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On August 1, 2007, a jury in Springfield, Massachusetts decided that the Visible name used by Visible Systems Corporation since 1984 for modeling tools and services is a protectable trademark.  Visible Systems was essentially the last of the early CASE Tool vendors still operating under it own name.  How did George Cagliuso and his company of a dozen employees in Lexington, Massachusetts, show a jury that they were the oldest name still standing in the market of data and enterprise modeling?  Lead trial counsel for Visible, Stephen Galebach of Boston, Massachusetts, will explain how the complex details of data, process, and enterprise modeling were successfully presented in an understandable manner to a jury.  Experts involved in the case were Peter Aiken of Data Blueprint and Virginia Commonwealth University (consulting expert) and Malcolm Lane of James Madison University (testifying expert).

As a discipline, data management is younger than software development.  (It turns out that we perform this function slightly better by some measures than software developers and/or systems integrators.)  This talk presents an historical perspective on how data management has evolved.  It draws on 25 years of published historical data including the 2007 data management survey conducted by the authors.  While, the talk portrays data management from its earliest incarnations and includes the latest data management buzzwords such as MDM, CDI, SOA, mashups, etc. the more important message from the talk is how today's data managers are able to leverage their understanding of our discipline's ability to contribute to effective and efficient organizational functioning. Delegates will conclude the session understanding:  1) why the past of our discipline greatly impacts current perception and practice; 2) how the discipline has necessarily evolved from strictly technical to socio-technical; 3) how embracing these changes will leave their organizations better prepared to meet current and future organizational data requirements.

Elizabeth Davis
Founding member and Unit Leader of the Information Quality Group
International Finance Corporation

Data Management principles like "accountability at the source" or "invest in data scrub projects only when you are able to protect clean data going forward" are common sense and well-known among data management professionals. But how can you make these and other principles work in practice? What kind of support do you need from senior management, from the IT Department, and from the subject matter experts to launch a data governance initiative? What do you need to give back to the business to build credibility and deliver on your mandate?

Peter brings a theoretical framework for assessing data management maturity across of variety of institutions and businesses. Elizabeth is a practicing data management professional with the war stories and practical experience of building an "information quality" function from the ground up in a large, global financial institution over the last five years.

Areas for discussion include:
· Essential data management principles that are the foundation of governance
· Preconditions for success and organizational hierarchy
· Establishing relationships and credibility across the organization
· Living the data governance role day by day
· Delivering value to the business

Data governance as a function is never "done", it never gets easier, and it's always political. Learn how to work hard but smart to provide effective data governance in any organization.

SOA is a current buzzword and it represents a tremendous challenge.  As some say "Everything must become SOA."  This of course represents a tremendous challenge to organizations.  This session explores the SOA implications from a data quality perspective.  Specifically, it will describe:

-       Specific SOA requirements as they pertain to data quality

-       How to avoid SOA hype perils during project planning

What are mashups and how might the be useful and/or impact me and my organization.? Mashups occurs when someone writes a website that uses data from another website. Sounds simple enough but the implications are huge particularly when considering other architectural configurations such as SOA. This talk describes and illustrates a number of mashups, describes the basic technology behind them, and will leave delegates with the ability to evaluate their potential utility for their own organizations.

Data Program Coordination
Enterprise Data Integration
Data Stewardship
Data Development
Data Support Operations

When considered under the CMM umbrella, understanding these five processes, their interactions and associated sub-processes will help organizations to better focus their efforts and lead to the development of better organizational data management capabilities.

- Governance defined, governing processes, governmental considerations.
- Performance to date (but don't give up).
- Top down, bottom up, or middle out? Where are you coming from and why?
- Communication: elevator speeches, folk lore, and re-education.
- Conditions for success.

This experience paper describes a repeatable model developed to address a class of data quality problems encountered when converting text data to ERPs.  Users often devise their own means of implementing system features not directly supported by the systems.  Often they employ what are known as clear-text, free-text, or "comment" fields to support the desired features.  Moving data from these fields to ERPs involves first extracting atomic data items.  Unlike most data, free text is not subject to structural or practice-oriented data quality measures when it is created. This results in a range of data quality challenges ranging from typing errors to structural errors such as prime key mismatch, duplication, and other issues. In our experiences with one large government system, a number of challenges were encountered that contained enough internal differences to require the development of a more generic framework for addressing this type of problem. The specifics of the actual issues confronted are not as interesting as the lessons that can be learned from the general approach to problems of this type. The solution type developed demonstrated a positive return on investment to the government. We will discuss the challenges, the costs associated with continuing along the original path, the solution developed, and its applicability to other organizations and situations. - Challenges that will increase DM complexity and scope by as much as fivefold in the coming years

Management typically does not understand metadata or the need for it. Consequently, data managers wanting to make a business case for metadata-based investments should consider building their case from legal and financial as well as technical ingredients. This talk will present several recipes for combining these ingredients into a successful business case for metadata management.Understanding these will help CIOs, CTOs, CIOs, VPs and Directors of IT as well as high-level technical staff, project support organizational BI demands. Implementing these changes early will secure significant advantages for your organization over your competition and help to correct the perception that DM does not matter.

