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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]
Resources, events, agents (REA) is a model of how an accounting system can be re-engineered for the computer age. REA was originally proposed in 1982 by William E. McCarthy as a generalized accounting model, [ 1 ] and contained the concepts of resources, events and agents (McCarthy 1982).
Data analysis focuses on the process of examining past data through business understanding, data understanding, data preparation, modeling and evaluation, and deployment. [8] It is a subset of data analytics, which takes multiple data analysis processes to focus on why an event happened and what may happen in the future based on the previous data.
The outer circle in the diagram symbolizes the cyclic nature of data mining itself. A data mining process continues after a solution has been deployed. The lessons learned during the process can trigger new, often more focused business questions, and subsequent data mining processes will benefit from the experiences of previous ones.
The refined representation of a process can be done in another data-flow diagram, which subdivides this process into sub-processes. The data-flow diagram is a tool that is part of structured analysis and data modeling. When using UML, the activity diagram typically takes over the role of the data-flow diagram. A special form of data-flow plan ...
To create a SIPOC diagram, one must first map the overall process in a few steps. Then one must identify process outputs, who will receive them, and what the necessary inputs and suppliers are for each process. The final step is to share the diagram with the stakeholders to evaluate and verify the results. [5]
This method aids the design of program structures by identifying the output and processing results and then working backwards to determine the steps and combinations of input needed to produce them. [5] The simple graphic method used in Warnier/Orr diagrams makes the levels in the system evident and the movement of the data between them vivid.
The purpose of this step is to identify, validate and select a root cause for elimination. A large number of potential root causes (process inputs, X) of the project problem are identified via root cause analysis (for example, a fishbone diagram). The top three to four potential root causes are selected using multi-voting or other consensus ...