enterprise semantic technology

©2014, Cognizant

Enterprise Semantic Technology

Industrializing Your Organization's Semantic Technology Platform

SPEAKER: Thomas Kelly, Practice Director Semantic Technology Center of Excellence Enterprise Information Management Cognizant Technology Solutions, Inc.

1 | ©2014, Cognizant

Cognizant Technology Solutions

20,000+ Projects in

40 countries

.……………………….. . Founded in 1994

(CTSH, Nasdaq)

………………………. .

Headquarters

Teaneck, NJ USA

……………….…… .

…………………… .

25+ Regional

sales offices

………………….…………..….…. .

………………………

75+ Global Delivery

Centers

……………….….. . Revenue

$8.84b in 2013 (up 20.4% YOY)

Q1 2014 – $2.42b

.

178,600+

employees (Mar 2014)

…………….….. .

. . . .…………………. .

Revenue Mix

NA: 77%, Europe:19%, RoW: 4%

1,223

active customers


Our Portfolio Across Industries

HEALTHCARE & LIFE SCIENCES

27 of the top 30 Global Pharmaceutical Companies

8 of the top 10 U.S. Healthcare Plans

9 of the top 10 Biotech Companies

2 of the top 5 Medical Device Companies

INSURANCE

7 of the top 10 Global Insurers

33 of the top 50 US Insurers

BANKING & FINANCIAL SERVICES

6 of the top 10 North American banks

8 of the top 10 European banks

MANUFACTURING,

LOGISTICS, ENERGY & UTILITIES

7 of the top 10 Automotive OEM

4 of the top 15 Industrial Manufacturers

4 of the top 15 Chemical Manufacturers

4 of the top 14 Logistics Providers RETAIL, TRAVEL & HOSPITALITY

9 of the top 30 Global Retailers

2 of the top 4 Global Distribution System Companies

3 leading U.S. Airlines

3 of the world’s leading Restaurant Chains

INFORMATION, MEDIA &

ENTERTAINMENT

4 of the top 10 Information Service

Companies Worldwide

4 of the top 10 Global Media Companies

6 of the major U.S. Movie Studios

TECHNOLOGY

4 of the top 5 Online Companies

7 of the top 10 ISVs

2 of the top 5 Semiconductor

Manufacturers

COMMUNICATIONS

7 of the top 10 Communications

Service Providers & Equipment

Vendors


Many Organizations are at an Inflection Point

Busin

ess I

mpact

Time

Project-based

Semantic Technology Engagement and Execution

Enterprise


Definition

industrialize To manufacture on an industrial scale or using industrial methods

yourdictionary.com

Our point of view: To engage people, practices and methods, and technologies that provide a repeatable, predictable, consistent, time-efficient, and cost-effective result


Semantic Industrialization Pyramid

Technology • Data Stores • Cross-Technology Integration • Query and Analytics • Access and Security

Practices and Methods • Data Governance • Knowledge Representation • Data Acquisition / Onboarding • Data Quality / Curation • Data Publication (Sharing)

People • Champions and

Stakeholders • Communities of Interest • Data Suppliers and

Consumers • Semantic Technology Team


Agenda

• Semantic strategy and roadmap

• Semantic technology competency center

• Align project and data governance objectives

• Semantic technology platform and practices

• Use and extend industry ontologies

• Leverage internal and external data assets

• Define reference architecture models to guide project teams

• Open access to data while securing those data assets from unauthorized users

• Rapid model-based data integration


Semantic Strategy and Roadmap

Business Discovery

Environment Evaluation

Consensus Building

Communicate Roadmap

Ap

pro

ach

• Interview stakeholders in relevant business units • Analyze the organization’s opportunities and challenges • Define alignment between business needs and semantic solutions • Determine resourcing requirements (time, funding, staffing, infrastructure) • Test and fine-tune recommendations with champions and stakeholders • Describe the strategy , its outcomes, and how it will be achieved

Ou

tco

mes

• Champion and stakeholder support for success • Plans for funding, resourcing, and delivery • Senior management will be able to see tangible benefits at the end of

every milestone mentioned in the roadmap • Clear description of how semantic technology will contribute to the

organization’s success

Planning


Key Enablement Themes of your Strategy

Semantic Technology is the Enabling Foundation for Business Agility


Key Roles that will Influence the Success of Your Strategy

Communities of Interest • Future stakeholder –

“disinterested (unbiased) party”

