The Accessibility of China Small and Medium Sized Enterprises to Big Data Market

Transcription

1 The Accessibility of China Small and Medium Sized Enterprises to Big Data Market Yan He Department of Business and Management Aalborg University The report of the 3rd semester project January 2014

2 Abstract With the widely use of social media, portable devices, and the Web, the humungous amount of data is being generated every day. The explorative growth of the data is termed as big data. Big data brings a revolution to traditional business processes and models. In the second biggest economic entity, China, big data becomes increasingly importance in the process of informationization. This report explores a complete insight of how enterprises (particularly small-medium sized enterprises (SMEs)) in China can benefit from big data. Specifically, this report has made the following contributions: First, this report introduces the past and future of big data, the current big data market in worldwide, and the trend of its future development. The report compares big data in China and USA, the lead of big data, and discusses the advantages and disadvantages of big data in China. Second, we explore big data applications from different industries, and discuss how SMEs can access the value of big data. We find that most of SMEs in China still have difficulties to access big data due to the shortage of investment, the experts of big data, and technology innovations. Third, this report dissects China big data market in terms of industries, introduces their characteristics, and analyzes the SMEs who have already embraced big data. In particular, this report uses the qualitative research method, a case of study, to conclude that SMEs can be applied big data by the following methods, i.e., if SMEs have sufficient resources such as talents and finance, they can develop their own big data platforms, otherwise, SMEs can link to the companies who have implemented big data, and benefit from their big data programs.

3 Fourth, this report comprehensively discusses the challenges of SMEs in China to embark on big data programs, analyzes their strengths and weaknesses, and points out the direction. e.g., an SME could focus on its expertised sub-domains of big data, improve the innovation capability, and strengthen the linkages to big data companies to utilize the values of big data, etc. In conclusion, big data has brought the unprecedented opportunities and challenges. Though, the SMEs in China still lags behind to the lead of big data, the proposed solutions in this report would be useful to bring them closer. 3

4 Contents 1 Introduction Definition of Big data Past and Future of Big Data Relevance Theories and Definitions of SMEs SMEs Challenges in China SMEs Innovations in Big Data Motivation Problem Formulation The Structure of Report Methodology Paradigms The Modern Scientific Thought of Marketing Research Assumptions and Methodologies Operative Paradigm Sub-conclusion Theoretical Foundation Theory of Data, Information and Knowledge Definition of Data, Information and Knowledge Models about Data, Information and Knowledge Model 1: Data Information Knowledge Hierarchy Model 2: Knowledge Information Data Hierarchy Model 3: Interactive Model of Data, Knowledge and Information Model 4: Knowledge Based Theory of Information i

5 3.3 The Partnership Model The Benefits of Partnership model The Framework of Partnership Model Metrics for Partnership Model Sub-conclusion Big Data Industry and Market Big Data Phenomenon The Fact of the Data Growth How Big Data Creates Value Big Data Store Varies Across Geography Big Data Market and Business Segmentation Worldwide Big Data Technology and Services Market Forecast The Outlooks of Big Data Businesses Sub-conclusion Big Data Application & China SMEs Access to Big Data Value Big Data Application in Retail Industry Big Data Application in Manufacture Industry The Big Data Market in China China Enterprises Involved with Big Data SMEs Get Values from Big Data Sub-conclusion Case Study and Analysis Based on the Theoretical Foundation Case Studies Chamate Accesses to Big Data in Restaurant Industry Glodon Accesses to Big Data in Construction Industry IQIYI Accesses to Big Data in Film and Television Industry SMEs Access to the Big Data Platform of Alibaba SMEs Access to Big Data Platform of Ali Small-loans The Analysis of Case Studies The Analysis of Chamate, Gloden and IQIYI The Analysis of SMEs Access to Alibaba Platform ii

6 6.3 Sub-conclusion The Discussion of Challenges and Suggested Solutions The Difficulties of Big Data Application for China SMEs The Proposed Solutions Sub-conclusion Conclusion and Reflection 62 Bibliography 64 iii

7 List of Figures 1.1 Definitions of SMEs in China context Characteristics of Quantitative and Qualitative Paradigms Data-Information-Knowledge Hierarchy The Reversed Hierarchy Knowledge-Based Theory of Information The Framework of Partnership Metrics of Partnership Model Conversion of the basic data unit The declining cost vs. the rising investment capital Impact on revenue due to 10% improvement in data accessibility and intelligence Amount of new data store varies across geography Segmentation of Big Data Technology and Services Revenue Share worldwide Forecast of big data technology and services market($m) Vendor positioning in big data storage market Big data application in retail industry Big data application in manufacture industry Case analysis based on data,information and knowledge models Case analysis based on Partnership model iv

8 Chapter 1 Introduction In recent years, the amount of data in the world has been exploded, and enterprises can capture, communicate, aggregate, store and analyze data source via high technologies. Thus, the big data is intrusive in every sector and function of the global economy. The significance of big data implies much of modern economic activity, such as innovation and growth could take place with having proper data. Many researches investigate big data volumes in order to find the business and economic possibilities. Besides, some researches also examine if big data can create potential value for organisations and enterprises. Meanwhile, in industry, the new emerged enterprises, big IT enterprises and new emerging consultant enterprises has already started to implement big data solutions for their businesses. If enterprises maintain the capabilities to overcome big obstacles such as data scalability, availability and security, they could find value from the big data source. However, there are not so many researches about how the small and medium sized enterprises transfer of value from big data to their businesses. Although SMEs produce massive of data in all industries, they are still lack of knowledge to analyze data source for further utility. On the other hand, SMEs are facing ever increasing competitions in worldwide. This project aims to research how SMEs could utilize big data information as a new strategy, and how the third party consultancy companies could help SMEs to achieve the solutions. Finally, researchers try to find some guidelines about how big data solutions can be a service business for consultancy companies. China SMEs play an important role for the rapid economic growth in the past two decades, and it accounted for more than 90% of total China business entities, which provides more than 75% of employment positions in entire employment market [1]. This project aims to research SMEs challenges and big data 1

