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© 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
HowWhy Not
Who are Dave and Mike?
• Architecture
• Security
• Innovation
• Solution engineering
• Integration
• ETL
David Read Michael Delaney
© Blue Slate Solutions 2013
Who is Blue Slate?
3
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
© Blue Slate Solutions 2013
What?
4
Let’s give semantic technology some context
© Blue Slate Solutions 2013
What is Semantic Technology?
Semantics
≡ meaning
Semantic Technology
≡ machine-readable meaning
5
© Blue Slate Solutions 2013
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
6
© Blue Slate Solutions 2013
Semantic Technology is a Team Player in an Architecture
• Integrates
• Federates
• Adapts
• Extends
7
© Blue Slate Solutions 2013
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
8
Subject
Predicate
Object
© Blue Slate Solutions 2013
Directed Graph Example
9
David
bioFatherOf
Sarah
Lisa
bioMotherOf
fullSiblingOf
Michael
SarahB
friendOf
friendOf
Carl
spouseOf
Blue Slate
Solutions
employeeOf
favoriteSport
Bowling
bioFatherOf fullSiblingOf
© Blue Slate Solutions 2013
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
10
Lisa
bioMotherOf
Michael
friendOf
Carl
favoriteSport
Bowling
© Blue Slate Solutions 2013
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
11
© Blue Slate Solutions 2013
What’s New?
Let’s compare semantic and other technologies
12
© Blue Slate Solutions 2013
Storage
13
Relational Efficient Use of Space
NoSQL
Document Stores
(Key-Value Pairs)
Semantic
Single, standardized schema
(the triple)
© Blue Slate Solutions 2013
Relationships
14
Relational
Relationships convey
meaning through table names
or data values
NoSQL No
Semantic
Relationships convey
meaning in their definition
© Blue Slate Solutions 2013
Query Language
15
Relational
Standardized Language
(ISO: SQL)
NoSQL No Language Standardization
Semantic
Standardized Language
(SPARQL)
© Blue Slate Solutions 2013
Schema Maintenance
16
Relational Rigid Schema
NoSQL Data is unstructured
Semantic
Relationships convey
meaning in their definition
© Blue Slate Solutions 2013
Maturity
17
Relational
Well established
(30+ years old)
NoSQL
Coming Along
(10+ years old)
Semantic
Relatively Young
(5+ years old)
© Blue Slate Solutions 2013
Indexing
18
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
© Blue Slate Solutions 2013
Optional Relationships
19
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
© Blue Slate Solutions 2013
Ecosystem
20
Relational
Very rich development
ecosystem, solid tooling
NoSQL
Rich development
ecosystem, limited tooling
Semantic
Immature ecosystem,
nascent tools
© Blue Slate Solutions 2013
Ontology
OWL
Semantic Technology: Cool Solution Looking for a Problem?
21
Triple
Graph SPARQL
Reasoner
Triple Store
RDFTurtleObject
Predicate
Subject
© Blue Slate Solutions 2013
Where?
22
Is this technology really being used?
© Blue Slate Solutions 2013
Where is Semantic Technology Today?
• Maturing standards and practices
• Expanding ecosystem
• Big and small players
• Diverse offerings
– Products and services
• Real-world usage
23
© Blue Slate Solutions 2013
Consider Relational Database Adoption
24
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
6
© Blue Slate Solutions 2013
Data and Analytic Diversity Continue to Expand
25
• Evolving Interactions
• Data Diversity
© Blue Slate Solutions 2013
Where has it been deployed successfully?
26
• BBC
– World Cup
– Olympics
• data.gov
• Facebook’s Graph Search
• Best Buy Product Catalog
• Cleveland Clinic
• Amazon
• …
© Blue Slate Solutions 2013
Why?
Why do enterprises use semantic technology?
27
© Blue Slate Solutions 2013
How Does Semantic Technology Benefit an Enterprise?
Integrates diverse
data sources
28
© Blue Slate Solutions 2013
How Does Semantic Technology Benefit an Enterprise?
Provides consistent meaning
across systems
29
© Blue Slate Solutions 2013
How Does Semantic Technology Benefit an Enterprise?
Federates and integrates
in real-time
30
© Blue Slate Solutions 2013
How Does Semantic Technology Benefit an Enterprise?
Supports multiple logical models
without ETL and warehouses
31
© Blue Slate Solutions 2013
How Does Semantic Technology Benefit an Enterprise?
Extends data and relationships
without refactoring databases
32
© Blue Slate Solutions 2013
How Does Semantic Technology Benefit an Enterprise?
Augments Analytics
33
© Blue Slate Solutions 2013
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
34
© Blue Slate Solutions 201335
When?
Recognizing good opportunities for
introducing semantic technology
© Blue Slate Solutions 2013
When should you consider Semantic Technology?
Heterogeneous
Data
36
© Blue Slate Solutions 2013
When should you consider Semantic Technology?
37
Evolving
Data
Structures
© Blue Slate Solutions 2013
When should you consider Semantic Technology?
