Modern analytics and business intelligence (BI) platforms are characterized by easy-to-use tools that support the full analytic workflow — from data preparation and ingestion to visual exploration and insight generation. They are most differentiated from traditional BI platforms by not requiring significant involvement from IT staff to predefine data models or store data in traditional data warehouses. The emphasis is on self-service and agility. Most modern analytics and BI platforms also have their own self-contained in-memory columnar engine to ensure fast performance and support rapid prototyping, but many can optionally use existing modeled data sources. The growing use of data lakes and logical data warehouses dovetails with the capabilities of modern analytics and BI platforms that can ingest data from these less-modeled data sources.
Vendors of traditional BI platforms have evolved their capabilities to include modern, visual-based data discovery that also includes governance, and more recently, augmented analytics. Newer vendors continue to evolve the capabilities that once focused primarily on agility, by extending them to enable greater governance and scalability, as well as publishing and sharing. The holy grail is for customers to have both Mode 1 and Mode 2 capabilities in a single, seamless platform that draws on existing assets but also has emerging best-of-breed capabilities.
As disruptive as visual-based data discovery has been to traditional BI, a third wave of disruption has emerged in the form of augmented analytics, with machine learning (ML) generating insights on increasingly vast amounts of data. Augmented analytics also includes natural language processing (NLP) as a way of querying data and of generating narratives to explain drivers and graphics.
This Magic Quadrant evaluates vendors of data science and machine learning (ML) platforms. These are software products that enable expert data scientists, citizen data scientists and application developers to create, deploy and manage their own advanced analytic models. Gartner defines a data science platform as:
A 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.
“Cohesive” means that the application’s basic building blocks are well-integrated into a single platform, that they provide a consistent “look and feel,” and that the modules are reasonably interoperable in support of an analytics pipeline. An application that is not cohesive — that mostly uses or bundles various packages and libraries — is not considered a data science and ML platform, according to their definition.
A data science and ML platform supports various skilled data scientists in multiple tasks across the data and analytics pipeline. These range from data ingestion, data preparation, interactive exploration and visualization and feature engineering to advanced modeling, testing and deployment.
Tableau remains, for seven years, the long-standing Leaders in the Magic Quadrant for Analytics and Business Intelligence Platforms, but its position is fiercely challenged by Microsoft. Tableau is a Leader, thanks to the popularity of its product, high customer satisfaction scores and strong roadmap. Tableau offers an intuitive, interactive, visual-based exploration experience that enables business users to access, prepare, analyze and present findings in their data without technical skills or coding. Tableau’s offering is primarily deployed on-premises, either as a stand-alone desktop application or integrated with a server for sharing content; Tableau Online is the cloud-based SaaS offering. In 2018, Tableau introduced a new, lower-priced Viewer role and now leads with named-user, subscription-based pricing. Tableau Prep was released to improve data preparation and profiling within Tableau Desktop — and more robust server-based scheduling capabilities are in beta testing. Tableau also acquired Empirical Systems in 2018 to broaden its augmented analytics capabilities.
- Easy visual exploration and data manipulation: Tableau enables users to rapidly ingest data from a broad range of data sources, blend them, and visualize results using best practices in visual perception. Data can be manipulated while visualizing — such as when creating groups, bins and new hierarchies — all with a high degree of ease of use.
- Customers as fans: Customers have a fanlike attitude toward Tableau, as evidenced by the record 17,000 users that attended its 2018 annual user conference. Reference customers placed Tableau in the top third of Magic Quadrant vendors for customer experience, and gave it high scores for achievement of business benefits. Tableau sets the industry standard for user enablement with Meetup groups, roadshows, online tutorials and availability of skills in the market.
- Momentum: Tableau grew its total revenue to just over $800 million through 3Q18 — double-digit growth compared with 2017. This was despite moving to subscription-based licensing, which often impairs a vendor’s growth. Tableau remains at the top of many customers’ shortlists, and continues to expand within its installed base. The Tableau Foundation and Tableau Public have been a force in the Data for Good movement, having recently pledged $100 million in funding over the next seven years.
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Alteryx has turned from a Leader into a Challenger by maintaining its position for Ability to Execute but demonstrating less vision relative to many other vendors in this Magic Quadrant. Nevertheless, Alteryx’s emphasis on making data science accessible to citizen data scientists and others across the end-to-end analytic pipeline is resonating in the market. Its approach provides a natural extension for a client base focused on data preparation but ready to take the next step into data science. A lack of innovation, relative to others, also contributes to Alteryx’s new position as a Challenger.
- Collaborative enablement of broad user base: Alteryx’s no-code approach is attractive to a broad spectrum of users, from business and data analysts to citizen data scientists. A focus on the ease of use and cohesiveness of its platform enables collaboration between users.
- End-to-end pipeline: Alteryx has focused on offering a complete, end-to-end data science platform. It has added two new products to its platform. Alteryx Connect focuses on data connections, data discovery and social connections. Alteryx Promote incorporates Alteryx’s Yhat acquisition and focuses on operationalizing analytic content.
- Marketing execution: Alteryx’s focus on addressing the end-to-end analytic process easily and clearly positions it as a vendor of a comprehensive platform. Alteryx’s value proposition is clear and resonating.
- Strong customer experience: Alteryx scored in the top quartile for customer experience in our survey of reference customers. Scores were consistently high for overall customer experience, plans to make additional investments, inclusion of product enhancements and requested features into subsequent releases, and overall product capabilities.
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DataRobot debuts as a Visionary. Its Completeness of Vision is supported by strong marketing and sales strategies and innovation. Its Ability to Execute is limited by being one of the newer entrants to this market, but supported by positive customer feedback. DataRobot sets the standard for augmented data science and Machine Learning. Reference customers scored DataRobot in the top quartile for overall experience with a vendor and in the top half for both overall product capabilities and inclusion of product enhancements and requests into subsequent releases.
- Thought leader in augmented data science: DataRobot sets the standard for augmented data science and ML. Significant funding has enabled expansion via acquisitions to address time series modeling and an augmented approach for developers to incorporate models into applications. These acquisitions give DataRobot the opportunity to extend its capabilities to new types of user, while focusing on its core competency of augmentation.
- Strong customer experience: Reference customers scored DataRobot in the top quartile for overall experience with a vendor and in the top half for both overall product capabilities and inclusion of product enhancements and requests into subsequent releases. DataRobot’s customer-facing data scientists, assigned to each client to jump-start initiatives, provide a unique approach to supporting and onboarding clients.
- Market responsiveness: DataRobot’s market responsiveness is strong. Despite being a relative newcomer to the data science and ML market, DataRobot has a solid installed base. In addition, the company is quickly gaining market traction.
Learn why DataRobot is included as a Visionary and request your copy of the report via DataRobot.