Search results
Results from the WOW.Com Content Network
Dirty data, also known as rogue data, [1] are inaccurate, incomplete or inconsistent data, especially in a computer system or database. [2]Dirty data can contain such mistakes as spelling or punctuation errors, incorrect data associated with a field, incomplete or outdated data, or even data that has been duplicated in the database.
Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database.It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. [1]
The FRACAS process is a closed loop with the following steps: Failure Reporting (FR). The failures and the faults related to a system, a piece of equipment, a piece of software or a process are formally reported through a standard form (Defect Report, Failure Report). Analysis (A). Perform analysis in order to identify the root cause of failure.
Database activity monitoring (DAM, a.k.a. Enterprise database auditing and Real-time protection [1]) is a database security technology for monitoring and analyzing database activity. DAM may combine data from network-based monitoring and native audit information to provide a comprehensive picture of database activity.
Spreadsheet risk is the risk associated with deriving a materially incorrect value from a spreadsheet application that will be utilized in making a related (usually numerically based) decision. Examples include the valuation of an asset , the determination of financial accounts , the calculation of medicinal doses, or the size of a load-bearing ...
Data validation is intended to provide certain well-defined guarantees for fitness and consistency of data in an application or automated system. Data validation rules can be defined and designed using various methodologies, and be deployed in various contexts. [ 1 ]
In this podcast, Motley Fool co-founder David Gardner is joined by superstar guests Randi Zuckerberg and Morgan Housel as they each share three stories -- one to educate, one to amuse, and one to ...
Data reconciliation is a technique that targets at correcting measurement errors that are due to measurement noise, i.e. random errors.From a statistical point of view the main assumption is that no systematic errors exist in the set of measurements, since they may bias the reconciliation results and reduce the robustness of the reconciliation.