Most analytics programmes are built on numbers. Sales volumes, inventory counts, operational costs. That is the 20% everyone knows how to work with.
The other 80% is text. Customer reviews, support transcripts, email threads, RFP documents, survey responses. It sits full of signal, and almost nobody is doing anything useful with it.
Why Keyword Matching Failed
The first generation of text analytics was just search. If a review contained "bad," flag it negative. The problem is obvious. "Not bad for the price" is positive. "I love how the app crashes every morning" is sarcasm. Keyword matching misses all of these.
Modern NLP works differently. It parses sentence structure, maps word relationships, and identifies entities. We have used this on PSU tender documents. A 100-page RFP that used to take two days can now be parsed in minutes, with compliance clauses flagged and risk areas ranked by severity.
Asking Questions in Plain Language
The old workflow: a business leader has a question, emails the data team, the team writes SQL, the answer comes back three days later, and by then the leader has moved on.
Conversational BI changes that loop. A head of operations can type "What were our top three delivery delays in Q3?" and get a synthesised answer in seconds. This works when two things are true: the underlying data model is clean, and the interface is simple enough that people actually use it.
What This Means for Your Data Strategy
NLP is not a product you buy. It is a capability you build into your existing stack. The practical starting points are narrow: take your highest-volume unstructured data source and run sentiment extraction. Pilot conversational BI with one team on one well-governed dataset. Use NLP parsing on your most painful document-heavy process.
The companies that get this right will be the ones that picked the right problem, started small, and proved the value before scaling.