9 solutions in China business environment, and to research if big data solutions could be one of the effective innovation ways for China SMEs. 1.1 Definition of Big data There are many definitions of big data, but the most common definition is symbolized by four high Vs as volume, velocity, variety and value. IDC defines big data technologies as a new generation of technologies and architectures designed to extract value economically from very large volumes of a wide variety of data by enabling high-velocity capture, discovery, or analysis [2] Big data is regarding a phenomenon which is characterised by an ongoing increment in volume, volocity and variety of data. It requires advanced technology to capture, store, distribute, manage, and analyze the data source. According to Mayer Schonberger [3], big data represents a unique new capability in today s society. It is using a profound way to achieve value by obtaining products and services, or a profound insight through massive data analysis. A Chinese author defines big data in his book: Big data technology is a kind of capability of extracting proper information in a fast speed from complicated, diversified, and abundant of data source [4]. According to MIT Technology Review: One of the biggest new ideas in computing is big data. There is unanimous agreement that big data is revolutionising commerce in the 21st century. When it comes to business, big data offers unprecedented insight, improved decision-making, and untapped sources of profit [5]. The IDC Report clarifies that Big data is a big dynamic that seemed to appear from nowhere. But in reality, big data is not new. Instead, it is something that is moving into the mainstream and getting big attention, and for good reason. Big data is not a thing but instead a dynamic activity that crosses many IT borders [6]. To sum it up, big data is regarding a dynamic phenomenon rather than an emerging technology. It has been a hot topic since it was regarded that enterprises can draw huge potential value from big data, which is the leading competitiveness in nowadays business. 2

10 1.2 Past and Future of Big Data According to the authoritative research institution, China ICT institution (CCW Research), big data market value is about 450 million Yuan in 2012, and it is estimated that it would increase to 1.12 billion Yuan in Furthermore, the increase rate would be over 100% in each year, and the market value would increase to 9.39 billion Yuan in 2016 [7]. Businesses increasingly attempt to learn more about their customers, suppliers, and operations by using ever-increasing amounts of big data. Therefore, data source becomes a raw material of economy and business growth, and a vital economic input, which can create a new form of economic value. This development raises the question of how enterprises manage to cope with the big data, and how to transfer of value from big data source to businesses. Although, it is considered that big data utilities has tremendous potentials to transform business and power revolutionary to customer experiences, there are still some big obstacles, such as scalability, availability and security, which enterprises have to overcome in order to make data source valuable. In the past few years, an increasing number of enterprises have their businesses involved in big data in China, such as E-commerce companies, new emerging Internet companies, and big IT companies, which have the resources and capabilities implementing big data solutions to enhance their strategies and competitiveness. However, big data involvement is not only limited in E-commerce companies, but also various industries. According to a market report conducted by ZDnet (The first cloud computing website in China), about understanding big data application in China market [8], it indicates that big data involvement also has been shown in energy and manufacturing industry, government and public fair, finance and insurance industry, logistics and retail industry, entertainment and tourism industry, ect. Although any enterprises do not have to be like ebay who has millions of concurrent users, which has big data problems, small and medium sized enterprises face the same challenges of dealing with big data. According to the Technical Report [9] of Accenture, the problem that an enterprise is facing is not the lack of data, but the lack of the proper data. For example, an executive needs proper customer data for decision making. They have to obtain the capability to analyze the raw data source. However, the majority of small and medium 3

11 sized enterprises do not have the abilities to analyze and implement their raw data source. 1.3 Relevance Unisphere Research [10] also provides some insights about involvement enterprises that big data is not just the matter of volume and new types of data source. Many SMEs also have massive amounts of data. That is, organizational size is not a significant factor in undertaking big data projects. On the other hand, predictive analytics and understanding customers are the top two big data opportunities. Many SMEs understand that by using the data they can make a predictive analysis which can help to know their customers, to make new offerings quickly, to compete effectively in the market, and to grow business revenue streams. This research also investigates the problems of SMEs implementing big data strategy, such as the lack of budget and skills, unsure of technology requirements and value; but the problem of big data implementation is not a business management priority for SMEs. In addition, the Report of China National Development and Reform Commission [11] shows SMEs, especially in manufacturing industry have big challenges, e.g., to shorten availability of land resources, to increase of energy and raw materials costs, to increase of labor costs, hostile competitiveness, and subprime crisis. Therefore, big data solutions can be an opportunity for SMEs to make technology innovation, and to overcome all the challenges under the ever-increasing competitive environment. It has been discussed that SMEs can buy the big data capabilities from the thirdparty service companies. Therefore, the supply of big data analytics as a consultancy business is rapidly increasing. The consultancy companies can offer a large variety of services to different industries. Therefore, SMEs are likely to find suitable solutions to tackle big data problems in specific contexts. Besides, due to the infrastructure of the analytics consultancy is hosted in the cloud, the costs for the services in organisations are far more flexible than the costs of implementing big data solutions [12]. In addition, the consultancy companies can perform big data analytics more efficiently than an average company could do. This is especially interesting for SMEs. Since SMEs often do not have sufficient resources and experts to do big data analytics. Thus, SMEs have an interest in utilizing off-the-shelf solutions with costs and resource efficiency. 4

12 A lot of discussion indicates that the big data market has tremendous potential values for consultancy companies. But, big data market is a new emerging market in China, and it is in its early stage. So, it is also a big challenging for consultancy companies to find proper models for their business services. 1.4 Theories and Definitions of SMEs SMEs have been researched extensively. Nevertheless, no unified definition exists, since different authors use different definitions of SMEs[13]. Most commonly quantitative variables, such as the number of employees or annual turnover, are being used [14]. These are often seen in definition of governments, although they can vary excessively from country to country [15]. The definition of an SMEs differs between China and the EU. The official EU definition narrows SMEs to the maximum size of 250 employees, and maximum turnover of 50 million EURO, or total assets of 43 million EURO, whereas in China the definition of an SME varies among different indus tries [16]. For this project, the notification on the issuance of China SMEs standard is being used, which defines SMEs with employee numbers based from 5 to 300, and the sales revenue is range from RMB 0.5million up to 80million [17]. Table 1.1: Definitions of SMEs in China context 5

