Werkon data services
Werkon starts data work with the fact, decision, and operation it must support. Source authority, definitions, identifiers, quality, lineage, access, transformations, delivery, retention, and ownership remain visible from capture through use.
Data service map
These responsibilities often combine, but they solve different problems. Beginning with the missing responsibility prevents a warehouse, dashboard, model, distributed platform, migration, or database replacement from becoming the goal by default.
Capture, validate, transform, test, deliver, observe, and recover dependable data flows while keeping schemas, identifiers, lineage, quality rules, failure, and ownership explicit.
Turn governed operational facts into defined metrics, reporting, analysis, explanations, and decision support without letting a dashboard become the source of truth.
Frame analytical and predictive questions, establish baselines, prepare protected datasets, evaluate segmented results, communicate limits, and operationalize only when the evidence supports use.
Design distributed storage and processing only when measured volume, velocity, variety, workload, latency, resilience, or concurrency exceeds a simpler owned path.
Inventory, map, cleanse, transform, move, validate, reconcile, cut over, recover, archive, and retire data while preserving authority, history, access, retention, and auditability.
Design, migrate, tune, secure, back up, restore, observe, and operate data stores around real consistency, query, transaction, retention, availability, and ownership requirements.
Data delivery method
A complete data slice is small enough to inspect and complete enough to expose the real contract. It joins capture, source authority, identity, definition, quality, transformation, delivery, access, use, correction, observation, and lifecycle for one useful fact or decision.
Observe the operation, decision, obligation, or product behavior, identify the fact it needs, trace where that fact is first created, and record current manual reconciliation, delay, error, ambiguity, and ownership.
Assign the source record and owner, define meaning, identifiers, grain, time, schema, allowed values, sensitivity, quality thresholds, correction, retention, access, interfaces, and consumers.
Compare process repair, source-system configuration, integration, pipeline, reporting, analytical model, migration, database change, or retirement against the measured constraint, risk, cost, continuity, skill, and ownership.
Use representative records and exceptions to prove capture, transformation, quality, lineage, access, delivery, metric or model behavior, reconciliation, performance where relevant, failure handling, and correction.
Monitor freshness, quality, volume, cost, use, failures, drift, access, and outcomes, manage contracts and consumers, rehearse recovery, keep correction usable, transfer ownership, and remove obsolete copies and paths with evidence.
Data boundaries
Data can become easier to query while becoming harder to trust. Copies, transformations, aggregates, models, and dashboards must not detach a result from the record authority, time, population, definition, quality, access, and correction path that make it usable.
Source basis
World Wide Web Consortium
Data Catalog Vocabulary Version 3The 2024 W3C Recommendation defines an interoperable model for describing datasets, data services, distributions, versions, series, provenance-related relationships, and catalog metadata without prescribing a particular storage format or access platform.
World Wide Web Consortium
Data on the Web Best PracticesThe W3C Recommendation covers metadata, licensing, provenance, quality, versioning, identifiers, access, formats, preservation, feedback, and enrichment so publishers and consumers can evaluate and reuse data responsibly.
UK Government Data Quality Hub
The Government Data Quality FrameworkThe framework treats quality as context-dependent and describes measurable dimensions such as completeness, uniqueness, consistency, timeliness, validity, and accuracy alongside governance, action plans, and user needs.
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.
Preserve source authority, schema, time, quality, lineage, delivery, replay, reconciliation, correction, and ownership from operational event to consumer.
Open path02Turn source observations into metric contracts, reports, dashboards, analysis, and alerts with explicit grain, time, quality, lineage, caveats, access, and correction.
Open path03Frame descriptive, causal, predictive, or prescriptive questions with protected evaluation, uncertainty, bias review, accountable authority, monitoring, and stop conditions.
Open path04Design partitioning, state, processing, checkpointing, recovery, reconciliation, capacity, security, privacy, and cost only when the workload requires distribution.
Open path05Inventory, map, transform, validate, reconcile, cut over, recover, stabilize, archive, and retire records with explicit write boundaries and accountable evidence.
Open path06Design records, transactions, queries, access, maintenance, backups, restores, replication, capacity, upgrades, retention, and ownership as one durable system contract.
Open path07Compare data responsibilities with AI, software, cloud, delivery, and operational services before choosing a treatment.
Open path08Map the wider operation, systems, records, friction, risks, and keep, connect, replace, or build decision before changing the data path.
Open path09Preserve record authority, identity, meaning, timing, delivery, reconciliation, recovery, and change when facts cross application boundaries.
Open path10Keep models downstream of governed sources, deterministic rules, permissioned context, evaluation, human authority, and accountable operation.
Open path