• Often new to semantic technology

• Approach: Engage and educate

Stakeholders • Success is influenced by the

semantic strategy • May be new to semantic

technology • May influence (or control)

resourcing the strategy • Often “risk conscious” • Must be “sold” on the value

of the semantic strategy • Approach: Educate,

demonstrate success, manage risk, overcome hard-to-solve challenges, achieve high-value ROI

Champions • Stakeholder • View semantic technology as

enabler of their future success

• Demonstrate their support • Help you to communicate and

“sell” the semantic strategy • Approach: Align with

successes, position as innovators, recognize their support


Roadmap Scenario

1 30/60/90 Day Plan

• Services • Skills Development • Processes/Methods • Technology

2 Build Team / Technology

• Staffing • Training • Technology Procurement • Establish Success Metrics

3 Execute Project(s)

• Define Data Requirements • Build Application Ontology • Map Source Data to Ontology • Construct Semantic Queries

4 Create Shared Ontology

• Identify Domain Concepts • Define Common Vocabulary • Describe Data Relationships • Build Shared Ontology

5 Create Linked Data

• Define Use Cases • Populate RDF Database(s) • Map / Load Data Links • Execute Validation Queries

6 Enterprise Data Integration

• Prioritize Domains • Create Domain Ontologies • Map Data Assets to Ontologies • Enrich Data with Data Links


Roadmap Scenario

1 30/60/90 Day Plan

• Organize Community of Interest • Select Project(s) to Execute • Services

• Project Ontology Services • Team / Skills Development

• Job Classification Development • Ontology Modeling • Controlled Vocabularies • SPARQL, R2RML

• Processes/Methods • Validating Ontologies • Managing Industry Ontologies • Semantic Query Performance

• Technology • Ontology Editor • Data Profiling • Relational-to-RDF Mapping • Automated Ontology Generation • SPARQL-based Visualization • RDF Database

2 Build Team / Technology

• People • Educate Stakeholders, Champions,

Community of Interest • Semantic Team • Project Team(s)

• Skills Development • Ontology Modeling • Mapping Relational Data to an

Ontology Model • SPARQL Data Query and Management • Inferencing

• Technology Procurement • Servers, Software, Network

• Establish Success Metrics • Coverage of Projects’ Business

Requirements • Support of Projects’ Performance

Requirements • Speed-to-Business Value • Support for Model Updates


Roadmap Scenario

3 Execute Project(s)

• Define Data Requirements • Current Data Rules • Industry-defined Data Rules • Future Data Requirements

• Build Application Ontology • Define Vocabulary • Define Standard Properties • Define Classes, Properties, and

Relationships • Map Source Data to Ontology Model • Construct Semantic Queries • Create Inferencing Rules • Visualize Data • Validate Functionality and

Performance • Define Cache / Persistence

Candidates • Define and Develop Persistence

Structures • Deploy and Train User Community


Agenda

Semantic strategy and roadmap

• Semantic technology competency center









Semantic Technology Competency Center

Data Governance

Domain Expertise

Ontology Modeling

Capture and Integrate Expertise

Align with the Organization’s Data Strategy,

Objectives, and Standards

Describe, Organize, and Connect Data and Knowledge Assets

Defining the Knowledge Capture and Management Services


Semantic Technology Services

Project Services

• Ontology Modeling

• Model-based Data Movement

• Relational-to-RDF Mapping

• NLP / Semantic Search

• Linked Data Integration

• Curation Automation

Ontology Management Services

• Ontology Modeling • Controlled Vocabularies • Business Rules and Inferencing

• Ontology Integration/Rationalization

• Provenance

• Integrating Semantic Modeling with Data Governance Activities

Infrastructure Services

• Capture and Validate Internal Knowledge • Knowledge Representation • Validation Methods

• Embedding Expertise in Information Management • Business Rules in Ontology Models • Frequently Used or Standard Analytics

Strategic / Enterprise Services Project Services

Ontology Management Services Domain Expertise

• Semantic Strategy and Roadmap

• Prioritizing and Building Enterprise and Business Unit Models

• Establishing Standards for use of Industry Ontologies

• Enterprise Data Integration

• Data Asset Cataloging, Search, and Authorization


Key Roles and Responsibilities

• Semantic Strategy & Roadmap • Establishing Standards for use of

Industry Ontologies • Integrating Semantic Modeling with

Data Governance Activities • Prioritizing Knowledge Capture and

Analysis

• Capture and Validate Internal Knowledge • Defining Business Unit Models • Capturing Domain Expertise in Information Management

