FlockData Overview from Startup Pitch Night

©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

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/


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 whichtype 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 ebolaObtain 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 daysSentiment, 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 Overview from Startup Pitch Night

Data & Analytics

Transcript of FlockData Overview from Startup Pitch Night