Model proposal on big data analytics and digital advertising management systems

Big data; It is a general term for a larger, more diverse, and more complex set of data that includes processes such as collection, storage, analysis, and visualization. With the help of big data, meaning is extracted from the data; providing foresight, behavioral models, obtaining insight and process optimization. Such phenomena bring big data and digital advertising closer together, both in practice and in theoretical discussions. Big data enables digital advertisers to take real-time advertising decisions, as well as take immediate action based on reliable and relevant information thanks to data generated in digital environments. Big data allows digital advertisers to better target users with more personalized ads they're likely to want to see. In this exploratory research that explores big data-driven digital advertising theories and practices; It is desired to analyze the details of how Amazon, Apple, Google, Meta and Microsoft platforms openly use big data as a business model. The research examines the big data-driven digital advertising, management and analysis processes of these technology platforms. The organizational structures of these platforms about big data and their orientation on how they have the necessary skills for this are discussed in the context of digital advertising. Research; It aims to reveal how platforms such as Amazon, Apple, Google, Meta and Microsoft are using big data techniques and business models as digital advertising capabilities. Another sub-objective of the research is to provide space to connect discussions about how platforms structure big data by analyzing the operating patterns of digital advertising in the big data environment. In this exploratory research, a qualitative research approach was adopted to reveal the processes of big data analytics approaches in digital advertising applications. In the research, subcategories, code generation models, cross tables, single case model and two case models were analyzed in accordance with the research design consisting of qualitative data using the MAXQDA program. Analyzing these platforms with qualitative approaches makes it easier to understand how the digital advertising market is complex, layered and globally interconnected ecosystem and partnerships. A horizontal analysis was conducted to determine how platforms are embedded in big data applications in the advertising industry. With horizontal analysis, MAXQDA program images were used to analyze the partnerships of the platforms, their analytical points, big data application integrations and visualize their associated networks. Big data-driven digital advertising technologies, partnerships and differences are illustrated with different program deliverables to begin to understand cross-platform relationship networks. In the last step of the research, it is discussed how big data mediated digital advertising should be realized. The theoretical and conceptual frameworks obtained from the first and second parts of the research and the big data-mediated digital advertising analyzes obtained from the third part of the research are combined with the discussions on how to manage the big data used in digital advertising in the last step. In this section, a digital advertising method model architecture based on big data is proposed by combining data sources, data collection, data integration, data analysis, data application, data visualization and advertising combinations. This model combines digital advertising capabilities with theoretical and industry applications from previous parts of the research to identify and characterize key technical priorities and sequence in big data technologies.