DataRobot Recognized by 451 Research for Differentiated Augmented Intelligence Platform (Part 1)
Here at DataRobot, we believe that machine intelligence empowers human intelligence. It’s a yin and yang relationship that is complementary and interconnected. By purposefully connecting the skills and experience of people to work in tandem with machine learning, we can move the outcome of artificial intelligence above and beyond simple automation. This is the philosophy behind DataRobot’s augmented intelligence approach, elevating the “A” in AI from “artificial” to “augmented.”
In the recently published 451 Research Market Insight Report DataRobot Rebrands as Augmented Intelligence Platform with Features to Support Repositioning, Senior Research Analyst Krishna Roy supports DataRobot’s more comprehensive perspective on the impact of data science in the market.
Roy writes, “DataRobot has clearly adapted to customer demand and market requirements to offer a broader platform for a wider audience. Branding all these assets as an augmented intelligence platform differentiates DataRobot from other vendors in the enterprise data science sector.”
The report goes on to outline DataRobot’s two-pronged approach for successfully implementing an augmented intelligence strategy. Combining the skill of human intelligence with the power of machine intelligence is the first step and creates a multiplicative effect. People bring to the table their innate intuition and learned experience, the capability to recognize patterns and connections, and the expertise to create advanced, custom offerings. Computers, on the other hand, flawlessly execute rules-based approaches and have the power to process heavy data volumes and complicated tasks. Within the DataRobot platform, integrating the intricate and complementary skills of human and machine intelligence and leveraging the strengths of both is key.
The second facet of this strategy is to expand the platform capabilities to appeal to three user personas with different skill sets—data scientists who want to code, business decision-makers who want straightforward predictive analysis, and IT operations teams.
Our customers made it clear that their data scientists want the ability not only to use their own code with DataRobot but also to have a seamless notebook experience in order to take advantage of the power of the platform. To meet this need, DataRobot acquired Zepl in May. Zepl provides a self-service data science notebook solution for advanced data scientists to do exploratory, code-centric work in Python, R, and Scala. It was built with enterprise-ready features such as collaboration, versioning, and security.
Data scientists who prefer to code can continue to use DataRobot’s automation capabilities to quickly build models and carry out tasks and then edit and customize them with their own code. These extensions are easily inserted back into DataRobot’s blueprints and can be shared and reused across projects and teams.
While expanding product offerings for the data scientist is an important growth strategy, our existing audience of executive and analytics leaders who want to use predictive analysis to drive data-driven decision-making and ROI are still front and center. Towards that end, DataRobot AI Applications, which launched earlier this year, are end-user apps that allow an organization’s entire ecosystem to understand customer requirements, optimize processes, and drive better business results in an accessible manner. Any predictive model can become an AI app in minutes – no coding required.
Automated machine learning also improves the lives of the third user persona, IT operations teams, giving them the transparency they need to know everything that is entering their networks and eliminating the misconception that machine learning is a “black box.”
In part two of this blog post series, we’ll provide an overview of DataRobot’s other key augmented intelligence deliverables, as outlined by Roy in the report.