13 1.5 SMEs Challenges in China SMEs play an important role in China s economy reform, contributing a significant share of GDP, employment and tax. According to the National Development and Reform Commission [13], China has more than 42 million SMEs, including individual industrial and commercial households, making up 99.5% of the total number of enterprises. The ultimate value of goods produced and services rendered by SMEs is over 50% of the Gross Domestic Product (GDP). Currently, SMEs have produced 65% of Chinas invention patents and over 80% of new product development. Furthermore, SMEs create 75% of all new jobs in China. However, SMEs operate mainly local, in where local work force is acquired and capital is supplied by the owners. SMEs are more affected by a number of risks compared to large companies, such as a financial instability, fluctuations of sales, market failure, competition and the relationship between employers [14]. Due to these risks, SMEs experience more difficulties in obtaining new capital, which results in a restricted access to resources and fewer technological innovations. China SMEs has been developing in latest decades. They are facing a lot of challenges, and the survival condition is competitive. The difficulties are summarized as two categories. First category is the difficulties of SMEs financing and accessing to capital has been a main challenge for a long time. In general, the capital funds are collected from a few business partners or from the company employees. In recent years, a lot of SMEs are planning to apply funds from commercial banks when labor costs, raw material and fuel costs are continue rising. However, mostly, SMEs are not able to provide professional documents or reliable credit to gain trusts from banks, which make the application less successful. Besides, due to the lack of the supports from government policies and laws, commercial banks do not have the motivation lending loans to SMEs. On the other hand, the high interest rate is a big risk for SMEs. Meanwhile, the availability of commercial loans for SMEs is limited. Therefore, the vast majority of SMEs competitiveness in the market is not strong. According to a survey, small business life cycle is generally around 3 to 5 years. Second category, the lack of technology innovation is another big challenge for SMEs. In China, The rapid growth of SMEs depends heavily on low operation costs. So, SMEs facilities and manufacturing techniques are in a low level. Due to the shortage 6

14 of capitals, SMEs innovations are lack of technologies, facilities, talents and proper information. Moreover, since the business and services environment play an important role on SMEs technology innovation, China SMEs are meeting huge challenges for technology innovations in current situation. 1.6 SMEs Innovations in Big Data After two years investigation of 4000 SMEs in five countries (China, America, Germany, India and Cuba), Boston Consulting Group published a report about how SMEs should apply new technologies as one of their leading strategy [18]. The report indicates that SMEs make huge contribution to the growth of their national economy, but they are all in the crossroad at the economic downturn. However, SMEs with technology innovations could achieve sales revenues up to 15% higher than those SMEs who do not have technology innovation. The report is also mentioned that the cutting edge technologies such as big data solutions could improve SMEs business operation with reasonable low costs. According to IBM 2012 CEO studies [19], enterprises believe that big data technology is the main investment area in next 3 to 5 years. The studies also indicate SMEs can achieve innovation via big data solutions. SMEs could make strategical plans through the analysis of big data information, and it is possible for SMEs spent less costs with the services provided by the third party professional companies. Therefore, in the later chapter, the researcher will investigate if SMEs have big data problems or if big data solution could help SMEs to deal with some of challenges, and so, what could be the analytical requirements and how to access the analytical abilities. 1.7 Motivation Big data is still a new and emerging field of research, and many big companies have started to research and invest in this areas.it seems that big data is only applicable for big companies, there are not so much research about how SMEs could make changes from the emerging phenomenon. So I summarize my research motivations as the following items: 7

15 Big data technology is regarding as a new phenomenon with great potential markets. It has been discussed that business environment would change due to the implementation of big data technologies. how big the market, and what are the industry trends, What are the big data truly value for industries? what kind of businesses can big data technology create? China SMEs have to find effective ways for their innovation as they are having huge challenges when the operation costs continue increasing. Is it applicable for SMEs designing their strategical plans by implementing big data solutions? Due to the characteristics of capital and talents shortage, how could SMEs access to big data solutions with the assistance of consultancy companies? 1.8 Problem Formulation Consequently, the aim of the research is to understand big data market in China, to identify what value SMEs can gain by implementing big data services. Then, the research will identify how SMEs assess big data value. Finally, the research will identify the accessibility difficulties that SMEs implement big data technologies, and make suggested solutions for SMEs. China Small and Medium Sized Enterprises access to big data market: In the direction of identifying the possibilities of SMEs access to big data application. Followings are the objectives of this research : Research of big data market trends, potential value, business services and problems in the emerging market, get the overview of the market. Research big data related to SMEs, or if big data solution could help SMEs to deal with some of challenges, and how SMEs could make value of big data technology. Case study of the most successful applications, and analyze the accessibility to big data value, find out the challenge of SMEs access to big data technology, and make suggested solutions. 8

16 1.9 The Structure of Report This report is structured as following: Chapter 1 introduces the motivation of this report, the background of the big data, the relevance to the business and marketing, the challenges of big data for the SMEs in China and the problem formulation and formulates the research problems of this project. Chapter 2 describes the research methodologies. Chapter 3 introduces the theoretical foundation for the big data business and marketing, and the information technologies. Chapter 4 introduces the big data phenomenon, the secret of the growth of big data, and how big data creates values. Chapter 5 investigates the big data market in different industries, and explains how the enterprises get the values from big data, especially for the SMEs. Chapter 6 uses several real-world cases to study the most successful big data applications, analyzes the case using the the theory models, and discusses how to use big data, e.g., by linking to large company s big data platform,. Chapter 7 discusses the research results including the difficulties of SMEs in China, and propose the solutions. Chapter 8 concludes this report and presents the reflection during this research. 9

