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Enterprise Data Modeling

20 March, 2011 | Anne Marie Smith, Ph.D. | Data Architecture

Enteprise data modeling is an essential component of strong enterprise data architecture, with subject, conceptual and enterprise logical models based on business concepts and requirements

Introduction

 

In many organizations, there is no way to understand the scope and variety of the data captured, stored and used by the applications and business activities that need information.  An enterprise data model is an integrated view of the data and related processes that consume and store data across the organization.  With a focus on business needs and not technology, an enterprise data model unites, formalizes and represents the concepts and items important to an organization, and displays the rules governing the data and its usage.  The development of an enterprise data model should be part of an organization’s enterprise data architecture, which is a component of their enterprise data management function

 

Occasionally, companies purchase what is referred to as an industry-standard model for their industry from a vendor.  For example, EWSolutions has created enterprise models for insurance, law enforcement, healthcare and financial accounting services.

 

These industry standard models can serve as the foundation for constructing an enterprise model for the organization, and can assist in refining those models that have already been constructed, if appropriate, by adding the integration of requirements across applications or subject areas.  This integration is essential if an organization is to realize the benefits of increased productivity and reduced systems development and maintenance costs.

 

Technology independent, enterprise models provide a stable, cohesive view of the corporate information resource and enterprise architecture for both data and process.  An interdisciplinary team that understands the enterprise’s requirements for data and the processes that act upon that data should develop the model.  Of course, the enterprise modeling team should be in control of all data modeling activities, so that the enterprise model reflects the organization’s data accurately and completely.

 

In many cases, the development of enterprise models solidifies the business’ views of its essential functions, and the data needed to perform those functions.  This effort should result in a single, enterprise agreement of the information and functions, including the business rules that a company uses to secure the value of its data and information assets.

 

Enterprise Data Modeling Purpose

 

According to the DAMA Data Management Body of Knowledge (DAMA-DMBOK©), an enterprise data model (EDM) enables effective data management and data governance through the understanding that comes from organizing the data by subject area rather than by application or other technical delineation.

 

The enterprise data model enables the identification of shareable and/or redundant data across functional and organizational boundaries and gives business users context for data that may be employed in multiple places in the company.  Integrated data provides a “single version of the truth” as a goal of master data management.  Modeling data at an enterprise level allows the organization to minimize data redundancy, disparity, and errors, which are essential for the performance of data consistency, accuracy, validity and other data quality dimensions.

 

Enterprise data models are an important companion to application data models, since they give the organization a holistic view of the data and its relationships unencumbered by any technology constraints that may be required in an application data model.  Using the enterprise model as a balance for the application data models, the organization can identify omissions in its data capture and usage methods in applications or processes that were not apparent during application development.  In organizations that purchase packaged software applications, developing an enterprise data model provides a common framework to evaluate all packages against for data requirements, ensuring that the organization’s needs will be met by the packages.

 

 

Enterprise Data Model Scope

 

The recommended scope of an enterprise model project includes:

enterprise-data-modeling-1

 

Enterprise Data Model Approaches

 

There are three main approaches to data modeling, and each part of an enterprise data model generally uses one of these approaches.

 

 

Subject area models use the top-down EDM technique that focuses on fact-finding discussions with participants from various business areas who are responsible for the collection or use of data.  The participants identify the major business objects and their definitions.  In the Subject Area Model, the unique business identifiers of those objects, and the data relationships between them, the significant attributes of those objects, and the main business rules for those attributes are not included.  The top-down data modeling technique produces a valid Subject Area Model quickly.  However, using this approach for an entire enterprise data model would be laborious, since it would require numerous sessions to identify and model all the complex business rules for each entity in each concept in every subject area.

 

Conceptual Data Modeling also uses the top-down EDM technique, identifying and defining entities for each subject area and adding the data relationships between the objects to the model.  Again, the unique business identifiers of those objects, the significant attributes of those objects, and the main business rules for those attributes are not included.  As in the Subject Area Model, this top-down approach creates an Enterprise Conceptual Data Model in a relatively short time.

 

Bottom-up enterprise data modeling involves the difficult task of normalizing existing data structures that are usually de-normalized into a set of approximately correct logical data models and merging these models into an integrated, non-redundant enterprise data model.  Alternatively, the bottom-up approach creates a data model starting with a single entity type and completing all the attributes for it, then building another entity and relating the second entity to the initially developed one.  The model development continues along this path, entity by entity for a specific concept until all the entities in a concept have been modeled, moving on to the next concept and beginning again.

 

Using the bottom-up data modeling technique produces a generally complete enterprise data model, despite the protracted time needed for this effort.  However, an enterprise data model based on approximately correct logical data models cannot be trusted until the EDM has been reviewed and validated by all the business stakeholders, throughout the enterprise.  Additionally, many business rules and metadata cannot be extracted from existing physical data models, so the completeness of an enterprise data model designed using the bottom-up approach should be questioned.

 

The third approach, center-out, describes the method of building the enterprise data model iteratively using both techniques, starting either top-down or bottom-up and then continuously alternating between the two approaches.  This technique is most frequently used for building the enterprise logical data model, and can be effective for reverse-engineering source files and databases to collect their integrated ETL processes, contributing robust metadata to the enterprise data model.

 

 

Uses and Benefits of an Enterprise Data Model

 

The Enterprise Data Management team should be a catalyst and reference point, informing a team about other efforts and how those groups solved a problem.  They should serve as the source of reusable objects (entities, attributes, definitions, abbreviations, etc.) and should enable development and maintenance to proceed faster, secure in the knowledge that their efforts will be integrated into the rest of the enterprise.  Data Management, in its role as the administrator of the corporate metadata repository, can provide corporately defined objects for reuse and the guidance to ensure their effective use.

 

One of the most important benefits of an enterprise data model is its use as a reference for data integration.  The enterprise data model becomes a blueprint for current state and future state data management, so any changes identified during an integration process should be implemented into existing applications to preserve the consistency of the enterprise view.  If these modeling integration activities are not incorporated into the existing applications, the modeling efforts will have been solely an intellectual exercise.

 

Questions arise during every enterprise modeling effort concerning the balance of strategic (enterprise) modeling versus tactical (application) modeling.  These efforts can occur simultaneously as long as the tactical modeling team communicates regularly with the strategic modeling team.  The tactical team must be expected to incorporate objects developed by the enterprise team so that reusability is achieved and maintained.  The strategic team must recognize the needs of the application modeling team for robust, flexible objects that can accommodate multiple applications.  This amalgamation is common in model-driven development and is successful in organizations that recognize the need for both types of models.

 

Conclusion

 

In conclusion, the development of enterprise models is an activity that requires commitment from Senior Management (to approve the project and to require adherence to the modeling purpose and results), from business management and staff (to provide knowledgeable, dedicated and empowered resources for the duration of the project), and from IT management and staff (to provide knowledgeable, dedicated and empowered staff for the duration of the project, and to adhere to the modeling principles and results in systems development and maintenance).  The organization discovers the business data needs for each area, its meaning and relationships, the uses of the data from business perspectives and learns how data and information can serve as organizational resources when they are organized at an enterprise level.

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