• Ontology Modeling • Relational-to-RDF Mapping • Embedding Domain Expertise in

Information Management • Business Rules in Ontology Models

(Data Quality and Security Rules) • Frequently Used or Standard Analytics

• Model-based Data Movement • Data Curation Automation • Linked Data Integration

Data Governance

Ontologist

Business Analyst

Semantic Developer

• Semantic Reference Architecture(s) • Data Integration Solution Architecture(s) • Transaction Models

Semantic Architect


Ontology Development Services for Projects

Establish

Scope Discovery

Ontology Modeling

Model Validation

Map to Data

Assets Visualize

• Define Subject Areas

• Identify and Recruit Domain Experts

• Prepare Interview / Validation Schedule

• Conduct Discovery Sessions with Domain SMEs

• Define Facts and Rules

• Document Findings

• Track Requests for Changes

• Identify Related Ontologies for Inheritance

• Construct New Classes

• Add New Attributes and Relationships

• Construct Rules Logic

In some cases, the Ontology Modeling and Model Validation steps may be conducted during the Discovery session(s), delivering a validated model more rapidly than traditional approaches.

• Create Model-Specific Test Cases

• Define and Build New Model Validation Rules

• Perform Automated Model Validation Checks

• Define Mapping Between Source Data Asset and Ontology Model

• Create Data Element-Level Mappings

• Create and Execute Test Cases

• Create Sample Visualizations through Model-based Queries

• Review Visualizations with Domain Experts and Project Stakeholders

• Track Requests for Changes


Agenda


Semantic technology competency center









Evolutionary Modeling for Enterprise Data Governance

App A Database

Application-Specific

Ontology

1

App C Database

App B Database

App D Database


Ontology

2 Application-Specific

Ontology

3

1 An application project maps an existing database to an ontology, providing semantic access to selected data elements

2 Another application project maps data elements from multiple databases to an ontology, providing semantic integration and access to the relational data

3 A third project maps the data elements to a data organization that meets their project needs, without changing the structure of the underlying data

Individual projects will independently create ontologies. These ontologies will focus on supporting a specific business process, but may re-engineer the same concepts, leading to ontology proliferation.

Industry Ontology


Evolutionary Modeling for Enterprise Data Governance


Ontology

App A Database


Ontology

App C Database

App B Database

App D Database

Databases


Ontology Applications

1 2 3

Inherits and Extends

North America R&D

Ontology

Departments 4

Enterprise Ontology Enterprise Inherits and

Extends

7


International Market

Ontology

8

Industry Industry Ontology


9

North America Commercial Ops

Ontology Inherits and Extends

5

North America Market

Ontology

Geographical Business Units Inherits and

Extends

6


Each remaining ontology retains the concepts that are unique to their domain

Common concepts are promoted to the highest level ontology in which they are shared


Governance-Driven Ontology Management Extends the Organization’s Data Ecosystem

Ingest new data sources (light

integration and curation) Reuse Expertise

Identify and leverage existing, relevant data assets and expertise

Analyze

Extend

Create and extend data relationships,

leveraging insights from previous study

cycles

Refine Capture insights from new data

analysis cycles, refining relationships to support new

analytics

Govern

Elevate proven data, relationships, and expertise

to organization-wise definition

Monitor and measure use and benefits

achieved; identify next set of priorities

Realize Benefits


Agenda



Align project and data governance objectives








Using and Extending Industry Ontologies

Evaluation Criteria

1. Fitness for a planned purpose

2. Industry adoption history and potential

3. Sponsoring standards body’s support and direction

4. Timing of a superceding ontology

Using the Industry Ontology

1. Identify domains and concepts relevant to the business

2. Execute proof-of-concept project to evaluate ontology concepts in real world use

3. Identify and evaluate abstraction methods (if needed)

1. Rename concepts

2. Mask/hide non-relevant concepts

4. Define adoption plans

5. Educate user community

6. Map data in current systems to the industry ontology

Extending the Industry Ontology

1. Identify gaps in the industry ontology

2. Document business rules for new / revised concepts

3. Define naming convention for new / revised concepts

4. Create ontology model that references the industry ontology

5. Define / design new / revised concepts

6. Validate the new ontology model


Extend existing investments in relational technology while delivering smart applications

Organizations have invested billions in relational technology

Relational technology powers high-performance transaction systems

Speed parity is good… but getting the job done faster is better

Semantic technology bridges relational databases with semantic features to help organizations to transition what they want, when they want it.


