In their webinar "Big Data Fabric 2.0 Drives Data Democratization" Ben Szekley, Cambridge Semantics’ SVP of Field Operations, and guest speaker, Forrester’s Noel Yuhanna, author of the Forrester report: “Big Data Fabric 2.0 Drives Data Democratization”, explored why data-driven businesses are making a big data fabric part of their data strategy to minimize data complexity, integrate siloed data, deliver real-time trusted insights, and to create new business opportunities. These are the slides from that webinar.

1. The Data Discovery and Integration Layer
for the Enterprise Data Fabric

2. We apply semantics and graph to the data
fabric – so anyone can find, understand,
blend, and use enterprise data.
At a Glance:
• Based in Boston
• 100+ employees
• Origins in IBM and Netezza
• Anzo 4.0 GA 2017
• Added enterprise-scale OLAP
graph database engine in 2015

3. A modern data discovery and integration platform
for your enterprise data fabric.
Anzo lets business users find, connect, and blend
enterprise data into analytic ready data products.
Map and Explore
Enterprise Data
Build Blended
Analytic-Ready Data
Products
Apply Enterprise-Ready
Data Management

4. RDBMS/OLTP Big Data / Hadoop Document Repositories
Traditional BI Cloud
CUSTOMERS
PRODUCTS
CLAIMS
COMPOUNDS
onboard
model
blend
access
ANZO IN THE DATA FABRIC
ARCHITECTURE

5. A modern data discovery
and integration platform
for your enterprise data
fabric.

6. Anzo Difference:
Graph Data Models & Semantics
Simplifies access to complex and blended data to address
unanticipated questions
Quickly profiles, connects and harmonizes data from multiple
sources, including unstructured textual sources
Presents tailored views and experiences to different personas
with conceptual models that use business terms
Flexibly accommodates new data sources and use cases on the fly,
with minimal impact
Scales horizontally to accommodate enterprise data fabric scale

7. Claim ID Process
Date
Subscriber
ID
44223 10/3/2015 ID-BA213
44224 10/7/2015 ID-234I2
… … …
Claims
On July 3, 2016, Patient BA213
experiencing headache and
nausea following 500mg dosage
of sleep aid therapeutic,
Narcoleptol.
On Site Doctor Note
Graph data models flexibly connect and transform new data sources.
Patient
ID
Condition Drug Name
BA213 Sleep Apnea Narcoleptol
CS289 Type II
Diabetes
Insulin
… …
Electronic Health Records
BA213
patient ID
Drug
prescribed
Narcoleptol
brand name
Sleep
Apnea
condition
Patient
Record
500mg
dosage
about
Note
3/7/2016
headache
and nausea
event
-.05
sentiment score
when
10/3/2015
process date
Subscriber
subscriber ID
ID-BA123
Claim
44223
claim ID
about

8. Real World Graphs
Get Big Fast
Vast
Hundreds of sources, representing
thousands of entity types
Siloed
Different technologies, schemas,
formats
Complex
Sprawling schemas, wide flat
records, and cryptic names
Unstructured
Documents, emails, web content
Valuable
Hidden connections and common
business definitions

9. ©2018 Cambridge Semantics Inc. All rights reserved.
ANALYZE
PREPARE
BLEND
PROTECT
EXPLOR
INGEST
AnzoGraph
What does it take to make graph work at scale in
the Enterprise Data Fabric?
• High Performance OLAP
• Work with existing landscape
• End-to-end capabilities
• Diverse user support and tools
• Collaboration and reuse
• Security and governance
CATALOG

10. Discovery and Integration in the Data Fabric - The User Experience
Catalog and map your
existing data assets –
structured or unstructured.
Translate datasets into graph
models. Add business
definitions, object types, and
relationships with semantics.
Create blended analytic
-ready data products.
Connect graph models.
Transform data. Harmonize
into canonical models.
Analyze data using semantic
and graph models. Export
data for use with BI,
analytics, and machine-
learning tools.
ONBOARD MODEL BLEND ACCESS

