Data
We pay close attention to what data means and how it is structured. Ambiguous definitions, inconsistent identifiers, and undocumented transformations create problems that spread into calculations, software, and operating processes. Resolving them at the data layer reduces the need for downstream workarounds.
Good data modeling makes assumptions explicit: what an observation represents, which units it uses, when it applies, where it came from, and how it relates to other observations. These details determine whether sources can be combined and whether the resulting analysis answers the intended question. A successful join or a plausible model output does not establish that the underlying interpretation is correct.
Statistical analysis and machine learning both depend on these foundations. Flexible models can exploit inconsistencies or accidental relationships that are difficult to detect from their outputs alone. We therefore emphasize verified data, explicit definitions, and traceable transformations. The storage design follows the requirements: normalization can help enforce consistency, while document and columnar structures serve other needs. The aim is to preserve meaning and make errors detectable without imposing unnecessary complexity.
We have developed LLM-based techniques for constructing metadata and mapping it to ontological hierarchies. We use these techniques in large-scale data modernization to help interpret existing data and reconcile definitions across systems.