Guide to Saving Time with Intelligent Data Logging

How to Simplify Data Analysis and Find Answers Quicker

When trying to define intelligent data Logging, it’s useful to compare it with traditional data logging. In the past, data loggers or recorders would typically be configured to sample a set number of channels at a fixed rate and store the recorded values in memory. The data could then be downloaded to a PC afterward and analyzed to identify trends, anomalies, and other events. The analysis was often very time-consuming, requiring the user to weed through thousands or millions of data points to find a region of interest to study. Consider for example a data logger that measures 2 inputs once a second; within one day this would fill more than 86,000 rows in a Microsoft Excel spreadsheet. Now imagine several months’ worth of data or a higher speed logger that samples at 100 to 1000 Hz. This amount of data can simply overwhelm the user and analysis becomes a task of finding a needle in a haystack.