Big-O notation describes how an algorithm's work grows as its input gets larger. It ignores machine speed and focuses on the shape of that growth. This lets you compare two approaches without running either on a specific computer.
The problem it solves is fair comparison. A fast laptop can make a bad algorithm look quick on tiny inputs. Big-O strips away hardware and fixed setup, so you see which method wins once data gets big.
You use it to predict scaling. If a report takes one second on a thousand rows, Big-O tells you whether a million rows means minutes or days. That decides whether your design survives real traffic instead of collapsing under it.
Rewriting in plainer words…
This answer doesn't lend itself to a diagram - it reads best . No credits were charged.
