At the same time, technology advances are changing the way we think about a database and its relationship to the supporting infrastructure. Data architecture uses processes, systems and technology, and human performance to optimize the storage, access, movement and organization of data.
Data architecture considers and balances the potentially conflicting requirements of transaction systems (fast response time), business intelligence (large quantities of data), and reporting (wide distribution). To effectively manage data architecture components, enterprise-wide product standards need to be defined. These standards need to be binding to the whole enterprise and must not be redundant. Enterprise-wide asset management needs to be in place for the data Architecture component to enable resource sharing and to reduce equipment and operations costs.
As the data volume increases, data migration is necessary to move information from a transactional database to a data store or alternate storage device. In the new location, data should be accessible only to authorized applications and businesses. Data Access needs to be transparent and system performance is a key concern, especially in the movement, migration and storage of data. The data storage architecture needs to meet the business requirements beyond just data access; it must also support high availability, archival and disaster recovery requirements. Data storage usually represents the highest portion of an enterprise's hardware costs, yet the costs can be controlled with efficient storage and archiving schemes.
The lifecycle of data needs to be understood from the start of a development effort and needs to be reflected in the design and implementation of data archiving and data retirement approaches. Archived data requirements vary depending on its state in the lifecycle and on the enterprise's current and future storage technologies. These technologies require careful consideration of corresponding operational procedures. It is important that data retirement plans are well documented in the analysis phase of a project. If these specifications are not determined and documented, data will not be retired in an organized fashion and the cost of the infrastructure to support the data will become unacceptable.
Our data architecture approach includes components that support the vision to improve data quality, productivity and operational efficiency as the organization grows. Accenture has the expertise to help clients define and put into place the processes, people and technology to manage data effectively across its lifecycle. Leveraging strong alliances with leading hardware and software vendors, we provide our clients with a wide perspective on all aspects of data architecture at each stage of the data lifecycle. In addition, we work with our alliance partners to help organizations manage the large expense associated with the growing data storage requirements.