17 Chapter 2 Methodology This chapter intends to clarify the approach to the studied problem. The basis of philosophy of science will be discussed in order to reflect on nature of the studied phenomenon defining the structure, and choose methods to find the answers. There has been much development in the art and science of marketing research in the past centuries or so of its formal existence. The development has been primarily in the adaptation of methodologies borrowed from other disciplines, in increased sophistication of both hardware and software for data collection and analysis, and in methods of reporting and data presentation. 2.1 Paradigms The common definition of paradigm is defined by Thomas Kuhn in his seminal book The Structure of Scientific Revolutions (1962) as: a paradigm is a set of linked assumptions about the world which is shared by a community of scientists investigating that world. Additionally, this set of assumptions provides a conceptual and philosophical framework for the organized study of the world, and referred as world of view. Paradigms, according to Kuhn, are fundamental for the day-to-day work of any science. More specifically, a paradigm accomplishes the following four objectives [20] 1. Paradigm serves as a guide to the professionals in a discipline for it indicates what are the important problems and issues confronting the discipline; 10

18 2. Paradigm goes about developing an explanatory scheme (i.e., models and theories) which can place these issues and problems in a framework which will allow practitioners to try to solve them; 3. Paradigm establishes the criteria for the appropriate tools (i.e., methodologies, Instruments, and types and forms of data collection) to use in solving these disciplinary puzzles; 4. Paradigm provides an epistemology in which the preceding tasks can be viewed as organizing principles for carrying out the normal work of the discipline. Kuhn argues that different fields of research are characterized by a set of general understandings of what has been studied, what kind of questions are useful for that research, what approach researchers should use in order to answer their research questions, and how the results should be understood. These characteristics create a paradigm. Paradigms are inventions of constructs by human beings. It represents the most essential view of its supporters. It is not possible to proof them right, but to give arguments for their utility [21]. Guba and Lincoln (1994) also define a paradigm as particular combination of our basic belief system or world views (ontology), with their associated epistemology. Therefore, understanding the nature of a paradigm enables a scientist to determine both what problems are worthy of exploration and also what methods are available to attack them. Four Dimensions of Paradigms: Most researchers of philosophy of science categorize paradigms in four dimensions of assumptions ontology, epistemology, human nature and methodology. Ontology: Ontology is the theory of what exists and is the foremost concern of metaphysics, which is the study of the most fundamental questions about being and the nature of reality [22]. Ontology closely relates to how the researcher sees the relationship between human beings and their environment [23]. The two thoughts of Objectives and subjectives are elaborated: Realism (Objectivism) and Nominalism (Subjectivism). 11

19 Epistemology: Epistemology is the theory of knowledge and as such is concerned with such matters as the analysis of knowledge and its relationship to belief and truth [22]. Kuada (2012) describes it as how we know what we know or what may be considered by the researcher as truth. It gives answer to what can be known, and what is the relationship between knower and researcher [21]. This concept has two different views of research as Positivism (Objectivism) and Anti-Positivism (Subjectivism). Human Nature: The term human nature describes in social sciences the relationship between humans and their social environment in the researcher s point of view. It has great impact to determine how knowledge is acquired and what is seen as truth. Again, two main thoughts can be described as Determinism (Objectivism) and Voluntarism (Subjectivism). Methodology: Methodology is the strategy or plan of action guiding the entire research. It describes the reasons underlying the choice and use of specific methods in the research process. Others refer to it as the research design or how the researcher finds out the knowledge he requests. According to Guba and Lincoln (1994)[21] the methodology of a scientific research finds answers to the question how one can find something out whatever it is believed can be known. Methods of doing research might fit to the following methodology: Nomothetic (Objectivism) and Ideographic (Subjectivism). Subjective vs. Objective Debate: After reviewing the content of four levels of understanding, it is clear that Burrel and Morgan distinguished between what is called subjective and objective approach. Such standpoint with relation to our study will have an influence on the paradigm that we will utilize. A discussion among philosophers of science has been held about how these two different approaches can or cannot be employed together. According to Kuada [23], one can be associated with purists, situationalists or pragmatists when answering the question above. Purists perceive the reality as either objective or subjective while these two cannot be combined. For pragmatists, it is every research itself which is determining whether subjective or objective approach or even combination of them is adopted [24]. The third group, 12

20 situationalists, is the one we incline to. The reason behind is that such researchers argue that all social phenomena have many sides and interpretations. Therefore, combination of both subjective and objective perspective provides deeper insight into subject matter under investigation. It perceives both approaches to be positioned on the continuum, rather than alternative standpoints such as in the case of purists, and every studied issue determines to what degree will be the research subjective or objective [24]. Adoption of this attitude leads us to choice of paradigmatic typology that will support our research. Arbnor and Bjerke s typology of paradigms will be incorporated because these are elaborated based on assumption that subjective and objective views are standing on a continuum and are not mutually exclusive [24]. 2.2 The Modern Scientific Thought of Marketing Research In order to understand how marketing developed, it is therefore useful to see how thinking in modern science has developed. This historical backdrop will provide the foundation for the later discussion on theoretical paradigms in marketing. At the stage of early science development, there is a strong faith in reason as a means of understanding the world in rationality existed. The perception of everyday scientific reality was in terms of human senses. For example, if a phenomenon could not be seen, heard, touched, smelled, or tasted, then it could not exist [25]. The positivists answer to the fundamental philosophical question mentioned earlier was: We know because of our abilities to sense phenomena. However, many scholars began to question the logic and method of science as if concerned understanding human beings. Idealists believed that this social world was created by the individuals who live with it [20]. Therefore, based on the philosophical positions of positivism and idealism, quantitative and qualitative paradigms are introduced to marketing research. The positivist seeks the facts or causes of social phenomena with little regard for the subjective states of individuals. The idealism is concerned with understanding human behavior from the actors own frame of reference. To quote Reichardt and Cook [26](1979): the quantitative paradigm is said to have a positivistic, hypothetico-deductive, particularistic, objective, out-come-oriented, 13

