Data Management • uSlness • ec Ive success Everyone knows that data volumes are increasing at enormous rates but is knowledge of the data, and more importantly knowledge within the business, increasing with it? Jason Tiret examines the best practices on how data models are used to improve service enterprise data management. IN TODAY'S WORLD OF DATA The latest technological trends Both web services and service-oriented architecture (SOA) are two hot technologies that are currently on most business's 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 schemas 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 schemas. This makes sense because they can use the same set of standards that are applied to physical data level ofthe 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 Nevertheless, this infor"mation needs to be incorporated into the models, otherwise it will just become yet another outdated artifact that IT needs to manage, with no tangible benefits to demonstrate to management. business feel if you told them 85 per cent 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 into 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 per cent. The scope of data governance extends beyond the data architecture team and it is very important that both the architects and modellers 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 90 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. very little ofthe data that is stored within a corporation is actually used to its benefit. Gartner estimates that only 15 per cent of data is actually used for the benefit of the organisation - nobody knows what the other 85 per cent of data is, where it is or what to do with it. How happy would executives in your governance, web services, regulatory compliance and heightened information security, data architects are asked to build much more than the classic data dictionary.The importance of well-documented models, both data and process, are at a premium.The traditional entity and attribute definitions are not cutting it when it comes to truly documenting the data and the processes surrounding it. 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. ,