Transform Raw Social Media Data into Real Competitive Advantage
There's real competitive advantage buried in today's deluge of social media data. If you know how to analyze it, you can increase your relevance to customers, establishing yourself as a trusted supplier in a cutthroat environment where consumers rely more than ever on "public opinion" about your products, services, and experiences.
Social Media Analytics is the complete insider's guide for all executives and marketing analysts who want to answer mission-critical questions and maximize the business value of their social media data. Two leaders of IBM's pioneering Social Media Analysis Initiative offer thorough and practical coverage of the entire process: identifying the right unstructured data, analyzing it, and interpreting and acting on the knowledge you gain.
Their expert guidance, practical tools, and detailed examples will help you learn more from all your social media conversations, and avoid pitfalls that can lead to costly mistakes.
You'll learn how to:
Focus on the questions that social media data can realistically answer
Determine which information is actually useful to you-and which isn't
Cleanse data to find and remove inaccuracies
Create data models that accurately represent your data and lead to more useful answers
Use historical data to validate hypotheses faster, so you don't waste time
Identify trends and use them to improve predictions
Drive value "on-the-fly" from real-time/ near-real-time and ad hoc analyses
Analyze text, a.k.a. "data at rest"
Recognize subtle interrelationships that impact business performance
Improve the accuracy of your sentiment analyses
Determine eminence, and distinguish "talkers" from true influencers
Optimize decisions about marketing and advertising spend
Whether you're a marketer, analyst, manager, or technologist, you'll learn how to use social media data to compete more effectively, respond more rapidly, predict more successfully...grow profits, and keep them growing.
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Für höhere Schule und Studium
Maße
Höhe: 228 mm
Breite: 154 mm
Dicke: 16 mm
Gewicht
ISBN-13
978-0-13-389256-7 (9780133892567)
Schweitzer Klassifikation
Dr. Matthew Ganis, a member of IBM's Academy of Technology, is currently an IBM Senior Technical Staff Member located in Somers, New York. His current areas of interest include social media analytics, the Internet of Things, and Agile software methodologies. He is an adjunct professor of computer science and astronomy at Pace University in Pleasantville, New York, where he teaches at both the undergraduate and graduate level.
Dr. Ganis holds a BS degree in computer science and an MBA in information systems from Pace University, an MS degree in astronomy from the University of Western Sydney, Australia, and a doctorate of professional studies in computing from Pace University. He has authored or co-authored over 40 papers in both of his fields of interest, ranging from programming techniques, computer system administration, computer networking, and topics on stellar evolution and radio astronomy. He is also the proud coauthor of A Practical Guide to Distributed Scrum published by IBM Press.
In his 30-year career at IBM, he has been responsible for a number of technological advances such as the creation of the first enterprise firewalls for IBM; the creation of highly available World Wide Web platforms to support the Atlanta, Sydney, and Nagano Olympics (which secured Dr. Ganis and his team a spot in the Guinness Book of World Records for the highest sustained rate of Internet web traffic); and the proliferation of advanced software development techniques across IBM's worldwide development laboratories.
He can be found on LinkedIn (https://www.linkedin.com/in/mattganis), on Twitter as @mattganis, or on his blog at http://mganis.blogspot.com.
Avinash Kohirkar is currently Manager of Social Business Adoption in IBM. His current areas of interest include deployment and adoption of social technologies within an enterprise, social engagement dashboards, and social media analytics. Avinash Kohirkar holds a BS degree in electronics and communications engineering from Osmania University (India), an MS degree in industrial engineering from NITIE (India), and an MBA in finance from California State University. He has contributed to IBM white papers and has given numerous presentations on social analytics in IBM and outside IBM. He has authored a number of articles on this subject that have been published in the Cutter IT Journal and Infosys Lab Briefings.
In his 19-year career at IBM, he has leveraged technologies such as e-business, Web 2.0, social collaboration, social graph technologies, big data, and social media and text analytics for the business benefit of IBM and IBM's customers. He is recognized as a thought leader in the project management profession within IBM and is certified as Executive Project Manager at the highest level within IBM. He has held several technical, business, and management positions during his career: Architect, Development Manager, Project Manager, Project Executive, Associate Partner, Project Executive, and Business Manager.
He can be found on LinkedIn (https://www.linkedin.com/in/ AvinashKohirkar) and on Twitter as @kohirkar.