This presentation examines the results of a real life business rules extraction exercise for a client. It attempts to analyze the productivity of the business rule extraction process and postulates some measures that may be useful in planning for (manual versus automation-assisted) business rule extraction metrics. It points out where the labor-intensive activities are and where opportunities for time and cost savings ought to be. These should be useful when developing a business case for extracting business rules from legacy code as part of a system migration and transformation process. They should also prove useful when prioritizing business rule extraction among candidate systems and developing reasonable project plans.Dr.

XML-based technologies are capable of transforming data management, administration, and architecture in profound ways. This tutorial shows you how to start incorporating XML capabilities into your data management activities. XML-based technologies permit new and more extensive integration possibilities and can be implemented with little or no change to the existing applications or data – the non-intrusive approach championed by industry expert, Rosemary H. Rock-Evans. Understanding these capabilities permits organizations to make better decisions regarding the adoption and use of XML and associated technologies. Thus equipped, organizations can develop XML-based architectures permitting them to implement solutions that are solid foundations for future development and not just the latest "silver bullet." Those of us concerned with data challenges (such as delivery, integration, quality, interchange, etc.) are gaining access to advanced technologies allowing us to address these challenges in a programmatic manner using structured techniques. The tutorial presents an overview of these possibilities including:

How well does your organization manage its one resource (described by Brackett) that it cannot use up, and is designed to be reusable? Chances are - not as well as it could. Over the past two years, the Institute for Data Research has surveyed more then 40 organizations of differing sizes - from both government and industry. The results of this survey are permitting the development of a model that can help organizations assess their organizational data management practices. Good data management practices can help organizations save the 20 - 40 % of their technology budget that is spent on non-programmatic data integration and manipulation (Zachman). This talk describes the Data Management Practice Maturity Survey and presents the results to date. Participants will be equipped to generally assess the state of their own organizational data management practices.* Architecting classes of problem engineering-based solutions instead of more expensive, point-to-point solutions! Many applications that have been seen as very complex can now be successfully implemented.

A survey of the 1,200 + attendees of the 2001 Data Management International Conference yielded statistically significant results permitting description of the state of current data management practice in more detail than has been previously available. When combined with other research conducted recently by the Institute for Data Research, it begins to explain a number of interesting challenges that the community has faced as well as some basic causes for a number of types of project failures. These include the areas of data repository technology, modeling/CASE tool usage, ERP implementation and a number of others. Knowing where we are will help us to move forward.* How XML and metadata management are inextricably linked as "the" new way of delivering data solutions to enterprise information challenges. Using XML – data managers can more easily integrate existing corporate data assets such as data in warehouses, legacy systems, e-mail and other office documents.

In the past, EAI, has focused on middleware-based solutions aimed at connecting disparate applications together. Now businesses are realizing that technical solutions alone cannot help us to tame the legacy dragon, integrating new and working applications, as well as new or existing data in databases or files, built using diverse technologies, across a network connecting the machines of a company or companies. XML-based EAI technologies permits implementation with minimal or no change to the existing applications or data – a non intrusive approach.” This talk highlights aspects of XML-based, EAI technologies that can deliver tangible integration, rapidly when implemented by data management.* How XML-based metadata engineering is required as we reconsider our approaches to data quality engineering and enterprise integration?

