Routinely-encountered
Routinely-encountered describes something frequently experienced or observed, common, and usual in a particular context. It signifies the commonplace nature of an object, situation, or phenomenon. This term emphasizes the predictable and expected presence, highlighting that the item or event is not novel or unexpected, but a standard part of daily life, scientific research or industrial production, implying a level of familiarity. This often implies a degree of ease or familiarity in interaction or approach and an element of mundane experience.
Routinely-encountered meaning with examples
- In medical settings, symptoms of a cold are routinely-encountered; doctors and nurses see them daily. A common symptom of this is that the patient will complain of sore throat, and the nurse will be familiar with how to deal with this symptom, such as by using the 'sore throat' protocol. These observations are so commonplace that they require little specialist investigation.
- Cybersecurity professionals work to defeat routinely-encountered phishing attacks. These attacks target unsuspecting people to access their systems, and gain information from them. It is a big part of the Cybersecurity professional's job to make sure that they know all the routinely encountered attacks and how to deal with them.
- In software development, bugs in code are routinely-encountered and corrected, or the whole program may be scrapped if the problem is too great, or if it is a serious threat, like a virus. Developers use debugging tools and strategies to deal with these flaws.
- Manufacturing companies are routinely-encountering supply chain disruptions in recent times, leading to inventory problems, so they are now trying to find new suppliers, and alternative sourcing is becoming more prevalent as a result. There's a large global problem in finding the sources for certain products.
- In many scientific fields, data with minor inconsistencies are routinely-encountered and handled through standard processes. The scientist can then evaluate the level of acceptable inconsistency, and if there's too much, throw out the data, and run the experiment again.
