Metrics Supported by Analytics Studio

These are the Metrics supported by Analytics Studio.

The Metrics Approach!

Fogwing IoT Analytics Studio, communicates with Users via metrics that draws valuable insights from raw data. There are a certain metrics type in Fogwing Analytics that are represented in either 'Bar charts' or 'Line charts' from which you can gain insights of your factory floor and various business operations.

Learn the Metrics Approach:

• Average, Minimum, Maximum - This type of Metrics displays data analytics as numbers within components of 'Minimum, Maximum and Average'.

Example: Users can choose this type of metrics for 'Temperature' and 'Humidity' attributes. The average, minimum and maximum of temperature and humidity conditions in a given environment can be tracked and monitored easily.

• Count - This type of Metrics will indicate the specific number of occurrence of an event.

Example: An event could be 'Closing / opening' of a door or 'Turning off / Turning on' of an Air Conditioner. When the Count metrics is selected, the number of times the door has opened / closed and the number of times the AC was turned on or turned off is made known.

• Sum - This Metrics type declares the total or a particular aggregate of an event / action.

Example: The cutting of metal in a manufacturing establishment, can be accounted for an action. The cutting machine performing the action is monitored when the Sum metrics is opted. The Sum metrics will extract the exact aggregate or total of the action performed within a stipulated time.

• Variance - This Metrics type indicates the fluctuations / difference in a data set.

Example: The variance in the rate of current flow can be monitored with the Variance metrics.

• Standard Deviation - This type of Metrics portrays average amount of variability in a data set.

Example: The standard deviation in the Voltage can be traced within stipulated time intervals.

• Median - This Metrics type measures the average value in a data set. It omits outliers that can negatively impact business decision making.

Example: This can be used detect the performance values of machinery on factory floors and estimate downtime for maintenance.