The document summarizes a presentation about social data and the DataSift platform. It discusses how social data is growing rapidly, the challenges of analyzing large volumes of social data, and how DataSift addresses these challenges by aggregating data from many social sources, enriching it with additional information, filtering it, and integrating it with other business data to power analytics, visualization, and actions. DataSift aims to make social data core to business intelligence and competitive advantage.

1. Todd Lloyd – Senior Director, DataSift
@ToddRLloyd
todd.lloyd@datasift.com
Welcome to the
Social Revolution
#SDWK13

2. Agenda
 Social Data and the Enterprise
 The DataSift Platform and Value
 Use Cases and Examples

3. Welcome to the
Social
Revolution
2.5 Billion People
67% of Internet Users on Social Networks.
#sdwk13

4. Source: IDC, Mary Meeker KPCB
DataShared(Zettabytes)
9x Growth in Shared
Content in 5 years
3.2Bn Facebook Likes
500M Tweets
100M Instagram Photos
250M Tumblr Posts
200M Bitly Clicks
70M+ Blogs, Forums
0
2
4
6
8
2009 2010 2011 2012 2013E 2014E 2015E

5. The Long Tail – Ravelry.com
3.2 Million Users
#sdwk13

6. Creating a wealth of data outside your enterprise
#sdwk13

7. - Jack Welch, Former CEO, GE
There are only two sources of
competitive advantage:
the ability to learn more about
our customers faster
than the competition and the
ability to turn that learning into
action faster than the
competition

8. Vast, Real-time
Data Volumes
Problem:
Social Data is Big Data
Integrating into
each social network
Integrating Social
with other data
Extracting insights
from text
Filtering out
the noise

9. Anatomy of a Single Tweet

10. Companies starting by listeningEarly days – try to understand what is going on
Simple tools to search
for brand #keywords
Engage & Respond
Report back
#sdwk13

11. Enterprise Proliferation of Social
Average Enterprise has
178 Social Account
13 Departments
Actively Engaged in
Social Media
#sdwk13

12. Social
Data
Business
Data
Social Data is not an Island.
Need to integrate business data to see whole pictur
Research
Marketing & Sales
Customer Service
Product Development
Customer Feedback
Competitive Campaigns
Customer Sentiment
Customer Interests
Customer Intent

13. integrating
Transactions with
Interactions
Customers
Purchases
#Social
Engagement
Interests
Opinions
Analytics Revolution
#sdwk13

14. 10 Steps to Extracting Value from
Social Data
1. Aggregate
2. Structure
3. Enrich
4. Filter
5. Deliver
6. Integrate
7. Analyze
8. Visualize
9. Share
10. Act
DataSift does this
Google does this
Tableau does this
You do this!
#sdwk13

15. We Enrich Social Data with up-to 400 data items
We do this 15,000 times / second
Gender
AgeRange
Hometown
Interests

16. Filter to extract relevant Social Data
Create Sophisticated Filters with 1,000s of criteria

17. SocialApps SocialApps EnterprisePlatforms
Deliver it for analysis and action

18. We make it easy to get started with Social
 Capturing data from
hundreds of sources
 Real time data
 Historical data
 Compliant and at scale

19. DataSift Powers the Social Ecosystem
DataSift + Tableau for Visual Social
Analytics
Integrate Business Data with Social Data
Leverage your BI Platform for Social
Insights

20. DataSift powers 100s of Businesses

22. http://www.nypost.com/p/news/business/no_
sachs_appeal_fji8rAnH1UkZtMUFDQZfXL
Feedback to Improve Products &
ServiceNet Promoter Score across 37 Product-Lines
150 Metrics influence Social Advocacy Score
Product-Teams Proactively Respond to
Issues

23. Understand your market for Advertising
What sites are most shared on Twitter by
18-24yr old Brazilians
June15th 2013-24-hoursnapshotoflinkssharedonTwitterbasedonDemographicProfiles
0 1,750 3,500 5,250 7,000
Tumblr
Facebook
Instagram
Ask.fm
YouTube
FourSquare
Twitter
Bitly
Unfollowers.me
Whrt.it
Male Female
Ask.fm a fast-emerging
destination for 18-24 yr
olds in Brazil
#sdwk13

24. Understand Burberry’s Social Followers

25. Industry Benchmark
Airline Social Responsiveness to Customer Issues

26. Operational - Click-traffic+Social
How did Dove Campaign go viral?
USA
Facebook Mobile
37% of Traffic
USA
Facebook Web
USA
Direct Traffic
USA
Upworthy.com
USA
Twitter
UK
BR CA
GR
NL

27. Predictive Models based on
Social Signals
Correlate Box Office Revenues
with Social Interest & Intent.

28. Predictive
Player popularity drives demand
= Optimize Supply Chain

29. Platform For The
Social Economy
Our Vision: Make Social Data Core to the Enterprise
Increasingly, the most valuable data for your business sits
outside your organization, not inside it.
Big Social Data Insight will become a key basis for competition.
DataSift will be the leading customer & marketing data delivery
platform to help companies respond to this opportunity.
#sdwk13