Summary:<\/em><\/strong> The Gartner Magic Quadrant for Data Science and Machine Learning Platforms is just out and once again there are big changes in the leaderboard.\u00a0 Some major incumbents have fallen and some new challengers have emerged.<\/em><\/p>\n \u00a0<\/p>\n The Gartner Magic Quadrant for Data Science and Machine Learning Platforms is just out and once again there are big changes in the leaderboard.\u00a0 Say what you will about our profession but as a platform developer you certainly can\u2019t rest on your laurels.\u00a0 Some traditional leaders have fallen (SAS, KNIME, H2Oai, IBM) and some challengers have risen (Alteryx, TIBCO, RapidMiner).<\/p>\n \u00a0<\/p>\n Blue dots are 2019, gray dots 2018.<\/em> This year and last year<\/u><\/em><\/a> saw big moves that seemed out of character with prior years when change had been more incremental.\u00a0 One possibility is that the scoring had changed or perhaps the nature of the offerings had evolved more rapidly than we thought.\u00a0 We started with a side-by-side comparison of the scoring criteria for last year and this.<\/p>\n \u00a0<\/p>\n Has the Scoring Changed?<\/strong><\/span><\/p>\n Actually the scoring criteria are pretty consistent from last year.\u00a0 The emphasis is on platforms that are: \u201cA cohesive software application that offers a mixture of basic building blocks essential for creating all kinds of data science solution, and for incorporating those solutions into business processes, surrounding infrastructure and products.\u201d<\/p>\n In other words from ingest and blending through modeling, implementation, and refresh.\u00a0 Looks like we\u2019re evolving toward ERPs for data.<\/p>\n There\u2019s no bias in favor of those that offer \u2018professional\u2019 free form coding platforms, versus \u2018augmented\u2019 drag-and-drop, and even an entry from the fully automated machine learning (AML) group this year.<\/p>\n There is an appropriate increasing bias toward platforms that serve all the data functions in the company, from data scientist to data analyst to line of business manager.\u00a0 Some extra points if:<\/p>\n \u00a0<\/strong><\/p>\n Machine Learning is the Focus – AI Gets Noticed but Not Scored<\/strong><\/span><\/p>\n Gartner specifically calls out that this is about machine learning ML model building, not AI.\u00a0 Although AI continues to grow in importance, the vast majority of users create value with ML models.<\/p>\n<\/p>\n Here are the Changes that Caught our Eye:<\/strong><\/span><\/p>\n Alteryx<\/strong> continues its rapid rise to the top in ability to execute but almost falls out of the Leaders box.\u00a0 Strong as they are, Gartner dings them for appealing mainly to citizen data scientists and entry level data scientists.<\/p>\n TIBCO<\/strong> moves from Challenger to high level Leader.\u00a0 TIBCO has fully digested its acquisitions of Statistica and Alpine Data to offer a strong end-to-end platform with added points for IoT and real time data capture and scoring.<\/p>\n SAS<\/strong> remains a leader but continues to slip.\u00a0 The continuing criticism is that its offerings are so numerous as to be confusing and complex to deploy.\u00a0 However, it has an almost unassailable install base.<\/p>\n IBM<\/strong> that was the exalted leader in 2017 has slipped for two straight years completely out of the leader box to be ranked as a mid-tier player.\u00a0 They confused the market with three separate DS platforms that are now down to two (the Data Science Experience having been folded into Watson Studio), but flagship SPSS still gets dinged in many areas.<\/p>\n RapidMiner<\/strong> gained slightly and KNIME<\/strong> slipped slightly but both are still strongly in the Leader box.<\/p>\n Databricks<\/strong> was new on the stage last year and has scored a big improvement in the visionary quadrant.\u00a0 Perhaps better known as a premier distributor of Spark 2.0, they are now clearly in the end-to-end analytic platform space.<\/p>\n Teradata<\/strong> was dropped altogether this year.\u00a0 Word is they are completely revamping their ML platform and will try again next year.<\/p>\n \u00a0<\/p>\n New and Startling<\/strong><\/span><\/p>\n Two adds are particularly noteworthy.<\/p>\n Google<\/strong> is now recognized as an end-to-end platform instead of just an ecosystem of components.\u00a0 As Google develops they will be tough to beat for breadth, scalability, and speed.\u00a0 Depending on how you think about it, the fact that all this is pretty much restricted to their cloud and not on prem is either a pro or a con.\u00a0 Can Amazon be far behind?<\/p>\n DataRobot<\/strong> certainly takes the prize as the best established automated machine learning platform (AML) having poured lots of its VC money into gaining market share.\u00a0 The entry criteria are at least $5 Million in 2017 revenue and either 150% YoY growth or 200 paying end user organizations.\u00a0 The AML field is now crowded with competent but much smaller competitors and it\u2019s good to see the AML segment recognized through DataRobot\u2019s success.\u00a0 It\u2019s worth noting though that DataRobot does not feature a full extract and prep front end and is usually paired with data prep platforms like Trifacta.\u00a0 Can an acquisition be far behind?<\/p>\n \u00a0<\/p>\n Love the One You\u2019re With<\/strong><\/span><\/p>\n Gartner goes out of its way to point out that a Leader may not be your best choice and that many organizations might find value in niche players more suited to their needs (FICO comes to mind for financial services).<\/p>\n Then again the Gartner methodology for ranking, while certainly sound is not the only way to pick winners.\u00a0 In fact Gartner publishes a completely different ranking \u201cBest Data Science and Machine-Learning Platforms Software of 2018 as Reviewed by Customers\u201d.<\/p>\n This is based on November 2018 data just as is the magic quadrant but the ranking here is done by current users.\u00a0<\/p>\n In total there were 1,859 reviews submitted for 37 different DS platforms, many too small to be included in the Magic Quadrant.\u00a0 It\u2019s a 5 point scale.\u00a0 Some of the major platforms had over 300 user reviews while several of the smaller platforms had only 1.\u00a0 Here are the top 17 (I\u2019ve eliminated more or less arbitrarily any that didn\u2019t have at least 40 reviews \u2013 about 2% of submissions.)\u00a0<\/p>\n The ranking is significantly different than Gartner\u2019s Magic Quadrant.\u00a0 On the other hand all of these were rated by their users within 6 tenths of a point of each other.\u00a0 Seems these users thought all these platforms were pretty good – so love the one you\u2019re with.<\/p>\n Both Gartner reports have much richer detail which you may want to investigate.\u00a0 The Magic Quadrant is available from several of the platforms that did well this year.\u00a0 I got mine here<\/u><\/em><\/a>.\u00a0 Likewise the Customer Review can be found here<\/u><\/em><\/a>.<\/p>\n \u00a0<\/p>\n \u00a0<\/p>\n Other articles by Bill Vorhies<\/u><\/em><\/a><\/p>\n \u00a0<\/u><\/em><\/p>\n About the author:\u00a0 Bill is Editorial Director for Data Science Central.\u00a0 Bill is also President & Chief Data Scientist at Data-Magnum and has practiced as a data scientist since 2001.\u00a0\u00a0\u00a0 He can be reached at:<\/p>\n Bill@DataScienceCentral.com<\/a> or<\/span> Bill@Data-Magnum.com<\/a><\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<\/a><\/p>\n
\n
<\/a><\/p>\n<\/p>\n