DAMA + Wilshire Meta-Data Conference - Data Strategy Track
Service Oriented Architecture (SOA) is taking the business and technologies spaces by storm. Yet, the challenges that face us all are to truly understanding what SOA is, and how it affects our discipline and role. This tutorial will provide a practical and real-world explanation for SOA, and guide the data practitioner through the supporting Web services standards. It will also point out where the data practitioner can have influence and add value.

You will learn about and understand:
  •  Service Oriented Architecture
  •  Supporting Web Services standards
  •  Service Artifacts and Metadata (real syntactical examples)
  •  Service Oriented Data Access - The "role" of the Data Practitioner
The move to adopt Service Oriented Architecture to better align Business and IT Strategy is well underway. What is becoming more obvious is that an effective SOA Asset Reuse Management stratgey is an integral part of transitioning an organization to SOA . This presentation highlights key technology trends and emerging metadata and SOA standards influencing SOA Development and Runtime registries and Repositories. The session will conclude with an overview of IBM\'s vision for Asset and Metadata Management across the development, deployment and operational aspects of the SOA Life cycle. Key topics covered include:
  •  Importance of SOA and SOA Governance
  •  Emerging standards in SOA Asset Based Development
  •  Business Process of governing SOA design and development
  •  Metadata management for SOA : What's different?
  •  Development VS Runtime repositories : Usage scenarios
  •  Where is the Industry headed? Where is IBM headed?

In the development of both Business Intelligence and Services Oriented Architecture solutions, there is a requirement for data integration architecture.

This session will take the attendees throught the development of a best practices data integration architecture that supports both the delivery of Business Intelligence and the incorporation of a SOA foundation.

Learn about the many facets of data integration architectures including:

  •  History of data integration architectures
  •  Key role of metadata
  •  Iterative development 11 step process
  •  Including Data Goverance & Stewardship
  •  Distributed vs. Centralized Models
  •  Architectural considerations
  •  Team composition and resourcing Real world examples and an open question and answer period will allow attendees to learn about the development of data integration architectures while receiving practical guidance and advise.
Business users are demanding more data, better analytics, and faster delivery times from their I/T organizations. And the tools to support these needs—BI, ETL, warehousing, mining, cheap storage—are readily available and no longer key differentiating factors. In fact, they may be driving behaviors that are inhibiting I/T’s ability to deliver what the business needs to compete. For example, when an application is built for a particular business function, it will typically manage its data within that function’s limited view of the enterprise data model. Over time, different applications will fragment the data model, as the needs of each business area evolve and time-to-market pressures make it easier to create loosely-coupled data stores for each application. Subsequently, one or more data warehouses will be built to collect and homogenize the data silos, leading to further inconsistency between the warehouse meta model and the source data models, and requiring heavy ETL transactions, redundant storage, and more data cleansing…not to mention higher development and maintenance costs. But if “the business is the business”, i.e., the business model is the same regardless of how its systems are structured, then why should each application need its own data model, and why should the data warehouse need a different meta model than the operational data stores? ESB (and more broadly, SOA) provides a framework and tools to help enforce better alignment of the enterprise data architecture across business applications and warehouses. In this presentation, we will discuss how ESB can be used to bring together multiple application data stores and data warehousing platforms under a common data access umbrella.
Three specific implementations will be discussed:
  1. Simplified data access services to replace localized (app-specific) services and eliminate cumulative calls and transformations;
  2. A common inquiry gateway for applications to navigate both operational data stores and a data warehouse;
  3. Right-sized access services that better fit specific data requirements and reduce the need for an application to “crawl the data model”.
Organizations in the early stages of SOA development face many challenges—governance is one of the most important. In order for SOAs to realize their intended business objectives—improved agility and reduced IT cost—organizations must always be in control of the services that are created, and then used and re-used. Governance that is implemented at design-time prevents an SOA from becoming simply ABOS—A Bunch of Services. Without proper design-time governance, services will be project-specific and redundant with other services. This will result in gaps across the business architecture, which increase IT costs and complexity without bringing any of the promised business benefits of SOA.

Brent Carlson, co-founder and vice president of technology at LogicLibrary, will explain how to initiate governance at the design and development phase and carry it through operations.

Carlson will
  •  discuss the role of an integrated registry/repository in keeping IT properly aligned with high-level business processes
  •  walk attendees through real-world success stories
  •  address the return on investment many companies are now seeing – up to twenty-times their original investment – by properly managing software development metadata.
If logical data models reflect the business view, one can think that they would be a strong basis for defining canonical structures to enable service oriented architecture, application agnostic archiving, etc. The challenge lies defining structures that can be implemented in the 'real world', with master data management repositories, inconsistent reference data across applications, and groups that may not agree with names and definitions.

This session will be based on actual lessons learnt in seeking to tackle the above, what worked, what did not work, how one can go from an ER model (which is a bidirectional graph) to one or more XML schema structures depending on the technical/business need.
This session focuses on reusing the existing enterprise logical data model to identify the business services as part of the SOA strategy. Specifically this session focuses on:
  •  Steps involved in service delivery (Service Identification, Service Specification and Service Realization)
  •  Discuss the Service Taxonomy and the applicability of logical data model in identifying the candidate business services
  •  Discuss the steps involved in business services identification - Discuss the creation of canonical data model using the logical data model through the service specification efforts
  •  Apply lessons learnt by Intel to create a sustainable service identification approach

Service Oriented Architecture is very hot now: most survey have about 60% of large firms starting an SOA project this year. Left to their own devices, many SOA projects will go bad. One of the main reasons is that a purely developer lead project has a tendency to re-implement Distributed Objects with XML interfaces, instead of applying the discipline needed to get to truly reusable services and messages. In this session will we review a methodology we have used successfully on several engagements we call the "Enterprise Message Model" (EMM). In this session attendees will:

  •  Learn the basics of SOA - Be able to describe why letting an SOA project run open loop is likely to fail
  •  Understand the basics of the EMM methodology Data Modeling professionals are in a great position to assist their firms with their SOA projects, if they seize the opportunity.
Data Modeling professionals are in a great position to assist their firms with their SOA projects, if they seize the opportunity.



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