2Getting Started with Analytics

Karl G. Kempf

Decision Engineering, Intel Corporation, Chandler, AZ, USA

“The secret of getting ahead is getting started. The secret of getting started is breaking your complex overwhelming task into smaller manageable tasks, and then starting on the first one.”

Mark Twain [1]

2.1 Introduction

In 1965, Gordon Moore made a prediction that computing would dramatically increase in power and decrease in relative cost at an exponential pace over time [2]. True to this speculation, computing power measured in millions of instructions per second (MIPS) per dollar has grown by a factor of 10 every 4–5 years [3]. Advances in computing have driven or enabled similar results in memory, storage, and networking that have in turn enabled the World Wide Web and a sequence of revolutions in analytics.

Davenport identifies Analytics 1.0, 2.0, and 3.0 [4] and since advances in computing and associated technologies show no signs of slowing, we can expect Analytics 4.0, 5.0, and so on into the future. Prior to 2010, Analytics 1.0 was characterized by small, structured, internally generated data sets. Analytics were confined to reporting and what we would now think of as descriptive analytics. Results took weeks if not months to produce and so organizations could not think of analytics as a competitive advantage. The rise of Analytics 2.0 has dramatically changed each part of that characterization. Data are assembled from a variety of internal and external sources, ...

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