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[[Category:Accounting templates]] to the <includeonly> section at the bottom of that page. Otherwise, add <noinclude>[[Category:Accounting templates]]</noinclude> to the end of the template code, making sure it starts on the same line as the code's last character.
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
For example, it could be doing a merge of two years of accounts payable tables/files into a single table/file. Import wizard : Specifies whether the product provides an import wizard to assist in importing (interpretation, conversion, formatting) data for analysis.
Analytics is the systematic computational analysis of data or statistics. [1] It is used for the discovery, interpretation, and communication of meaningful patterns in data, which also falls under and directly relates to the umbrella term, data science. [2] Analytics also entails applying data patterns toward effective decision-making.
The Portable Format for Analytics (PFA) is a JSON-based predictive model interchange format conceived and developed by Jim Pivarski. [ citation needed ] PFA provides a way for analytic applications to describe and exchange predictive models produced by analytics and machine learning algorithms.
Note: If you really need to get data from a QIF file into an account that does not support QIF imports (e.g. Quicken 2005 and later), you can import from the QIF file into a (temporary) Cash account. Make sure the first line in the QIF file says "!Type:Cash" for importing it into a Quicken Cash account. (QIF files can be edited in any text editor.)
Record to report or R2R is a Finance and Accounting (F&A) management process which involves collecting, processing and delivering relevant, timely and accurate information used for providing strategic, financial and operational feedback to understand how a business is performing. [1]
Challenges in adopting master data management within large organizations often arise when stakeholders disagree on a "single version of the truth" concept is not affirmed by stakeholders, who believe that their local definition of the master data is necessary. For example, the product hierarchy used to manage inventory may be entirely different ...