Search results
Results from the WOW.Com Content Network
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]
While there are numerous analysis tools in the market, Big Data analytics is the most common and advanced technology that has led to the following hypothesis: Data analytic tools used to analyze data collected from numerous data sources determine the quality and reliability of data analysis.
DataOps applies to the entire data lifecycle [3] from data preparation to reporting, and recognizes the interconnected nature of the data analytics team and information technology operations. [4] DataOps incorporates the Agile methodology to shorten the cycle time of analytics development in alignment with business goals. [3]
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.
Life cycle energy analysis (LCEA) is an approach in which all energy inputs to a product are accounted for, not only direct energy inputs during manufacture, but also all energy inputs needed to produce components, materials and services needed for the manufacturing process. [111] With LCEA, the total life cycle energy input is established. [112]
It documents data's origins, transformations and movements, providing detailed visibility into its life cycle. This process simplifies the identification of errors in data analytics workflows, by enabling users to trace issues back to their root causes. [2] Data lineage facilitates the ability to replay specific segments or inputs of the dataflow.
The Kimball lifecycle is a methodology for developing data warehouses, and has been developed by Ralph Kimball and a variety of colleagues. The methodology "covers a sequence of high level tasks for the effective design , development and deployment " of a data warehouse or business intelligence system. [ 1 ]
The Cross-industry standard process for data mining, known as CRISP-DM, [1] is an open standard process model that describes common approaches used by data mining experts. It is the most widely-used analytics model. [2]