Towards a Framework Driven by Use Cases in Data-heterogeneous Environments
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I presented our research work on developing a use case-driven framework for data-heterogeneous environments at the JISBD 2023 conference organized by SISTEDES in Ciudad Real. This work is part of my industrial PhD research focused on helping companies transition to data-driven operations.
The presentation addressed a common challenge: while most organizations rely on data analysis for strategic decisions and increasingly use machine learning techniques, the tools available require significant effort to adapt to each company’s specific context. Companies face barriers such as infrastructure costs, the need for specialized profiles to build and maintain these tools, and lack of skills, leadership, or policies that limit return on investment.
Our proposed framework aims to simplify the process of consuming and analyzing data over time by emphasizing the definition of data use cases that drive the phases of data enablement, consumption, discovery, and storage. The framework is being developed and validated through real-world implementation in companies evolving toward data-driven practices.
Related publication: Towards a Framework Driven by Use Cases in Data-heterogeneous Environments