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The first release of Power BI was based on the Microsoft Excel-based add-ins: Power Query, Power Pivot and Power View. With time, Microsoft also added many additional features like question and answers, enterprise-level data connectivity, and security options via Power BI Gateways. [10] Power BI was first released to the general public on 24 ...
Value-stream mapping, also known as material- and information-flow mapping, [1] is a lean [2]-management method for analyzing the current state and designing a future state for the series of events that take a product or service from the beginning of the specific process until it reaches the customer.
Value-stream-mapping software is a type of software that helps prepare and/or analyze value stream maps. The software typically helps design maps through utilizing a series of symbols representing activity and information/material flow, and as a supplement to manual calculations [ 1 ]
Prior to the release of Power Pivot, the engine for Microsoft's Business Intelligence suite was exclusively contained within SQL Server Analysis Services.In 2006, an initiative was launched by Amir Netz of the SQL Server Reporting Services team at Microsoft, codenamed Project Gemini, with the goal of making the analytical features of SSAS available within Excel.
A value stream is the set of actions that take place to add value to a customer from the initial request through realization of value by the customer. The value stream begins with the initial concept, moves through various stages of development and on through delivery and support. A value stream always begins and ends with a customer.
Business intelligence (BI) consists of strategies, methodologies, and technologies used by enterprises for data analysis and management of business information. [1] Common functions of BI technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text ...
In the future, tools based on semantic web languages such as RDF, the Web Ontology Language (OWL) and standardized metadata registry will make data mapping a more automatic process. This process will be accelerated if each application performed metadata publishing. Full automated data mapping is a very difficult problem (see semantic translation).
The goal of the pattern is to keep the in-memory representation and the persistent data store independent of each other and the data mapper itself. This is useful when one needs to model and enforce strict business processes on the data in the domain layer that do not map neatly to the persistent data store. [2]