Model-Based Development & Management
the biggest challenges facing today’s organizations is ensuring the right
information reaches the right people so data is used appropriately and
accurately. Unfortunately, few organizations treat data as the corporate asset
it truly is.
architects can design the best possible data model, but that doesn’t mean
developers will use the information how it was intended, or that business
analysts will immediately understand the relationships between different data
integrated solution for modeling can help reduce this problem by streamlining
information exchange and contextualizing the data model to minimize errors. By
using models as the lingua franca for all data stakeholders–-whether they are
application and Extract, Transform and Load (ETL) developers, Business
Intelligence (BI) professionals, or business users–-organizations can
effectively translate otherwise disparate requirements in context of one
another. For example, a data architect passing a logical data model to a
development DBA needs to ensure the design is properly implemented in the
database so the business’s rules are enforced. With an integrated approach,
models–-and the underlying metadata–-are smoothly and accurately passed across
models for the data architect and the DBA remain similar in look and feel, and
utilize the same types of notation. Deploying modeling solutions with tight
integration across functional areas can reduce the amount of time, effort, and
error involved in sharing information. And sharing at the model level simplifies
potentially complex processes because models can more readily be consumed and
understood by all required parties.
context, a model isn’t just a diagram with boxes and arrows. Yes, a data model
must include the visual representation of the data (such as an
entity/relationship diagram), but it also must be backed up by metadata. A model
without metadata might as well be just a quick picture scrawled on a napkin
somewhere. It’s the powerful combination of a clear diagram and the metadata
definitions for the elements in the diagram that make a model useful.
Facilitating Data Integration
other objectives, a good data modeling solution can help you reverse engineer
existing schemas into models and forward engineer models into new target
schemas. This is basically what you do during an ETL process: You reverse
engineer an existing data source (you extract the data along with the
accompanying metadata, and it’s the latter that’s described in the schema),
transform that data, and then forward engineer the target data.
this reason, it makes sense to utilize models as a mechanism to facilitate data
movement and data warehouse population. A model-driven approach to data
integration can enable data architects and BI professionals to import data
models and the associated metadata for use during ETL. This visual approach to
data integration helps simplify the process while ensuring the complete and
accurate migration and integration of data between systems.
Simplifying Application Development
can also be used to guide application development. The comprehensive data model
can be integrated into a process model to streamline the development process.
With knowledge of and access to the metadata, developers are better prepared to
build the correct processes the first time.
Furthermore, application modeling solutions used by development teams enable
clear application models to be created that can then be translated into code.
Model-based application development enables the deployment of higher quality
applications in less time. And with the appropriate tools, you can then generate
code directly from models into popular application development technologies
(e.g., Java, C, VB, etc.).
Data Integrity Across the Enterprise
Creating data models and collecting metadata that spans the breadth of an
organization can deliver significant benefits such as better decision support
and business information, or richer customer information and support. However,
these benefits are realized only when the information is accessible and
accurate. Using integrated modeling solutions to implement model-driven data
management and development can help organizations share knowledge effectively
and with minimum effort. And it will certainly produce higher quality
applications and data, too.
logical data model should be used as the blueprint for designing and creating
physical databases. Modeling can be integrated into your IT infrastructure,
thereby helping to exploit the synergy that exists within your databases, IT
activities, and application systems.