Explore AI-ready data models, metrics, and business semantics.
Bitemporal modeling separates when a fact was valid in business from when the system knew it, supporting original publication, late correction, and restated views.
Read articleTreat quantity and unit as one value, with physical dimension, standard unit, packaging version, conditions, effective period, precision, and controlled conversion paths.
Read articleUse stable nodes, effective-dated parent-child edges, closure paths, and controlled multi-parent bridges instead of fixed level columns.
Read articleSeparate stable customer identity from controlled, rapidly changing profile combinations, then stamp both keys onto facts so historical state remains queryable without dimension explosion.
Read articleEvery amount needs a currency and governed exchange-rate context. Preserve original amounts, date roles, rate types, quote direction, adjustments, rounding, and policy versions.
Read articleModel a governed cost pool and a versioned allocation bridge. Preserve eligible scope, drivers, weights, allocated amounts, rounding residuals, replay evidence, and the distinction between attribution and causality.
Read articleA business key identifies an entity in a source; a surrogate key anchors a warehouse row across source integration, key reuse, and historical versions.
Read articleAttendance, eligibility, assignment, and promotion coverage can be modeled as factless event or coverage facts, counted by rows and compared through anti-joins.
Read articleBalances can aggregate across accounts or items at one point in time but not over time. Ending, opening, average, and flow each need distinct semantics.
Read articleReuse one conformed date dimension through order, ship, receipt, and refund roles, with a declared default role for every metric.
Read articleGroup related flags and small enumerations to reduce fact-table clutter and govern combination meaning without creating many tiny dimensions.
Read articleTransaction facts preserve each event; an accumulating snapshot updates milestones on one row. Most mature workflows use both for different questions.
Read articleA direct join duplicates facts. A bridge must define membership, effective time, allocation weight, and valid aggregation behavior.
Read articleTransaction identifiers can stay in the fact table as degenerate dimensions for grouping and drill-through without an empty key-only dimension.
Read articleRetain the fact with a source-aware placeholder member, then enrich and audit it when customer or product master data arrives.
Read articleCalendar months, 4-4-5 periods, and business dates have different boundaries; model periods, close status, and comparison explicitly.
Read articleReplacing every missing value with zero creates false conclusions; model observation state and missing-record semantics explicitly.
Read articleBusiness definitions, data quality, implementation, semantic publication, and change approval need distinct responsibilities.
Read articleShared product, store, customer, and date dimensions align facts across sales, inventory, and finance; equal field names are not enough.
Read articleRatios are usually non-additive; aggregate numerator and denominator first to avoid an average of averages.
Read articleEntity resolution needs normalization, candidate generation, evidence, golden records, and reversible decisions, not fuzzy matching alone.
Read articleFacts record measurable events or states; dimensions describe analytical entities. Mixing roles causes bad aggregation and joins.
Read articleTransactions preserve change; snapshots simplify point-in-time analysis. Inventory, accounts, and device state often need both.
Read articleClassify changes, find dependents, run parallel versions, and enforce a deprecation window so reports and AI agents do not fail unexpectedly.
Read article“What does one row represent?” is foundational metadata; unclear grain distorts joins, metrics, and natural-language analysis.
Read articleA join contract declares allowed paths, cardinality, effective time, and failure behavior so model changes cannot silently inflate metrics.
Read articleSemantic change needs lineage, versions, effective dates, regression questions, and release communication instead of overwriting a definition.
Read articleA field name is only an entry point; AI also needs meaning, validity, aggregation rules, and ownership.
Read articleSemantic tests must cover structure, metrics, permissions, and real phrasing, not only data-quality assertions.
Read articleQuality spans completeness, accuracy, consistency, freshness, and usability, and each dimension must connect to a business action.
Read articleMetrics are not static formulas; owners, scope, effective dates, and impact belong in every version.
Read articleCurrent-only attributes rewrite history; SCD models preserve explainable past and current views.
Read articleWide tables start quickly; star schemas improve reuse and governance. Choose by change and ownership.
Read articleMake grain, definitions, quality status, lineage, and ownership explicit before data reaches AI.
Read articleMany wrong numbers come from undeclared grain, joins, and aggregation paths rather than weak reasoning.
Read articleModeling tools organize data structure; semantic layers make business meaning reusable.
Read articleReading tables is not understanding business; metrics, relationships, terminology, permissions, and versions need governance.
Read articleThe path requires scoped reflection, profiling, relationship review, business approval, tests, and versioned publication.
Read articleIntegration, development, semantic metrics, and AI-ready modeling are different layers.
Read articleAI needs more than tables and columns. It needs stable definitions, clear relationships, and traceable business meaning.
Read articleOrganize metrics, terminology, rules, and permissions into reusable AI context.
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