Browsing by Author "Laine, Matti"

Since data-driven business and decision making have ben trending, data has found to be a valuable part of organizations capital. Centralized data warehouse has typically been a solution for organizations to collect, manage and exploit data. While data volumes have gained, also data warehouse technologies have evolved over time and technical architectures have become more complex. In the process of that, metadata has become more essential instrument for managing large-scale data warehouse and analytics environments. Though metadata-based data warehouse architecture and automation have become quite common concepts, there are still potential areas and subsets of metadata left that has been quite out of the spot. One of those is metadata generated by the usage of data warehouse and analytics platform. Capability to measure a product and its usage is one of the key things to improve product technically, but also get insights to the ways that data has been consumed by end users. Before usage metadata and metrics can be analysed and results can be exploited, data must be collected and stored in a format that facilitates its further utilization purposes. That raises questions: how usage-related metadata and metrics should be modelled and stored to facilitate its utilization with other metadata of the data warehouse environment? How that collected usage metadata should be managed? Depending on the technical environment and setup, how usage metadata and metrics can be collected in the first hand? As a result of this study, design process to model database usage by analysing requirements and conceptualizing usage in a hierarchical model is presented. Also logical level reference model for usage metadata and methods for usage metadata management are presented. In addition, concepts for usage metadata and metrics collection process are discussed in the context of Amazon Redshift data warehouse service. Though presented reference model is an example outcome of the design process, phases of the design process can be generalized and exploited when designing usage metadata model in different use cases and technical environments.