Glossary · IIoT, data and AI
Big data
German: Big Data
In information technology, big data refers to data sets whose volume, velocity or variety exceeds what conventional databases and tools can store and process efficiently, requiring distributed storage and processing. In industry it typically means high-frequency sensor data combined with production, quality and business data.
- IIoT
In one sentence
Big data means data sets too large, fast or varied for conventional tools, such as high-frequency sensor data from many machines.
Example
A steel plant stores millisecond vibration data from 400 motors, which amounts to several terabytes per month and is processed on a distributed cluster.
How it applies
- Engineering: Collecting everything is rarely the right strategy. Decide which signals, at which sampling rate, answer which question; high-frequency raw data can often be reduced at the edge (Edge computing).
- Operation: Storage and processing costs grow with volume. Retention rules keep a Data lake from turning into an unusable archive.
- Documentation: Volume does not replace meaning. Without Metadata, units and asset context, large data sets are hard to use. Document data sources, sampling rates and retention in a data catalog.
Big data vs. good data
More data does not automatically lead to better analytics or better AI models. Data quality, representative coverage of operating conditions and clear labeling often matter more than raw volume.