One of the biggest challenges facing today’s organizations is ensuring the right information reaches the right people so data is used appropriately and accurately. In the age of Big Data and analytics, there is even more data being churned out more frequently than ever before. And few organizations treat data as the corporate asset it truly is, so documentation is sparse to non-existent.
Data architects can design the best possible data model, but it does not necessarily follow that developers will use the information how it was intended, or that business analysts will immediately understand the relationships between different data elements.
An 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 common language for all data stakeholders–-whether application or data warehouse developers, Business Intelligence (BI) professionals, data scientists 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 functional boundaries.
The 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.
In this 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
Among other objectives, a good data modeling solution can help you reverse engineer existing schemata into models and forward engineer models into new target schemata. 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.
For 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
Models 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.
Additionally, 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.
Data Modeling, NoSQL and Big Data
It is sometimes said that Big Data and NoSQL database implementations benefit from schema flexibility. This means that one record may contain different data elements than another record within the same database row (or object or document or…) This would seem to obviate the need for a data model.
But it should not! If you simply determine that the developer or user can add any data at any time anywhere in the database then you will end up with a pile of mismatched, unmanaged data – or what I like to call a data outhouse. So sure, the flexibility offered in the NoSQL world can be a useful tool, but without a data model it turns into a disaster.
NoSQL database systems grew out of the need to resolve certain big data management issues. But running systems without a definition of the data being managed should not be the result. If it is, then things will go from good to bad to worse!
Enhancing 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.
A 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.