetrix were organised to exploit. Unfortunately, while valuable in the UK market, many substitutes existed and Bluemetrix did not have the requisite market knowledge.

Big Data and Business Value

As Bluemetrix works with big data outside the cloud, its operations may be analysed from the perspective of the adapted version of 4V model of big data outside the cloud (Figure 1). This model suggests that three characteristics of big data—volume, velocity and variety—may generate value for providers and users of big

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data. The case of Bluemetrix confirms that being in the big data business may occasionally expand the range of potential sources of business value and reveal a series of business opportunities. Bluemetrix got a chance to move from being a reseller of third party web analytics services to developing and marketing proprietary analytics service.

Figure 3 presents the 4V model of big data outside the cloud for Bluemetrix and suggests that Bluemetrix had the opportunity to move from an existing web analytics reseller business to a new business developing and marketing proprietary analytics software services. Having opened up a possibility for generating larger profit margins, the new business became a source of greater business value. Bluemetrix now has similar opportunities to leverage their in-depth knowledge of big data and analytics to enter emerging vertical markets.

In addition, the model contains a set of moderators represented by networking capability, cultural intelligence, dynamic capabilities, dependence upon TNS and lack of experience with big data. Networking capability, cultural intelligence and dynamic capabilities are factors that enable positive effects of big data on business value. By contrast, dependence upon TNS and lack of experience with big data appear as factors that inhibit positive effects of big data on business value.

Figure 3: 4V model of big data outside the cloud for Bluemetrix

Big Data and Cloud Computing

The proposed model of business effects of big data in the cloud (Figure 2) assumes that big data in the cloud contains some potential benefits for businesses. These benefits have two major implications for developing sustainable competitive advantage. First, they help to free scarce resources for pursuing future opportunities. Second, they help to make timely decisions that enhance a dynamic capability. Bluemetrix trials suggested that migrating their big data service to a public cloud would improve Bluemetrix’ cost efficiency. However there were issues, at the time, a public solution would have been financially burdensome, there were perceived

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quality of service issues, and there were also perceived data security concerns. Because of this Bluemetrix invested in infrastructure to create a “private cloud”.

Figure 4 visualises the above ideas of business effects of big data in the private cloud of Bluemetrix. Transferring big data to the private cloud has a series of business effects. These are associated with lower costs of pushing data on the cloud, lower risk costs due to greater perceived data security of the private cloud and lower costs of user data management. The second and third phases of the Bluemetrix case also reveal enablers generating sustainable competitive advantages from having big data in the cloud (illustrated in Figure 4). The most salient of such capabilities originate from a combination of high quality human resources, risk intelligence, networking skills and knowledge of big data business. As well as a sustaining strategy in offering big data services using Hadoop, Bluemetrix can offer other big data services, such as social network analysis, and offer services into other verticals including retail, government and mobile sectors. When analysed using a VRIO model (Barney, 1991; 1995), the opportunity for Bluemetrix establishing a sustainable competitive advantage would seem promising (See Table 2).

Figure 4: Model of business effects of big data in a private cloud for Bluemetrix

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Table 3: Can big data in a private cloud generate sustainable competitive advantage?

Figure 5 presents a visual summary of the above ideas in a model of business effects of big data in the public cloud for Bluemetrix. The model shows several business benefits of big data in the cloud and specifically lower infrastructure-related costs. The key enablers of business value, however, are the same as the private cloud and driven by Bluemetrix’ internal capabilities. However, one might argue that the unique heterogenous (and therefore not mobile) capabilities combined with those of a public cloud provider can create additional value in the form of composite capabilities. Given this, future studies into the area might examine research models including interaction effects among capabilities of the cloud provider and internal capabilities of the adopting firm.

Figure 5: Model of business effects of big data in the public cloud for Bluemetrix

Bluemetrix’ decision is to either gain as much value from their infrastructure as possible, to migrate to public or hybrid cloud, or to offer all of the above. It should be noted that the TNS project scaled down and most likely Bluemetrix has more than adequate redundancy in its existing “private cloud” to cater for demand from its existing customer base.

