Curated guidance for integrating SCADA/OT data with BI and decision workflows in manufacturing and industrial operations.
Welcome — turn machine signals into decisions that stick. Operational teams hear the promise of business intelligence: dashboards, alerts, and models that help people act faster and reduce surprises. But too often SCADA and OT data ends up duplicated, misunderstood, or left behind because production rhythms, security...
Practical guide — integrate SCADA/OT data with BI and decision workflows. Many integration attempts fail not because the data is bad, but because the process ignores operational constraints: signal meaning, sampling rhythm, ownership, and safety. This guide shows an end-to-end, pragmatic path to make operational data...
Operational-to-BI Integration Checklist (practical verification). Use this checklist before connecting plant data into your BI environment or before deploying analytic alerts into operations. Each item should have an owner and a date. Decision & scope Target decision is clearly defined and measurable. Expected...
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Signal → KPI Mapping Template. Use this worksheet to translate raw tags into business-facing KPIs and the actions they enable. Keep the mapping compact—start with 5–10 tags for a single decision. Signal / Tag Friendly Description Source (PLC/Historian) Sampling Rate KPI / Derived Metric Transformation / Formula Owner...
Composite example — from noisy telemetry to actionable alerts. This composite example synthesizes common patterns and lessons from multiple operational projects (not an account of a single company). It illustrates how small, disciplined steps reduce friction and create value. The hunger. Operations wanted fewer false...
An interactive, practical checklist to guide teams through integrating SCADA/OT telemetry with enterprise BI. Covers inventory, time alignment, sampling, tagging, asset mapping, contracts, latency, security, provenance, quality checks, example KPI queries, monitoring, and implementation ownership.
A practical, stepwise recipe for ingesting SCADA/OT data, aligning timestamps and labels, making time-series data queryable for BI teams, and producing reliable operational KPIs while preserving OT security and reliability.
A practical, step‑by‑step guide to turn time‑series and OT (SCADA/PLC) telemetry into operational dashboards, maintenance signals, and decision playbooks. Covers sensor selection, sampling and aggregation patterns, smoothing vs raw data, OEE calculation, common anomaly detection approaches, short‑latency dashboard design, and concrete playbook snippets for routing alerts into maintenance workflows. Includes an actionable sensor‑to‑dashboard lifecycle checklist you can adapt for your site.
An actionable, savable checklist to guide safe and reliable integration of SCADA/OT telemetry into BI and analytics systems. Record owners, acceptance checks, notes, risk, and site metadata to build operational memory, follow up on actions, and reduce integration risk.
Practical, actionable checklist to guide safe, reliable, and analytically useful integration of SCADA/OT data into BI platforms. Covers connectivity, security, timekeeping, metadata, ingestion patterns, storage, edge decisions, safety controls, visualization needs, testing, and handover.
An actionable, fillable checklist to assess sensor/tag selection, sampling and timestamp rules, data quality checks, aggregation and OEE inputs, safety and security controls, and the communication plan needed to integrate OT/SCADA data into BI and analytics systems.
A practical operational playbook for modeling, aggregating, and monitoring time-series data from sensors, SCADA/OT, IoT, logs and product metrics. Provides concrete patterns for aggregation windows, irregular sampling, seasonality and holiday adjustments, baseline forecasting for anomaly detection, alerting strategy and escalation, dashboard design, and evaluation metrics — with common pitfalls and sample queries to apply immediately.
A practical, operational playbook for scoping predictive‑maintenance use cases, choosing sensors, creating target labels, validating models, integrating alerts into maintenance workflows, and keeping models and people aligned over the asset lifecycle.
A practical, operations‑focused playbook for framing predictive‑maintenance use cases, choosing sensors and features, labeling and validation strategies (including lead‑time analysis), integrating models into maintenance workflows, tuning alarms, and defining clear human‑in‑the‑loop responsibilities so predictive signals become trusted, actionable work.