TYPES OF HARD AND SOFT DATA
Data is subdivided into two categories, namely soft and hard data (Kittel, 2018). Hard data is easily measured, quantifiable, objectively based, and always credible with management. Hard data represent rational facts and are always quick to capture. Moreover, hard data may be categorized as output, quality, finances, and time.
Hard data examples include sales, customer complaints, downtime, operating cost, turnover, and absenteeism. One type of hard data is the results of blood tests in conducted medical study research. This information can be measured, tracked, and validated. Most healthcare organizations use data obtained from the results of blood tests to come up with valid conclusions.
Soft data entails measures that are not easily quantifiable, immeasurable, behavior-based, and subjective. In contrast to hard data, however important, the measures are mostly considered less credible when converted to monetary value. Soft data is inherently subjective hence often perceived less reliable. Since soft data is difficult to collect and analyze, it is often used only to supplement hard data.
Soft data examples include customer satisfaction, customer loyalty, brand awareness, and reputation. One type of soft data is observing patients and asking them to rate the symptoms they feel. This type of soft data gets collected from subjective observations that cannot be quantified. Soft data’s nature does not make it unreliable. Crucial business decisions are based on soft data, such as customer satisfaction and product reviews.
Reference
Kittel, W. (2018). Hard and Soft Data. World Scientific.