Big Data and Business Analytics by Liebowitz Jay

Author:Liebowitz, Jay [Liebowitz, Jay]
Language: eng
Format: epub, pdf
Published: 2013-04-02T17:45:47+00:00

8

Innovation Patterns and Big Data

Daniel Conway and Diego Klabjan

CONTENTS

Introduction ....................................................................................................131

Contexts for Understanding Big Data .........................................................132

Big Data as a Natural Resource ................................................................133

Big Data as Big Digital Inventory ............................................................133

Big Data as a More Granular View of the Past ...................................... 134

Big Data and Organizational Challenges ................................................135

Big Data as a Role in Process Innovation ...............................................136

P- TRIZ: Repeatable Process Innovation......................................................138

Notation ......................................................................................................138

Examples of P- TRIZ and Technology ..........................................................140

Summary .........................................................................................................145

References ........................................................................................................146

INTRODUCTION

Big data is often generated by devices configured for collection based on the occurrence of events. Events can occur based on scan rates (collect yield from a combine every five seconds), from status change (pitch is a strike, count is now 3-1), or from rule execution (S&P 500 VIX > 24.5).

Domains such as finance and physics, where big data was first collected and analyzed, were the first to create new theories and innovative new

markets, and those innovations are now finding their way into domains

where data collection has recently become feasible. For example, the

financial options pricing method known as Black– Scholes is now used to estimate the future value of baseball players. These innovations are often the answer to questions formulated with innovation theory. Innovation

theory would suggest new domains where big data is now available, and it 131

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should allow for the creation of new markets based on predictive analytics applied over robust event histories. We look at these innovation patterns and apply them to the important area of global food supply with particular focus on the opportunities the agricultural market participants will encounter while moving from traditional manufacturing and distribution

to competing on analytics over big data.

Big data is a hype peddler’s dream come true. Big is somewhat ambiguous and modestly confident, yet invokes a sense of challenge. What’s big to me might not be big to you, and what’s big to you today might not be big to you next month. It is a fairly versatile term and thus likely enduring.

Data is somewhat dormant and passive, yet invokes a sense of opportunity. Data can be dense with value or sparse. It can be meaningless unless combined with other data. It can be useful at one time and useless at

another time. Perhaps Yogi Berra might have given clarity to the term as well as anyone: Data ain’t big until it’s big.

An economic approach might require coupling big data with analytics

and thus attempt to measure the derived value based on the computa-

tional effort expended. The cost side of this effort is often estimated with the usual suspects of CPU/ cluster cycles, storage costs, labor, and utilization rates for cloud services, for example. If we consider the three Vs of big data (velocity, volume, and variety), then our traditional measures primarily address velocity and volume. The variety of data implies the need for integration, and while we have improved data integration in practice, it remains an endeavor with domain- specific challenges.

The value side of the economic approach involves a transformation from

digital assets into actionable insights. The difficulty in measures of value is that one often doesn’t know how the assets will be leveraged in the future if at all.

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