Data mining is used wherever there is digital data available today. Notable examples of data mining can be found throughout business, medicine, science, and surveillance. A common way for this to occur is through data aggregation. Data aggregation involves combining data together possibly from various sources in a way that facilitates analysis but that also might make identification of private, individual-level data deducible or otherwise apparent. Data may also be modified so as to become anonymous, so that individuals may not readily be identified. This indiscretion can cause financial, emotional, or bodily harm to the indicated individual.
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By Gregory Piatetsky , KDnuggets. The report evaluated a new set of 16 analytics and data science firms over 15 criteria and placed them in 4 quadrants, based on completeness of vision and ability to execute. While open source platforms like Python and R play an important role in the Data Science market, Gartner research methodology does not include them, so this report evaluates only commercial vendors. As we did in our popular post last year: Gartner Magic Quadrant for Advanced Analytics Platforms: gainers and losers , we compared Magic Quadrant with its version.
Below we examine the changes, gainers, and losers. Fig 2: Gartner Magic Quadrants for Data Science Platforms compared, vs Fig 2 shows a comparison of MQ greyed background image and MQ foreground image , with arrows connecting circles for the same firm.
Arrows are colored green if the firm position improved significantly further away from origin , red if the position became weaker. Green circles indicate new firms, while red Xs mark vendors dropped in IBM strengths include its vast customer base and continued innovation of its data science and machine learning capabilities.
SAS provides a many software products for analytics and data science. SAS retain a strong position in the Leaders quad, but confusion about its multiple products and concerns about high cost led to decline in Ability to Execute. It is strong in several industries, especially in manufacturing and life sciences.
It lost somewhat along the Vision dimension due to weaker marketing and innovation compared to other leaders. RapidMiner offers GUI-based data science platform, suitable for beginner and expert data scientists. It also offers access to open-source code. RapidMiner is available both as a free version and a commercial edition with extra functionality for large data and connections to more data sources.
RapidMiner is in leaders quad due to its market presence and well-rounded product. Quest, the result of the sale of Dell Software in to a private equity firm, now sells the Statistica Platform. Quest is in Challenger quad while Dell was in Leaders quad as a result of the second change of ownership of Statistica in 3 years and lack of cloud-related product improvements which however are on the roadmap.
Alteryx, offers an easy to use data science platform, with self-service data preparation and advanced analytics. It also added simulation and optimization capabilities. Compared to , it moved from Visionaries to Challengers quad due to its solid customer growth.
Angoss provides visual data mining and predictive analytics tools, as well as prescriptive analysis and optimization. Angoss remained in almost the same position in Cha quad as in Visionaries: Microsoft evaluation was based the Azure Machine Learning platform, part of the Microsoft Cortana Intelligence Suite, which offers a strong cloud-based data science platform.
Gartner kept Microsoft in the visionaries quad, due to the lack of a comparable onsite solution. It was placed in Visionaries quad due to innovative nature of DSS, openness, collaboration features, and suitability for different skill levels. Domino Data Lab a new entry with its Domino Data Science Platform, which focuses collaboration and supports a wide range of open-source technologies. Alpine Data offer a "citizen data science" platform, Chorus, enabling collaboration between business analysts and front-line users in building and running analytic workflows.
Compared to , Alpine remained in Visionaries quadrant, but was dropped in its ability to execute due to its struggle to gain market share. SAP decline in ability to execute caused it to drop from Challengers quad to Niche quad, and it is lagging in Spark integration, open-source and Python support, and cloud deployment.
It stayed in the niche quadrant in , losing on vision but gaining a little on ability. Teradata offers Aster Analytics platform, with 3 layers: analytic engines, prebuilt analytic functions, and the Aster AppCenter for analysis and connectivity to external BI tools. It is in the Niche quad to to low-level of adoption. You can download the Gartner Magic Quadrant report for Advanced Analytics Platforms from RapidMiner , Dataiku , and probably other vendors favorably mentioned in this report.
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