Check sensor data quality before building analytics
Use simple checks for completeness, range, timing and context before trusting a trend or training a model.
· 2 min
Industrial data / EN
TOPIC HUB
Industrial data, historians & data quality
Analytics is only as good as the signal that reaches it. These guides follow industrial data from the sensor to storage: checking units, timestamps and quality codes, choosing between a historian and a data lake, sizing retention and compression, and cleaning data without removing real events.
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A suggested reading order
Industrial data / EN
Write a data contract your automation and IT teams can both use
Define meaning, timing, quality and ownership so an industrial tag remains understandable outside its source system.
· 2 min
Industrial data / EN
Historian vs Data Lake: Where Should Industrial Data Actually Live?
Historians are optimized for fast time-series; data lakes for flexible analytics. The two are complementary - if you design the boundary.
· 2 minRELATED TOOLS
Try what you read.
Sensor Data Analyzer
Inspect CSV data for variation, trends and outliers.
CSV → statisticsIndustrial Data Converter
Convert and scale CSV, JSON and Modbus register data.
CSV · JSON · ModbusIIoT Data Budget
Estimate data volume from tag count, sampling and retention.
Bandwidth · storageProcess Unit Converter
Convert pressure, temperature, flow and other process units in batches.
bar · °C · m³/hSensor Data Generator
Generate multi-channel test data with noise, trends and anomalies.
CSV · simulationPDF RESOURCES
All resources Take it to the meeting.
PDF · 2 pages
Write a data contract your automation and IT teams can both use
Define meaning, timing, quality and ownership so an industrial tag remains understandable outside its source system.
ALL ARTICLES
Open in the article list Every article on this topic
Industrial data / EN
Check sensor data quality before building analytics
Use simple checks for completeness, range, timing and context before trusting a trend or training a model.
· 2 min
Industrial data / EN
Data Quality in Manufacturing: Why Garbage Time-Series Breaks Analytics
The analytics model is only as good as the data pipeline that feeds it. The most common data quality failures and how to catch them early.
· 2 minIndustrial data / EN
Write a data contract your automation and IT teams can both use
Define meaning, timing, quality and ownership so an industrial tag remains understandable outside its source system.
· 2 min
Industrial data / EN
Reading a sensor CSV: outliers, drift and flatlines before analytics
A short routine for checking an exported sensor file: timestamps, missing samples, z-scores and outliers, drift and frozen values, and what to do before data reaches a dashboard or a model.
· 4 min
Industrial data / EN
Process unit conversion pitfalls: gauge vs absolute, normal vs standard volume
The conversions that cause real mistakes in process data: gauge versus absolute pressure, temperature offsets, volumetric versus mass flow, and normal versus standard cubic meters.
· 4 min
Industrial data / EN
Time synchronization for industrial data: NTP, PTP and timestamp discipline
Why timestamps decide whether data can be trusted, where NTP is enough, when PTP is worth the effort, and how to choose a single time source and timestamp convention.
· 4 min
Industrial data / EN
Sizing an IIoT data budget: sampling, deadband and retention
How tag count, sampling interval, payload size, overhead and retention combine into bandwidth and storage numbers, and how deadband and compression change them.
· 4 minIndustrial data / EN
Historian vs Data Lake: Where Should Industrial Data Actually Live?
Historians are optimized for fast time-series; data lakes for flexible analytics. The two are complementary - if you design the boundary.
· 2 min
Industrial data / EN
Size historian retention with a measured data budget
Estimate the data path, separate sampling from storage and validate compression before choosing capacity.
· 2 min
Industrial data / EN
How Time-Series Database Compression Keeps Historians Fast
Historians keep very large volumes of samples manageable. Understanding lossless and lossy compression helps you size and tune them.
· 2 min
Industrial data / EN
Industrial Data Historians: Choosing Between OSIsoft PI, Ignition, and InfluxDB
Every factory needs a historian — but the choice between enterprise platforms, SCADA-native solutions, and open-source time-series databases depends on your scale, budget, and team skills.
· 5 min
Industrial data / EN
Time-Series Data at the Industrial Edge: MQTT, InfluxDB, and the Architecture Behind It
Storing and querying industrial time-series data at the edge requires a different mindset than cloud-native architectures. Here is what works in practice.
· 3 min
Industrial data / EN
Data Preprocessing for Industrial ML: Cleaning Sensor Noise Without Losing the Signal
Raw industrial sensor data is messy. Before any ML model can deliver value, it needs to survive proper preprocessing. Here is the pipeline that actually works in the field.
· 3 minIndustrial data / EN
Industrial Data Modeling Principles with HighByte Intelligence Hub
Principles for modeling industrial data in an Industrial DataOps platform such as HighByte Intelligence Hub: model the plant first, condition data near the source, name consistently and version your models.
· 3 min
Industrial data / EN
Running Lightweight ML Models at the Industrial Edge with ONNX
Why and how to run compact machine learning models on edge devices with ONNX Runtime: model export, quantization trade-offs, input validation, local buffering and safe model updates.
· 3 minCOMMON QUESTIONS
Short answers.
Historian or data lake?
A historian suits fast time-series queries and operational visibility; a data lake suits long-term analytics across many sources. Many plants use both.
How do I measure data quality?
Start by measuring missing data, stale values, clock drift, unit errors and flatline (frozen sensor) behavior.
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