21 and natural science world view. In contrast, the qualitative paradigm is subscribed to a phenomenological, inductive, holistic, subjective, process-oriented, and social anthropological world view The latter view of the world assumes the importance of understanding situations from the perspective of the actors or participants in that situation. Proponents of this world view are on the opposite end of an objectivity-subjectivity continuum from those of the positivist school of thought. In fact, the very use of the terms quantitative and qualitative implies certain preferences in the kinds of research designs and analyses subsumed by each paradigm. Table 2.1 is the comparison of quantitative and qualitative paradigm Table 2.1: Characteristics of Quantitative and Qualitative Paradigms 2.3 Assumptions and Methodologies In this section, I will describe the perception of the reality while utilizing ontological, epistemological and human nature-related assumptions for this project. In order to make the philosophical assumptions and to distinguish what degree is the reality subjective or objective to us, the four paradigms methodological view will be made. In terms of ontology, it describes how the reality is. Generally, this project is positioned within the environment of big data technology field. Further, since big data is still a new and emerging field of research, the understanding of big datas basic constituents is fragmented. So, the research would be the case of nominalism 14

22 but rather organized, tangible structures which constitute the area to be studied and explored. Besides, the research about how China SMEs apply big data technology is complicated, but objectively existed. So, the reality can not be structured by the researchers, and researchers are as independent observers outside the cases to capture the facts. Therefore, this project inclines to reality as an objective feature under ontological assumptions. In terms of epistemology about how knowledge can be obtained, due to the study is made up of different parts, the subject and anti-positivist approaches are difficult to guide the research activities. Instead, the positivism approach can be used to research the cases of how China SMEs link with big data technology. The reality can by revealed and predicted by searching for regularities and causal relationships between its constituent elements [27]. The assumptions about human nature with regard to this project might be labelled as rather deterministic.the researchers of this project are influenced by the external environment and the information that the researcher is capable to reach. For example, the choosing of cases is determined by the external companies. However, since there are information on the Internet and books, it is possible to process it in relation to the subject matter, and find the accuracy of the research. In a certain degree, the research can determine what cases to be discovered for the research subjects. The selective process is determined by the external environment, but the research inputs are subjectively influenced in order to meet the goals of the report. Due to the assumptions or ultimate assumptions made in previous section, the consideration of the systems approach to be the most appropriate for our research. The reality of multiple constituent parts which are positioned and influenced by each other complies aptly with systems view. Also we admitted that certain amount of subjectivity will support our research and since we perceive analytical view as the most subjective one, this one could not be acknowledged. Subjective deductions would partly help to explore the fact-filled reality in answering the research questions corresponds to the mindset of systems view. 15

23 2.4 Operative Paradigm This section begins with definition of methodical procedures and methodologies, which as mentioned earlier belong to the operative paradigm. Arbnor and Bjerke define methodical procedure as following: A methodical procedure refers to the way the creator of knowledge incorporates, develops, and/or modifies some previously given technique (e.g., a technique for selecting the units of study, for collecting data, or for analyzing results) in a methodological view. Adopting and possibly modifying a previous result and/or theory is also called a methodical procedure. Further, methodologies are defined as the way in which the creator of knowledge relates to and incorporates these techniques made-into-methods into to his/her study process, and the way the study is planned and conducted is called methodics [28]. With regard to methodical procedures, this project intends to utilize a technique of qualitative paradigm to do the case study. Chosen technique should be always adjusted to the chosen methodological perspective in order to increase the fit between methodological approach and problem under investigation [28]. Since the problem of SMEs applied big data technology is a new business trends, and the market has not formed yet, all cases are the early successful applications. In order to identify the possible ways and rational principles of SMEs applied big data technology, we choose five cases for the qualitative research and come up with some assumptions. As indicated in previous section on nature of science paradigm that if marketers in general subscribe to a logical empiricist philosophy of how science is done, then the set of research methods used will be quantitative paradigm such as those are characterized as objective, obtrusive, controlled, and reductionist. In contrast to quantitative approaches, qualitative methodologies assume that there is some value to analyzing both inner and outer perspectives of human behaviour [29]. Furthermore, when the research topic is uncertain and subjective, researchers could learn towards more exploratory and qualitative research tools and tactics such as focused group interviews, projective rather than experimental designs or laboratory studies. Therefore, qualitative method is applicable for the system research. There are three primarily areas that qualitative fieldwork can make contribution to marketing research as project design, data collection and analysis. 16

24 Initial Project Design Project idea: This project idea initially comes from a vivid understanding about how the new technology could affect industry environment, and what value the new technology could bring to business development. Especially, how the new technology could create business opportunities for the consultancy companies. The consultancy company I worked for my internship heard about big data technology, but they are willing to understand more about the entire industry trends, and how China SMEs could create value from big data technology, so that they could identify in what directions they would work for to close this industry and have business opportunities in future. Since big data technology is an emerging field of market, there are much fragmented information around in China market, the research to identify successful application is necessary for assuming the business trends. Therefore, the project idea of investigation big data technology application on SMEs is formed. Project concept: Once the project idea has been agreed upon, a project concept paper should be developed in order to flesh out the idea, and enable researchers to have an overview about the project at the first stage. Therefore, the project concept contains definition of big data, overview of value creation in past and future big data, the important relevant of big data, as well as SMEs challenges and possibility of the new technology. Finally, the problem formulation of the project is discussed to have a clear research target. Besides, the research methodology is also discussed before the project starts. Appraisal: The appraisal is an internal examination of the merits and feasibility of the project. Before starting the project, this project concept is screened, discussed and guided by the supervisor. Further, questions concerted with the project research are asked during the design phase. Last, the final evaluation will be made and approved by the supervisor. Data Collection Primary data: The primary data support the case studies of the project, and it is the main method for the research. So, the cases should be real, rich, and 17

