Big data monetization market and business implications through startup ecosystem

The main purpose of this thesis is to investigate and illustrate the possibilities an organisation has to monetize its Big Data outside of the firm’s boundaries using data as an asset. In addition, this thesis also aims at offering a framework, which can be used to classify startups operating in the Data Monetization business and thus, allowing to map and assess the maturity of the startups operating in the Data Monetization landscape. Furthermore, this thesis is supported by an empirical research on 380 startups operating in the Data Monetization business. To fully exploit the potential of data as an asset, data has to be used as an asset external to the firms boundaries by selling it to third parties, bartering it for tangible and intangible benefits or wrapping it around other services or products to gain a premium price and thus, leverage data to create an additional revenue stream. However, to leverage data as an asset, a certain degree of infrastructure, resources and capabilities supporting a data driven organisation are required. To set up an organisation able to monetize its data as an asset outside of the firms boundaries, an organisation can build up the required infrastructure, resources and capabilities internally or outsource it by deploying third parties such as startups offering data monetization products and services. Thus, this thesis provides an External Data Monetization framework which companies can use to map the startup landscape operating in the Data Monetization business. More in detail, the framework summarizes the three main macro-categories a company can rely on to monetize its data as an asset. Macro-categories are defined as: Data Enrichment & Sharing, Internal Data Monetization and Data Management. In addition, the proposed framework can be also seen as a checklist to assess the different requirements a company might need to set up a data-driven organisation.

(No abstract in italian available) The main purpose of this thesis is to investigate and illustrate the possibilities an organisation has to monetize its Big Data outside of the firm’s boundaries using data as an asset. In addition, this thesis also aims at offering a framework, which can be used to classify startups operating in the Data Monetization business and thus, allowing to map and assess the maturity of the startups operating in the Data Monetization landscape. Furthermore, this thesis is supported by an empirical research on 380 startups operating in the Data Monetization business. To fully exploit the potential of data as an asset, data has to be used as an asset external to the firms boundaries by selling it to third parties, bartering it for tangible and intangible benefits or wrapping it around other services or products to gain a premium price and thus, leverage data to create an additional revenue stream. However, to leverage data as an asset, a certain degree of infrastructure, resources and capabilities supporting a data driven organisation are required. To set up an organisation able to monetize its data as an asset outside of the firms boundaries, an organisation can build up the required infrastructure, resources and capabilities internally or outsource it by deploying third parties such as startups offering data monetization products and services. Thus, this thesis provides an External Data Monetization framework which companies can use to map the startup landscape operating in the Data Monetization business. More in detail, the framework summarizes the three main macro-categories a company can rely on to monetize its data as an asset. Macro-categories are defined as: Data Enrichment & Sharing, Internal Data Monetization and Data Management. In addition, the proposed framework can be also seen as a checklist to assess the different requirements a company might need to set up a data-driven organisation.