In recent months, as COVID-19 has spread and social distancing has become a necessity, spring conferences that build community within the industry, such as the Gartner Data and Analytics Summits and even our own Rev Summit for Data Science Leaders, have been postponed. Not surprisingly though, data science networking and community-building initiatives are more important than ever. Data science remains a priority (and will prove even more vital) as companies work to predict what’s next in these unprecedented times. Data science teams, now working remotely, need to share best practices and figure out how to get their work done more efficiently as companies and communities cope with the social and economic fallout from this crisis.
As you look to connect with colleagues and stay on top of trends in the data science domain, one resource we’d recommend checking out is Gartner Peer Insights*. It offers a place for enterprises to rate and review products including the Domino Data Lab platform. Reviews are submitted anonymously, and vetted by Gartner, which works to ensure “no vendor bias….just the real voices of enterprise users.”
One important principle at the heart of our platform is the open workbench. Companies can run Domino on any infrastructure — cloud, on-prem, and even in their own Kubernetes clusters. And data scientists can use their preferred languages, tools, services, and infrastructure as they develop models.
On openness, one Principal Data Scientist for a manufacturing organization shared that:
“Access to computing and data science tools has greatly extended the types of work that we can do, leading to tangible value for the company.”
Two other key principles we focus on are reproducibility and collaboration. Our reproducibility engine automatically tracks all aspects of data science experiments (code, environment, data, and more) so data science teams never lose work and can always reproduce their results. Our collaboration capabilities, like Experiment Manager and Project Portfolio Dashboard, help data science teams share ideas, learn from each other, and more easily collaborate with IT and business stakeholders.
On collaboration and reproducibility, the Head of Data Science and Analytics at a Finance Company wrote:
“The core functionality has delivered all that we were hoping for to make our Data Science community more productive and get our assets into production—self-service environments excellent—collaboration has improved—publishing of APIs simple—workflow versioning is fantastic—prototyping and apps developed more quickly.”
And as the Chief Data Scientist at a Manufacturing company put it:
“It [Domino Data Lab] has allowed us to deploy machine learning solutions to customers 10x faster than we could have before, and opened up entire new possibilities for collaboration across the corporation.”
These are just a few quotes from the more than a dozen reviews added in the past twelve months. Customers represented a wide range of industries—from finance and manufacturing (where many the majority of our customers are from) to services and transportation. Companies ranged in size from less than US$50 million in revenue to more than US$10 billion.
If you’re considering the Domino Data Lab platform, we encourage you to take a look at Gartner Peer Insights. It’s a great way to get the scoop from those actively using our products.
If you’re a current customer, we hope you’ll share your experiences and knowledge with others on forums like Gartner Peer Insights, and share your needs with us on the Domino Community forum so we can continue to add the features that help data science teams succeed.
In February, we announced that Domino Data Lab has been named a “Visionary” in the 2020 Gartner Magic Quadrant for Data Science and Machine Learning Platforms.** It was our third time receiving this positioning, which we believe recognizes the three key principles at the heart of the Domino Data Lab platform—openness, reproducibility, and collaboration. A complimentary copy of the full Magic Quadrant for Data Science and Machine-Learning Platforms can be found here.
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
*Gartner Peer Insights ‘Voice of the Customer’: Insight Engines, 6 February 2020.
**Gartner Magic Quadrant for Data Science and Machine Learning (DSML) Platforms, Peter Krensky, Pieter den Hamer, Erick Brethenoux, Jim Hare, Carlie Idoine, Alexander Linden, Svetlana Sicular, Farhan Choudhary, February 17, 2020.