Careful analysis and preparation is required in order to prepare for XML-based delivery of data via Corporate Portals. This process is refereed to as Engineering Enterprise Portals. Two phases are required when engineering Enterprise Portals: metadata engineering and metadata implementation. This presentation describes the use of the metadata model to guide the metadata engineering as a precursor to metadata implementation in preparation for XML-based delivery. In metadata engineering, logical models representing the "as is" system data are developed by reverse engineering the data. Once derived this metadata is typically maintained using entity relationship diagrams. Metadata about entity relationship diagrams can be maintained with a many to many association between two metadata entities: LOGICAL DATA ENTITY and LOGICAL DATA ATTRIBUTE. The two metadata entities form the basis of a metadata model that can be used as a structure facilitating the subsequent metadata implementation. Understanding the requirements of metadata engineering is a necessary prerequisite to delivering data via Corporate Portals via XML.* Standardized delivery of organizational data via an XML-based portal provides a central point of integration. This permits organizations to begin accruing tangible savings (of hundreds of millions) on many aspects of organizational information integration and delivery. The technology is so powerful that virtually all organizations in industry, academia, and the public sector will need to develop XML-based portal capabilities to remain competitive. Organizations are easily and tangibly profiting from this technology.

Whether automated or manual techniques are employed, reverse engineering goals remain identical. This paper describes the development and application of a manual reverse engineering analysis of a large, legacy system. The manual analysis was required because circumstances prevented the application of automated reverse engineering techniques. The paper describes: the larger organizational systems reengineering context in which the reverse engineering was required; the circumstances motivating the specific reverse engineering analysis goals; the situational characteristics preventing application of automated techniques; the manual reverse engineering process developed to achieve the analysis goals; the evolution of the analysis products during the course of the analysis; the analysis results; the resources required to produce the results; and management's evaluation of the process effectiveness. This research also offered an opportunity to note the similarities and differences between manual and automated approaches as well as ...* In many cases XML permits a simple to use and inexpensive to implement yet more robust means of electronically exchanging data than - electronic data interchange (EDI). Some say that XML is EDI for the rest of us!

Data reverse engineering (DRE) is a relatively new approach used to address a general category of data disintegration problems. DRE combines structured data analysis techniques with rigorous data management practices. The approach is growing in popularity as an integrative systems reengineering method because of its ability to address multiple problem types concurrently. Integrative problem solving is key to effective application of DRE. Four problem scenarios are described as typical of those facing practitioners confronted with data disintegration problems. A general DRE template is described as both an activity model and as a data model to be populated with reverse engineered data. DRE is shown to offer an integrated common solution methodology for addressing the problems. In addition, DRE outputs can be used to develop a more flexible and useful reengineered system. The four scenarios describe: 1) harnessing data assets to address organizational data integration problems; 2) developing organizational data migration strategies 3) specifying distributed systems architectures; and 4) successfully implementing and propagating organizational CASE tool usage to address system maintenance problems. Selectively applied DRE can be an important first step toward eventual organization-wide data integration.* Recovery and management of XML-based metadata can often be accomplished as a by-product of other information engineering tasks with just incremental cost structures.

Data reverse engineering (DRE) is a relatively new approach used to address a general category of data disintegration problems. DRE combines structured data analysis techniques with rigorous data management practices. The approach is growing in popularity as an integrative systems reengineering method because of its ability to address multiple problem types concurrently. Integrative problem solving is key to effective application of DRE. Four problem scenarios are described as typical of those facing practitioners confronted with data disintegration problems. A general DRE template is described as both an activity model and as a data model to be populated with reverse engineered data. DRE is shown to offer an integrated common solution methodology for addressing the problems. In addition, DRE outputs can be used to develop a more flexible and useful reengineered system. The four scenarios describe: 1) harnessing data assets to address organizational data integration problems; 2) developing organizational data migration strategies 3) specifying distributed systems architectures; and 4) successfully implementing and propagating organizational CASE tool usage to address system maintenance problems. Selectively applied DRE can be an important first step toward eventual organization-wide data integration.* How the existing XML component architecture ensures that it can provide the basis for solving many forms of data integration that have been challenging organizations for years.

In the past, EAI, has focused on middleware-based solutions aimed at connecting disparate applications together. Now businesses are realizing that technical solutions alone cannot help us to tame the legacy dragon, integrating new and working applications, as well as new or existing data in databases or files, built using diverse technologies, across a network connecting the machines of a company or companies. XML-based EAI technologies permits implementation with minimal or no change to the existing applications or data – a non intrusive approach.” This talk highlights aspects of XML-based, EAI technologies that can deliver tangible integration, rapidly when implemented by data management.* How the data group can develop and deliver complete information delivery solutions to organizational clients - solving forever the "what have you done for me lately" problem.

This talk presents a more in-depth look at XML for Data Management. The content is culled from a longer seminar (see http://www.irmuk.co.uk/) that has been popular with organizations. It discusses major XML architectural components and how they have been applied in organizational data management contexts. Eamples are used to illustrate XML's utility in data management contexts.