Hats off to Gartner for resisting the temptation to call Advanced Analytics “Analytics 3.0” because no one knows what that means. Some say that 3.0 suggests “analytics for all," referencing “all” as average workers.

Advanced Analytics, by Gartner's definition, are anything but that, according to the Magic Quadrant for Advanced Analytics Platforms (registration required).

Instead, Gartner defines them as “the analysis of all kinds of data using sophisticated quantitative methods (for example, statistics, descriptive and predictive data mining, simulation and optimization) to produce insights that traditional approaches to business intelligence (BI) — such as query and reporting — are unlikely to discover."

In other words, advanced analytics are the scalpels used by highly trained data scientists.


Extracting Value

This year’s Magic Quadrant for Advanced Analytics report acknowledges that the tools are becoming friendlier to business analysts and citizen data scientists. But based on the reporting we’ve done around the various vendors in the past 16 months, we’re not holding our breath.

Data workers using great, clean data sets and the right tools extract value.

That being said, here are the vendors Gartner selected as leaders for its MQ for Advanced Analytics Platforms.

Still SAS-sy

SAS’s analytics software has been around since 1976, yet no one is ready to call it a relic. In fact, with 40,000 customers and the largest ecosystem of users and partners, it’s the clear leader in this year’s MQ with only IBM ranking anywhere near it.

What’s so special? Aside from its completeness of vision and ability to execute, Gartner claims it has the widest product stack in the industry. Not only that, but its community is strong and users praise its training as well as the performance, scalability, stability and reliability of the platform.

On the downside Gartner notes that SAS offers multiple products with similar capabilities (for example, predictive modeling) and that this can cause confusion. Ditto for interoperability and consistent UIs between them.

And though third generation advanced analytics vendors argue that they offer a different and more modern approach (browser vs. client server, big data and Hadoop versus data warehouses) Gartner indicates price is the primary reason companies look for SAS alternatives.

We should note that SAS is getting friendly with open source via integrations and initiatives such as ODP.

IBM Analytics Before and After Watson

With all the press we see about IBM’s consistently poor quarterly results, it is easy to forget that it is a leader and a groundbreaker on other fronts. Analytics is one of them

Gartner says one of the strengths of IBM’s SPSS Modeler products and solutions is that they apply to a gamut of analytic challenges relating to customers, operations, physical assets and risks.

It would be interesting to see how they relate to Watson’s new industry offerings. Gartner says that it might behoove IBM to clarify this for us all.

That being said, IBM’s community of experienced employees and users rate its products highly, especially when you look at them one at a time. The challenge for any vendor that has been in a market for many years is keeping it simple and experiences consistent.

On the downside, IBM seems weak on the non-geeky stuff like account management, training, technical support and that user feedback isn’t necessarily taken account on product road maps.

KNIME Proves Open Source Is Viable

Is Open Source the way to go with big data and analytics? Maybe not so much. Or at least not yet. That being said, KNIME breaks that rule, and quite impressively. Gartner likes its openness, which allows companies to build their own customized features and to work with the tools of their choosing, like R and Python. Not only that, but any open source project that succeeds owes it largely to an enthusiastic community that KNIME has.

Still, Gartner has some concerns, many KNIME enthusiasts are data miners. So it would be good to broaden the platform's appeal. Not only that, but the UI and visualizations could also be more attractive.

RapidMiner Moves Toward the Cloud, Big Data

RapidMiner is open source, client-server based solution but it will cost you to take it to the cloud. Still, it could be worth your dollars and cents. Gartner likes the ground that RapidMiner covers both in terms of breadth and depth. Ditto for its innovation as it moved toward big data, cloud and industry accelerators.

On the downside the user base is mostly data scientists. In addition, a whole gamut of customer oriented services are on the slide. That being said, there’s a new leadership team in house and (this is our two cents). Maybe it’ll focus on fixing the shortcomings.

Visionaries: They Could Be Leaders

Alteryx, Alpine Data and Microsoft are all speeding toward Gartner’s leaders’ quadrant, in many cases offering something that the leaders don’t or at least not via as delightful a user experience.

Learning Opportunities

Alteryx Is Just a Hair Away

The promise of the big data age is to generate better insights using significantly more data. But blending data sources is difficult unless you use Alteryx, that is. At least that’s the company’s promise.

If it’s true that organizations that leverage data best will win the future then Alteryx or a tool like it could be key. It helps users integrate internal data, third party data and cloud data without any coding. Drag and drop replaces R, which, through almost anyone’s eyes, is a beautiful thing.

Gartner likes Alteryx’s new sales strategy, which encourages customers to first go after small wins, the idea being that based on that, it can later expand its footprint. It also noted its integrations with Revolution Analytics and Apache Spark as big plusses.

On the downside its visualization capabilities which are commonly expressed via Tableau are sometimes criticized by customers—they want Alteryx to offer their own (they do offer some, but they don’t yet compare to Tableau’s. And though its documentation is weak, customer service is strong, according to Gartner.

Our experience interacting with Alteryx executives is that they’re confident in the value their product provides, determined to meet customer needs, and delight. There’s only a fine line between their positions in the visionaries quadrant and where the leaders reside. We expect to see them there next year.

Alpine's Vision for the Big Data Era

Alpine Data Labs built its analytics solutions for the age of big data and the third platform. Their solution is browser based and it analyzes big datasets by running analytic workflows natively within existing Hadoop or other parallel platforms. Alpine’s Chorus tool makes it easy for business users and data analysts to collaborate while creating models.

Gartner likes Alpine’s commitment toward cloud based analysis which shortens time to value (and results). Its scalability and attention to customer feedback rates highly.

And while some are concerned by Alpine’s size and market share, it’s worth noting that yesterday the company announced that they have ten times as many users as they had in 2013. So this could be a short-lived problem.

“Sure we’re not as big as SAS, but we’re not 30 years old either,” Bruno Aziza, Alpine Data Labs’ CMO told me in a recent conversation. And there’s a benefit to that, they can spend their energies building what’s new, and ground breaking — it’s harder for older companies to do that because they have existing products to maintain and protect.

Not only that, but “no one else can do what we can do,” Aziza pointed out, explaining that Alpine is one of the few (or maybe the only) Gartner rated Advanced Analytics vendor that leverages all brands of Hadoop, Apache Spark, and other data sources.

Microsoft Capitalizes on Reach, Machine Learning

Microsoft’s broad enterprise reach via Windows, Office, SharePoint, SQL Server and Exchange product lines provide it inherent advantages. Though its predictive analytics capability (SQL Server Analysis Services [SSAS]) embedded within SQL Server may be a bit stiff and tired, its new Azure Machine Learning (AML) capabilities are as cool as anything you can find at a smart, ambitious startup. If it wasn’t for them, Microsoft may have missed a spot in the Visionaries MQ.

Gartner says that Microsoft's AML is impressive in that it brings together best of breed components (extraction, transformation and loading [ETL], data storage, data preparation, different analytics and presentation layers) and integration with T and R packages. AML is a radically different product than SSAS and will likely overtake it before long.

Microsoft can also leverage its cloud to provide PaaS, which give it clear advantages.

If you’re an Enterprise in search of Advanced Analytics solutions, these are good times. There are many, well-differentiated, great choices.

Creative Commons Creative Commons Attribution 2.0 Generic License Title image by familymwr.