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Data[ edit ] Business operations can generate a very large amount of information in the form of e-mails, memos, notes from call-centers, news, user groups, chats, reports, web-pages, presentations, image-files, video-files, and marketing material. The management of semi-structured data is an unsolved problem in the information technology industry.
BI uses both structured and unstructured data. The former is easy to search, and the latter contains a large quantity of the information needed for analysis and decision making.
This can ultimately lead to poorly informed decision making. In the context of relational database systems, unstructured data cannot be stored in predictably ordered columns and rows.
One type of unstructured data is typically stored in a BLOB binary large objecta catch-all data type available in most relational database management systems.
Unstructured data may also refer to irregularly or randomly repeated nonrepetitive column patterns that vary from row to row within each file or document.
Metadata can include information such as author and time of creation, and this can be stored in a relational database. Therefore, it may be more accurate to talk about this as semi-structured documents or data,  but no specific consensus seems to have been reached.
Unstructured data can also simply be the knowledge that business users have about future business trends. Business forecasting naturally aligns with the BI system because business users think of their business in aggregate terms.
Capturing the business knowledge that may only exist in the minds of business users provides some of the most important data points for a complete BI solution. Limitations of semi-structured and unstructured data[ edit ] There are several challenges to developing BI with semi-structured data.
Couple that with the need for word-to-word and semantic analysis. 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.
This can be done by adding context through the use of metadata.
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