Data Denormalization is a process in which internal schema is developed from conceptual schema. The data denormalization although done by adding redundant data is actually a process of optimizing a relational database s performance. This is often done with relational model database management system which is poorly implemented. At the logical level...
Data warehouses where a rich repository of company data may be found are being run by database management systems that need to see one homogenous data in order for it to flow smoothly and process data to be able to come up with statistical report about company trends and patterns. But the problem arises because data warehouses gather extract and...
Data warehouses of an organization are filled with data which would reflect all the activities within the group. Data may come from various sources and gathered using routing business processes. It is imperative that the processes in the data warehouse should be precise and accurate because the usefulness of data goes far beyond the software applications...
Consistent Data Quality refers to the state of a data resource where the quality of existing data is thoroughly understood and the desired quality of the data resource is known. It is a state where disparate data quality is known and the existing data quality is being adjusted to the level desired to meet the current and future business information...
Data Generalization is the process of creating successive layers of summary data in an evaluational database. It is a process of zooming out to get a broader view of a problem trend or situation. It is also known as rolling-up data. There are millions and millions of data stored in the database and this number continues to increase everyday as a...
Data Naming Convention refers to a convention established to resolve problems with Traditional data names. Many of these conventions are in use today such as the Of Language entity— attribute— class role— type— class prime— descriptor— class entity— adjective— class entity— attribute— class...
Data Optimization is a process that prepares the logical schema from the data view schema. It is the counterpart of data de-optimization.  Data optimization is an important aspect in database management in particular and in data warehouse management in general. Data optimizations is most commonly known to be a non-specific technique used by several...
Data Normalization is a process to develop the conceptual schema from the external schema.  In its very essence data normalization is the process of organizing data inside the database in order to remove data redundancy. The presence of many redundant data can have very undesirable results which include significant slowing of the entire computer...
Data Quality indicates how well data in the data resource meet the business information demand. Data Quality includes data integrity data accuracy and data completeness. Today s business organization cannot function at its optimum without relying on information. Data sources supplying information such as data warehouses and data marts are fast...
Data Quality Activity is an activity in the data architecture component that ensures the maintenance of high-quality data in an integrated data resource. Data Quality Process is a process that documents and improves data quality by using both the deductive and inductive techniques. It is a systematic process of examining the data resource to determine...
Data Refining is a process that refines disparate data within a common context to increase the awareness and understanding of the data remove data variability and redundancy and develop an integrated data resource. Disparate data are the raw material and an integrated data resource is the final product. Data refining process may be composed of...
Data Refreshing is the process of updating active data replicates based on a regular known schedule. The frequency and timing of data refreshing must be established to match business needs and must be known by clients. Today companies operate in an information-centric and fast-paced world. As such data and information is very plentiful and readily...
Data Scrubbing is a technique to correct error by using a background task in order to periodically inspect the memory for errors.  The technique helps to  decode merge filter and even translate the source data so that the data for data warehouse remains valid. The error correction used is often the ECC memory or another copy of the...
Disparate Data are heterogeneous data. They are neither similar nor can be easily integrated with an organisations database management system. It differs in one or more aspects of an information system. Disparate data may be characterized by these basic problems 1. In an organization implementing a database system there is no one complete integrated...
Disparate Databases are heterogeneous databases In such database systems  are neither compatible electronically nor operationally. Technically speaking a database is repository of data that can provide for a centralized and homogeneous view of data to be used for multiple applications. The data in a database are not just randomly placed there...
Disparate Metadata Cycle is a kind of cyclic process in which disparate metadata gets  produced  rapidly.  With data warehouse implantations becoming more ubiquitous these days due to the fact that businesses can no longer function without being supplied by relevant data the use of metadata has become relevant too. As a short backgrounder...
Disparate Operational Data is generally used to support day-to-day business transactions.  They reflect current status of an organizations operational data. Dealing with disparate operational data is a very common everyday process in a data warehouse implementation. Since a data warehouse is built to contain the repository of current and historical...
In any database implementation it is always advised to normalize the database for optimal performance by reducing redundancy and maintaining data integrity. The process of normalization involves putting one fact in its appropriate place to make updates optimized at the expense of data retrievals. As opposed to having one fact in different places a...
The most general sense data quality is an indication of well the data is in terms of integrity and accuracy as it is stored in the data resource in order to meet the demand of business information. Other indicators of good quality data pertain to completeness timeliness and format though this may never apply anymore with today s advanced data cleansing...
Database reverse engineering is an important process in improving the understanding of data semantics. Many aspects of the database evolution especially those that pertain to old and legacy databases where the semantics of their data have been lost through the years need a database reverse engineering process to understood in detail. Today there...
Normalized Data is the data in the data view schema and the external schema which have gone through data normalization. Maintaining a data warehouse means dealing with millions of data as the data warehouse itself is the main repository of a company s historical data or its corporate memory. Thus data should be well managed and one of the ways to...
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...

 

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