This presentation, When to Consider Semantic Technology for Your Enterprise, was presented by Mike Delaney and David Read at the 2013 Semantic Technology and Business Conference.

1. © Blue Slate Solutions 2013
When to Consider Semantic Technology for
Your Enterprise
Michael Delaney, Senior Consulting Software Engineer
David Read, CTO
Semantic Technology and Business Conference
San Francisco, CA, US
June 3, 2013

2. HowWhy Not

3. Who are Dave and Mike?
• Architecture
• Security
• Innovation
• Solution engineering
• Integration
• ETL
David Read Michael Delaney

4. © Blue Slate Solutions 2013
Who is Blue Slate?
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About
 Driven by a total commitment to customers, company,
colleagues and community
 30+ consultants – operations, strategy, technology and
industry experts
 Founded in 2000, headquartered in Albany, NY
Clients
 Industry Leaders seeking to drive value for
shareholders and end customers
 Include ten Blues organizations and four commercial
payers
 Innovators looking to grow beyond their core markets

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What?
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Let’s give semantic technology some context

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What is Semantic Technology?
Semantics
≡ meaning
Semantic Technology
≡ machine-readable meaning
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What Makes Up Semantic Technology (in this talk)?
• Standards → RDF/RDFS/OWL/SPARQL
• Definitions → Ontologies
• Storage → Triple Stores
• Inferencing → Reasoners
• Data Access → SPARQL
• APIs → Jena, Sesame
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Semantic Technology is a Team Player in an Architecture
• Integrates
• Federates
• Adapts
• Extends
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What Is Different About Semantic Technology?
• Structure is (mostly) logical not physical
– Triple
– Directed Graph
• Federation is assumed
– SPARQL
• Web is the natural platform
– URI
– HTTP
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Subject
Predicate
Object

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Directed Graph Example
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David
bioFatherOf
Sarah
Lisa
bioMotherOf
fullSiblingOf
Michael
SarahB
friendOf
friendOf
Carl
spouseOf
Blue Slate
Solutions
employeeOf
favoriteSport
Bowling
bioFatherOf fullSiblingOf

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The Physical Structure is Flexible by Design
• Triples are an extreme form of normalization
• Any data can be related without the need for
foreign keys
• Relationships can be added or removed as
they are found, explored, accepted or
discredited
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Lisa
bioMotherOf
Michael
friendOf
Carl
favoriteSport
Bowling

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The Logical Structure is Built to Relate and Define
• Directed graph relates
data
• RDF, RDFS and OWL
define data
• Reasoners build models
on the fly
• Relationships are
flexible:
– Groups can use different
relationship rules
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What’s New?
Let’s compare semantic and other technologies
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Storage
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Relational Efficient Use of Space
NoSQL
Document Stores
(Key-Value Pairs)
Semantic
Single, standardized schema
(the triple)

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Relationships
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Relational
Relationships convey
meaning through table names
or data values
NoSQL No
Semantic
Relationships convey
meaning in their definition

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Query Language
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Relational
Standardized Language
(ISO: SQL)
NoSQL No Language Standardization
Semantic
Standardized Language
(SPARQL)

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Schema Maintenance
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Relational Rigid Schema
NoSQL Data is unstructured
Semantic
Relationships convey
meaning in their definition

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Maturity
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Relational
Well established
(30+ years old)
NoSQL
Coming Along
(10+ years old)
Semantic
Relatively Young
(5+ years old)

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Indexing
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Relational
Indexing can be complicated,
but is vital to scalability
NoSQL
Indexing can be complicated,
but is vital to scalability
Semantic Indexing can be automatic

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Optional Relationships
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Relational
Optional relationships can be
difficult
(NULL’s or Dummy Values)
NoSQL None
Semantic
All relationships are optional
by default, but can be
enforced if needed

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Ecosystem
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Relational
Very rich development
ecosystem, solid tooling
NoSQL
Rich development
ecosystem, limited tooling
Semantic
Immature ecosystem,
nascent tools

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Ontology
OWL
Semantic Technology: Cool Solution Looking for a Problem?
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Triple
Graph SPARQL
Reasoner
Triple Store
RDFTurtleObject
Predicate
Subject

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Where?
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Is this technology really being used?

