Domino Data labs

Domino Data labs by sennchi

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Predictive Analytics And Machine Learning Solutions

Domino Data Labs wants coders to collaborate across open source. the open source options for data science coders have never been richer. Data science notebooks such as Jupyter allow coders to be more productive and collaborative, and language libraries of algorithms continue to proliferate. the problem: Many of these tools are not integrated, leading to a disjointed development experience that becomes a burden to maintain, especially for larger enterprise teams. to counter this, Domino Data labs’ solution aims to package the most popular open source coding tools and libraries and provide a unifying interface for teams of data science coders. But this approach also has a drawback: Many of the features critical to enterprises, such as model management and advanced workbench tools, lack open source options. We don’t think Domino can wait for the open source community to add critical enterprise features.

Domino (Domino Data Lab), headquartered in San Francisco, California, U.S., offers the Domino Data Science Platform. This is an end-to-end solution for expert data science teams. The platform focuses on integrating tools from both the open-source and proprietary-tool ecosystems, collaboration, reproducibility, and centralization of model development and deployment. Founded in 2013, Domino is a recognized name in this market and continues to gain mind share among expert data scientists.

Domino maintains its position as a Visionary. Its Ability to Execute, though improved, is still hampered by weaker functionality at the beginning of the machine-learning life cycle (data access, data preparation, data exploration and visualization). Over the past year, however, Domino has demonstrated the ability to win new accounts and gain traction in a highly competitive market.

STRENGTHS

• **Open-source innovation and tool-agnostic approach: **Domino's offering is well-designed and positioned to capitalize on the popularity of open-source technologies by offering freedom of choice in terms of tools. Of the vendors in this Magic Quadrant, Domino earned the highest overall score for flexibility, extensibility and openness.

• **Outstanding customer service and support: **Domino has maintained the high standard of customer satisfaction that we recognized in the prior Magic Quadrant. Reference customers gave favorable reviews to Domino's service and customer support (onboarding and troubleshooting). Reference customers also praised their overall experience with the vendor.

• **Responsiveness to customers' requests for product improvements: **Domino received one of the highest overall scores for inclusion of requested product enhancements into subsequent product releases. Domino is proactive in supporting the latest open-source tools and has acted on requests for strong delivery and model management capabilities. Consequently, it received excellent scores for its overall capabilities and ability to meet clients' needs.

• **Collaboration: **Domino has two years of excellent customer feedback about its collaboration functionality, which earned it the overall highest score for this critical capability among the vendors in this Magic Quadrant. Domino provides excellent features to enable data scientists to offer transparency and work effectively with nontechnical users.

CAUTIONS

• **High technical bar: **Domino's offering is not a good choice for citizen data scientists, as it offers neither visual pipelining nor a visual composition framework. However, some expert data scientists did recognize improvements to the interface's ease of use. Domino's scores for analytic support (training and technique selection) were below average.

• **Beginning of the machine-learning life cycle and business exploration: **Domino's score for data access was in the bottom quartile. Its scores for data preparation, data exploration and visualization, and business exploration were mediocre.

• **Few quick or "precanned" solutions: **Domino's offering is not a lightweight tool for "quick and easy" data science. Users must navigate a vast open-source ecosystem for precanned solutions for common use cases in marketing, sales, R&D, finance and other areas.

• **Advanced enterprise-grade capabilities: **Some reference customers complained of a lack of functionality when, for example, working with cloud infrastructures and supporting large and complex hybrid cloud environments.