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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.
Process validation is the analysis of data gathered throughout the design and manufacturing of a product in order to confirm that the process can reliably output products of a determined standard. Regulatory authorities like EMA and FDA have published guidelines relating to process validation. [ 1 ]
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]
Continued process verification (CPV) is the collection and analysis of end-to-end production components and processes data to ensure product outputs are within predetermined quality limits. In 2011 the Food and Drug Administration published a report [ 1 ] outlining best practices regarding business process validation in the pharmaceutical ...
Process qualification is the qualification of manufacturing and production processes to confirm they are able to operate at a certain standard during sustained commercial manufacturing. Data covering critical process parameters must be recorded and analyzed to ensure critical quality attributes can be guaranteed throughout production. [ 1 ]
In 2000, Microsoft released an initial version of an XML-based format for Microsoft Excel, which was incorporated in Office XP. In 2002, a new file format for Microsoft Word followed. [9] The Excel and Word formats—known as the Microsoft Office XML formats—were later incorporated into the 2003 release of Microsoft Office.
Otherwise, the process of IQ, OQ and PQ is the task of validation. The typical example of such a case could be the loss or absence of vendor's documentation for legacy equipment or do-it-yourself (DIY) assemblies (e.g., cars, computers, etc.) and, therefore, users should endeavour to acquire DQ document beforehand.
Data cleansing may also involve harmonization (or normalization) of data, which is the process of bringing together data of "varying file formats, naming conventions, and columns", [2] and transforming it into one cohesive data set; a simple example is the expansion of abbreviations ("st, rd, etc." to "street, road, etcetera").