38
Rule-based
data
interactions
© Blue Slate Solutions 2013
When should you consider Semantic Technology?
39
Language
Flexibility
© Blue Slate Solutions 2013
When should you consider Semantic Technology?
40
Vendor
Independence
© Blue Slate Solutions 2013
When should you consider Semantic Technology?
41
Augmenting
Current
Systems
© Blue Slate Solutions 2013
When Not?
42
Avoid the round hole, square peg syndrome
© Blue Slate Solutions 2013
When Won’t Introducing Semantics Make a Splash?
• Common and well-defined systems
– Claims Processing
– Order Entry
– ERP
– Payroll
– CRM
43
© Blue Slate Solutions 2013
Semantic Technology is Not “The” Answer
Not a dessert topping, engine
lubricant, elixir-of-life, hand
soap and
cloud solution
all-in-one!
44
© Blue Slate Solutions 2013
When is Semantic Technology a bad fit?
Transactional,
high volume
45
© Blue Slate Solutions 2013
When is Semantic Technology a bad fit?
46
Homogeneous
or contained
data
© Blue Slate Solutions 2013
When is Semantic Technology a bad fit?
Problems that have already
been solved/abstracted
47
© Blue Slate Solutions 201348
When is Semantic Technology a bad fit?
Very large
data stores
© Blue Slate Solutions 2013
Case Study
49
We’re giving you the story first hand
© Blue Slate Solutions 2013
Case Study: Client
• Medicare Administrative Contractor
• Struggling with key metrics
• Competitive landscape (15  10  ~7)
50
© Blue Slate Solutions 2013
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
51
© Blue Slate Solutions 2013
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
52
© Blue Slate Solutions 2013
Case Study: Semantic Focus
• Agile checklist support
• Flexible data classifications for denials
• Immediate aggregation and feedback of reviews
• Legacy integration
53
• Detailed association
of denials and
provider education
• Integration of claim
data and reviews for
analytics
© Blue Slate Solutions 2013
• 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
54
© Blue Slate Solutions 2013
How?
55
Make it so!
© Blue Slate Solutions 2013
How do I know when to take the leap?
56
© Blue Slate Solutions 2013
How do I start?
Picking a project
Staffing
Technology
Scheduling
57
© Blue Slate Solutions 2013
Questions + Contact Info
58
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

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When to Consider Semantic Technology for Your Enterprise

  • 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
  • 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? 3 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
  • 5. © Blue Slate Solutions 2013 What? 4 Let’s give semantic technology some context
  • 6. © Blue Slate Solutions 2013 What is Semantic Technology? Semantics ≡ meaning Semantic Technology ≡ machine-readable meaning 5
  • 7. © Blue Slate Solutions 2013 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 6
  • 8. © Blue Slate Solutions 2013 Semantic Technology is a Team Player in an Architecture • Integrates • Federates • Adapts • Extends 7
  • 9. © Blue Slate Solutions 2013 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 8 Subject Predicate Object
  • 10. © Blue Slate Solutions 2013 Directed Graph Example 9 David bioFatherOf Sarah Lisa bioMotherOf fullSiblingOf Michael SarahB friendOf friendOf Carl spouseOf Blue Slate Solutions employeeOf favoriteSport Bowling bioFatherOf fullSiblingOf
  • 11. © Blue Slate Solutions 2013 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 10 Lisa bioMotherOf Michael friendOf Carl favoriteSport Bowling
  • 12. © Blue Slate Solutions 2013 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 11
  • 13. © Blue Slate Solutions 2013 What’s New? Let’s compare semantic and other technologies 12
  • 14. © Blue Slate Solutions 2013 Storage 13 Relational Efficient Use of Space NoSQL Document Stores (Key-Value Pairs) Semantic Single, standardized schema (the triple)
  • 15. © Blue Slate Solutions 2013 Relationships 14 Relational Relationships convey meaning through table names or data values NoSQL No Semantic Relationships convey meaning in their definition
  • 16. © Blue Slate Solutions 2013 Query Language 15 Relational Standardized Language (ISO: SQL) NoSQL No Language Standardization Semantic Standardized Language (SPARQL)
  • 17. © Blue Slate Solutions 2013 Schema Maintenance 16 Relational Rigid Schema NoSQL Data is unstructured Semantic Relationships convey meaning in their definition
  • 18. © Blue Slate Solutions 2013 Maturity 17 Relational Well established (30+ years old) NoSQL Coming Along (10+ years old) Semantic Relatively Young (5+ years old)
  • 19. © Blue Slate Solutions 2013 Indexing 18 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
  • 20. © Blue Slate Solutions 2013 Optional Relationships 19 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
  • 21. © Blue Slate Solutions 2013 Ecosystem 20 Relational Very rich development ecosystem, solid tooling NoSQL Rich development ecosystem, limited tooling Semantic Immature ecosystem, nascent tools
  • 22. © Blue Slate Solutions 2013 Ontology OWL Semantic Technology: Cool Solution Looking for a Problem? 21 Triple Graph SPARQL Reasoner Triple Store RDFTurtleObject Predicate Subject
  • 23. © Blue Slate Solutions 2013 Where? 22 Is this technology really being used?