25 deep data. In order to find real, rich and deep data, I used many ways to reach the case companies. 1. Based on the resource of internship company, get overview of the industry, and made an initial plan for case study. 2. Research media information form business and technology magazines, and contact media reporters to get detailed information from industry and business perspectives. 3. Phone or in person Interview media people for certain case information. 4. Confirm with companies about the detailed information. 5. Contact university professor (Chengdu Science and Technology University) who works on big data to confirm the case information I have got in order to make the cases more accurate. 6. Further research about relative field to confirm the cases I ve got. 7. Secondary data: Literature sources: data from published sources such as reports, conference proceedings, theses, academic and professional journals, books and newspapers, as well cases company investigation, reports, ect. Analysis After the cases have been investigated and discussed, the qualitative data analysis can help researchers to find the conclusion and make some suggestions. 1. Reading and understanding all cases and try to find an overview about all the cases. 2. Deciding and labeling relevant parts, making a conclusion of cases studies. 3. Finding the most relevant parts and common phenomena 4. Describing the findings and how they become the phenomena, write the interpretations and discuss the results. 18

26 2.5 Sub-conclusion In this chapter, we have discussed the philosophy of doing scientific research, which uses the four dimensions of paradigms, i.e., ontology, epistemology, human nature and methodology. We also discussed the research methods specific to marketing, and how we apply these methods onto the big data investigation. 19

27 Chapter 3 Theoretical Foundation In this chapter, we will present the investigation of data theory and partnership model in order to understand how organizations utilize data and information as knowledge. In addition, we use the partnership model to analyze SMEs link with shared platform of large companies. 3.1 Theory of Data, Information and Knowledge Nowadays, it is widely recognized that information systems and databases technology are essential for managers in the competitive and digital business environment. Organizations need information systems and database technology to survive and prosper, and extend their business to other locations, offer new products and services, reshape jobs and work flows, and perhaps profoundly change the way they conduct business. For example, the change presents the transformation of industrial economies and societies into knowledge, information and data-based service economies. According to [30], the question about how the knowledge is related to information and data has been under debate since the beginning of knowledge research. According to [31], many people have the belief that knowledge is a more valuable form than information, and information is a more valuable form than data. Besides, the datainformation-knowledge hierarchy is a common belief in many of information system textbooks. Nevertheless, many researches proposed the reversed knowledge information data hierarchy and info-logical equation [32, 33]. So, three models with different 20

28 views on the relationship between data, information and knowledge will be discussed in the later section Definition of Data, Information and Knowledge Definition of Data: In the information field, data is the basis and important factor of the development in information system. Data modelling and processing technologies are the main driving forces of the information discipline to the contemporary digital economy. According to [34], scholars generally define data as the measure or description of objects facts or events. Data is the measure or description of objects or events, usually referred to as a set o f interrelated data items that measure the attributes o f the objects or events [36]. Definition of Information: There are many different definitions of information. In the information system field, it is defined as data processed into a form that has meaning to the user and is of real or perceived value in current or prospective action or decision [35]. Many scholars challenged this definition by questioning how meaning and data interact to produce information. So, the common definition is that information is data that has been interpreted so that it has meaning for the user. Definition of Knowledge: According to [37], in information system research, knowledge is typically expressed in some formal structures, such as production rules, knowledge frames, knowledge maps, and knowledge networks. However, researchers who take normative discourse and depict knowledge describe that knowledge is as set of rules produced by human societies. According to [30], in practice, knowledge can be of several different types including know-why, know-how, and know-when. So, knowledge can be defined as: is a combination of information, experience and insight that may benefit the individual or the organization. 3.2 Models about Data, Information and Knowledge Data, information, and knowledge are the most fundamental concepts in the information system field. The relationship between three concepts is a major topic in the field of research. Several models of their relationship have been developed, following 21

29 is the discussion of the most three popular models. The research try to find a proper model for this project Model 1: Data Information Knowledge Hierarchy This model is also called as a value chain model. It represents the value creation from data to information to knowledge with a dominant hierarchy. Data contains the discrete elements, such as words, numbers, code, tables, and databases. Data is visualization in the primary process of the value chain. Information contains sentences, paragraphs, equations, concepts, ideas, questions, and simple stories. Information is the processed data, and to transfer value from data to information, the system has to be designed to gather primary data. Knowledge is the organized information, such as chapters, theories, axioms, conceptual frameworks and complex stories facts. Knowledge is the high value from information, and it can be structured, interpreted and evaluated from information accumulation. The knowledge helps organizations take further action. In essence, the relationship between the three concepts is determined by the amount of value associated with each concept and the accumulation of value from one concept to another. Figure 3.1 shows the Hierarchy. Figure 3.1: Data-Information-Knowledge Hierarchy Model 2: Knowledge Information Data Hierarchy Although model1 is the common belief of the relationship between data, information and knowledge, there is a reversed model with knowledge to information to data 22

30 hierarchy, which is developed by Tuomi [32]. Tuomi argues that data emerges only after organizations have information, and information emerges only after organizations have knowledge. Therefore, the reversed hierarchy of knowledge is shown to lead to a different approach in developing information systems. The common belief is that knowledge weights more than information, but there are several different views on their exact relation. According to Earl [38], knowledge has to be interpersonal and objective. Earl argues that when organizations design information systems, the explicit and articulated knowledge is the prerequisite. For example, the system designers implicitly rely on culturally shared and accumulated stocks of knowledge. In this way, data exists as a solution to a practical problem. Since the computer does not have the ability to process meaningful processes, computer programmers have to represent meaning that enable automatic processing. The reversed hierarchy explains that organizations use knowledge to create objective of information, and data can emerge only if a meaningful structure or the fixed semantics can be used to represent information. Based on the availability of knowledge and information, data emerges last, so others can access to these data and recover the potential value. Figure 3.2 (from [32]) shows the reversed hierarchy. Figure 3.2: The Reversed Hierarchy 23

