Jason Tiret of Embarcadero (below) looks at how data models are used to better service enterprise data management

As new projects are undertaken, ensuring that business requirements are adequately addressed and accurately implemented can be a challenge unless the metadata (i.e. the data about the data) has kept apace with the growing needs of the business, continually evolving alongside it.

The Importance of data governance
Data governance is becoming ever important to businesses as they strive to meet the needs of regulatory compliance measures such as Sarbanes-Oxley, Basel II and most recently MiFID.

However, very little of the data that is stored within a corporation is actually used to its benefit. Gartner estimates that only 15% of data is actually used for the benefit of the organization – 85% of data nobody knows what it is, where it is or what to do with it. How happy would executives in your business feel if you told them 85% of your data was unusable and just taking up disk space?
Data governance entails many things but the basic premise is a set of standards or guidelines for managing data on an enterprise wide scale with the goal of making it more useful, more secure, and more valuable - i.e. turning a storage cost in to an asset for the business. By ensuring best practice around data within a company you can automatically drive down data centre costs and gradually begin to utilise that ‘missing’ 85%.

The scope of data governance extends beyond the data architecture team and it is very important that both the architects and modelers are involved with the data governance initiatives to ensure the business is aligned correctly. This means creating standards for how your data is secured and documenting what, if any, sensitivity to compliance laws it may have. It means defining the stewards of your data as it relates to responsibilities of managing it, such as the quality, design and business rules. It also means creating standards for database development as new databases are built and existing databases are re-architected. It is critical that these standards be integrated into the models to service the data governance initiatives of your business. A general definition of an entity in a typical data model very rarely documents the sensitivity level of the data it represents, the use of the data on an enterprise or departmental level, the last time the represented data was checked for accuracy, or the last time the structure was changed in the database. Most organisations are just happy that an entity has a definition at all. Nevertheless, this information needs to be incorporated into the models, otherwise it will just become yet another outdated artifact that IT needs to manage.

The latest technological trends
Both web services and service-oriented architecture are two hot technologies that are currently on most business’ radars. SOA enables the integration and reuse of data throughout the enterprise which can, amongst other things, speed-up processes and increase data sharing across the organisation. This can often improve efficiency across various business areas such as customer service and technical support call centres or sales, order management and accounting. A big component of SOA and web services is XML and XML schemes that represent the data and structure in a message. These, like everything else representing the structure of data, need to be governed.

Many organisations are actually using data models as the origin of XML schemes. This makes sense because they can use the same set of standards that are applied to physical data models and databases and leverage them for creating the XML schema structure. This often starts with creating logical models which represent the canonical form of the XML messages. A canonical model will typically be somewhere in between a conceptual and a logical model but will be fully attributed and enforce stricter vocabulary and stronger typing for the attributes. The benefit is that the same vocabulary and naming standards can be used for the XML as it is to create databases which are typically where the data originates any way.

A safe repository
The importance of storing the data models in a repository as opposed to a network drive cannot be understated. Models represent a large part of the intellectual property of a business. The worst thing that can happen is to have them stored on personal hard drive or network drives with no process for backup and recovery, no ability to analyse what the sum of the parts is, and no way of knowing what is truly out there. It gets very difficult to align the information about IT assets with the knowledge and rules of the business. Getting them into one central container maximises the benefit they can provide to an organisation. This can help isolate areas of redundant data and reduce the overall cost of storage for the data. In addition, most repositories have the ability to reuse information across various models to promote reuse and further drive down the cost of the managing common data in systems throughout the organisation.