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Conclusions Our study drew upon a research question making an enquiry into how cloud

computing enhances competitive advantages of big data firms. In order to answer the research question, we referred to the case of Bluemetrix. We found that the continuous stream of data under analysis results in higher data transport costs and introduces perceived data security challenges when deployed on a “public cloud”. By adopting cloud computing technologies on its own infrastructure, Bluemetrix managed to increase substantially its cost efficiency, address data security issues, and has positioned the company to benefit from an easier access to the latest data as well as faster processing of data while addressing previous scalability issues. While within the timeframe of the case, public cloud solutions were not economically viable for Bluemetrix, this may provide an opportunity for cloud service providers which, if addressed, may further enhance Bluemetrix’ competitiveness.

Our analysis of Bluemetrix suggests that the convergence of cloud computing and big data and the offering of such a solution by any given firm does not automatically imply a greater competitive advantage over non-cloud solutions. Firms need to possess certain strategic capabilities to be able to generate competitive advantages from big data in the cloud.

Our study makes a series of theoretical contributions. For example, it suggests an extension to the 4V model (Figure 1), which may serve as a tool helping to analyse how firms whose big data is outside the cloud generate value and sustain their competitive advantage. Also, the study proposes a model of business effects of big data in the cloud (Figure 2). We illustrated application of the model in the context of both the private (Figure 4) and public cloud (Figure 5). In addition, our study has several methodological implications. For instance, it contributes to the discussion about key methods of theorising from process data. The study demonstrates application of the narrative strategy and the temporal bracketing strategy to analyses of competitiveness of Bluemetrix. It is expected that the discussion and application of the two methods will be particularly useful for scholars whose data draws from a single rich case.

Our study concludes that moving big data to the cloud may help a firm to enhance its sustainable competitive advantage depending on the nature of interaction with the big data. First, the transfer of big data to the cloud may result in several business benefits such as cost efficiency, access to the latest data in a real-time format and fast processing of data. These benefits free additional resources and facilitate timely decisions. Also, our study concludes that competitive advantages associated with cloud computing technologies are not limited to deployment in clouds. In the case of Bluemetrix, the company was able to accrue benefits by deployment of cloud technologies on its own infrastructure in a limited “private cloud”. This is because, apart from cost efficiency, access to the latest data in a real-time format and fast processing of data, migration to a private cloud may also lead to a series of other business benefits. For example, it may facilitate increased security and decreasing risks associated with perceived security problems.

Practitioners should take into account a series of factors that enable positive effects of decisions to transfer big data to the cloud. Other firms may have a different set of factors, than Bluemetrix in this case, which strengthen positive effects of big data in the cloud. Managers of big data firms should carefully evaluate such factors

17

before making decisions about cloud adoption and migration. As such, any extensive generalisation of conclusions from our study of Bluemetrix to other big data firms should be done with caution. We also propose a new managerial tool—a model of business effects of big data in the cloud (Figure 2). Managers may use this model in order to visualize formation of sustainable competitive advantage from big data in the cloud possibly in conjunction with other tools including the VRIO framework

Our study has several limitations that may be addressed in further research. For example we use only a single case. On the one hand, we agree with many other scholars that a study of one rich case may bring about a number of valuable insights and may help in the earlier stages of theory development. On the other hand, we believe that a larger number of cases are needed to confirm ideas of this study.

Future Research Finally, we would like to point out directions for further research into the area. It

would be worthwhile to confirm our model of business effects of big data in cloud (Figure 2). Future studies might test this model on a sample of, firms whose big data is in the public cloud, and firms whose data is in the private cloud. Furthermore, some studies might make a particular emphasis on investigating the enablers in the model including composite capabilities. Such studies might examine a set of factors that reinforce positive effects of big data in the cloud on sustainable competitive advantage, a set of factors that hinder competitiveness of a firm whose big data has been moved to the cloud, and interaction effects between the two sets of factors. In addition, we advise conducting a longitudinal study in order to compare competitiveness measures of firms before and after transfer of their big data to the cloud.  

 

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