This is a revised and expanded version of a story that first appeared in the weekly roundup for February 15.
Gartner publishes its 2016 Magic Quadrant for Advanced Analytics Platforms. You can get a free copy here from RapidMiner (registration required.) The report is a muddle that mixes up products in different categories that don’t compete with one another, includes marginal players, excludes important startups and ignores open source analytics.
Other than that, it’s a fine report.
The advanced analytics category is much more complex than it used to be. In the contemporary marketplace, there are at least six different categories of software for advanced analytics that are widely used in enterprises:
- Analytic Programming Languages (e.g. R, SAS Programming Language)
- Analytic Productivity Tools (e.g. RStudio, SAS Enterprise Guide)
- Analytic Workbenches (e.g. Alteryx, IBM Watson Analytics, SAS JMP)
- Expert Workbenches (e.g. IBM SPSS Modeler, SAS Enterprise Miner)
- In-Database Machine Learning Engines (e.g. DBLytix, Oracle Data Mining)
- Distributed Machine Learning Engines (e.g. Apache Spark MLlib, H2O)
Gartner appears to have a narrow notion of what an advanced analytics platform should be, and it ignores widely used software that does not fit that mold. Among those evaluated by Gartner but excluded from the analysis: BigML, Business-Insight, Dataiku, Dato, H2O.ai, MathWorks, Oracle, Rapid Insight, Salford Systems, Skytree and TIBCO.
Gartner also ignores open source analytics, including only those vendors with at least $4 million in annual software license revenue. That criterion excludes vendors with a commercial open source business model, like H2O.ai. Gartner uses a similar criterion to exclude Hortonworks from its MQ for data warehousing, while including Cloudera and MapR.
Changes from last year’s report are relatively small. Some detailed comments:
— Accenture makes the analysis this year, according to Gartner, because it acquired Milan-based i4C Analytics, a tiny little privately held company based in Milan, Italy. Accenture rebranded the software assets as the Accenture Analytics Applications Platform, which Accenture positions as a platform for custom solutions. This is not at all surprising, since Accenture is a consulting firm and not a software vendor, but it’s interesting to note that Accenture reports no revenue at all from software licensing; hence, it can’t possibly satisfy Gartner’s inclusion criteria for the MQ. The distinction between software and services is increasingly muddy, but if Gartner includes one services provider on the analytics MQ it should include them all.
— Alpine Data Labs declines a lot in “Ability to Deliver,” which makes sense since they appear to be running out of money (*). Gartner characterizes Alpine as “running analytic workflows natively within Hadoop”, which is only partly true. Alpine was originally developed to run on MPP databases with table functions (such as Greenplum and Netezza), and has ported some of its functions to Hadoop. The company has a history with
Greenplum Pivotal and EMC Dell, and most existing customers use the product with Greenplum Database, Pivotal Hadoop, Hawq and MADlib, which is great if you use all of those but otherwise not. Gartner rightly notes that “the depth of choice of algorithms may be limited for some users,” which is spot on — anyone not using Alpine with Hawq and MADlib.
(*) Of course, things aren’t always what they appear to be. Joe Otto, Alpine CEO, contacted me to say that Alpine has a year’s worth of expenses in the bank, and hasn’t done any new venture rounds since 2013 “because they haven’t needed to do so.” Joe had no explanation for Alpine’s significantly lower rating on both dimensions in Gartner’s MQ, attributing the change to “bias”. He’s right in pointing out that Gartner’s analysis defies logic.
— Alteryx declines a little, which is surprising since its new release is strong and the company just scored a pile of venture cash. Gartner notes that Alteryx’ scores are up for customer satisfaction and delivering business value, which suggests that whoever it is at Gartner that decides where to position the dots on the MQ does not read the survey results. Gartner dings Alteryx for not having native visualization capabilities like Tableau, Qlik or PowerBI, a ridiculous observation when you consider that not one of the other vendors covered in this report offers visualization capabilities like Tableau, Qlik or PowerBI.
— Angoss improves a lot, moving from Niche to Challenger, largely on the basis of its WPL-based SAS integration and better customer satisfaction. Data prep was a gap for Angoss, so the WPL partnership is a positive move.
— Dell: Arguing that Dell has “executed on an ambitious roadmap during the past year”, Gartner moves Dell into the Leaders quadrant. That “execution” is largely invisible to everyone else, as the product seems to have changed little since Dell acquired Statistica, and I don’t think too many people are excited that the product interfaces with Boomi. Customer satisfaction has declined and pricing is a mess, but Gartner is all giggly about Boomi, Kitenga and Toad. Gartner rightly cautions that software isn’t one of Dell’s core strengths, and the recent EMC acquisition “raises questions” about the future of software at Dell. Which raises questions about why Gartner thinks Dell qualifies as a Leader in the category.
— FICO fades for no apparent reason. I’m guessing they didn’t renew their subscription.
— IBM stays at about the same position in the MQ. Gartner rightly notes the “market confusion” about IBM’s analytics products, and dismisses yikyak about cognitive computing. Recently, I spent 30 minutes with one of the 443 IBM vice presidents responsible for analytics — supposedly, he’s in charge of “all analytics” at IBM — and I’m still as confused as Gartner, and the market.
— KNIME was a Leader last year and remains a Leader, moving up a little. Gartner notes that many customers choose KNIME for its cost-benefit ratio, which is unsurprising since the software is free. Once again, Gartner complains that KNIME isn’t as good as Tableau and Qlik for visualization.
