How the Gartner Magic Quadrant is Used to Analyze Industries
Published on 17 March 14
Douglas0
The Gartner Magic Quadrant Business Intelligence model provides insight into what drives executives in technology industries. By analyzing various industries once every few years, the resulting matrix provides a qualitative analysis as to the direction of the industry, relevant maturity of the field, and the major and minor players in the field.
Investors, analysts, bankers and stakeholders view the quadrant with interest as it reflects current position in an evolving marketplace.
Company Position
Business intelligence (BI) that the Magic Quadrant uses to figure into the mix include details of the enterprises at issue. The resulting output ranks them into one of four quadrants based on Gartner's assessment of the completeness of vision the company demonstrates as well as its ability to execute on its vision.
1. "Leaders" are the class considered to score well on both of the indicators, the completeness of vision and the ability to execute; often large, more developed companies.
2. "Challengers" are those that do well on the ability to execute, but lack the completeness of vision seen in the Leaders; generally large companies with a perceived less thorough vision of the future or development anticipated for the industry.
3. "Visionaries" are the group that scores lower on ability to execute but higher on completeness of vision; generally thought of as smaller companies in an industry.
4. "Niche" players are companies that score lower on both indicators, the completeness of vision and ability to execute; generally newer companies to the field.
Harnessing the power of big data to provide relevant analytical scale in creating its Magic Quadrant, Gartner can intelligently condense the mass of data into functional business intelligence (BI) that can be used to graph the relative positions of the market contenders relative to one another.
Recognizing the power of data to measure things as diverse as consumer attitudes, supply chain optimization and more, the company focuses on what it is in each particular enterprise that contributes to one of the two indices, the wide vision for the future and or the ability to execute on that vision with palpable results.
How Data Speaks To Users
Converting big data into useful information has traditionally been the realm of the sophisticated IT operation, making use of complicated algorithms crafted by data engineers and scientists with massive hardware infrastructure and software support. To make the flood of data useful to business managers and directors, BI platforms permit access to the relevant contextual information needed to make key decisions and implement progress.
What's an example of BI application? Business intelligence for finance can improve the cash conversion process and reduce the number of delinquent accounts, and help to increase revenue and reduce costs.
Depending on the needs and the placement within the corporate structure, the individual may require summary data with a large scope or more detailed information focused narrowly on one geographic region or product line, for instance. The challenge for the BI user is to make the massive quantity and scope of information present in the business, useful for a particular need at one level, and for a different purpose at another locale within the enterprise.
A user can leverage insights gained to help the organization succeed.
Check out Marv Dumon's technology blog on Examiner.com
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