Advanced Analytics for Big Data

Advanced Analytics for Big Data

Stephen Kaisler (SHK and Associates, USA & George Washington University, USA), J. Alberto Espinosa (Kogod School of Business, American University, USA), Frank Armour (Kogod School of Business, American University, USA) and William Money (School of Business Administration, George Washington University, USA)
Copyright: © 2015 |Pages: 10
DOI: 10.4018/978-1-4666-5888-2.ch747
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Background

“Big Data” originally meant the volume of data that could not be processed (efficiently) by traditional database methods and tools. The original definition focused on structured data, but most researchers and practitioners realize that most of the world’s information resides in unstructured data, largely in the form of text and imagery, both still and video, and in audio. Today, big data refers to data volumes in the range of tens of petabytes (1016) and beyond. Such volumes exceed the capacity of current on-line storage and processing systems. Big Data was originally described by the 3Vs (Laney 2001), but Kaisler, Armour, Espinosa, and Money (2013) have suggest two more.

Table 1.
Five Vs of Big Data
VDescription
Data VolumeThe amount of data collected and available for use. It is estimated that over 2.5 Exabytes (1018) of data are created every day as of 2012 (Wikipedia 2013).
Data VelocityThe rate at which data is accumulated or the speed at which the data arrives, and how quickly it gets purged, how frequently it changes, and how fast it becomes irrelevant or outdated.
Data VarietyThe different types of data required for analysis, which can be either structured, such as RDF files, databases, and Excel tables or unstructured, such as text, audio files, and video.
Data ValueThe value derived from processing the data that contributes to decision making and problem solving. A large amount of data may be valueless if it is perishable, late, or imprecise.
Data VeracityThe accuracy, precision and reliability of the data. A data set may have very accurate data with low precision and low reliability based on the collection methods and tools used.

Up until 30 years ago, simple business models often sufficed for international business. Globalization, brought on by advances in digital technology, information available at our fingertips, and a rapidly changing, even chaotic, international political environment, has up-ended these models. It has increased the diversity and uncertainty in outcomes when complex systems such as financial flows and markets, regional economies and political systems, and transnational threats involving multiple actors are in constant flux.

Key Terms in this Chapter

Analytics: The process of transforming data into insight for the purpose of making decisions.

Population Imbalance: In very large data sets, events of interest occur relatively infrequently.

Advanced Analytic: A set of analytics integrated through an analytic architecture to solve a complex problem.

Analytic Architecture: A software architecture or application framework designed to solve a set of problems within a complex domain.

Visual Analytics: The science of analytical reasoning facilitated by interactive visual interfaces.

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