— Lavastorm makes it to the MQ this year, for some reason. Lavastorm is an ETL and data blending tool that does not claim to offer the native predictive analytics that Gartner says are necessary for inclusion in the MQ.
— Megaputer, a text mining vendor, makes it to the MQ for the second year running despite being so marginal that they lack a record in Crunchbase. Gartner notes that “Megaputer scores low on viability and visibility and there is a lack of awareness of the company outside of text analytics in the advanced analytics market.” Just going out on a limb, here, Mr. Gartner, but maybe that’s your cue to drop them from the MQ, or cover them under text mining.
— Microsoft gets Gartner’s highest scores on Completeness of Vision on the strength of Azure Machine Learning (AML) and Cortana Analytics Suite. Some customers aren’t thrilled that AML is only available in the cloud, presumably because they want hackers to steal their data from an on-premises system, where most data breaches happen. Microsoft’s hybrid on-premises cloud should render those arguments moot. Existing customers who use SQL Server Analytic Services are less than thrilled with that product.
— Predixion Software improves on “Completeness of Vision” because it can “deploy anywhere” according to Gartner. Wut? Anywhere you can run Windows.
— Prognoz returns to the MQ for another year and, like Megaputer, continues to inspire WTF? reactions from folks familiar with this category. Primarily a BI tool with some time-series and analytics functionality included, Prognoz appears to lack the native predictive analytics capabilities that Gartner says are minimally required.
— RapidMiner moves up on both dimensions. Gartner recognizes the company’s “Wisdom of Crowds” feature and the recent Series C funding, but neglects to note RapidMiner’s excellent Hadoop and Spark integration.
— SAP stays at pretty much the same place in the MQ. Gartner notes that SAP has the lowest scores in customer satisfaction, analytic support and sales relationship, which is about what you would expect when an ankle-biter like KXEN gets swallowed by a behemoth like SAP, where analytics go to die.
— SAS declines slightly in Ability to Deliver. Gartner notes that SAS’ licensing model, high costs and lack of transparency are a concern. Gartner also notes that while SAS has a loyal customer base whose members refer to it as the “gold standard” in advanced analytics, SAS also has the highest percentage of customers who have experienced challenges or issues with the software.
15 thoughts on “Gartner’s 2016 MQ for Advanced Analytics Platforms”
Lavastorm offers several options to perform advanced analytics in conjunction with its traditional data preparation and aggregation capabilities, namely: a bundle of pre-built functions for statistical and predictive analysis, a Power R implementation that enables technically-sophisticated users to use an enterprise-grade version of R, and an open source R integration. The pre-built functions enable users to perform such analysis as linear regressions, logistic regressions, time series forecasts, affinity analysis, and random forests – amongst other methods – with minimal complexity and configuration. Both the Power R and open source R implementations allow users to build custom processes around the R language, with the former being optimized for speed, scale, and stability.
Thanks for reading and commenting. I based my post on your own product marketing materials, which make no reference to the capabilities you say you have.
In any case, while I’m sure the capabilities you describe are useful, Gartner’s inclusion criteria state clearly that tools must have native analytics for inclusion, and that R integration does not count. Your “Power R” pack is an OEM version of TIBCO’s TERR runtime engine, which isn’t actually an enterprise-grade version of R but an implementation of S, an R-like predecessor to R that runs most but not all R functions.
My comments about Lavastorm aren’t intended as a criticism of your software; they are intended to highlight the flaws in Gartner’s analysis.
TIBCO’s TERR is a close-to-complete, white-room re-implementation of R, rather than anything to do with S.
It is exactly an enterprise-grade version of R.
In its Marketing materials, even TIBCO admits it’s based on S. TIBCO acquired S when they acquired Insightful.
Also, TIBCO does not claim that TERR is “an enterprise-grade version of R”; they position the product as a runtime engine for R programs developed separately.
That’s fine, I suppose, unless your R program includes any of these:
Excellent insightful assessment as always. Thanks !
Thanks for reading!
I agree with Charlie. Excellent. Thank you!
Predixion doesn’t use Microsoft for IOT. It’s an all-Java play. Do your homework!
Presumably you refer to Predixion’s “Machine Learning Semantic Technology”, which they say they are trying to patent. Four points on that:
(1) Interestingly, Predixion took down the page I linked that says what operating systems they support. When I linked, it said Windows only. I can only “do my homework” to the extent that vendors do theirs.
(2) Predixion does not disclose what language it uses for MLST. If it’s in Java, it will run anywhere you can run Java, which isn’t quite “everywhere”, but not bad.
(3) Data mining software packages have exported Java for twenty years, so it’s not exactly visionary. The only thing “visionary” is Predixion’s decision to spin this capability as something well suited for IoT.
(4) Nobody — neither Predixion nor anyone else — has figured out how to export a predictive model that you can deploy anywhere without modification.
Seems like Gartner (and others) could use a course in basic research methods.
Just discovered your blog; thanks for doing it. Very informative/insightful.
Thanks for reading!
Thank you for this summary.
I’ve read the Gartner’s version but it is great to see the movements.
Interesting – and it reflects on the images – that real innovation is missing in PA. Among the challenger / top companies I personally miss the real sustainable distinctive factors. Beyond that some of them seem to suffer demonstrating what they particularly offer as competitive advantage to their customers on scaled enterprise level.
Excellent insightful assessment. Nicely explained about Gartner’s Advance Analytics Platforms. I think Information Technology organization firms like Gartner, need to acknowledge what they wantedly rejected from many years.