Enterprise decision-makers look up to Gartner for its recommendations on enterprise software stack. The magic quadrant report is one of the most credible, genuine, and authoritative research from Gartner. Since it influences the buying decision of enterprises, vendors strive towards getting listed in the report.
Gartner recently published its magic quadrant report on data science and machine learning (DSML) platforms. The market landscape for DS, ML and AI is extremely fragmented, competitive, and complex to understand. Gartner attempted to stack rank the vendors based on a well-defined criterion. Refer to the inclusion and exclusion criterion for details on the parameters considered by Gartner.
At its core, Gartner evaluated vendors based on their support for a cohesive, complete and end-to-end pipeline for data science platform. Commercial availability and customer adoption of platforms acted as major influencers in defining the vendor ranking. There is special emphasis on the availability of the platform in the public cloud, hybrid cloud, and on-premises data center. Gartner also considered AutoML as a key component of the platform which enables non-developers to build ML models. The weightage based on these parameters impacted the ranking which decided the quadrant for a vendor.
The Leadership Quadrant
According to Gartner, leaders have a strong presence and significant mind share in the DSML market. They demonstrate strength in depth and breadth across the full data exploration, model development and operationalization process.
For 2020’s magic quadrant, 6 companies made it to the leadership quadrant. What’s common among these vendors is their proven track record of delivering end-to-end data science platforms. SAS, TIBCO, MathWorks have a heritage of building data and analytics-based platforms. Interestingly, some of the young companies such as Alteryx, Databricks and Dataiku have found a place in the top right quadrant.
All the vendors in the leadership quadrant offer commercially viable, platform-agnostic, mature data science platforms.
The Challengers Quadrant
Gartner calls challengers as someone with an established presence, credibility, viability and robust product capabilities but falls short of becoming a leader by a few notches.
IBM is the lone ranger of the challengers’ quadrant. The legacy of SPSS combined with modern Watson Studio features makes IBM a strong contender. The positioning of Watson Studio as the ML PaaS and IBM Cloud Paks for on-premises makes IBM a unique player in the market. But IBM’s frequent rebranding and renaming of product portfolio hurt its ability to establish Watson as a top brand for ML and AI.
The Niche Players Quadrant
According to Gartner, niche Players demonstrate strength in a particular industry or approach or pair well with a specific technology stack. They should be considered by buyers in their particular niche.
Altair and Anaconda share the bottom-most left quadrant. Altair leapfrogged to this quadrant through the acquisition of Datawatch which in turn acquired Angoss. Both Datawatch and Angoss were a part of the niche players’ quadrant in 2019.
Anaconda is a focused data science company that offers both open source and commercial platforms. A massive community combined with the simplification of Python and R-based libraries and packages make Anaconda a niche player in the market.
The Visionaries Quadrant
Gartner calls companies with products that have the potential to influence the market. The companies range from early-stage startups to well-established platform companies but their product offering could still be new and emerging.
In 2020 DSML MQ report, this is the most crowded quadrant. With 7 diverse players sharing the space, it does make it an interesting space to watch out.
DataRobot, Domino, Google, H20.ai, KNIME, Microsoft and RapidMiner are the DSML visionaries for 2020. Following the marketing campaigns from Google and Microsoft, an average user might assume that they would be in the leaders’ quadrant. But based on Gartner’s criterion, both Microsoft and Google lack a viable on-premises DSML platform that works against their score.
Google has a solid vision but many building blocks of the platform are in beta for a long time. When it manages to bring parity between Cloud AI platform and on-prem AI platform based on Anthos, Google has the chance to become a leader.
Microsoft has made genuine efforts in making AI and ML accessible to developers and data scientists. But it lost time going back and forth with its tooling and developer experience strategy. The current ML PaaS based on Azure ML looks promising. If Microsoft brings Azure ML to Azure Arc and Azure Stack, it will become a challenger.
Gartner has put AWS in the honorable mentions along with SAP, Oracle, and Teradata. Startups like Cloudera, FICO and Iquazio found a place in the honorable mentions.
I am personally surprised that AWS didn’t get a slot in the quadrant. If Google and Microsoft are positioned as visionaries, AWS deserves a place in the visionaries’ quadrant.
One reason why AWS didn’t make it is the announcement of SageMaker Studio and SageMaker Autopilot came after the cutoff period for MQ. Both these features were announced at re:Invent in December while Gartner’s cutoff was November. The other reason is that these new products are still in preview and available in just one region (US-East-2).
If AWS makes SageMaker Studio and SageMaker Autopilot generally available this year while porting the ML platform to AWS Outposts as a managed service, there are bright chances that Amazon will be positioned alongside Google and Microsoft.
Both Oracle and SAP are in the process of revamping their DSML platforms. Oracle announced its Cloud Data Science Platform last week.
Overall, Gartner MQ for DSML reflects the current state of the market. As always, its evaluation and recommendations are accurate and apt.
Credit: Google News