Magic Quadrant BI 2018 – Gartners Business Intelligence

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Magic Quadrant BI 2018 – Gartners Business Intelligence

Before we examine the maturity model of BI solutions and the guidelines for its development, it is necessary to perform an analysis of the capabilities of BI solutions, as well as the market for these solutions.
As a starting point for this research we take Gartner’s “Magic Quadrants for BI
platforms”,

Magic Quadrant BI 2018 – Gartners Business Intelligence

In it Gartner defines BI platforms as software
platforms providing 12 capabilities, divided into 3 basic categories:
✓ Delivery of information;
✓ Integration;
✓ Analytics
Information technology is a highly dynamic field of research. As part of it, business
intelligence systems (BIS) also develop very quickly. In this paper we shall adhere to
the following definition of BIS: “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, it is necessary to perform an analysis of the capabilities of BI solutions,
as well as the market for these solutions.
As a starting point for this research we take Gartner’s “Magic Quadrants for BI
platforms”, published in 2007 (fig. 1). In it Gartner defines BI platforms as software
platforms providing 12 capabilities, divided into 3 basic categories:
✓ Delivery of information;
✓ Integration;
✓ Analytics.
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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
and 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)
have no capacity for data analysis unless they possess some knowledge about different
type of data. That is, a search engine can easily follow any event that happened at a
certain moment in time, while using conventional RDBMS a search can be performed
only within strictly defined data fields

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