Optimized data are essential to an efficient running, maintenance and management of a data warehouse in particular and an information system of a company in general. Optimized Data are data in the logical schema and conceptual schema that have been through data optimization.
Although optimized data may come from different IT considerations, they are primarily the result of a general data optimization process which prepares the logical schema from the data view schema.
Data optimization, from the context of a data warehouse is optimizing the database being used. Most data optimizations in this respect are commonly known to be on-specific technique used by several applications in fetching data from a data sources so that the data could used in data view tools and applications such as those used in statistical reporting.
Since optimized data are data in the logical schema and conceptual schema, let us first know what a logical schema is. A logical schema is a non-physical dependent method of defining a data model of a specific domain in terms of a particular data management technology without being specific to a particular database management vendor. It contains the semantics that describe a certain technology for data manipulation. The descriptions may include terms of tables, columns, XML tags and object oriented classes.
On the other hand, a conceptual schema is a mapping of concepts and their relationships. It is also non-physical describing an organization’s semantics representing assertions about its nature. This schema describes the things that are of significance to the organization in terms of entity classes, attributes and relationships.
Optimized data adhere to the semantics described in these two mentioned schemas. They work according to the rules and specifications and not violate each of these. These data can be said to have been mapped to the semantics of both the conceptual and logical schemas.
In a business enterprise, optimized data help address excessive use of exchange feeds and high-end solutions as well as vendor penetration in an organization. It also solves certain problems related to high costs related to data licensing, technology, fragmented sub-optimized processes and regulatory and audit requirements.
Data centers need to have optimized data in order for it to be efficient, secure, flexible and agile service driven environment. In order for a business to have immediate paybacks and long term transformations, its enterprise management system must emphasize IT operation as a strategic business driver working on optimized data to ensure smooth flow of operation.
Enterprise data optimization can be achieved by having a careful planning and identifying of key issues and initiatives need to optimize the data center. IT professionals behind the data center need to establish a longer roadmap and outlook of projects and integrate tactical solutions into the overall data management strategy.
There are many vendors that manage data centers to be working on optimized data. These vendors make sure that data across the entire organization are optimized while addressing issues on scalability for the future. They can also manage various data sources into a reliable, integral and consistent manner while delivering high data volumes with low latency to multiple applications within the enterprise.
While having a robust infrastructure that ensures data are optimized may entail a high cost, the benefits many be tremendous and long term. Developing and executing a good data management strategy must be performed with a focus on the elimination of risks associated with change. By optimizing data, a company can also optimize supply and demand management as well as improve data distribution. Information system using optimized data does not just give the benefit to the database system but to all running programs as well.