Businesses are making slow progress in data and machine learning. In 2018 these companies will become dark horses.

The Gartner survey shows that companies are making slow progress in data and analysis. Few organizations are able to use data at the "transitional" level, and two-thirds of organizations surveyed by Gartner are still considering "corporate reports to address their most critical data and analytic applications."

Nick Heudecker, vice president of Gartner, provided some cautionary advice: “Machine learning and artificial intelligence can easily be stolen.” But traditional forms of analysis and business intelligence are still a critical part of how organizations work today, and this is not too May change."

How can companies determine if they should "stamp on" in artificial intelligence and machine learning programs? Is there a problem with the growing maturity of the data and analytics industry?

Gartner's report summarizes the biggest winners in data and machine learning and may help you find the answer.

The 2018 report evaluated 16 analytical and data science companies using multiple criteria and placed them in four quadrants based on product proactiveness and execution, as follows:

Leader (5): KNIME, Alteryx, SAS, RapidMiner, H2O.ai

Challenger (2): MathWorks, TIBCO Software (New Entry)

Foresight (5): IBM, Microsoft, Domino Data Labs, Dataiku, Databricks (New Entry)

Specific areas (4): SAP, Angoss, Anaconda (new entry), Teradata

The Magic Quadrant of the Gartner Data Science and Machine Learning Platform, 2018

In the past year, TIBCO Software, Anaconda, and Databricks were included in this quadrant. FICO, Quest and Alpine data were removed.

The figure above shows the comparison between 2017MQ (gray background image) and 2018MQ (foreground image), with the dots connected by arrows indicating the same company.

The survey shows that in the past three years, Nasdaq, Tableau, and Qliktech have maintained their leading three suppliers. Likatech accidentally stays in the leader's quadrant, although their CEO Thoma Bravo left after a one and a half months after the company was acquired for $3 billion.

IBM used to be a leader in the past, but it was placed in the visionary quadrant due to its low execution capacity.

Oracle was withdrawn from the quadrant in 2016 and returned to the list in 2017. It is currently in the category of "Leather."

MicroStrategy is one of the earliest suppliers in the industry and is included in the Challenger category.

There are also cases in this quadrant that may exceed people's expectations, including - Alteryx, ClearStoryData, ZoomData, Datameer, and Pentaho.

This area has witnessed many changes in the past year. Industry analyst Jen Underwood said the competition for new employees has increased. New models of machine learning and data science may be coming soon.

Despite the sensation caused by artificial intelligence and machine learning, Gartner is still cautious about adopting this new technology.

Jim Hare, vice president of research at Gartner, warned that before they calculated their "data strategy," companies eager to adopt artificial intelligence said that nearly one-third of CIOs are planning to deploy artificial intelligence. He said: "Data is an artificial intelligence booster, so organizations need to now prepare to store and manage larger amounts of data for artificial intelligence programs."

Gartner also pointed out in the report some key trends in machine learning and data science:

46% of CIOs have plans to deploy artificial intelligence, but only 4% are actually implemented.

Google and Amazon are still investing heavily in this area. Microsoft does not enter the leader quadrant.

definition

Gartner's definition of a data science and machine learning platform is that it is a cohesive software application that provides the basic ability to integrate component modules. It can both create various data science solutions and integrate the solution. Go to business processes and the surrounding infrastructure and products involved.

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