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Where is Semantic Technology Today?
• Maturing standards and practices
• Expanding ecosystem
• Big and small players
• Diverse offerings
– Products and services
• Real-world usage
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Consider Relational Database Adoption
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1 2 3 4 5 7
1. Edgar Codd begins relational data research
2. Codd publishes, “Relational Model of Data for Large Shared Data Banks”
3. Oracle v2 released (marketing gimmick, no v1)
4. DB2 announced
5. Sybase released
6. DB2 released
7. Sybase forked to MS SQL Server
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Data and Analytic Diversity Continue to Expand
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• Evolving Interactions
• Data Diversity

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Where has it been deployed successfully?
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• BBC
– World Cup
– Olympics
• data.gov
• Facebook’s Graph Search
• Best Buy Product Catalog
• Cleveland Clinic
• Amazon
• …

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Why?
Why do enterprises use semantic technology?
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How Does Semantic Technology Benefit an Enterprise?
Integrates diverse
data sources
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How Does Semantic Technology Benefit an Enterprise?
Provides consistent meaning
across systems
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How Does Semantic Technology Benefit an Enterprise?
Federates and integrates
in real-time
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How Does Semantic Technology Benefit an Enterprise?
Supports multiple logical models
without ETL and warehouses
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How Does Semantic Technology Benefit an Enterprise?
Extends data and relationships
without refactoring databases
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How Does Semantic Technology Benefit an Enterprise?
Augments Analytics
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Volume, Velocity, Variability, Variety
• Triple stores do well with volume and velocity
– Commercial products scale to billions of triples
• Directed graphs simplify variability
– Nodes and vertices can come and go
• URIs support variety
– Addressable ≡ accessible
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When?
Recognizing good opportunities for
introducing semantic technology

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When should you consider Semantic Technology?
Heterogeneous
Data
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When should you consider Semantic Technology?
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Evolving
Data
Structures

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When should you consider Semantic Technology?
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Rule-based
data
interactions

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When should you consider Semantic Technology?
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Language
Flexibility

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When should you consider Semantic Technology?
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Vendor
Independence

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When should you consider Semantic Technology?
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Augmenting
Current
Systems

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When Not?
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Avoid the round hole, square peg syndrome

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When Won’t Introducing Semantics Make a Splash?
• Common and well-defined systems
– Claims Processing
– Order Entry
– ERP
– Payroll
– CRM
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Semantic Technology is Not “The” Answer
Not a dessert topping, engine
lubricant, elixir-of-life, hand
soap and
cloud solution
all-in-one!
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When is Semantic Technology a bad fit?
Transactional,
high volume
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When is Semantic Technology a bad fit?
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Homogeneous
or contained
data

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When is Semantic Technology a bad fit?
Problems that have already
been solved/abstracted
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When is Semantic Technology a bad fit?
Very large
data stores

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Case Study
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We’re giving you the story first hand

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Case Study: Client
• Medicare Administrative Contractor
• Struggling with key metrics
• Competitive landscape (15  10  ~7)
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Case Study: Challenge
• Poor medical review focus
– Only denying ~30% of reviewed claims
• Slow reaction to review
outcomes
• High repeat defects from
providers
• Struggling to implement
meaningful analytics
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Case Study: Root Causes
• Manual “copying”
between systems
• Lack of agility
around review
checklists
• Review details not
captured
• Providers mystified
regarding denial reasons
• Multiple systems with parts of the story
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Case Study: Semantic Focus
• Agile checklist support
• Flexible data classifications for denials
• Immediate aggregation and feedback of reviews
• Legacy integration
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• Detailed association
of denials and
provider education
• Integration of claim
data and reviews for
analytics

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• Details support
targeted data mining
• Ontology easily
updated to address
identified risks
• Semantic reasoner
leverages rules
discovered through
analytics
• Flexible classifications provide agility dealing with
changing healthcare payer issues (errors, fraud,
abuse, waste…)
Case Study: Outcomes
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How?
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Make it so!

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How do I know when to take the leap?
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How do I start?
Picking a project
Staffing
Technology
Scheduling
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Questions + Contact Info
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Thank you for attending our session.
Feel free to contact us if you have
questions:
Michael.Delaney@blueslate.net
David.Read@blueslate.net
www.blueslate.net