魔力象限BI 2018 – Gartner的商業智能

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魔力象限BI 2018 – Gartner的商業智能

Before we examine the maturity model of BI solutions and the guidelines for its development, 有必要進行的BI解決方案的能力的分析, as well as the market for these solutions.
作為一個起點,這個研究我們採取Gartner的“魔力象限為BI
平台“,

魔力象限BI 2018 – Gartner的商業智能

在Gartner公司它定義BI平台軟件
平台提供 12 功能, 被分成 3 基本類別:
✓信息傳遞;
✓集成;
✓分析
信息技術是研究的一個高度動態的領域. 作為其中的一部分, 商業
智能系統 (TO) 也發展很快. 在本文中,我們將堅持以
BIS的定義如下: “BIS combine the activities of data mining and data
processing and knowledge management through analytical means in order to present
complex competitive information to consumers who draw plans and make decisions.”1
1. Analysis of today’s capabilities of BI platforms
Before we examine the maturity model of BI solutions and the guidelines for its
development, 有必要進行的BI解決方案的能力的分析,
as well as the market for these solutions.
作為一個起點,這個研究我們採取Gartner的“魔力象限為BI
平台“, published in 2007 (fig. 1). 在Gartner公司它定義BI平台軟件
平台提供 12 功能, 被分成 3 基本類別:
✓信息傳遞;
✓集成;
✓分析.
1
Articles
To the first category belong the capacities for:
✓ Generating reports;
✓ Navigation panes;
✓ Ad hoc queries;
✓ Integration with MS Office.
The second group includes:
✓ BI infrastructure;
✓ Metadata management;
✓ Developing environments;
✓ Workflows;
✓ Cooperation.
The third category covers the capabilities for:
✓ Online analytical processing;
✓ Visualization;
✓ Delivery of knowledge and forecasting;
✓ Maps of results.
Five years later Gartner published a new “Magic Quadrant for BI platforms”,
that is still current for 2012. In this document BI platforms continue to be viewed as
software platforms, delivering the capabilities described above. But two more features
have been added to the “delivery of knowledge” category, namely search-based BI
mobile BI.
The basic capabilities of BI platforms have been widely discussed and studied,
so for this reason they will not be considered in detail in this paper. More attention will
be paid to the two new characteristics, offered by Gartner.
The first opportunity is search-based BI. Essentially it is an application of a
search-index in structured and unstructured data sources and their division (organization)
into a classification structure of measures and dimensions, which consumers can easily
navigate and explore, using a Google-like interface.
The basic difference between search engines and data warehouses is that search
engines are very flexible and support any kind of format and type of information – be
it structured or unstructured. Thus search engines can cope with increasingly evolving
data structures. The indexing of both existing and new data (unknown so far) does not
require additional data modeling. Conventional data warehousing architecture has limited
capabilities for dealing with unstructured data that are necessary for facilitating decision-
making and search engines “fill this gap”. In comparison, data warehouses require
time not only for creating the warehouse model, but also for adding new data. Another
positive feature of search engines is their ease of “navigation” through contents. At
each step of the navigation, search engines provide different opportunities for filtering
the results according to contents into the multitude of data that have been indexed and
analyzed in nearly real time. Relational database management systems (RDBMS)
有數據分析沒有容量,除非他們擁有關於不同的一些知識
數據的類型. 那是, 搜索引擎可以很容易地遵循發生在任何情況下
某些時刻, 而使用常規RDBMS可以執行搜索
只有內嚴格定義的數據字段

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