  • 24. © Blue Slate Solutions 2013 Where is Semantic Technology Today? • Maturing standards and practices • Expanding ecosystem • Big and small players • Diverse offerings – Products and services • Real-world usage 23
  • 25. © Blue Slate Solutions 2013 Consider Relational Database Adoption 24 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 6
  • 26. © Blue Slate Solutions 2013 Data and Analytic Diversity Continue to Expand 25 • Evolving Interactions • Data Diversity
  • 27. © Blue Slate Solutions 2013 Where has it been deployed successfully? 26 • BBC – World Cup – Olympics • data.gov • Facebook’s Graph Search • Best Buy Product Catalog • Cleveland Clinic • Amazon • …
  • 28. © Blue Slate Solutions 2013 Why? Why do enterprises use semantic technology? 27
  • 29. © Blue Slate Solutions 2013 How Does Semantic Technology Benefit an Enterprise? Integrates diverse data sources 28
  • 30. © Blue Slate Solutions 2013 How Does Semantic Technology Benefit an Enterprise? Provides consistent meaning across systems 29
  • 31. © Blue Slate Solutions 2013 How Does Semantic Technology Benefit an Enterprise? Federates and integrates in real-time 30
  • 32. © Blue Slate Solutions 2013 How Does Semantic Technology Benefit an Enterprise? Supports multiple logical models without ETL and warehouses 31
  • 33. © Blue Slate Solutions 2013 How Does Semantic Technology Benefit an Enterprise? Extends data and relationships without refactoring databases 32
  • 34. © Blue Slate Solutions 2013 How Does Semantic Technology Benefit an Enterprise? Augments Analytics 33
  • 35. © Blue Slate Solutions 2013 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 34
  • 36. © Blue Slate Solutions 201335 When? Recognizing good opportunities for introducing semantic technology
  • 37. © Blue Slate Solutions 2013 When should you consider Semantic Technology? Heterogeneous Data 36
  • 38. © Blue Slate Solutions 2013 When should you consider Semantic Technology? 37 Evolving Data Structures
  • 39. © Blue Slate Solutions 2013 When should you consider Semantic Technology? 38 Rule-based data interactions
  • 40. © Blue Slate Solutions 2013 When should you consider Semantic Technology? 39 Language Flexibility
  • 41. © Blue Slate Solutions 2013 When should you consider Semantic Technology? 40 Vendor Independence
  • 42. © Blue Slate Solutions 2013 When should you consider Semantic Technology? 41 Augmenting Current Systems
  • 43. © Blue Slate Solutions 2013 When Not? 42 Avoid the round hole, square peg syndrome
  • 44. © Blue Slate Solutions 2013 When Won’t Introducing Semantics Make a Splash? • Common and well-defined systems – Claims Processing – Order Entry – ERP – Payroll – CRM 43
  • 45. © Blue Slate Solutions 2013 Semantic Technology is Not “The” Answer Not a dessert topping, engine lubricant, elixir-of-life, hand soap and cloud solution all-in-one! 44
  • 46. © Blue Slate Solutions 2013 When is Semantic Technology a bad fit? Transactional, high volume 45
  • 47. © Blue Slate Solutions 2013 When is Semantic Technology a bad fit? 46 Homogeneous or contained data
  • 48. © Blue Slate Solutions 2013 When is Semantic Technology a bad fit? Problems that have already been solved/abstracted 47
  • 49. © Blue Slate Solutions 201348 When is Semantic Technology a bad fit? Very large data stores
  • 50. © Blue Slate Solutions 2013 Case Study 49 We’re giving you the story first hand
  • 51. © Blue Slate Solutions 2013 Case Study: Client • Medicare Administrative Contractor • Struggling with key metrics • Competitive landscape (15  10  ~7) 50
  • 52. © Blue Slate Solutions 2013 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 51
  • 53. © Blue Slate Solutions 2013 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 52
  • 54. © Blue Slate Solutions 2013 Case Study: Semantic Focus • Agile checklist support • Flexible data classifications for denials • Immediate aggregation and feedback of reviews • Legacy integration 53 • Detailed association of denials and provider education • Integration of claim data and reviews for analytics
  • 55. © Blue Slate Solutions 2013 • 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 54
  • 56. © Blue Slate Solutions 2013 How? 55 Make it so!
  • 57. © Blue Slate Solutions 2013 How do I know when to take the leap? 56
  • 58. © Blue Slate Solutions 2013 How do I start? Picking a project Staffing Technology Scheduling 57
  • 59. © Blue Slate Solutions 2013 Questions + Contact Info 58 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

Editor's Notes

  1. Start from scratch?Layer on top of existing stack?Critical problem?Consultants, hire from outside or train up?What are the pieces that I need to get started?OntologyReasonerTriple StoreCan we suggest Open Source solutions to get started with and highlight the vendor independence of the space? That way when they’re ready to productionalize they can choose the best vendor for them.