Data collection

Tracking implies an automatic registration of events to carry on a quantitative approach. It is performed by tools set up exclusively for this purpose. Data points are rarely reviewed individually. Trend identification is often the main goal. The accuracy can be high but fidelity of the data is very low. Fidelity can be increased by tracking multiple types of data, creating context and potential correlation.

Logging in the pre-big-data days referred to a manual process to provide both a data point and often a unique note from the observer. The Medium-fidelity quality of the entry makes it less scalable than tracking. A log often includes multiple data points. Logs may be reviewed individually.

Checking in is qualitative. The degree of detail in the observation often involves a high effort and more time than for logging. Entries will be revisited in-depth to remember specific details. Think of this as a therapy session, whatever the subject matter, imagine it's sitting on a couch getting psychoanalyzed.

The context should determine the method collection based on frequency and fidelity desired. Conversely, the method of collection will tell the frequency and fidelity of the data. As often, context is key.

Everything that follows is the realm of interpretation.

← Index / Published on 2021-02-21