At Gartner Data & Analytics Summit 2017 Alok Prasad, President, was joined by Peter Horowitz of PricewaterhouseCoopers in presenting a session on how Cambridge Semantics' in-memory, massively parallel, semantic graph-based platform delivers an accelerating edge to data-driven organizations, while maintaining trust with security and governance.

1. Accelerating Insight
Smart Data Lake Customer Success Stories
Peter Horowitz
Principal - Advisory
PwC
Alok Prasad, President
Ben Szekely, VP – Solution Engineering
Cambridge Semantics

2. ©2017 Cambridge Semantics Inc. All rights reserved.
Anzo Smart Data Lake: Accelerating Insight
A Graph-based Platform to Solve Today’s Data and Analytics Challenges
Rapid Ingestion
& Mapping
Load and Map
any data…….
from anywhere…
at any time!
Active Metadata
Management
Organize,
Integrate, Share
and Govern
any data assets..
more efficiently
greater security
HiRes
Anaytics™
Ask & Answer
any questions…
any combination
any user…..
Graphmarts
On-Demand
Analytic
Exploration
In-Memory MPP
Query Engine
Interconnected …
trillions of facts…
100x the speed…
Enterprise
Data Sources
Cloud
Storage
“Last Mile”
Analytics
Exploratory
Analytics
HiRes Analytics
Search and
Discovery
Anzo Smart Data Lake™

3. ©2017 Cambridge Semantics Inc. All rights reserved.
Anzo Smart Data Lake: Accelerating Insight
A Graph-based Platform to Solve Today’s Data and Analytics Challenges
Accelerated Insights – 100x faster than the nearest competitor
Automated
ETL Generation
Collaborative
Mapping
Text Processing
Enterprise Knowledge Graph

4. ©2017 Cambridge Semantics Inc. All rights reserved.
Based in Boston, MA – Privately Held – 100% annual growth
Leading customers:
Strategic partnerships:
Industry recognition:
Cambridge Semantics
MIT Innovation Showcase
Business Intelligence /
Analytics Solutions

5. ©2017 Cambridge Semantics Inc. All rights reserved.
Peter Horowitz
Principal – Advisory Banking
and Capital Management
PwC
Ben Szekely
VP Solution Engineering
Cambridge Semantics
Accelerating Insight – Customer Success Story

6. Accelerating Insights: Using Smart Data
Lakes Successfully
March 2017

7. PwC
Overview
$53 bn 1000%The Dilemma – Given current landscape within many financial
service institutions, the standard data journey takes TOO LONG
Typical data journey
Winning Strategy - Attack every step in the process to reduce time
to market.
7
Requirements Sourcing
Quality
Evaluation
Quality
Remediation
Analysis and
Reporting
7

8. PwC
Why is this so important?
Sources: Average OM% from Morningstar, Inc.; IAIG Index
For global top banks, comparing average operating margin percent for
previous 5 years against index of information architecture, investment and
governance demonstrates strong correlation.
Proper investment in information strategy, architecture and governance
PAYS OFF!
Proper data
architecture
improving or in
place.
Insufficient or
lagging data
architecture
investment,
execution,
governance.

9. PwC
Old approach is Challenged
The existing landscape presents great challenges since it is based on
relational databases and point-to-point mappings.
Operational
Data Source
Operational
Data Source
Staging
DB
Metadata
Aggregated
Data
Detailed
Data
Data Mart
Reporting
Analytics
Predictive
modelingFiles
ETL
Data Mart
Data Mart
EDW
Integration Barriers
Restricted
entry
Narrow
payload
Data source mapping
Multi-stage, brittle ETL required to
integrate diverse data sources
Standard model required
Enforces a standard model, no
exceptions, leading to loss of valuable
information context
Limited insights
Rigidity undermines the agility
required in data analytics
Challenges

10. PwC
Original data and
models maintained
Data is loaded seamlessly without
transformation or mapping,
irrespective of format
New approach
Unified model
Consolidated model unifies
disparate source models into a
comprehensive and shared canonical
model
The smart data lake provides a centralized data store with a flexible schema
where data is first loaded and then transformed; a process known as
schema-on-read.
Operational
Data Source
Operational
Data Source
Files
Smart
Data Lake
Model 1
Model 2
Model 3
Operational
Data Source
Operational
Data Source
Files
Richer insights
Cross region and domain view
supports population-based
analytics, hidden relationship
discovery and reduces risk of
siloed or inaccessible
Reporting
Analytics
Predictive
modeling
Benefits
Graph Data Model
(i.e. Ontology)
Graph store designs capture the nature of data relationships and supports a variety of data
representations, known as “Shared Semantics”

11. PwC
Case Study: Next generation insider trading and fraud surveillance driven
by structured and unstructured data
Requirements Sourcing
Quality
Evaluation
Quality
Remediation
Analysis and
Reporting
Accelerators:
Firm Wide Risk Dashboard
Employee Risk Dashboard
Analysis Overview
• Ingested emails, IMs, web browser logs,
cookies, phone records, contacts, news
feeds
• Ingested futures, options trades, market
price data
Impact
• Data was ingested and linked in hours.
• Profiles developed for employee roles
• Outlier behavior effortlessly identified
• Employees given risk ratings allowing for
prioritization of detailed compliance
investigation.

