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Automatic Data Science: DataRobot, Quill and Loom Systems

Automatic data science is on the rise. This post examines three recent visionary solutions: DataRobot, Quill and Loom Systems.

By Beerit Goldfarb, Loom Systems.

Companies today are turning to data science to extract actionable insight from their big data. But data scientists are scarce and are expected to grow even scarcer as this demand rises.

Software companies are looking to bridge this human resource gap by weaving mathematical and statistical capabilities into automated data science tools.

But data science is an expansive field, matched in scope by the diverse methodologies developed to derive insight from big data.

Today’s we’ll be examining three radically different and technologically visionary solutions.

Data science automation

The first is DataRobot, a platform designed to help data scientists expedite their predictive model selection and deployment. It allows data scientists to deploy their models with ease and speed. DataRobot seeks to address the cross-industry shortage of data scientists by enabling data scientists to accomplish more in less time.

The second is Quill by Narrative Science. Quill transforms metric data into natural language, narrative-driven and highly customizable reports.The company has recently celebrated its 6th year and 10th patent, a testimony to its (relative) longstanding in the field of AI-powered predictive analytics.

The third solution comes from Loom Systems. Loom Systems seek to radically simplify how organizations derive insight from their data by fully automating humans’ analytical process and combining it with computational speed, accuracy and diligence. It presents insights and recommendations in simple, actionable reports anyone can read and act on.

These automation tools utilize artificial intelligence and machine learning methodologies to provide services to a wide variety of industries, agnostically deriving insights from unlimited amounts of datasets. Happily, this is more or less where the similarities end and each tool’s unique value comes into play.



DataRobot is a predictive analytics platform used by data scientists to help construct and deploy predictive models. Because the deployment process can drag on for months, models can grow obsolete by the time it takes them to be deployed. This is a critical pain point in data science, and one that DataRobot seeks to mitigate by speeding up the process.

DataRobot builds, tests and refines hundreds of models to help data scientists find the ones best suited for their data. The investigation is transparent, allowing users to trace and verify the validity of its results. Platform users can also code, train, test and compare their own models on the platform.

DataRobot also finds key drivers in business metrics, identifies key words and phrases within unstructured text, and can output basic visualizations of its findings.

As DataRobot is built to enhance rather than replace the work of a data scientist, DataRobot users need to have at least a basic understanding of data science methodologies and business intelligence tools like Tableau or Excel.

Features and Capabilities

  • Predictive Analytics: Yes
  • Automatic: Partially, targets need to be set manually, the system cannot generate insights without guidance
  • Data types: Structured only, except for a basic identification of key phrases within unstructured text
  • Natural Language Generation: Yes, concisely
  • Professional Training: Yes, “DataRobot University” trains customers in data science
  • Transparency: Users can track DataRobot’s model selection process, from its features to its algorithms
  • Data Visualization: Yes, as a supporting feature
  • Delivery Model: Cloud and on-Premise
  • Implementation Time: Details unavailable
  • Who Is It Right For:Companies with at least one data scientist or skilled data analyst, who are looking for a tool to enhance and empower their big data analysis capabilities.