My name is Shinobu Yokoyama, and I am a Data Scientist (former System Architect).
Education
- 2007 May – BA, University of Central Oklahoma;
- 2018 March – MSc, Kyoto University;
Scope of our services
Applying data science and mathematics to practical decision-making
We support organizations by applying data-driven and quantitative approaches to practical decision-making. Our work draws on methods from data analysis, systems engineering, and applied mathematics when they are appropriate to the problem at hand.
We are often engaged when decisions involve uncertainty, incomplete information, or competing constraints. In such cases, our role is to help clarify assumptions, reduce ambiguity where possible, and support more transparent reasoning. Rather than offering fixed answers, we aim to make the structure of a problem more explicit so that decisions can be made with greater confidence and accountability.
IT systems design and evaluation as a long-term business partner
Our background is in information technology and system design, with experience ranging from small, self-contained services to large, distributed systems built on modern architectures. We assist clients in understanding how technical systems support business objectives over time, including issues of maintainability, scalability, and operational clarity.
In working with complex systems, we place particular emphasis on evaluation: how performance, quality, and outcomes are defined and assessed. Frameworks such as the PDCA cycle remain widely used in practice, but their effectiveness depends on how evaluation criteria are specified and applied.
We have observed that, in some organizational settings, evaluation relies heavily on informal judgment or precedent, which can make improvement difficult to sustain. Our contribution is to help formalize evaluation criteria where appropriate, while recognizing that not all aspects of a system or organization can be reduced to quantitative measures. We aim to balance rigor with practical constraints, and to support evaluation processes that are both meaningful and communicable to stakeholders.
Consultation, research support, and knowledge sharing
The scope of data science and related technical fields continues to expand beyond traditional analysis toward modeling, inference, and visualization. At the same time, the systems that support these activities must address operational concerns such as reliability, security, and observability.
We offer consultation at different levels of engagement. In some cases, this involves exploratory discussions around open-ended questions or early-stage ideas. In others, it involves more concrete work such as requirements definition, system assessment, or technical planning. A recurring focus is helping clients articulate what they actually need to achieve, and distinguishing essential requirements from assumptions that may unnecessarily complicate solutions.
We do not assume that there is a single optimal plan applicable to all contexts. Instead, we work with clients to narrow overly broad or ambiguous requirements, with the aim of reducing complexity and avoiding systems that are costly to build or maintain without delivering corresponding value.
For information on academic research, publications, and presentations, please see the dedicated researcher page.
