What is Augmented Analytics and Where Does DataRobot Fit In?
Gartner defines augmented analytics as the use of machine learning to automate data preparation, insight discovery and insight sharing i.e. it is the use of automation in these common analytics processes that make them augmented.
“We are already seeing the augmented analytics features make their way into modern BI and analytics and data science and machine learning platforms. This is happening largely in response to disruptive innovations from startups such as…DataRobot” says Gartner.
Where does DataRobot fit within the augmented analytics segment?
DataRobot’s “automated machine learning” is closely related to Gartner’s “augmented” analytics.
“Augmented analytics will enable expert data scientists to focus on specialized problems and on embedding enterprise-grade models into applications. Users will spend less time exploring data and more time acting on the most relevant insights with less bias than is the case with manual approaches.” says Gartner.
Gartner splits augmented analytics into 3 sub-categories:
1. Augmented Data Preparation is defined as the use of machine learning to enhance and enrich data. DataRobot has several features that fit within this sub-category, including missing value imputation, target leakage detection, time series feature generation, and importing external calendars.
2. Augmented Data Discovery is defined as the use of machine learning to enable citizen data scientists to find, visualize and narrate findings, without having to manually build models or write algorithms. Since the time it was first released, DataRobot has automatically generated data insights such as exceptions, segments, links and predictions. After Gartner’s report was first published, DataRobot released its new automated model documentation capabilities, which automatically writes a detailed narrative of the data insights, algorithms and results, suitable for regulatory reporting purposes.
3. Augmented Data Science and Machine Learning is defined as the automation of key aspects of advanced analytic modeling, to reduce the requirement for specialized skills to generate, operationalize and manage models. These are all core skills for DataRobot, the pioneer of automated machine learning, which automatically chooses algorithms, selects features, deploys models and monitors deployed models to pro-actively identify when models need to be refreshed.
“A number of data science and machine learning platforms — such as those of DataRobot…are making it easier for expert data scientists and less-skilled citizen data scientists to build advanced descriptive, predictive and prescriptive models.” says Gartner
For More Details and to see Gartner’s Recommendations…
Gartner sees augmented analytics as a source of competitive advantage, and recommends that organizations launch a pilot to assess the viability of augmented analytics.
“By 2020, the number of users of modern business intelligence and analytics platforms that are differentiated by augmented data discovery capabilities will grow at twice the rate — and deliver twice the business value — of those that are not.” says Gartner
To learn more about DataRobot’s augmented analytics capabilities, contact DataRobot.
About the Author:
Colin Priest is the Director of Product Marketing for DataRobot, where he advises businesses on how to build business cases and successfully manage data science projects. Colin has held a number of CEO and general management roles, where he has championed data science initiatives in financial services, healthcare, security, oil and gas, government and marketing. Colin is a firm believer in data-based decision making and applying automation to improve customer experience. He is passionate about the science of healthcare and does pro-bono work to support cancer research.
VP, AI Strategy, DataRobot
Colin Priest is the VP of AI Strategy for DataRobot, where he advises businesses on how to build business cases and successfully manage data science projects. Colin has held a number of CEO and general management roles, where he has championed data science initiatives in financial services, healthcare, security, oil and gas, government and marketing. Colin is a firm believer in data-based decision making and applying automation to improve customer experience. He is passionate about the science of healthcare and does pro-bono work to support cancer research.
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