Data Historian

What is A Data Historian?

A data historian is a software program that records the data of processes running in a computer system. Organizations use data historians to gather information about the operation of programs to diagnose failures when reliability and uptime are critical. Data historians are most common in data centers and industrial control systems. Data historian records include such things as:

  • Analog data – for instance CPU temperature, fan, and other equipment RPMs, flow rates, fluid levels, and pressure levels.
  • Digital readings – for instance valve positions, limit switches, discrete level sensors, and motor status.
  • Quality assurance data such as process, product, and custom limits.
  • Alerts such as out-of-limit and return-to-normal signals
  • Aggregate data such as average, standard deviation, process capability, and moving average


Why is a Data Historian Important?

The data that goes into a data historian is time-stamped and cataloged in an organized, machine-readable format. The data is analyzed to compare such things as day vs. night shifts, different work crews, production runs, material lots, and seasons. Organizations use data from data historians to answer many performance and efficiency-related questions. Organizations can gain additional insights through visual presentations of the data analysis called data visualization.


How Enables Organizations to Use Data Historians

Data historians are among the many sources from which the C3 AI® Suite and applications can pull data, including such sources as eDNA data historian, OSI Pi historian, SQL databases, and Oracle databases. For example, C3 AI Predictive Maintenance can can ingest data from multiple sources through batch, stream, or message-based integrations. The application’s prebuilt connectors can access many standard data stores, including Postgres, Oracle, SAP, HBase, HDFS, Apache Kafka, AWS Kinesis, OSI PI, and Cassandra. Data integration services are extensible, enabling developers to configure and add additional connectors to scale with the underlying infrastructure. Additionally, Predictive Maintenance can be configured to integrate data from other disparate databases, legacy storage media, and streaming data sources.