Enterprise Semantic Technology

Transcription

1 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. 2014, Cognizant

2 Cognizant Technology Solutions Founded in 1994 (CTSH, Nasdaq) Headquarters Teaneck, NJ USA..... Revenue $8.84b in 2013 (up 20.4% YOY) Q $2.42b. Revenue Mix NA: 77%, Europe:19%, RoW: 4% ,223 active customers 178,600+ employees (Mar 2014) 75+ Global Delivery Centers 20,000+ Projects in 40 countries 25+ Regional sales offices , Cognizant

3 Our Portfolio Across Industries HEALTHCARE & LIFE SCIENCES COMMUNICATIONS 7 of the top 10 Communications Service Providers & Equipment Vendors 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 TECHNOLOGY 4 of the top 5 Online Companies 7 of the top 10 ISVs 2 of the top 5 Semiconductor Manufacturers INFORMATION, MEDIA & ENTERTAINMENT BANKING & FINANCIAL SERVICES 6 of the top 10 North American banks 8 of the top 10 European banks MANUFACTURING, LOGISTICS, ENERGY & UTILITIES 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 RETAIL, TRAVEL & HOSPITALITY 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 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 , Cognizant

4 Business Impact Many Organizations are at an Inflection Point Semantic Technology Engagement and Execution Enterprise Project-based Time , Cognizant

5 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 , Cognizant

6 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 , Cognizant

7 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 , Cognizant

8 Outcomes Approach Semantic Strategy and Roadmap Planning Business Discovery Environment Evaluation Consensus Building Communicate Roadmap 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 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 , Cognizant

9 Key Enablement Themes of your Strategy Semantic Technology is the Enabling Foundation for Business Agility , Cognizant

10 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 hardto-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 , Cognizant

11 Roadmap Scenario 1 30/60/90 Day Plan Services Skills Development Processes/Methods Technology 4 Create Shared Ontology Identify Domain Concepts Define Common Vocabulary Describe Data Relationships Build Shared Ontology 2 Build Team / Technology Staffing Training Technology Procurement Establish Success Metrics 5 Create Linked Data Define Use Cases Populate RDF Database(s) Map / Load Data Links Execute Validation Queries 3 Execute Project(s) Define Data Requirements Build Application Ontology Map Source Data to Ontology Construct Semantic Queries 6 Enterprise Data Integration Prioritize Domains Create Domain Ontologies Map Data Assets to Ontologies Enrich Data with Data Links , Cognizant

12 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 , Cognizant

13 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 , Cognizant

14 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 , Cognizant

15 Semantic Technology Competency Center Defining the Knowledge Capture and Management Services Align with the Organization s Data Strategy, Objectives, and Standards Data Governance Domain Expertise Capture and Integrate Expertise Ontology Modeling Describe, Organize, and Connect Data and Knowledge Assets , Cognizant

16 Semantic Technology Services Strategic / Enterprise Services 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 Ontology Management Services Ontology Management Services Ontology Modeling Controlled Vocabularies Business Rules and Inferencing Ontology Integration/Rationalization Provenance Integrating Semantic Modeling with Data Governance Activities Project Services Ontology Modeling Model-based Data Movement Relational-to-RDF Mapping NLP / Semantic Search Linked Data Integration Curation Automation Project Services Domain Expertise 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 , Cognizant

17 Key Roles and Responsibilities Data Governance Semantic Strategy & Roadmap Establishing Standards for use of Industry Ontologies Integrating Semantic Modeling with Data Governance Activities Prioritizing Knowledge Capture and Analysis Ontologist 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 Business Analyst Capture and Validate Internal Knowledge Defining Business Unit Models Capturing Domain Expertise in Information Management Semantic Developer Semantic Architect Model-based Data Movement Data Curation Automation Linked Data Integration Semantic Reference Architecture(s) Data Integration Solution Architecture(s) Transaction Models , Cognizant

18 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 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 Modelbased Queries Review Visualizations with Domain Experts and Project Stakeholders Track Requests for Changes 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 , Cognizant

19 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 , Cognizant

20 Evolutionary Modeling for Enterprise Data Governance 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 Application- Specific Ontology 1 Application- Specific Ontology 3 Application- Specific Ontology 2 App A Database App B Database App C Database App D Database , Cognizant 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

21 Evolutionary Modeling for Enterprise Data Governance Industry Enterprise Geographical Business Units Common concepts are promoted to the highest level ontology in which they are shared Inherits and Extends Industry Ontology 6 Inherits and Extends 9 North America Market Ontology Inherits and Extends Enterprise Ontology Inherits and Extends 7 Inherits and Extends International Market Ontology 8 Departments Applications Inherits and Extends Application- Specific Ontology North America R&D Ontology Application- Specific Ontology North America Commercial Ops Ontology 5 Inherits and Extends Application- Specific Ontology Databases , Cognizant App A Database Each remaining ontology retains the concepts that are unique to their domain App B Database App C Database App D Database

