A regular expression, almost always shortened to regex or regexp, is a sequence of characters that defines a search pattern. Instead of looking for one fixed string, a regex describes a shape of text - for example "any sequence of digits", "an email-like token", or "a word that starts with a capital letter". Nearly every programming language, text editor, and command-line tool supports regex, which makes it one of the most transferable skills in data work.
The power of regex comes from a small set of building blocks: character classes like \d (digit) and \w (word character), quantifiers like + (one or more) and * (zero or more), anchors like ^ and $ (start and end of line), and groups written with parentheses. Combining these lets you express surprisingly precise rules in a single line, such as \b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b to approximate an email address.
For marketers, recruiters, and analysts, regex is the fastest way to clean and extract data from messy exports. You can pull every phone number out of a scraped page, strip tracking parameters from a column of URLs, or validate that a list of SKUs follows a required format - all without writing a full program. The same pattern that finds matches can also drive find-and-replace operations to reshape data in bulk.
Regex has real limitations worth respecting. It is poor at parsing nested structures like HTML or JSON, where a real parser is safer, and overly greedy patterns can produce catastrophic backtracking that hangs on large inputs. The practical approach is to test patterns against representative samples, prefer non-greedy quantifiers when matching between delimiters, and reach for a dedicated parser when the data is genuinely hierarchical.