Extracting Information from Data

Data is formatted in various ways (e.g. transactional records, time-series points, etc.), however, this section will focus on extracting information from time-series data sources, like Industrial Tag Historians.

Industrial Tag Historians store high resolution time-series data, generally at sub-second intervals, and provide detailed trends, like the kWh totalizer shown below. This detailed information is incredibly useful for real time monitoring and troubleshooting process problems, but is generally not useful for answering questions on Operational Performance (e.g. “How many kWh did we use to produce the last batch of Apple Juice?”)


Management teams demand more than just trends. They expect context rich information to be “lifted” out of the detailed data. This involves summarizing the data into a form that is quickly and accurately understood at a management level. The summarized information is not only demanded by management teams, but is important to empower operators and team leaders to make their own decisions more frequently.

To achieve this, they need a system that gives them decision support and insight. They want to know things like:

  • “Which machines are performing most efficiently?”
  • “What is different about the machines that perform well?”
  • “Do the machines run more efficiently when running specific products?”
  • “Are some shift teams able to achieve better efficiencies than others?”
  • “How often are the machines stopping? Do we know why?”
  • “Do we have unexpected electricity, or water, over usage scenarios?”

Let’s see how the Flow Information Platform can help provide this decision support and insight.

Was this article helpful?
0 out of 0 found this helpful
Have more questions? Submit a request