Use Case – Hospital Supply Mobile Sales App

Skyland Children’s Hospital 1123 Hillcrest Drive Washington, DC

Primary Contact Silas Monroe, M.D.

Account Diagnostic

Aug 20, 2014 -- As the Affordable Care Act takes effect and healthcare shifts from a fee-for-service based model to a value-based one, leading hospitals and


Use Case – Hospital Supply Mobile Sales App

Hospital Supply Mobile

Sales App

SPARQL Access Point

Ontology Model

R2RML Mappings

CRM System

Sales Mgmt

Order / Inventory

Newsfeed to RDF

Mapping

Benefits

• Fast integration of federated relational data and public data

• No data mart required just to manage data integration


The Imperative for Universal Data Access

Increasing need for access to the right data at the right place at the right time

Organizations’ environments and processes change

frequently and unpredictably

There is an unmet need to connect and

engage cross-organizational data

There is more data in more places and

in more formats than ever before

Business Units R&D Partners

Commercial and Public

Data Publishers

Customers

Distribution Network


Semantic Enterprise Data Integration

R&D Manufacturing Finance Sales &

Marketing Administration

Enterprise • Small Model to Demonstrate Value

at the Business Unit Level • Evolutionary Modeling provides

Incremental, Continuous Improvement • Domain Expertise is Added to the

Models to provide Descriptive, Predictive, and Prescriptive Insights

• Data Governance Guides the Definition of Shared Data

• Externally-Hosted Data can be Mapped to Business Unit and Enterprise Models for Easy Access

Business Unit Level Models can use Vocabulary and Data Organization that Best Fit their Operations


Leveraging External Data Assets

(2) Replicated, Internally Federated

External Databases

Firewall

(3) Internally Merged

External Databases

Firewall Characteristics

(1) Externally Federated

(2) Replicated, Internally Federated

(3) Internally Merged

Data Location

Some or all datasets reside outside the firewall

All datasets reside inside the firewall

All data resides in a merged, shared database

Data Integration Internal and External

Internal Internal

Data Latency

Data is updated on system of record’s schedule

Data is replicated/ refreshed on internal schedule, but still dependent on systems of record’s schedule

Data is replicated/ refreshed on internal schedule, but still dependent on systems of record’s schedule

Query / Analysis Performance

Performance dependent on external systems of record’s infrastructure

Performance dependent on internal databases’ infrastructure

Performance dependent on internal merged database‘s infrastructure

External Databases

Firewall

(1) Externally Federated

• Internal management of external data can address performance concerns for infrequently updated small- to mid-size external databases

• Physical integration (option 3) may achieve a specific performance or management benefit


Agenda





Use and extend industry ontologies

Leverage internal and external data assets





Define Reference Architecture Models to Guide Project Teams

Technology Products

Define product features that enable the data ecosystem

• Technology Product Features • Supported databases and data

structures • Semi-structured and unstructured

data • Supported access methods

(SPARQL, API, web services) • Modeling tools • Data caching options

• Fit with Current/Planned Architecture • Benchmark of Representative

Transactions

Reference Architecture Transaction Models

Define fit-for-purpose integration of technology products to meet transaction

requirements

• Transaction Requirements for Execution

Frequency, Throughput, Response Time • Source Data Profile

• Formats, Latency • Frequency and Volume of Updates • Critical Processing Time Windows

• Source System Impact • Performance Profiles for Representative

Transactions • Data and Query Results Caching Techniques • High Availability / Failover Options

Bring Best-of-Breed Technology Products to Data Management and Delivery


Industrialize Your RDF Data Store

What are your requirements?

• Planned Data Volumes

• ACID Properties

• Runs on Multi-Server Platform with Load Balancing

• Runs on a Cloud Platform

• Parallel Processing

• Connectors to Relational, Document, and other Data Management Technologies

• Backup and Recovery

• High Availability

• Automatic Failover

• User-, Role-, Class-, and Rule-Based Security

• Product Support (8x5, 24x7)

• Track Record of Quality Product Releases

• Match products against your requirements (platforms, resilience, product functionality)

• Configure your infrastructure (servers, storage, network) to the performance requirements of your workload

• Plan for vendor support (operating hours, response time, locations, language support)

• Performance test RDF data store products

• Test your suite of transactions, rather than just comparing standard benchmarks

