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Unstructured data may also refer to irregularly or randomly repeated column patterns that vary from row to row  or files of natural language that do not have detailed metadata. 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. Where do you start? It takes you step by step through the technologies and the alphabet soup, so you can choose the right technology and implement a successful BI environment.
You'll see how the applications and technologies work together to access, analyze and present data that you can use to make better decisions about your products, customers, competitors and more.
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Making everything easier Chapter 2 focuses on answering questions faced by individuals interested in using storage or database technologies to solve their Big Data problems. In this excerpt, Neef focuses on doing business in the Big Data world, including the scope of projects already underway, the primary drivers behind initiatives, and how Big Data is changing both the organization and c-level leadership roles.
The chapter excerpt focuses on how effective data architecture must lay out the life cycle of data, from definition to capture, storage, management, integration, distribution, and analysis.
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Which topic are you interested in?Successfully reported this slideshow. While valuable, GIS applications required special- ized software and expert-level training, making them difficult to implement and scale.
QuinStreet does not include all companies or all types of products available in the marketplace. For example, waste receptacle sensors can save a city millions of dollars by understanding when they need to be serviced and optimizing waste pickup routing.
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