11. Automated Deployment and Operations
Storage and Compute Integration
MODEL
Graph Data Model
• Lift Data into
Data Fabric
• Design Ontologies
• Connect Data
Models
ONBOARD
Ingest & Map
• Automated ETL
• Collaborative
Mapping
• Metadata
Capture
Enterprise
Data Sources
Machine
Learning and AI
Enterprise
Search
“Last Mile”
Analytics Tools
Metadata Catalog
Semantic-based Metadata Management, Governance, and Lineage
Cloud or On-Prem Data Storage Infrastructure
Data Storage Layer
Ingest
BLEND
GraphMarts
• Combine and Align
Related Data Sets
• In-memory MPP
OLAP Query Engine
• Data Layers
ACCESS
Hi-Res Analytics
• Analyze All
Data Together
• Fast, Iterative Queries
Ad Hoc, What if
• Code-Free or API
Graphical Application Interface

12. ©2018 Cambridge Semantics Inc. All rights reserved.
Industry Trends Driving our Roadmap and Innovation
The graph data model becomes the foundation for machine learning and AI
due to its flexibility when dealing with the complex and connected nature of
data.
The enterprise data fabric is the modern architecture for digital transformation
and requires graph and semantics for integration and discovery at scale.
Kubernetes is the standard for multi/hybrid-cloud and on premises automation
of deployment and operations.
Diverse data consumers require easy access to data products and analytics-
ready data sets in their existing tools, systems, and workflows.

13. Anzo in Action
Modernizing clinical data standards
management to accelerate drug development
Integrated data layer to take cross-trial analytics
questions from weeks to hours.
Operationalized text analytics for
consumer feedback
Building an integrated view of materials data
across materials manufacturing processes
Accelerate drug development through an
enterprise data fabric
Building a platform of clinical and treatment
data for oncology patients and their doctors to
improve diagnosis and care
Accelerate scale and growth of M2Gen’s
ORIEN Program and Pharma partnerships
Modernizing Customer 360 and Data Center
Management through semantics and graph
Accelerate value from enterprise data lake
investments across multiple business units

14. ©2019 Cambridge Semantics Inc. All rights reserved.
Example Graph Model
● 10.5 million Cases
● 20 million Products
● 12 million Events
● 16 billion facts
Unstructured Data - Case Narratives
● 700k Unique Case Narratives annotated
using Scibite
● Indexed for search using ElasticSearch
Data Model, Blend,
and Access
● 22 Classes
● 18 Datalayers
● 9 Dashboards
● 20+ visualizations
Internal Data Source
● 2 million Case records
● 12 million Product records
● 4 million Events records
● 12 billion total facts
The Data Fabric for Digital Health and Patient Safety
Public Data Source
● 9 million cases
● 19 million FDA records
● 2.5 billion total facts

15. ©2019 Cambridge Semantics Inc. All rights reserved.
Example Graph Model
● 400 million events
● 50 million reference records
● 321 million EComms
● 45 billion triples
Unstructured Data - Text-based Communications
● 321 million messages
● 36 billion triples
● Indexed for search using ElasticSearch
Data Model, Blend, and
Access
● 30 classes
● 32 data layers
● 7 dashboards
● 20+ visualizations
The Data Fabric for Compliance and Surveillance
Structured Data - Transactions and
Reference data records
● 400 million transactions
● 50 million reference records
● 9 billion triples

16. What parties are connected
to this claim?
Policy, claim, counter party, employer, experts,
law enforcement, co-insurer, MGA, reinsurer
What products should I suggest?
CRM, policy, social media, connections
The Data Fabric allows insurers to connect, understand, and use
enterprise data to answer unanticipated business questions.
customer
policy
How can I price policies more
competitively and profitably?
Claims, risk, vehicle, property, medical, weather, crime
Is this a fraudulent claim?
Claim report, Incident data, police report, claim
history, customer’s connections, social media
Lifestyle, financial
And demographics
personal
profile
Is this an in market high value
prospective customer?
CRM, Financial, credit, vehicle,
property, marketing, web, social media
Social Media
Contact
info
connections
spouse
friend
claim
settlement
incident
Social Media
adjuster
counter party
vehicle
propertylost item
How can I personalize the
customer experience?
Marketing, CRM, social media, web

17. Begin your journey.
Identify your
initial use case
Define the
IT/business
partnership
Quick start
deployment
4 - 8 weeks
Leverage
CSI technical
expertise /staff

18. ©2019 Cambridge Semantics Inc. All rights reserved.
Click below to watch the full webinar on-demand.
Thank You!
Watch the Webinar