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Data quality assurance is the process of data profiling to discover inconsistencies and other anomalies in the data, as well as performing data cleansing [17] [18] activities (e.g. removing outliers, missing data interpolation) to improve the data quality.
KPI information boards. A performance indicator or key performance indicator (KPI) is a type of performance measurement. [1] KPIs evaluate the success of an organization or of a particular activity (such as projects, programs, products and other initiatives) in which it engages. [2]
Offline metrics are generally created from relevance judgment sessions where the judges score the quality of the search results. Both binary (relevant/non-relevant) and multi-level (e.g., relevance from 0 to 5) scales can be used to score each document returned in response to a query.
To calculate the recall for a given class, we divide the number of true positives by the prevalence of this class (number of times that the class occurs in the data sample). The class-wise precision and recall values can then be combined into an overall multi-class evaluation score, e.g., using the macro F1 metric. [21]
Academic articles that provide critical reviews of performance measurement in specific domains are also common—e.g. Ittner's observations on non-financial reporting by commercial organisations,; [10] Boris et al.'s observations about use of performance measurement in non-profit organisations, [11] or Bühler et al.'s (2016) analysis of how external turbulence could be reflected in ...
The objective of this stage is to take the data and conform it into information, specifically metrics. Developing KPI: This stage focuses on using the ratios (and counts) and infusing them with business strategies, referred to as key performance indicators (KPI). Many times, KPIs deal with conversion aspects, but not always.
Data profiling utilizes methods of descriptive statistics such as minimum, maximum, mean, mode, percentile, standard deviation, frequency, variation, aggregates such as count and sum, and additional metadata information obtained during data profiling such as data type, length, discrete values, uniqueness, occurrence of null values, typical string patterns, and abstract type recognition.
The metrics reference model (MRM) is the reference model created by the Consortium for Advanced Management-International (CAM-I) to be a single reference library of performance metrics. This library is useful for accelerating to development of and improving the content of any organization's business intelligence solution.
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