FlockData Pitch Overview

©2013-2015 FlockData LLC - All rights reserved
Open-source information management and data integration
COLLECT | CONNECT | COMPARE
TURNING DATA INTO INFORMATION

Companies
resistant to data  
do not thrive.  
Even if you have the
desire… 
you can’t get the
data…  
It exists in 27
different computer
systems with
different structures.

What do the world’s largest & most
complex data stores have in common?
Sources: https://gigaom.com/2013/06/06/heres-how-the-nsa-analyzes-all-that-call-data/ 
https://gigaom.com/2013/06/07/under-the-covers-of-the-nsas-big-data-effort/ 
http://neo4j.com/blog/why-the-most-important-part-of-facebook-graph-search-is-graph/
•multiple instances each storing tens of petabytes
•backend of the agency’s most widely used analytical
capabilities
•Accumulo is especially adept at analyzing trillions of data
points in order to build massive graphs
•Technology giants such as Facebook, Google, and Twitter
have all built graph technologies from the ground up to
differentiate and grow their business. Building and
maintaining one’s own database management system
however is not a practical solution if you’re not Facebook.
•PageRank changed the fundamentals of web search - taking
into account how the pages are connected
•Facebook = Social graph
•Google = Knowledge graph
•Twitter = Interest graph

Multi-model is the key to
modern and future data

Connections Analytics Timeline

Graph Search Document

Any data source(s)
HTTP RESTful API integration

Reports Apps Structure
Any data source(s)
HTTP RESTful API integration

FlockData provides a single,
uniﬁed multi-model access
point for both data storage and
information retrieval

Connect Meta Data
Disclaimer: image not generated by AuditBucket
Meta-data captured
and stored
Connections are made for
analysis
See hidden relationships
fast!

Dashboards

Versions of Data

Industries:
- Online media
- Telecommunications
- Financial Services
- Healthcare
- Logistics
©2013 AuditBucket Pty Ltd & Entiviti LLC - Proprietary & Conﬁdential - DO NOT SHARE

Solution Categories:
- Recommendation Engines
- Network mapping & analysis
- Cross-source analytics
- Data-driven apps
- Universal search & audit

Recommendation Engine

What is a recommendation engine?
Incorporates any number of factors about users 
Notably including products or services consumed 
Leverages multiple related factors (similar products,  
similar users, etc) 
Traverses these factors as connections 
Returns the most connected nodes as  
recommended products or services
An algorithm that:

Why build on graph data?
Only need to specify which 
type of relationships to use 
As little as 2-line queries 
Performance: 
1M rows —> ~20ms 
But very little scale effect 
Fast-enough for real-time performance 
Efﬁcient and ﬂexible for expanded use
Lookalikes

Why build a recommendation engine?
Original search
Bought together = up-sell
Also bought = up-sell
Targeted ads = cross-sell
Also viewed = conversion

Overlay social graph of users
Insert taxonomy here
Add as many factors as you have
Each factor improves quality

Merged graph

Graph as the basis for recommendations

Case Study: Macro view of ebola
Obtain a sample data set of 48K Twitter posts.
Send tweets through NLP engine for tag capture, entity &
concept extraction, sentiment analysis
FlockData JSON transformation and import definition in
under 1 day
Leverage our automatic analysis tools (word cloud, graph,
visualizations) to find connections
Use dashboards to get overview of breakdown
Use cluster analysis to find “hot spots” in the data

Quick ﬁndings: From concept to insights in under 2 days
Sentiment, tags and concepts
are sortable, reportable, and
can be integrated with real-
time data feeds
Geo-location of user gives
automatic mapping

Quick ﬁndings: Locate hot spots
Data categories sorted by
co-occurrence - shows
organizations where to focus
for maximum impact
FlockData data proﬁling
during data load is used to
drive reporting

FlockData Pitch Overview

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