Category: Web Technologies Copyright © 2015, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 7584 Advanced Analytics for Big Data INTRODUCTION Big Data has emerged as one of the most challenging aspects of business data and scientific processing within the past 20 years. A major problem is that although we may be able to collect the data, we often have inadequate means for analyzing it. Kaisler, Armour, Espinosa, and Money (2013) identified some of the issues and challenges associated with using Big Data. INFORMS defines analytics as “the process of transforming data into insight for the purpose of making decisions” (2013). It involves formulating specific problems or questions; identifying, gathering and organizing the relevant data; and selecting and applying the appropri- ate methods, algorithms, heuristics and procedures to solve the problems or answer the questions. Analytics are quantitative and qualitative, linear and non-linear, large and small, numerical versus symbolic, and vary along other dimensions as well. 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 (10 16 ) 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. Up until 30 years ago, simple business models often sufficed for international business. Globaliza- tion, brought on by advances in digital technology, information available at our fingertips, and a rapidly changing, even chaotic, international political environ- ment, 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. Traditional big data analysis has focused on data mining. The explosion of social media has provided massive amounts of data that can be analyzed and ex- ploited for a wide variety of purposes. Understanding what patterns exist and determining how to use them has been the primary problem for the past decade. Identifying the relevant information from the plethora of ambiguous and often contradictory data will require sophisticated diagnostic, predictive, and prescriptive analytical methods. Advanced analytics is the application of multiple analytic methods that address the diversity of big data to provide descriptive results and to yield action- able predictive and prescriptive results that facilitate decision-making. Advanced analytics go beyond data mining and statistical processing methods to encompass logic-based methods, qualitative analytics, and non- statistical quantitative methods. Advanced analytics 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 William Money School of Business Administration, George Washington University, USA DOI: 10.4018/978-1-4666-5888-2.ch747