31 3.2.3 Model 3: Interactive Model of Data, Knowledge and Information Model 1 and 2 both have strengths and weaknesses in applying information system research and practice. Nonaka [39] clarifies that model1 highlights the roles of information in knowledge creating, which model 2 emphasizes that the replication of information is transferred between persons [33]. Model3 suggests that information is produced from data and knowledge, so it is also called an interactive model. According to Langefors [33], information is the interpretation of a person who makes a message based on her pre-knowledge or specific amount of time. Similarly, Drucker [40] argues that information is data endowed with relevance and purpose, and converting data into information requires knowledge. So, model 3 emphasizes the interactivity between data and knowledge in producing information. However, model3 has several issues that account for the distinctions. First, the definition of information differs significantly, so, different views expresses differences between the relationship of other elements. Second, model3 does not explain the relationships between data and information, information and knowledge Model 4: Knowledge Based Theory of Information By comparing the strengths and weaknesses o f several alternative models (e.g., [32, 30] and [33]) and searching reference disciplines for the intellectual basis of these concepts, a new theory named as Knowledge-Based Theory o f Information (or KBI theory) is developed. Figure 3.3 (from [36]) shows the knowledge-based decision making process. Conceptually the decision making process is a black box with inputs and an output. The functionality of the black box is to convert the inputs into the information for decision making, and finally to produces the decision outcome. The information is from data and knowledge which both can be influenced by the factors, including decision support system (DSS) functionalists, decision makers attitudes and task characteristics, etc. The factors can have the influence on the performance, the accuracy, the complexity, and the cost of decision making. Therefore, the final decision outcome is the result of a combination of various factors. 24

32 Figure 3.3: Knowledge-Based Theory of Information 3.3 The Partnership Model According to Alexander, Lasker et al., partnerships are strategically formed relationships between organizations that involve varying degrees of resource sharing, joint decision making, and collaborative work to address common interests, achieve shared goals or benefit mutual stakeholders [41].Partnership goals are generally premised on the need for organizations to combine their resources and strengths to produce client or community outcomes [42]. Holliday, Schmidheiny and Watts define partnership business model in their book [43] as: Partnership plans to allocate and generate necessary resources for implementation and contribute to the stated goal. From the above definitions, partnership means an organizational form based around a common goal, where participating organizations share resources and power, as well as benefits and risks. With the development of economy, the industry and other associations in bringing partners together to share resources and pool know-how is critical for partnership development. It is recognized that a sophisticated knowledge sharing mechanism is the platform of partnership. Concerning SMEs on partnership, SMEs have limited resources in terms of personnel, finances, and knowledge pertaining to management, marketing, commercialization, or information technology. So, to compare large organizations with established hierarchy, SMEs are usually more entrepreneurial and willing to experiment and innovate in terms of partnership. 25

33 3.3.1 The Benefits of Partnership model Nowadays, SMEs plan to link with large partnership in order to survive in the competitiveness of electronic business environment. Market dislocation is another emerging phenomenon arising in the world of e-commerce. According to [44] e-readiness is the level of preparedness ability of being able to exploit Internet technology via the rapid adoption of electronic business. Besides, e-readiness is the ability to use information and communication technologies to develop electronic business. Furthermore, Partnership is an essential dimension of e-readiness. Because the partnership can fertile e-readiness environment, and take complete responsibility for the business environment. Industry associations and other organizations need well defined e-readiness strategies and plans if all entities wish to obtain competitive advantages from the revolutionary technology. So, partnership model presents a sustainable e-readiness climate that facilitates the adoption of e-business environment The Framework of Partnership Model There are six dimensions framework, which could support SMEs linking to large partnership. According to [44], SMEs involve in each of these dimensions of partnership can help to ensure the critical growth of digital economy. Table 3.4 (from [44]) shows the framework of partnership model. Table 3.4: The Framework of Partnership 26

34 3.3.3 Metrics for Partnership Model According to [45]: A metric is a composite of measures that yields systematic insight into the state of processes or products and drives appropriate action. A metric may be made up of multiple measures. Table 3.5 (from [44]) shows the metrics of partnership model with two categories, strategic alliance and shared platform. Table 3.5: Metrics of Partnership Model 3.4 Sub-conclusion In this chapter, we have discussed the technical theory about data, information and knowledge. We first gave their definitions, then presented the three models for explaining their relationship. We also explained the partnership model and the benefits of using this model. They form the theoretical foundation for our research. 27

35 Chapter 4 Big Data Industry and Market Big data is an emerging trend of business, and the market for big data just starts to formulate. Therefore, customers, competitors and other factors have not well developed yet for doing a comprehensive investigation. This chapter will study the big data phenomenon, investigate the reasons of big data growth, and discuss how big data could create value for businesses. In addition, this chapter will also present the trend of big data market, business services, and the challenges for organizations to implement big data technologies. At the end of this chapter, we could understand the industry involvement, and market factors of influencing the development of industry in big data. The investigation could also help industries to determine their profit potentials. 4.1 Big Data Phenomenon Big data is not only a technology, but also a phenomenon. With the development of information technology, digitization now deeply penetrates into our lives. People are not the passive recipients of information any more, but the creators of information. For example, the big data report [46] shows that the number of s sent per second is 2.9 million, and the products ordered on Amazon per second are 72.9 items. Obviously, big data means the volume of data is over 10 Trillion byte. But in reality, the volume of data has reached petabyte. According to the technical conference [48], the active data of Taobao s daily volume has exceeded 50TB, with a total of 400 million product messages, and 200 million registered users. The daily data of Baidu is 10TB, and its systems have to deal with 1PB of data per day. Table 4.1 shows the 28

