Social data analysis has evolved from a qualitative endeavor done at a small scale to a quantitative process done at a large scale using machine learning and analytics. Over the past 10 years, social media analytics has grown from basic monitoring and measurement to providing more advanced social intelligence through automated analysis of sentiment, opinions, and factors that influence consumer choice at scale. However, the demand for insights from large amounts of social data has outpaced the supply of data scientists needed to analyze it, requiring continued innovation in analytics techniques.

1. Social data research is not strictly a quantitative endeavor,
it’s qualitative insights on a quantitative scale.
The Social Data Analysis Challenge: Scale
DataCreation&Storage
Machine-Generated
Data
Human-Generated
Data
Time
→ includes Social Data

2. ~10 Years-In: How We Got Here
Social Media Analytics
• Brand and category monitoring
• Engagement performance measurement
• Community management support
• Crisis management & mitigation support,
(PR/Corporate Communications)
• Dashboards
Social Listening
• Category or industry ‘share of
conversation’
• Competitive ‘share of voice’
• Channel / media types analysis
• Opinion mining
• Sentiment analysis (rudimentary)
• Mostly manual process for insights
development – does not scale
Social Intelligence
• Factors that govern choice
• Brand health, brand lift
• True sentiment
• Ethnographic research
• Market research and business
intelligence augmentation & integrations
• Text analytics, natural language
processing (NLP), machine learning
• Scalable, mostly automated process for
insights with anomaly intervention &
study oversight by highly-skilled analysts
The Evolution
~10 Years Ago:
“The Intern”
Today:
“The Data Scientist”
Business requirements have outpaced the availability of
highly-skilled resources that can operate at scale
Analytics Intelligence