A Primer on the Theory of Data

A Primer on the Theory of Data provides a short, accessible entry point to the Theory of Data, a foundational framework for governed analytical data, lawful transformation, and certification. The Primer begins with the smallest meaningful analytical assertion—a datum—and develops the conceptual progression from datums to series, governed members, measures, anchors, universes, and value-generation regimes. It distinguishes an analytical object from its physical representation in tables, rows, columns, SQL, or dataframes. A universe governs which analytical points exist and why; event and spine are its two foundational types. A regime governs how values are generated at those points. Observation, provenance, and epistemic evidence status are treated as further independent dimensions. The Primer also explains why a successful calculation does not by itself establish that the result retains the same analytical identity, and why movement across anchors, populations, or value-generation regimes requires explicit governing contracts. The practical aim is to provide a compact conceptual map for analysts, data engineers, semantic-layer developers, statisticians, and AI systems: know where a value lives, which population gives its point existence, under what regime it was generated, how it was observed or produced, which governed family it belongs to, and which transformations preserve the right to keep using its name.