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Mdm Is Not Enough, Semantic Enterprise Is


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A broader view on MDM

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Mdm Is Not Enough, Semantic Enterprise Is

  1. 1. MDM Is Not Enough Semantic Enterprise Is by Semyon Axelrod SemanticWebEnterprise “The significant problems we face today cannot be solved at the same level of thinking we were at when we created them.” Albert Einstein “By far the most common mistake is to treat a generic situation as if it were a series of unique events, that is, to be pragmatic when one lacks the generic understanding and principle.” Peter Ferdinand Drucker
  2. 2. Agenda • Modern Enterprise modus operandi • Integration of disparate information systems • Issues – Data integration versus system integration • Data Integration Techniques and Technologies • Master Data – Modern enterprise complexity – Lack of business processes architecture • Solution – Enterprise Architecture – Semantic Enterprise • Q&A
  3. 3. Integration in the modern enterprise • No business is static – the only constant is change • Business processes and business systems – Integration crosses existing enterprise boundaries • Partners • Suppliers • Clients • Vendors • New systems are being built and legacy systems are being modified • All systems need to be connected – integrated
  4. 4. Data and Systems Integration • Theoretical Perspective: Data integration is the process of combining data residing at different sources and providing the user with a unified view of these data – Maurizio Lenzerini, quot;Data Integration: A Theoretical Perspective”. Principles of Database Systems (PODS) Perspective” symposium (2002). – Works well for OLAP and in case where operational context is highly homogeneous and thus can be standardized • US Postal Address • Practical Perspective: Systems interoperability is based on the exchange of data between systems – Works well for OLTP • For this presentation: Data integration ≡ Systems integration
  5. 5. Data Integration Techniques and Technologies • Techniques – technology independent approaches/styles: – Propagation, Consolidation, Federation • Technologies – practical implementations of techniques: – Data Replication, ETL, EAI, EII, ECM • Tools – COTS applications – Colin White, “A roadmap to Enterprise Data Integration”, BI Research, November 2005
  6. 6. Modern Enterprise Information Flow Sales Enterprise DataWarehouse Product Development Long Term Trend Marketing ODS 1 Analysis Master Data GL North America ODS 2 GL1 International
  7. 7. MDM – integration perspective • Master Data is shared data that has a single content and format and is available to all the systems within the enterprise that need to reference it – Product – Supplier – Customer • Master Data Management (MDM) is the capability to create and maintain a single, authoritative source system of “master” enterprise-level data. • MDM application (or system) is a system that provides consistent view of dispersed data. – Colin White, “A roadmap to Enterprise Data Integration”, BI Research, November 2005
  8. 8. MDM – semantic perspective • It is always possible, and arguably, quite easy, to misinterpret any shared data in the absence of rich contextual information that unambiguously distinguishes between different possible meanings – Customer • Current customer • High-value customer • Returning customer
  9. 9. Master Data Management as semantic integration problem • Customer for different operational units – Sales – Marketing – Customer Service – Legal – Regulatory Operational Risk • Primary Borrower – Primary Financial v Primary Legal – Origination, Secondary Acquisition, Risk Analysis, Primary Servicing, Investor Servicing, etc • Bankruptcy Indicator – Legal – Operational as used in loan servicing
  10. 10. Senseless Conclusions or Meaningful Integration • “Integrating two “loss” relations with (implicit) heterogeneous semantics leads to erroneous results and completely senseless conclusions. Therefore, explicit and precise semantics of integratable data are essential for semantically correct and meaningful integration results.” • “Note that none of the integration approaches above helps to resolve semantic heterogeneity; neither is XML that only provides structural information solution.” – Three decades of data integration – all problems solved? Chapter 4, from Structural to Semantic Integration Patrick Ziegler and Klaus R. Dittrich. University of Zurich.
  11. 11. Modern Enterprise Complexity • Scale – Local global • Time – Significant latency NRT • Technology – Ubiquitous and omnipresent – Operational Silos Enterprise-level view – Static applications with substantial manual steps Composite applications and SOA-type services
  12. 12. Solutions • Business processes contextual information contains the answers that we are looking for • Data and Process – yin and yang
  13. 13. Semantic reconciliation • Vickie Farrell, Cerebra WebMethods Software AG: “Lack of quot;semantic reconciliationquot; among data from different sources is inherent in a diverse, dynamic and autonomous organization. … Resolving discrepancies in metadata descriptions from multiple tools, not to mention cultural and historical differences, involves more than physically consolidating metadata into a common repository.” “The Need for Active Metadata Integration: The Hard-Boiled Truth”, DM Direct, September 2005;
  14. 14. EA: 4 Domains and 3 Perspective I
  15. 15. Yin and Yang of Information Management
  16. 16. MDA-inspired Architectural Domains I Business Strategy Computationally Independent Business Capabilities Domain Business Business Business Business Enterprise IT Principles Capability Capability Capability Capability Governance and 1 2 3 4 Framework Heuristics Conceptual Enterprise Information Model Logical Enterprise Information Model Platform Independent System Specification Domain Technology Enterprise Enterprise Enterprise LOB-Level Standards Integration System A System B Systems and Guidelines Model Specification Specification Interfaces Platform Specific Physical Implementation Domain Physical Enterprise Information (a.k.a. Data) Model ITIL Business Technology DB Schema/ XML Schemas Components CMDB Services Services Tables
  17. 17. MDA-Inspired Architectural Domains II
  18. 18. Semantic Enterprise • Well-engineered business enterprises – Process-driven information-centric and context-rich – Well-defined Governance – Co-evolution between business and IT • Enterprise Architecture – Unifying organizing logic at the enterprise level – Develops and maintains all EA domains • Uses modern approaches to address the issues long term – Ontologies and other semantic technologies – Domain modeling – SOA based • MDA
  19. 19. Semantic Enterprise Technologies - Ontologies • Ontologies – Ontology in addition to taxonomy characteristics, with formal subtyping and rules for inclusion and exclusion, will also include other relationships, i.e., part of • UML diagrams: Class, Activity, State Transition Diagrams, etc
  20. 20. Semantic Enterprise Technologies -- SOA • Enterprise SOA Governance should include Enterprise-level ontologies – Semantic technologies (OWL, RDF) should be part of the SOA technology suite along with UDDI, WSDL, etc – Service repositories and registries should be able to handle ontological operations in addition to UDDI – Semantic of each service operation should be completely unambiguous from both operational and informational perspectives
  21. 21. Semantic Enterprise – where to start • Culture change • Use models • UML • Business capabilities model – Information modeling instead of data modeling – Connecting business success to EA
  22. 22. Q&A • •?