22 Governance-Driven Ontology Management Extends the Organization s Data Ecosystem Identify and leverage existing, relevant data assets and expertise Reuse Expertise Analyze Ingest new data sources (light integration and curation) Monitor and measure use and benefits achieved; identify next set of priorities Realize Benefits Extend Create and extend data relationships, leveraging insights from previous study cycles Elevate proven data, relationships, and expertise to organization-wise definition Govern Refine Capture insights from new data analysis cycles, refining relationships to support new analytics , Cognizant

23 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 , Cognizant

24 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 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 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 , Cognizant

25 Extend existing investments in relational technology while delivering smart applications Organizations have invested billions in relational technology Relational technology powers highperformance 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 , Cognizant

26 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, 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 , Cognizant

27 Use Case Hospital Supply Mobile Sales App Hospital Supply Mobile Sales App Ontology Model SPARQL Access Point R2RML Mappings Newsfeed to RDF Mapping CRM System Sales Mgmt Order / Inventory Benefits Fast integration of federated relational data and public data No data mart required just to manage data integration , Cognizant

28 The Imperative for Universal Data Access There is more data in more places and in more formats than ever before Organizations environments and processes change frequently and unpredictably There is an unmet need to connect and engage crossorganizational data Partners Customers Business Units R&D Commercial and Public Data Publishers Distribution Network Increasing need for access to the right data at the right place at the right time , Cognizant

29 Semantic Enterprise Data Integration 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 Enterprise R&D Manufacturing Finance Data Governance Guides the Definition of Shared Data Externally-Hosted Data can be Mapped to Business Unit and Enterprise Models for Easy Access Sales & Marketing Administration Business Unit Level Models can use Vocabulary and Data Organization that Best Fit their Operations , Cognizant

30 Leveraging External Data Assets (1) Externally Federated External Databases Firewall External Databases (2) Replicated, Internally Federated Firewall Characteristics Data Location (1) Externally Federated Some or all datasets reside outside the firewall (2) Replicated, Internally Federated All datasets reside inside the firewall (3) Internally Merged All data resides in a merged, shared database External Databases (3) Internally Merged Firewall Data Integration Internal and External Internal Internal Data Latency Query / Analysis Performance Data is updated on system of record s schedule Performance dependent on external systems of record s infrastructure Data is replicated/ refreshed on internal schedule, but still dependent on systems of record s schedule Performance dependent on internal databases infrastructure Data is replicated/ refreshed on internal schedule, but still dependent on systems of record s schedule Performance dependent on internal merged database s infrastructure Internal management of external data can address performance concerns for infrequently updated small- to midsize external databases Physical integration (option 3) may achieve a specific performance or management benefit , Cognizant

31 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 , Cognizant

32 Define Reference Architecture Models to Guide Project Teams Bring Best-of-Breed Technology Products to Data Management and Delivery 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 , Cognizant

33 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 , Cognizant

34 Shared Rules Industrialize Your Data Quality Management Evolution of Requirements for Data Quality Rules The Organization s Data Quality Rules Unit A Rules Unit B Rules Traditional One-Size-fits-All Implementation Second Repository Unmet Demand , Cognizant

35 Industrialize Your Data Quality Management Evolution of Requirements for Data Quality Rules Processing Onboard New Data Regular Data Loads Dynamic Data Quality Management New Data New Data New Data New Data Data Quality Tests Data Quality Tests And / or Internal Data Store Unfiltered Evaluation Results Filtered Internal Data Store Query-level Data Quality Tests Query Results Effective when data is mostly static, and data quality is consistent Addresses variable data quality with a consistent set of rules Ontology model contains data quality rules that are executed at query time. Business unit models may contain rules that are specific to their requirements , Cognizant

36 Opening Access to Data while Securing those Data Assets from Unauthorized Users Leverage RDF data store security features Use Internet security features, including certificates, to authenticate and authorize users Build semantic models for specific user groups, defining only the objects and properties that the user groups are authorized to access Build a semantic model that defines security rules and access for groups, roles, and users. Direct queries through the security model , Cognizant

37 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 , Cognizant

38 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 , Cognizant

39 2. Profile the Data Light Profiling focusing on Understanding Key Data Elements Identify Initial Data Filtering Candidates Capture Insights about Key Data Relationships , Cognizant

40 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 , Cognizant

41 4. Generate the Initial Ontology for the Existing Data (if Necessary) Map Selected Entities and Critical Attributes for Existing Data Source(s) to the Source-specific Ontology Existing Data Add Reference to the Source-specific Ontology to the New Data Ontology Model Ontology Models Refinements will be made in Future Iterations New Data New Data Ontology manages integration with Existing Data until the ontology is sufficiently mature to be promoted into an enterprise ontology , Cognizant

42 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 , Cognizant

43 6. Create URI Links Customer Customer cust:zipcode JOIN Geography geo:zipcode Geography The Data has Common Values that can be used in Join Operations, but doesn t have Links Links Reduce Query Complexity and Can Improve Query Performance Focus on Key Queries, Identify Complex or Time-Sensitive Joins cust:zipcodeuri LINK Add Linking URI Attribute to Dependent Entity Amend Selected Queries to Leverage the New Link , Cognizant

44 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 , Cognizant

45 , Cognizant Questions?

46 Thank You , Cognizant

47 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 Thomas.Kelly@cognizant.com and For more information, check out our website at , Cognizant