Business intelligence and analytics
Werkon builds reporting and analytics around the decision a person must make. Each number keeps its source, definition, grain, time, population, filters, quality, lineage, access, caveats, owner, and correction path attached.
Analytics contract
The contract starts with what a person must understand or decide, then defines the observations that can support it. Sources, grain, dimensions, measures, time, population, filters, quality, lineage, access, explanation, action, correction, and ownership move as one design.
Analytics path
The first useful slice should answer one recurring question with fewer reconciliation steps and clearer evidence. Visual form follows the decision, comparison, exception, accessibility, and action the user actually needs.
Follow who asks, calculates, reconciles, explains, approves, and acts; capture current reports and spreadsheets; identify conflicting definitions, hidden filters, missing exceptions, delay, quality concerns, and the cost of uncertain interpretation.
Assign source authority, grain, dimensions, measures, units, time, population, formulas, filters, exclusions, quality, status, lineage, access, caveats, owners, and approved examples before building the view.
Transform governed source records into reusable observations and metrics, preserve lineage and version, test calculations and joins against independent examples, reconcile meaningful totals and segments, and expose quality and freshness.
Choose table, chart, text, drill-down, detail, alert, or export from the comparison and action; preserve context, units, labels, focus, keyboard, responsive behavior, status, caveats, exceptions, and a path back to records where allowed.
Test with representative users and records, compare independent calculations, review accessibility and interpretation, stage subscriptions and alerts, monitor use and quality, correct errors, communicate definition changes, and retire unused or superseded views.
Analytics form
The same governed observations can support several forms, but each adds different operating responsibilities. Frequency, variability, interactivity, explanation, audience, action, delivery channel, and change ownership should determine the form.
Use a table or scheduled report when users need a complete, sortable, exportable, auditable set of records, statuses, exceptions, or period results rather than an abstract visual summary.
Evidence: Audience and cadence, record grain, columns and definitions, sort and grouping, filters, cutoff and status, totals, quality notes, access, export control, reconciliation, and owner.
Use a dashboard when a stable audience repeatedly compares a small set of governed measures across time, segment, target, or threshold and can take an identified action from the view.
Evidence: Decision and action, metric contracts, comparison, dimensions, target authority, refresh, quality and caveats, accessible chart and table alternatives, drill-down, usage, and owner.
Use analysis when the purpose is to investigate patterns, segments, hypotheses, outliers, or possible explanations before promoting a result into a maintained metric or product surface.
Evidence: Question and method, data and population, transformations, code or query, assumptions, uncertainty, sensitivity, alternative explanations, reproducibility, reviewer, limitations, and next decision.
Use an alert or embedded recommendation when a bounded condition requires timely attention, while keeping thresholds, suppression, identity, authority, false positives, escalation, acknowledgement, correction, and fallback explicit.
Evidence: Trigger definition, source and latency, threshold rationale, recipient and authority, rate and suppression, context, action options, acknowledgement, audit, false-result review, escalation, and recovery.
Metric controls
Many analytics disagreements are structurally valid calculations over different observations. A governed result makes grain, dimensions, status, filters, version, and quality visible enough to distinguish a real change from a different question.
Engagement fit
Source basis
World Wide Web Consortium
The RDF Data Cube VocabularyThe stable W3C Recommendation models statistical observations through explicit dimensions, measures, attributes, units, code lists, slices, and reusable data-structure definitions so consumers can interpret and validate multidimensional data.
World Wide Web Consortium
PROV-O: The PROV OntologyThe stable W3C Recommendation defines interoperable provenance concepts for entities, activities, agents, generation, use, derivation, and responsibility that can keep analytical outputs traceable to their production history.
UK Government Data Quality Hub
The Government Data Quality Framework GuidanceThe guidance ties quality rules and metrics to user needs and business objectives, recommends communicating caveats and lifecycle changes, and treats completeness, uniqueness, consistency, timeliness, validity, and accuracy as context-dependent dimensions.
A Systems Audit is the usual starting point. If the opportunity is already clear, we can move directly into a focused build.
Show Us the WorkflowStart with the free automation readiness checklistOBSERVEQUANTIFYDECIDEBUILD
Continue with the work
Stay with the operating question. The technology can wait until the work is clear.
Place metrics and reports inside the wider engineering, science, scale, migration, database, quality, lineage, access, and lifecycle responsibility map.
Open path02Preserve source authority, schema, time, quality, lineage, delivery, replay, reconciliation, correction, and ownership before analytical use.
Open path03Make analytics content, comparison, responsive layout, focus, keyboard behavior, caveats, empty states, errors, detail, and actions usable and testable.
Open path04Verify calculations, contracts, data, access, accessible interaction, interpretation, release, and correction without overstating a green result.
Open path