1. Big Data & Taxonomies for
Actionable Intelligence
Mondeca
Ghislain Atemezing
Director, Research and Innovation
@gatemezing
Taxonomy Boot Camp
Track 2: Taxonomy Applications
Nov 5, Washington, DC

2. Help companies forestall and assess trade risks while protecting them against overdue items
Healthy snacking from the best of nature
Food industry
Healthy Snacks
Credit
Insurance
USA, France,
Mexico
USA, France, UK,
China, Portugal, Brazil
French, Portuguese, English,
Chinese
French, English,
Spanish
50+ power users
10+ power users
Our clients businesses are very different

3. Things both of our clients shared
3
None of them had a taxonomy department
None of them employed a taxonomist
Both wanted to analyze a large volume of diverse unstructured
content for the purpose of future risk identification

4. GoGo squeeZ wanted to listen to consumers
and detect risk related consumer issues as
soon as they were raised
Coface intended to go beyond traditional
balance sheet analysis and identify solvency
risk from information published in the media
How did it all start?
4
Risk profiles vary in time
and need to be adjusted
asap
Analysts need to detect
risks asap
We need to catch all
consumer-related
events asap
We need the big picture and
view results across a set of
categories (time, country,
activity, risk events)
Voice of consumer is
captured in multiple
different channels
We are missing important
messages because of the
volumes of User
Generated Content
Collecting a body of
evidence helps to assess
companies performance
We need to share alerts
proactively across our
marketing and quality
management teams

5. From business objectives to solution requirements
 Detect consumer risk
 Assess credit risk
Objectives
• Provide alerts/insight on product quality and associated risks
• Support quick decision making
Audience
Service & Quality managers and Executives
Requirements
• Monitor consumer feedback in real-time
• Collate data from different open and internal sources
• Reconcile data, identify and rank consumer risks
• Generate color-coded alerts
• Be 100% automated – end to end
Requirements
• Dynamically define online sources to monitor
• Collate data from open source to establish risk profiles
• Reconcile data, identify different types of trade risks
• Generate risk scores
• Be automated and allow human evaluation of information
Objectives
• Assess early events affecting companies’ solvency
• Streamline the risk evaluation process
Audience
Credit risk analysts

6. Common high level architecture
6
API-based connectors
Persistence DB
Datapipe/ETL
Text analytics
Auto-classification
Taxonomy
Semantic
Rules
Search index
Data visualization portal
Social
web
Consumer service
Call centers
Business managers Risk analysts
+800,000 messages/year
10 sources
News feeds
Websites
Scrapers
Persistence DB
Datapipe/ETL
Text analytics
Auto-classification
Persistence DB
Search index
Knowledge portal
Taxonomy
Semantic
Rules
+1,500,000 articles/year
English, Spanish, French 200+ sources
Chinese, Portuguese
English, Spanish, French

7. GoGo squeeZ UX targets alerts and trend changes
Synthetic dashboards Interactive analytics Focused on trend / time series
Extended search features and instant access to source data
Real-time, visual, easy-to-use, interactive, fast analytics

8. Coface UX is process oriented
Risk grades
Rationale
Terms & concepts detected
Acceptance or rejection of detected events
Relevance evaluation

9. The vocabulary was already there
More complex content, newspaper style,
POS analysis a must
Not many new terms, but more complex
sentences
Disambiguation was an issue requiring
complex ML & Word2Vec approach
Removing duplicates was an issue
Focus on precision, UX and response time
One size does not fit all … fine tuning was required
The taxonomy was build from scratch
Short, simple content, social media like, no
requirement for POS analytics
Evolving, informal language; candidate terms
discovered all the time
Little ambiguity – almost all content
somewhat relates to the subject matter
No issue in deduplicating content
Focus on recall and real time data availability

10. Key project metrics are similar for both projects
6 months elapsed
Taxonomy work Front endBack end
40% 40% 20%
Day 1 Go Live

11. Take away and lessons learned
The architecture was
generic enough to
respond to both client
requirements
UI/UX design was very
different and critical to
achieve user acceptance
Results achieved
through combination of
rule-based and machine
learning approach

12. 12
Questions ?
Thank you