12. PwC
Cast Study: Terabytes of data ingested and analyzed in a fraction of the time
revealing complex relationships between clients and transactions
12
Analysis Overview
• 5.5 billion transactions on behalf of 25
million customers for 35 million accounts.
• Based on reference data and public
information, prepared “social” network of
clients analyzing edges (i.e., connections)
between them.
Impact
• Data was ingested and linked in hours.
• Edges developed and weighted based on
Pointwise Mutual Information
algorithms.
• Resulting graph database supported
recursive analysis, which would have been
dramatically longer using RDBS, allowing
for identification of suspicious networks
Suspicious
Account Bridge
Customer Network
Requirements Sourcing
Quality
Evaluation
Quality
Remediation
Analysis and
Reporting
Accelerators:

13. ©2017 Cambridge Semantics Inc. All rights reserved.
INDEFINITE
Drug Discovery Preclinical Product
Development
FDA Review Scale-Up to Mfg.
Post-Marketing
Surveillance
ONE FDA-
APPROVED
DRUG
0.5 – 2
YEARS6 – 7 YEARS3 – 6 YEARS
NUMBER OF VOLUNTEERS
PHASE
1
PHASE
2
PHASE
3
5250~ 5,000 – 10,000
COMPOUNDS
PRE-DISCOVERY
20–100 100–500 1,000–5,000
INDSUBMITTED
NDA/BLASUBMITTED
The Information Fabric – A Semantic Layer for the Enterprise
R & D
Intelligence
(CI)
Product
Development &
Regulatory
PV & Safety
Case
Management
Source of Influence
Commercial
Analytics
Clinical Trial
Operations
Medical Advisory
Board Analytics
Real World
Research
Clinical Data
Standards
Management
Voice of the
Customer
Analytics
Clinical Trial
Exploratory
Analytics

14. Enterprise Knowledge Graph
Enabling on-demand
access to data
by those seeking
answers and insight
Scalability
Security
Governance
Lineage
Automated
Structured
Data Ingestion
Natural
Language
Processing and
Text Analytics
Rich
Models
Anzo Smart Data Lake™
Data On Demand

15. Enterprise Knowledge Graph Data On Demand
Automated Ingestion of
Patient Data
Patient
Safety
Clinical
Trial Ops
R & D
Health
Economics
Lineage
The Smart Data Lake for Digital Patient Health
Insight for
Decision Makers
Improving patient
outcomes, safety, and
comfort
Reducing the time
bring medicines to
patients
Lowering the cost of
healthcare
Insurance
Claims
Clinical Trials
Rx Data
Health Records
Genetic Data
Wearables
+

21. The Semantic Layer disrupts business inhibitors
Question or
Idea
Insights
Delivered
Data Prep
and Clean
IT Data
Extraction
Anzo Smart
Data Lake™
Insights
Delivered
Load or
Locate Data
Load Data
Semantic Layer
TIME TIMETIME
Question or
Idea

22. ©2017 Cambridge Semantics Inc. All rights reserved.
Anzo Smart Data Lake
A Graph-based Platform to Disrupt Today’s Data and Analytics Challenges
Connectors Models Rules
Analytics &
Tools
ASDL Customer Fingerprint - Intellectual Property
Data Ingestion
& Mapping
Automated
ETL Generation
Collaborative
Mapping
Text Processing
Data
Cataloging
Data & Model
Governance
Active Metadata
Management
Role-Based Security
Discovery &
Analytics
Automated Query
Generation
User Dashboards
and Custom UI/UX
Self-Serve Live
Extracts
In-Memory MPP
Query
Graphmarts
on Demand
ELT, Model Based
Data Integration
Document Search
Enterprise
Data Sources
Enterprise
Data Lakes
“Last Mile”
Analytics
Exploratory
Analytics
Search and
Discovery
HiRes Analytics

23. ©2017 Cambridge Semantics Inc. All rights reserved.
An Open Platform for the Semantic Layer based on Knowledge
Graphs
Rapid Ingestion
& Mapping
Automated
ETL Generation
Collaborative
Mapping
Unstructured Data
and Text
Processing
Active Metadata
Management
Data
Cataloging
Model
Governance
Role-Based
Security
HiRes
Anaytics™
Automated
Query
Generation
Dashboards
Custom UI/UX
Analytics
Endpoints
Graphmarts
On-Demand
Data Layers for
Prep, Cleaning,
Transformation
Analytic Query
In-Memory MPP
Query Engine
Enterprise
Data Sources
Cloud
Storage
“Last Mile”
Analytics
Exploratory
Analytics
HiRes Analytics
Search and
Discovery
Anzo Smart Data Lake™

24. ©2017 Cambridge Semantics Inc. All rights reserved.
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