Foreword xviii
Preface: Mining for Gold (or Digging in the Mud) xx
Just What Do We Mean When We Say Social Media? xx
Why Look at This Data? xxi
How Does This Translate into Business Value? xxii
The Book's Approach xxiv
Data Identification xxiv
Data Analysis xxv
Information Interpretation xxvi
Why You Should Read This Book xxvii
What This Book Does and Does Not Focus On xxix
Acknowledgments xxxi
Matt Ganis xxxi
Avinash Kohirkar xxxi
Joint Acknowledgments xxxii
About the Authors xxxiv
Part I: Data Identification
Chapter 1: Looking for Data in All the Right Places 1
What Data Do We Mean? 2
What Subset of Content Are We Interested In? 4
Whose Comments Are We Interested In? 6
What Window of Time Are We Interested In? 7
Attributes of Data That Need to Be Considered 7
Structure 8
Language 9
Region 9
Type of Content 10
Venue 13
Time 14
Ownership of Data 14
Summary 15
Chapter 2: Separating the Wheat from the Chaff 17
It All Starts with Data 18
Casting a Net 19
Regular Expressions 23
A Few Words of Caution 27
It's Not What You Say but WHERE You Say It 28
Summary 29
Chapter 3: Whose Comments Are We Interested In? 31
Looking for the Right Subset of People 32
Employment 32
Sentiment 32
Location or Geography 33
Language 33
Age 34
Gender 34
Profession/Expertise 34
Eminence or Popularity 35
Role 35
Specific People or Groups 35
Do We Really Want ALL the Comments? 35
Are They Happy or Unhappy? 37
Location and Language 39
Age and Gender 41
Eminence, Prestige, or Popularity 42
Summary 45
Chapter 4: Timing Is Everything 47
Predictive Versus Descriptive 48
Predictive Analytics 49
Descriptive Analytics 53
Sentiment 55
Time as Your Friend 57
Summary 58
Chapter 5: Social Data: Where and Why 61
Structured Data Versus Unstructured Data 63
Big Data 65
Social Media as Big Data 67
Where to Look for Big Data 69
Paradox of Choice: Sifting Through Big Data 70
Identifying Data in Social Media Outlets 74
Professional Networking Sites 75
Social Sites 77
Information Sharing Sites 78
Microblogging Sites 79
Blogs/Wikis 80
Summary 81
Part II: Data Analysis
Chapter 6: The Right Tool for the Right Job 83
The Four Dimensions of Analysis Taxonomy 84
Depth of Analysis 85
Machine Capacity 86
Domain of Analysis 88
External Social Media 88
Internal Social Media 93
Velocity of Data 99
Data in Motion 99
Data at Rest 100
Summary 101
Chapter 7: Reading Tea Leaves: Discovering Themes, Topics, or Trends 103
Validating the Hypothesis 104
Youth Unemployment 104
Cannes Lions 2013 110
56th Grammy Awards 112
Discovering Themes and Topics 113
Business Value of Projects 114
Analysis of the Information in the Business Value
Field 115
Our Findings 115
Using Iterative Methods 117
Summary 119
Chapter 8: Fishing in a Fast-Flowing River 121
Is There Value in Real Time? 122
Real Time Versus Near Real Time 123
Forewarned Is Forearmed 125
Stream Computing 126
IBM InfoSphere Streams 128
SPL Applications 129
Directed Graphs 130
Streams Example: SSM 131
Step 1 133
Step 2 134
Step 3 134
Step 4 135
Steps 5 and 6 136
Steps 7 and 8 136
Value Derived from a Conference Using Real-Time
Analytics 138
Summary 139
Chapter 9: If You Don't Know What You Want, You Just May Find It!: Ad Hoc Exploration 141
Ad Hoc Analysis 142
An Example of Ad Hoc Analysis 144
Data Integrity 150
Summary 155
Chapter 10: Rivers Run Deep: Deep Analysis 157
Responding to Leads Identified in Social Media 157
Identifying Leads 158
Qualifying/Classifying Leads 160
Suggested Action 161
Support for Deep Analysis in Analytics Software 163
Topic Evolution 163
Affinity Analysis in Reporting 165
Summary 167
Chapter 11: The Enterprise Social Network 169
Social Is Much More Than Just Collaboration 170
Transparency of Communication 171
Frictionless Redistribution of Knowledge 172
Deconstructing Knowledge Creation 172
Serendipitous Discovery and Innovation 172
Enterprise Social Network Is the Memory of the Organization 172
Understanding the Enterprise Graph 174
Personal Social Dashboard: Details of Implementation 175
Key Performance Indicators (KPIs) 177
Assessing Business Benefits from Social Graph Data 183
What's Next for the Enterprise Graph? 185
Summary 186
Part III: Information Interpretation
Chapter 12: Murphy Was Right! The Art of What Could Go Wrong 189
Recap: The Social Analytics Process 190
Finding the Right Data 193
Communicating Clearly 195
Choosing Filter Words Carefully 198
Understanding That Sometimes Less Is More 198
Customizing and Modifying Tools 201
Using the Right Tool for the Right Job 204
Analyzing Consumer Reaction During Hurricane Sandy 204
Summary 209
Chapter 13: Visualization as an Aid to Analytics 211
Common Visualizations 212
Pie Charts 213
Bar Charts 214
Line Charts 216
Scatter Plots 218
Common Pitfalls 219
Information Overload 219
The Unintended Consequences of Using 3D 220
Using Too Much Color 221
Visually Representing Unstructured Data 222
Summary 225
Appendices
Appendix A: Case Study 227
Introduction to the Case Study: IBMAmplify 228
Data Identification 228
Taking a First Pass at the Analysis 234
Data Analysis 241
A Second Attempt at Analyzing the Data 243
Information Interpretation 244
Conclusions 247
Index 249
