When to Consider Semantic Technology for Your Enterprise

© 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

New York, NY, US

October 2, 2013

How Why Not

Where

Who are Dave and Mike?

• Architecture

• Security

• Innovation

• Solution engineering

• Integration

• ETL

David Read Michael Delaney


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


What?

4

Let’s give semantic technology some context


What is Semantic Technology?

Semantics

≡ meaning

Semantic Technology

≡ machine-readable meaning

5


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


Semantic Technology is a Team Player in an Architecture

• Integrates

• Federates

• Adapts

• Extends

7


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


Directed Graph Example

9

David

bioFatherOf

Sarah

Lisa

bioMotherOf

fullSiblingOf

Michael

SarahB

friendOf

friendOf

Carl

spouseOf

Blue Slate

Solutions

employeeOf

favoriteSport

Bowling

bioFatherOf fullSiblingOf


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


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


What’s New?

Let’s compare semantic and other technologies

12


Storage

13

Relational Efficient Use of Space

NoSQL Document Stores

(Key-Value Pairs)

Semantic Single, standardized schema

(the triple)


Relationships

14

Relational

Relationships convey

meaning through table names

or data values

NoSQL No

Semantic Relationships convey

meaning in their definition


Query Language

15

Relational Standardized Language

(ISO: SQL)

NoSQL No Language Standardization

Semantic Standardized Language

(SPARQL)


Schema Maintenance

16

Relational Rigid Schema

NoSQL Data is unstructured

Semantic Relationships convey

meaning in their definition


Maturity

17

Relational Well established

(30+ years old)

NoSQL Coming Along

(10+ years old)

Semantic Relatively Young

(5+ years old)


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


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


Ecosystem

20

Relational Very rich development

ecosystem, solid tooling

NoSQL Rich development

ecosystem, limited tooling

Semantic Immature ecosystem,

nascent tools


Ontology

OWL

Semantic Technology: Cool Solution Looking for a Problem?

21

Triple

Graph SPARQL

Reasoner

Triple Store

RDF Turtle Object

Predicate

Subject


Where?

22

Is this technology really being used?


Where is Semantic Technology Today?

• Maturing standards and practices

• Expanding ecosystem

• Big and small players

• Diverse offerings

– Products and services

• Real-world usage

23


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


Data and Analytic Diversity Continue to Expand

25

• Evolving Interactions

• Data Diversity


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

• …


Why?

Why do enterprises use semantic technology?

27


How Does Semantic Technology Benefit an Enterprise?

Integrates diverse

data sources

28



Provides consistent meaning

across systems

29



Federates and integrates

in real-time

30



Supports multiple logical models

without ETL and warehouses

31



Extends data and relationships

without refactoring databases

32



Augments Analytics

33


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 2013 35

When?

Recognizing good opportunities for

introducing semantic technology


When should you consider Semantic Technology?

Heterogeneous

Data

36



37

Evolving

Data

Structures



38

Rule-based

data

interactions



39

Language

Flexibility



40

Vendor

Independence



41

Augmenting

Current

Systems


When Not?

42

Avoid the round hole, square peg syndrome


When Won’t Introducing Semantics Make a Splash?

• Common and well-defined systems

– Claims Processing

– Order Entry

– ERP

– Payroll

– CRM

43


Semantic Technology is Not “The” Answer

Not a dessert topping, engine

lubricant, elixir-of-life, hand

soap and

cloud solution

all-in-one!

44


When is Semantic Technology a bad fit?

Transactional,

high volume

45



46

Homogeneous

or contained

data



Problems that have already

been solved/abstracted

47


Case Study

49

We’re giving you the story first hand


Case Study: Client

• Medicare Administrative Contractor

• Struggling with key metrics

• Competitive landscape (15 10 ~7)

50


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


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


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


• 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


Case Study: Results (Just Published)

55

Activity Established

Goal Current Results

Probe (Hypothesis) Research Duration

250% Improvement

370% Improvement

Last Claim Reviewed to Date of Notification

240% Improvement

2070%

Improvement (months to days)

Probe Edits Above 35% Charge Denial Rate

62% 100%

After 9 months of production experience…


How?

56

Make it so!


How do I know when to take the leap?

57


How do I start?

Assign an Owner

58

Pick a Project

Iterate over Technology


Assign an Owner

59

• Point person

• Responsible and accountable

• “Gets” the big

picture value

proposition

• Educator

• Evangelist


Pick a project

60

• Aligned with semantic technology value proposition

• Creative team

• Education investment

• Agile process

• Visible

• Schedule

flexibility

(phases)


Iterate over Technology

61

• Learn

• Try

• Select

• Full speed!


Questions + Contact Info

62

Thank you for attending our session.

Feel free to contact us if you have

questions:

[email protected]

[email protected]

www.blueslate.net

http://www.blueslate.net/

When to Consider Semantic Technology for Your Enterprise

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