In a recent Gartner survey, it's clear that many companies are progressing slowly when it comes to leveraging data and analytics. Only a small number of organizations have reached the "transitional" level of data maturity, while two-thirds are still relying on traditional corporate reports to address their most critical data and analytics needs.
Nick Heudecker, vice president at Gartner, warned that while machine learning and AI are powerful tools, they can be easily misused or stolen. However, he emphasized that traditional analytics and business intelligence remain essential for today’s organizations and are unlikely to disappear soon.
The question remains: How can businesses decide whether to fully commit to AI and machine learning initiatives? Is there an issue with the growing complexity of the data and analytics industry?
Gartner’s latest report highlights the top performers in data science and machine learning, offering insights into which platforms are leading the way. In 2018, the report evaluated 16 data science and analytics companies across four quadrants based on product innovation and execution capabilities:
- **Leaders (5):** KNIME, Alteryx, SAS, RapidMiner, H2O.ai
- **Challengers (2):** MathWorks, TIBCO Software (new entry)
- **Visionaries (5):** IBM, Microsoft, Domino Data Labs, Dataiku, Databricks (new entry)
- **Niche Players (4):** SAP, Angoss, Anaconda (new entry), Teradata
Over the past year, TIBCO Software, Anaconda, and Databricks joined the quadrant, while FICO, Quest, and Alpine Data were removed. The 2018 Magic Quadrant also showed shifts in market dynamics, with some companies moving up or down based on performance and strategy.
Nasdaq, Tableau, and QlikTech have consistently ranked among the top three suppliers over the past three years. Likatech managed to stay in the leader category despite a leadership change after being acquired for $3 billion.
IBM, once a dominant player, was moved to the Visionary quadrant due to lower execution strength. Oracle returned to the list in 2017 after being excluded in 2016, currently placed in the “Niche†category. MicroStrategy, one of the earliest players in the field, is now in the Challenger group.
Some unexpected names also made appearances in the quadrant, including Alteryx, ClearStoryData, ZoomData, Datameer, and Pentaho. These companies show how dynamic the landscape has become.
Industry analyst Jen Underwood noted that competition is intensifying, and new models in machine learning and data science may soon emerge.
Despite the hype around AI and machine learning, Gartner remains cautious. Jim Hare, vice president of research at Gartner, pointed out that nearly a third of CIOs plan to implement AI, but only 4% have actually done so. He warned that data is crucial for AI success, and organizations must prepare to manage larger volumes of data effectively.
Gartner also identified key trends in the field, including heavy investments from Google and Amazon, and Microsoft’s absence from the leader quadrant. According to Gartner, a data science and machine learning platform is a cohesive software application that enables integration of various components, supports the creation of data science solutions, and integrates them into business processes and surrounding infrastructure.
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