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首页解锁大数据价值:企业决策新动力
"Making Big Data Work for Your Business" 是一本由Sudhi Sinha撰写的书籍,出版于2014年,旨在帮助企业管理层理解并利用大数据来推动业务发展。书中强调了如何将大数据与决策相结合,克服将数据转化为实际商业洞察的挑战。 在当前的商业环境中,大数据已经成为一个关键的驱动力。它为企业提供了前所未有的数据财富,但同时也带来了如何有效利用这些数据的问题。Sudhi Sinha作为一名管理、技术和可持续性专家,通过这本书提供了一个独特的视角,阐述如何通过深入且精确的分析从大数据中获取最大价值。 书中的核心观点是,大数据的有效运用需要与组织的战略紧密集成。只有这样,数据才能从单纯的数字和统计转变为具有目标性和影响力的工具。作者首先引导读者构建大数据分析项目的战略框架,并将其融入更广泛的企业战略。他强调,缺乏策略的大数据只是无目的的数字堆砌,无法产生有意义的洞见。 为了将大数据潜力转化为实际效益,书中的内容涵盖了创建大数据战略、识别关键机会、组建最佳团队以及选择合适技术实施和管理项目等方面。此外,还讨论了如何通过有效的组织调整和变革管理,充分利用大数据洞察带来的价值。 尽管大数据为企业创造了巨大的机遇,但对于许多企业来说,将数据转化为能够指导关键业务决策的清晰洞察并不容易。本书提供了一条实践路径,通过战略规划、项目执行和组织适应性的结合,帮助企业克服这一难题。 《Making Big Data Work for Your Business》是一本面向希望理解和探索大数据的管理者的重要读物。它不仅提供了快速而实用的入门知识,还针对大数据领域快速变化的特点,为读者提供了与时俱进的指南。
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Content III
Chapter 4: Building the Right Technology Landscape 57
Designing Big Data storage 58
Evolution of storage technology 58
Big Data storage architecture 60
Big Data storage calculations 61
The hardware and operating system needs for Big Data 62
Identifying the different technology layers 62
Quality check 64
Cleansing 64
Correlation 65
Enrichment 65
Data cataloging 66
Modeling for analysis 66
Classification and clustering 67
Statistical summary for preliminary insights 68
Human explorations 68
Selecting from your technology choices 68
An overview of key Big Data technology components 69
Hadoop 69
MapReduce 69
Programming languages 69
NoSQL 71
Other Hadoop components 72
Technology choices by layers 72
Making the right technology choices 75
Creating a visualization of your Big Data 75
Understanding the difference between Enterprise Data Warehouse
and Big Data 76
Summary 77
Chapter 5: Building a Winning Team 79
Understanding the distinctive skills you need 80
Data scientist 81
Skills of a data scientist 82
Sourcing data scientists 83
Experimental analyst 83
Skills of an experimental analyst 84
Sourcing experimental analysts 84
Application developer 85
Skills of an application developer 85
Sourcing application developers 86
Infrastructure specialist 86
Skills of an infrastructure specialist 87
Sourcing infrastructure specialists 87
Change leader 87
Skills of a change leader 88
Sourcing the change leader 89
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IV Content
The project manager 89
Skills of a project manager 90
Sourcing the project manager 90
Defining the team and structure 90
Building an extended ecosystem 92
Educational institutes 92
The engagement framework 92
Best practices 93
Consulting organizations 93
The engagement framework 94
Best practices 95
Improving team alignment and performance 96
Training and orientation 96
Quick wins 97
Rotational leadership 97
Motivating your team towards progress 97
Summary 99
Chapter 6: Managing Investments and Monetization of Data 101
Understanding how data creates value 103
Insights and influence 103
Immediate and future value 104
Value creation for data 104
Capturing the value of data 105
Identifying the value 106
Building a value catalog 107
Understanding and capturing your Big Data costs 108
Data collection costs 109
Storage and processing costs 110
Software licensing costs 111
People costs 111
Infrastructure and administrative costs 112
Maintenance costs 112
Capturing the costs 112
Cost Context Framework 113
Monetizing your Big Data 113
Direct business impact 114
Selling data externally 114
Valuation of Big Data 115
Managing your Big Data investments 116
Summary 118
Chapter 7: Driving Change Effectively 119
Understanding changes caused by Big Data 121
The significance of changes 124
Applying the IMMERSE framework to manage change 125
Identify 126
Modulate 127
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Content V
Mitigate 128
Educate 129
Role play 131
Show 131
Effect 132
Creating stakeholder groups to drive change 132
Project group 132
Work group 133
Review group 133
Summary 133
Chapter 8: Driving Communication Effectively 135
Identifying your communication needs 137
Communicating with the internal audience 137
Providing a general overview of Big Data 137
Sharing the strategic cascade 138
Communicating with the external audience 139
Engaging with customers 139
Swaying the business influencers 139
Roping in your ecosystem partners 140
Communicating with the shareholders 140
Selecting your communication channels 141
Communication channels 141
Channel effectiveness 143
Building your communication strategy 144
Building your communication plan 146
Engaging executives effectively 148
Monitoring and modulating your communication program 149
Effectiveness metrics 149
Measuring effectiveness 149
Summary 150
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Preface
In the 2008 historic presidential election in the United States, President Barack Obama
captured the imagination of the nation with his
"Yes we can"
slogan and his different but
definitive ideas. He employed social media very well to get his message out to the millions
of new voters. Fast forward to 2012; he was facing a very difficult re-election campaign.
This time he employed data and analytics to win this election. In his November 16, 2012
article in
The Atlantic
, Alexis Madrigal captures this transformative experimentation in
details. For the first time in history, a political campaign had a Chief Technology Officer
in Harper Reed. Mr. Reed assembled an eclectic team from Google, Facebook, Twitter,
and many other new age technology companies and initiated the project Narwahl. They
mined through huge volumes of data—demographic, past voting patterns, economic data,
social media interactions, and others to predict how the campaign is going to perform in
each seat and how they can persuade individual voters. Big Data is here and now.
In the past 50 years, the world has seen itself transforming to the era of the information
age; in the last 5 years, we have seen ourselves gravitating towards the Big Data age. Big
Data touches every aspect of our lives. Talking about Big Data, we often refer to huge
volumes, variety, and velocity of data—lots of data, different types of data, and data
getting created, captured, and processed at breakneck speeds. We often use examples of
the 1 billion plus Facebook users, the 3 billion plus likes they click on every day, the 100
billion credit card transactions that happen across the world, the millions of transactions
that occur in individual retail chains every hour, the 200 million + e-mails sent every
minute, and so on. Sometimes it is difficult for us to fathom the quantum of data.
Let me share an excellent representation that I saw in one of the infographics published by
EMC (http://www.emc.com/campaign/global/big-data/hfbd-infographic-
4web-1500.jpg
). Data is measured in bytes or multiples of that. In the following table,
we compare these multiples with more physical equivalents of words/pictures and sand:
Data Numerical representation Words/pictures Sand
Byte 1 Single character One grain of sand
Kilobyte 1,000 A sentence Couple of pinches
of sand
Megabyte 1,000,000 A 20 slide
PowerPoint
Tablespoon of
sand
Gigabyte 1,000,000,000 Ten yards of books
on a shelf
Shoebox full of
sand
Terabyte 1,000,000,000,000 300 hours of good
quality video
Playground sized
sandbox
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