Chapter 10 Key Terms

Beschreibung

These are the critical words for Chapter 10.
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Zusammenfassung der Ressource

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Big Data Datasets that are too large and complex for businesses’ existing systems to handle utilizing their traditional capabilities to capture, store, manage, and analyze these datasets.
structured data Highly organized data that fits nicely in a table or a database. A balance sheet or income statement is a good example.
unstructured data Data without internal organization (or structure). Blogs, social media, and pictures posted on Instagram are examples of unstructured data.
semi-structured data Data that has elements of both structured (highly organized) and unstructured (not highly organized) data.
Data Analytics The science of examining raw data (now often Big Data), removing excess noise from the dataset, and organizing the data to conclude decision-making.
extract, transform, and load (ETL) The process of cleaning and scrubbing the data before data analysis can take place.
data integrity Maintaining and assuring the accuracy and consistency of data during transmission and storage.
numerical data Data is associated with a number that has a logical order.
categorical data Data with no logical order can’t be translated into a numerical value.
Audit Data Standards (ADS) A standard format for data files and fields typically needed to support an external audit in a given financial business process area was developed by the AICPA.
descriptive analytics Analysis performed that characterizes, summarizes, and organizes features and properties of the data to facilitate understanding.
diagnostic analytics An analysis is performed to investigate the underlying cause that cannot be answered by simply looking at the descriptive data.
predictive analytics An analysis is performed to provide foresight by identifying patterns in historical data by judging likelihood or probability.
prescriptive analytics Analysis performed that identifies the best possible options given constraints or changing conditions.
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