Business Analytics

An Introduction

Jay Liebowitz

• Publisher: Auerbach Publications
• Year: 2013
• Print ISBN: 9780367241940
• eBook ISBN: 9781498760256
• Edition: 1
• Topic: Negocios y empresa
• Subtopic: Minería de datos

Together, Big Data, high-performance computing, and complex environments create unprecedented opportunities for organizations to generate game-changing insights that are based on hard data. Business Analytics: An Introduction explains how to use business analytics to sort through an ever-increasing amount of data and improve the decision-making cap

Chapter 1

The Value of Business Analytics

Evan Stubbs

Learning Objectives

By the end of this chapter, you should understand:

• The importance of business analytics.
• The difference between business analytics and analytics.
• How value creates a reason to act and support change.
• The importance of value definition and quantification in business analytics.
• How to take communication preferences into account when communicating value.

Concepts

Why Business Analytics?

Knowing more than one’s competitors creates advantage. The intelligence generated by Bletchley Park arguably shortened World War II by as much as two to four years.* Gosset’s research gave Guinness market advantage through clarifying the relationship between crop yields and beer production.† Firms that are able to generate information asymmetries through data-sourced insight have the opportunity to build competitive differentiation.‡

While not always easy to empirically test, there are many examples of how information asymmetries create advantage in various forms. Whether it is in selling cars,§ the service industry,¶ research and development,** or even real estate,†† unique insight that is acted on improves results. In the private sector, this has the potential to fuel competitive differentiation. In the public sector, it enables better social outcomes. In intelligence and defense, it reduces risk and improves national security.

The Changing Role of Insight

The idea that insight leads to competitive differentiation is not new.‡‡ Three things, however, happened in the late 20th century that drastically changed the potential influence that insight has on organizational advantage. These are:

• Large-scale digitization of information, also known as Big Data.
• Exponential increases in processing power, also known as high-performance computing.
• Technological automation, also known as decision management.

The Rise of Big Data

The data we have access to today dwarfs that which we had even a decade ago. Thanks to the personal computer and constantly falling storage costs, data volumes have grown geometrically over the last few decades.§§ There is more to the rise of “Big Data” than just data volumes: by common definition, it also includes a significant increase in data variety and the velocity at which data is being generated.

While there is no strict litmus test that distinguishes a “large data” repository from a “Big Data” repository, Big Data repositories tend to be characterized by orders of magnitude more information than a “typical” data warehouse. They also tend to include large amounts of unstructured and semi-structured data in addition to the usual structured data. They are often designed to accommodate extremely frequent data updates, sometimes in the order of microseconds.

This massive increase in data has been driven in no small part by:

• Increasing consumer demand for intangible goods such as digital music and online entertainment.
• The pervasiveness of the Internet across OECD nations leading to greater online communication.
• The shift toward online and electric banking and commerce.

In an online world, every interaction leaves a trail. Every purchase, every click, and every message creates a data footprint. This data, if analyzed, has the potential to generate historically unimaginable insight.

The Advent of High-Performance Computing

In isolation, Big Data itself does not create advantage; data is useless without the ability to effectively analyze it. The key to creating advantage from Big Data has been parallel exponential increases in processing power, sometimes referred to as high-performance computing. Without the ability to cost-effectively analyze data in a timely way, it becomes nothing more than noise. Moore’s law¶¶ and its storage-focused counterpart, Kryder’s law,*** are well known but their impact cannot be underestimated. Current smartphones that fit in a pocket are now thousands of times faster than the Apple II, a revolutionary personal computer by any measure.

What once might have needed thousands of people in a room performing calculations in parallel††† or even a supercomputer the size of a building can now be completed in real time by a piece of commodity hardware. Together, Big Data and sheer computational horsepower create an unprecedented ability to generate insight. And this insight, when held disproportionately to the market, has the potential to create a significant advantage.

The Role of Decision Management

Data and processing power create insight. Insight alone, however, does little to improve outcomes; realizing value requires action. Insight without action is the same as doing nothing. If Big Data and high-performance computing are, respectively, the fuel and the engine, the driver is the move from manual to automated systems. Together, these are changing...

Table of contents

• Preface
• About the Editor
• Contributors
• Chapter 1 - The Value of Business Analytics
• Chapter 2 - Producing Insights from Information through Analytics
• Chapter 3 - Executive/Performance Dashboards
• Chapter 4 - Data Mining: Helping to Make Sense of Big Data
• Chapter 5 - Big Data Analytics for Business Intelligence
• Chapter 6 - Text Mining Fundamentals
• Chapter 7 - Neural Network Fundamentals
• Chapter 8 - Measuring Success in Social Media: An Information Strategy in a Data Obese World
• Chapter 9 - The Legal and Privacy Implications of Data Mining
• Chapter 10 - Epilogue: Parting Thoughts about Business Analytics