Data Goods Development And Pricing Method Based On Data Value Chain

With the rapid development of IoT,cloud computing and e-commerce,data grows up with exponential rate from private person,business,and government.Big data era has come.These data are benefit to company management and academic research as well.While data growing up rapidly,data has become a kind of commodity to sell on emerging data marketplaces,like Factual and Infochimps.The academic researches on big data focus on machine learning,power distribution,ignoring the economic analysis and business model study.How to stretch value from data,how to innovate data goods and services to meet market demand,and how to measure the value and price of data,few researches to follow now.Big data market model,effective data goods pricing and quality design method are more important under the underground of data trading.So this thesis investigates the big data market model using data value chain concept,and develops data goods design approach that reaches quality selection and pricing method.Main innovative research topics are as follows:(1)Introduces the stakeholders in data market,analyses the characteristics for data goods.Summarizes the research review from information goods pricing,data calue chain,data goods pricing.The data goods development and pricing strategy problem considering data volume is investigated.Firstly,data volume,one factor impacts the quality and value of data goods,refers to the quantity of raw data to be used for service providers.However few existing literatures develop data goods pricing problem considering raw data volume.So this thesis presents the concept called data goods development complexity with data volume,proposes a new data goods pricing approach,analyses how the complexity impact goods quality,pricing and decisions in linear and non-linear Stackelberg models.Study shows quality of data goods upgrade and service provider get discount with raw data quantity's growing.(2)The data strategy problem considering both data volume and quality is investigated.On the basis of studying the data goods development problem with raw data volume,another important factor-the data quality,which can also affect the value of data goods is considered.This paper stands from the perspective of multidimensional data quality,analyses the influence to entire data quality from the interaction between dimensions.On the basis of assumption that marginal quality cost of data providers grows,this paper proposes a Stackelberg game pricing model to analyse the influence of quality cost parameter to decisions.Study shows data provider will cut raw data quality level rationally when data quality is too high to upgrade.(3)The data strategy problem considering both data volume,quality and multi-source data integration is investigated.This paper investigates big data market with one service provider and multiple data providers,considering the relevance between multi-source heterogeneous data,which refers to potential complementary knowledge and data conflicts.Study proposes a Stackelber game pricing for multiple data sources,and influence of data relevance to decisions.Analisis shows service provider want to collect data from more sourcrs not more data from only one source when relevance is big enough.Besides,service have to offer some allowance to data providers to develope better goods.In this paper,the author proposes a data goods quality selection and pricing approach using Stackelberg game theory,taking raw data colume,quality and integration into account,and analyses big data market operation model with data value chain,decision processes as well.This study has great significance to acanemic reaearch and industrial practice.