Your organization wants to be customer-centric. The cost of strengthening an existing customer is far less than what it takes to acquire a brand new one. Unfortunately, the information needed to profile an individual customer is scattered across a myriad of account and product-oriented systems. How do you find the customers buried in your application portfolio?
Integrating customer data isn’t easy. Many stumbling blocks may be encountered along the way. However, techniques exist that can prove useful in the process of re-architecting your application portfolio to be customer-centric. This talk will explore the models, information architecture, data quality issues you are likely to encounter when your company decides to become customer-oriented.
The statement "a premier customer is allowed to have up to five unpaid orders outstanding at one time" represents a typical business rule. Further analysis will result in the following questions: "What is a customer?", "What differentiates a premier customer from any other customer?", "What is an order?". In traditional information engineering methodologies, the answers to these types of questions are normally recorded as meta-data in our models. When you look at the natural language statements that are the basis of business rules, do you see the same statements that we normally use in defining an enterprise’s meta-data?
Meta-data is the representation of an enterprise’s business rule statements according to a classification scheme that can be readily transformed into business information systems. Using this definition as its theme, this seminar provides an introduction to the business rule approach and its relationship to meta-data. The following topics will be discussed:
- Business Rules and Meta-data
- What is Meta-data?
- How business rule classification schemes relate to traditional modeling techniques
- Integrating the business rule meta-model into an organization’s meta-data model
- Business Rules and the Dynamic Business Model
- Illustrates how business rules are configured within the dynamic business model
- Demonstrates how dynamic business model's meta-data structures provide flexibility for changing business rules
- Business Rule Discovery
- Object-oriented techniques which are useful in business rule discovery
- The Concept Catalogue
- Object Class Models
- Scenarios
- Swimlanes™
- Mining for Business Rules
- Finding Golden Nuggets in Application Code
- Tools that support meta-data extraction
- Business Rules Management Environment
- How the meta-data management environment must be enhanced to support business rules
- From natural language statements to applications: how far have we come?
Swimlane is a trademark of Frazier Technology, Inc.
With the emergence of the data warehouse and the interest in business rules, metadata is finally being recognized as an essential component of the Business information resource. We always knew that metadata has value. We just had a hard time finding the right business stakeholders who also saw that value. Now, the data warehouse has created the need for metadata. By describing the data available through the data warehouse, metadata provides the brains of the decision support environment. It is metadata that provides the context that transforms the warehouse data into meaningful business information. The users of the data warehouse, business management and executives, are the most powerful individuals within the organization. What they want, they normally get. And, right now, they want access to metadata. Never before has the opportunity existed to elevate metadata to a position of value equal to that of the business data maintained in our corporate databases. The moment is right and we must seize it.
Managing metadata is not a casual activity. To manage information as a business resource, a system of management controls must be in place that governs how information is created, maintained, used delivered and secured . This one-day seminar will explore what is necessary to manage meta-data as a corporate resource by discussing:
- What is meta-data? Why is there so much interest in met-data right now?
- How meta-data helps to enable the learning organization
- What environment is required to manage meta-data
- How a repository is the central component in a meta-data management environment
- Criteria for evaluating how well commercial repositories meet our meta-data management needs
- A status report on where our industry is in its ability to manage meta-data
It’s been nearly a decade since Object-Oriented approaches made their debut to the data management community. Since then, data administrators have been wrestling with the issues surrounding data management in an object-oriented development world. As object oriented approaches are embraced by Information Technology (IT) organizations, an interesting dilemma has emerged. Object Oriented programming languages have matured faster than their Database Management Systems (DBMS) counterpart. Likewise, most organizations have taken a "wait and see" attitude towards object-relational databases. Consequently, IT is developing OO applications that derive their persistence from traditional relational databases. Modelers who attempt to marry the OO and data disciplines are often finding themselves with some strange bedmates.
Drawing on Inastrol's experiences with OO projects, some of the burning questions a data administrator has are addressed:
- how are object and data models different
- do we still need data models? or is the object model sufficient to support data and database design?
- which comes first, the data model or the object model?
- what’s the role of data administration in an OO world?
- are Data Modelers from Venus; Object Modelers from Mars?
The goal of this presentation is to alleviate unanticipated difficulties that can be encountered when an Object model is transformed into the corresponding, normalized data model required to incorporate relational technology into a persistent object framework. A formal approach for conducting this transformation can serve as a guideline in ensuring that the migration from Object model to relational data model occurs smoothly. Object model diagramming notation provides a richer vocabulary for stating business rules than most data modeling methodologies. The challenge for any Object Model to Data Model transformation is ensuring that all the business rules stated in the Object model are accounted for.
Some of the topics addresses include:
- obvious and subtle differences in an object model versus a data model
- key issues in achieving object and data synergy
- insights into object modeling issues not fully addressed by data modeling techniques and vice versa
The presentation highlights selected Object model constructs and their transformation into the corresponding data model structures, illustrated with guidelines and examples.