Editing Business intelligence (section)

===Limitations of semi-structured and unstructured data===
{{update|part=section|reason=It's dubious that searchability and semantic analysis are still limitations at the current stage of NLP and AI development|date=December 2023}}
There are several challenges to developing BI with semi-structured data. According to Inmon & Nesavich,<ref name = inmon>Inmon, B. & A. Nesavich, "Unstructured Textual Data in the Organization" from "Managing Unstructured data in the organization", Prentice Hall 2008, pp. 1–13</ref> some of those are:

* Physically accessing unstructured textual data&nbsp;– unstructured data is stored in a huge variety of formats.
* [[Terminology]]&nbsp;– Among researchers and analysts, there is a need to develop standardized terminology.
* Volume of data&nbsp;– As stated earlier, up to 85% of all data exists as semi-structured data. Couple that with the need for word-to-word and semantic analysis.
* Searchability of unstructured textual data&nbsp;– A simple search on some data, e.g. apple, results in links where there is a reference to that precise search term. (Inmon & Nesavich, 2008)<ref name = inmon /> gives an example: "a search is made on the term felony. In a simple search, the term felony is used, and everywhere there is a reference to felony, a hit to an unstructured document is made. But a simple search is crude. It does not find references to crime, arson, murder, embezzlement, vehicular homicide, and such, even though these crimes are types of felonies".