Browsing by Subject "Analytics"

Data collection and analytics are the core of firms' development in digital economies and have an enormous impact on consumer welfare. We build a monopolistic competition model with heterogeneous firms to incorporate both data collection and analytics investment. The model studies how the complementary effect between data collection and analytics affects firms' pricing, profit and consumer welfare. Data is divided into two categories: raw data and effective data. Raw data is a byproduct of production and does not benefit firms on its own. Effective data is a signal on consumers' taste and must be produced with both analytics and raw data. We then find analytics can not only reduce firms' uncertainty but also lower user cost of capital and markup. Lower cost of data analytics can increase consumers' welfare by increasing competition. We allow firms to differ in the size of complementary effect. The model shows that cheaper analytics has asymmetric effects on heterogeneous firms' product quality and profit. Firms with strong complementary effects produce higher quality goods, charge lower price-per-utile and benefit from the cheaper analytics. The opposite is true for firms with weak complementary effects.

In the second paper, We build a model to incorporate the buy-now-pay-later (BNPL) platform and study its welfare implication. BNPL platforms lend money to consumers, provide private data to partner firms and charge fee from in-platform merchants. Data can lower production cost. Two types of data are available: public data and private data. Data size of both types increases in the number of firms. Private data is only available for in-platform merchants. We find BNPL platforms can hurt non-platform users. The reason is that the platform fee can decrease the number of firms in the market and reduce public data, which increases out-of-platform firms' product prices. We then study a duopoly model with two platforms competing with each other. The model predicts that competition between platforms benefits non-platform users but can hurt platform users. The intuition is that competition splits the in-platform merchants and reduces private data for both platforms.

Cloud computing appears to be a paradigm shift in service oriented computing. Massively scalable Cloud architectures are spawned by new business and social applications as well as Internet driven economics. Besides being inherently large scale and highly distributed, Cloud systems are almost always virtualized and operate in automated shared environments. The deployed Cloud services are still in their infancy and a variety of research challenges need to be addressed to predict their long-term behavior. Performance and dependability of Cloud services are in general stochastic in nature and they are affected by a large number of factors, e.g., nature of workload and faultload, infrastructure characteristics and management policies. As a result, developing scalable and predictive analytics for Cloud becomes difficult and non-trivial. This dissertation presents the research framework needed to develop high fidelity stochastic models for large scale enterprise systems using Cloud computing as an example. Throughout the dissertation, we show how the developed models are used for: (i) performance and availability analysis, (ii) understanding of power-performance trade-offs, (ii) resiliency quantification, (iv) cost analysis and capacity planning, and (v) risk analysis of Cloud services. In general, the models and approaches presented in this thesis can be useful to a Cloud service provider for planning, forecasting, bottleneck detection, what-if analysis or overall optimization during design, development, testing and operational phases of a Cloud.