36 conversion of basic data unit. Table 4.1: Conversion of the basic data unit Big data is not only generated in the Internet industries, but also in many traditional industries such as retail, wholesale and construction. Wal-Mart handles more than 1 million customer transactions per hour. Its database size is estimated more than 2.5 petabytes which is equivalent to 167 times of the books in America s Library of Congress [47]. With the explosion of the amount of data, Big data now has become a commercial phenomenon for many enterprises. According to IDC s report [2], 2010 is the world of ZB era, and data is expected to reach 1.8ZB in In 2020, the size of data around the world will reach to 35ZB. Therefore, the storage of big data would be a challenge for many enterprises. Moreover, the chief technology officer (CTO) of Teradata Corporation, Stephen Brobst [49], said that the data generated in global in the past three years is even more than the amount of data generated in past forty thousand years, and no doubt that people are facing the age of changes. The emerging data will become the top priority for the development of enterprises. Only the enterprises that are able to make use of Big data could create sustainable competitive advantages for successful businesses in the future. 4.2 The Fact of the Data Growth First, the cost of generating data declines, which causes the exploration of the amount of the data. IDC analyst of database management, Carl Olofson, has pointed out the following factors causing the growth of data. Constantly increasing compute power lowers the costs, particularly for big enterprises such 29

37 as Google, Amazon, Baidu, etc. Now various systems have been able to perform parallel computing, and the hard memory cost is plummeting. Therefore, the systems can handle much more data than ever in memory. Now it is very simple to build a cluster using multiple computers. Besides, the decreasing of data generation cost and the increasing of the investment scales both result in the growth of data storage capacity. Figure4.2 shows the Moore s Law between the declining cost and the rising investment capital. Note:($:cost per Gigabyte) Figure 4.2: The declining cost vs. the rising investment capital Second, the new type of data source increases data variety. The declining of data cost increases the data volume, and the new data source and collection greatly increase the complexity of the big data. The obvious example of data source growth is the data from entertainment, media, health care and video source. The other new source is the data from Weibo, Facebook, Twitter, Youtube and other social media. They have established consumers to provide continuous data stream. Furthermore, due to the network effect, the data can be generated quickly and widely. A large number of new data sources, on the one hand, have changed the situation that data is generated internally. On the other hand, the new data sources also increase the difficulty of collecting data since the new data sources generate a large amount of texts, figures, pictures, voices, video and other unstructured data. IDC reports that the current unstructured data accounts for 80% of the global amount of data, and still keep high growing. With the expansion of data volume and the diversity of data 30

38 sources, the process of collection, storage, management, processing, encryption would be very challenging to many enterprises. Third, the purpose of implementing Big Data technology is to extract value in a velocity collection, discovery and analysis. Big Data would not only bring significant finance value for large enterprises, but also improve the operation and sales efficiency. Figure 4.3 shows the example that the improvement of data accessibility can increase sales revenue from products [51] Figure 4.3: Impact on revenue due to 10% improvement in data accessibility and intelligence 4.3 How Big Data Creates Value According to a research from the Mckinsey Global Institute [50], big data can create value by several ways for nowadays business: First, big data can unlock the value by making information transparent and usable. A common challenge for big data implementing to organizations is the difficult process of collecting and analyzing large sets of information. The prerequisite to make the information more transparent and usable is government agencies collect a large amount of data on individuals and businesses regularly. Besides, if agencies pre-filled forms for citizens and businesses, and stored data in government databases, it would save time for the submitters as well as government. In this way, the data from large public sector databases are made 31

39 more accessible for both external stakeholders such as citizens and businesses, and internal stakeholders such as government employees and agencies. They can improve their efficiency when the database is more accessible. However, make data more transparent, it requires policy and statutory declaration to sharing data for the public. Second, big data can help organizations discover more accurate and detailed internal information. When organizations create and store transactional data in digital form, they can collect more accurate and detailed information, which cover all level information such as product inventories to employees sick days. By storing those data regularly, the information can discover organization needs, expose variability, and improve organization performance For example, organizations can use data collection and analysis to conduct experiments and make better management decisions; others can use data for basic forecasting to adjust their operation accordingly. Third, big data can make segmentation, and help organizations precisely tailored products or services to external customers. By collecting proper customer data frequently, organizations are able to identify target customers and make market segments, in which they can deliver the products and services demand more effectively. Organizations can treat customers differently according to customer segmentation. Four, big data can make sophisticated analytics that help organization improve decision making. Big data can help managers to use wealth of information to make sophisticated analytics, and produce accurate sales forecasts. By having an information about near term predictable future of the business, managers can make decision for what types of budgets or goals need to be set for the next quarter or year, also identify early warning signals and risks. Five, big data can help organizations make innovations possible. Having proper data about the market, products and customers, organizations are able to come up with innovation ideas. For example, manufactures can use big data to make sophisticated analytics about product and service offerings, which organizations can identify innovation ideas much quickly. Big data becomes a key point of 32

40 business growth as organizes make the innovation efficient in their processes when they have obtained first hand data information. 4.4 Big Data Store Varies Across Geography According to McKinsey Global Institute [50], the new data store varies across geography as shown in Figure fig:bigdatastore: Figure 4.4: Amount of new data store varies across geography The report identifies the use of big data can drive significant value across geographies. It suggests that the significant potential value is by applying big data levers in health care and retail industries in developed market. But in personal location data domain, it suggests 20 to 40 percent of the global potential is in emerging markets Big Data Market and Business Segmentation Although organizations are not conversant with big data technologies of its emerging trends, the widespread adoption of big data technologies outside of high-performance computing has already begun, and it is considered as fast-growing market with the expectation to accelerate rapidly. According to IDC, it expects the big data technology and services market to grow from $3.2 billion in 2010 to $16.9 billion in This represents a compound annual growth rate (CAGR) of 39.4% or about seven times that of the overall information and communication technology (ICT) market 33

41 [2].In 2011, the big data technology and service market was $4.8 billion. Following Figure 4.5 (from [2]) depicts the distribution of the market by segment worldwide. Figure 4.5: Segmentation of Big Data Technology and Services Revenue Share worldwide 4.5 Worldwide Big Data Technology and Services Market Forecast The above figure shows big data services contains the most biggest share of the entitle sales revenues. IDC furthermore made a services market forecast from 2010 to 2015 as shown in Table 4.6. The worldwide CAGR for the market through the five-year period is expected to be 39.4%. However, the growth of individual segments of the market varies from 27.3% for servers to 61.4% for storage. Table 4.6: Forecast of big data technology and services market($m) 34