• Test on your planned platforms and connected technologies

• Talk to other customers about their experience with the products


Industrialize Your Data Quality Management

Evolution of Requirements for Data Quality Rules

Traditional One-Size-fits-All Implementation

The Organization’s Data Quality Rules

Unit A Rules

Unit B Rules Sh

are

d

Ru

les

Second Repository

Unmet Demand


Industrialize Your Data Quality Management

Evolution of Requirements for Data Quality Rules Processing

New Data

Onboard New Data

Data Quality

Tests

Evaluation Results

Effective when data is mostly static, and data quality is consistent

Internal Data Store

New Data

Dynamic Data Quality Management

Query-level Data Quality

Tests

Ontology model contains data quality rules that are executed at query time. Business unit models may contain rules that are specific to their requirements.

New Data

Query Results

And / or

Unfiltered

Internal Data Store

New Data

Regular Data Loads

Data Quality

Tests

Addresses variable data quality with a consistent set of rules

Filtered


Opening Access to Data while Securing those Data Assets from Unauthorized Users

Use Internet security features, including certificates, to authenticate and authorize users

Leverage RDF data store security features

Build a semantic model that defines security rules and access for groups, roles, and users. Direct queries through the security model.

Build semantic models for specific user groups, defining only the objects and properties that the user groups are authorized to access


Agenda





Use and extend industry ontologies

Leverage internal and external data assets

Define reference architecture models to guide project teams

Open access to data while securing those data assets from unauthorized users



1. Define Preliminary Objectives

1. Discuss Functional and Timing Objectives, and Priorities

2. Clarify Immediate, Short-Term, and Long-Term Business Value (SMART *)

a. Cost Reduction/Avoidance b. Meet Critical Customer Need

3. Is This the Right Solution?

4. Set Expectations a. Evolutionary Process b. Initial Results Quickly c. Frequent, Active Participation d. Feedback Critical to Making Refinements

5. Brainstorm Deliverables that Produce Business Benefits; Define a Few Sample Queries

6. Ask for Commitment to Benefits Realization

7. Start the Clock!

* SMART -- Specific, Measurable, Attainable, Realistic, and Traceable


2. Profile the Data

Light Profiling focusing on Understanding Key Data Elements

Identify Initial Data Filtering Candidates

Capture Insights about Key Data Relationships


3. Generate the Initial Ontology for the New Data (if necessary)

Reverse-engineer Ontology from New Data

Load New Data into the RDF Store (or Create Link to the Data)

Create Business-relevant Synonyms for High-Importance Attributes

Refinements will be made in Future Iterations


4. Generate the Initial Ontology for the Existing Data (if Necessary)

Existing Data

New Data

Ontology Models

Map Selected Entities and Critical Attributes for Existing Data Source(s) to the Source-specific Ontology

Add Reference to the Source-specific Ontology to the New Data Ontology Model

Refinements will be made in Future Iterations

New Data Ontology manages integration with Existing Data until the ontology is sufficiently mature to be promoted into an enterprise ontology


5. Integrate Entities over Common URIs

Different URIs, Separately Maintained

Focus on Key Entities

Equivalence Functions Logically Integrate the Federated Data

Reduces Query Complexity and Can Improve Query Performance


6. Create URI Links

Links Reduce Query Complexity and Can Improve Query Performance

The Data has Common Values that can be used in Join Operations, but doesn’t have Links

Focus on Key Queries, Identify Complex or Time-Sensitive Joins

Add Linking URI Attribute to Dependent Entity

Amend Selected Queries to Leverage the New Link

cust:ZipCode

geo:ZipCode

JOIN

Customer Geography

cust:ZipCodeURI

LINK

Customer Geography


Summary

Enterprise Semantic Technology

• New Solutions – Solve previously hard to solve challenges

• Accelerate Benefits – Deliver business value sooner

• Better Engagement – Champions and stakeholders support execution, while communities of interest prepare to engage

• Reduce Risk – Provide more-predictable execution, and increasing likelihood of successful delivery


Questions?


Speaker

Thomas (Tom) Kelly

Practice Director, Enterprise Information Management,

Cognizant

Thomas Kelly is a Director in Cognizant’s Enterprise Information Management (EIM) Practice and heads its Semantic Technology Center of Excellence, a technology specialty of Cognizant Business Consulting (CBC). He has 20-plus years of technology consulting experience in leading data warehousing, business intelligence and big data projects, focused primarily on the life sciences and healthcare industries. Tom can be reached at [email protected] and at @tjkelly3va.

For more information, check out our website at www.smartEIM.com

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