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Data wrangling typically follows a set of general steps which begin with extracting the data in a raw form from the data source, "munging" the raw data (e.g. sorting) or parsing the data into predefined data structures, and finally depositing the resulting content into a data sink for storage and future use. [1]
A standard representation of the pyramid form of DIKW models, from 2007 and earlier [1] [2]. The DIKW pyramid, also known variously as the knowledge pyramid, knowledge hierarchy, information hierarchy, [1]: 163 DIKW hierarchy, wisdom hierarchy, data pyramid, and information pyramid, [citation needed] sometimes also stylized as a chain, [3]: 15 [4] refer to models of possible structural and ...
AAA also focuses on: information systems, artificial intelligence/expert systems, public interest, auditing, taxation (the American Taxation Association is a Section of the AAA), international accounting, teaching, and curriculum. [4] AAA publishes The Accounting Review, [5] Accounting Horizons and Issues in Accounting Education.
The acceptance of these transaction records into any new system could be very important for any verification of such imported data. [citation needed] In research, gaining access to source data may be cumbersome. Particularly where sensitive personal data is involved, security and redaction (obscuring information) may be an issue. [5]
Data collection and validation consist of four steps when it involves taking a census and seven steps when it involves sampling. [3] A formal data collection process is necessary, as it ensures that the data gathered are both defined and accurate. This way, subsequent decisions based on arguments embodied in the findings are made using valid ...
Raw data (sometimes colloquially called "sources" data or "eggy" data, the latter a reference to the data being "uncooked", that is, "unprocessed", like a raw egg) are the data input to processing. A distinction is made between data and information , to the effect that information is the end product of data processing.
Data hierarchy refers to the systematic organization of data, often in a hierarchical form. Data organization involves characters, fields, records, files and so on. [1] [2] This concept is a starting point when trying to see what makes up data and whether data has a structure. For example, how does a person make sense of data such as 'employee ...
Metadata means "data about data". Metadata is defined as the data providing information about one or more aspects of the data; it is used to summarize basic information about data that can make tracking and working with specific data easier. [15] Some examples include: Means of creation of the data; Purpose of the data; Time and date of creation