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Foreword xixto the increased desire to process and analyze all available data, rather thananalyzing a sample of data and extrapolating the results; exploration relates toanalytic approaches in which the schema is defined in response to the natureof the query; and frequency refers to the desire to increase the rate of analysisin order to generate more accurate and timely business intelligence.Since it is important to consider the ongoing importance of existing datamanagement, processing, and analytic approaches, we add a fourth factor:dependency, which relates to the need to balance investment in existing technologiesand skills with the adoption of new techniques.While the authors of this book may not use the term “Total Data,” we seeevidence of these four factors influencing the technology and business processchoices being made by businesses in the numerous Big Data success storiescontained within these pages, as well as the portfolio of technologies beingassembled by IBM to address its customers’ Big Data needs.This brings us back to definitions, and the futility of getting hung up onthe meaning of Big Data rather than embracing the associated new data managementtechnologies and approaches. IBM’s research has highlighted thatcompanies that invest in analytics and understanding their business andtheir customers are significantly more likely to achieve competitive advantageover their rivals.Whatever you might think of the term Big Data, the adoption of new dataprocessing technologies and techniques that complement established analyticsapproaches promises to deliver new levels of business intelligence, customerservice, and revenue opportunities. Many companies are finding that is notsomething they can afford to ignore.Matt AslettResearch Manager, Data Management and Analytics, 451 Research