The Top 5 AI & Machine Learning Trends for 2021 And Beyond

The Benefits and Challenges of Augmented Data Discovery Tools

December 18, 2018

Augmented data discovery is an emerging BI capability.  It can automatically prepare and organize enterprise data for self-service BI.  This is particularly challenging for unstructured data from sources like email, social media channels, IoT feeds, and customer service interactions.

Traditional BI tools have supported basic capabilities for joining, manipulating and transforming structured data. Augmented data discovery can build on these basic capabilities.  It uses augmented data preparation and automated pattern discovery for self-service BI, according to research firm Gartner Inc.  Augmented data preparation streamlines processes for data profiling, managing quality, cleaning data, modeling, enriching, and labeling metadata in a manner that supports reuse and governance. Automated pattern detection builds on traditional BI tools to support complex, large data sets with more than 10 columns.

Augmented data discovery focuses on providing insight for citizen data scientists. In Gartner’s view, these are similar but somewhat different to augmented data science platforms used for building data inference models that can be embedded into apps.  Consequently, augmented analytics tools also tend to include natural language query and natural language generation features.  This ease of access promises many benefits, but enterprises also face several challenges in making the tools work well in practice.

Read the full article featuring dotData CEO, Ryohei Fujimaki, on SearchDataAnalytics.


dotData Inc.

dotData Automated Feature Engineering powers our full-cycle data science automation platform to help enterprise organizations accelerate ML and AI projects and deliver more business value by automating the hardest part of the data science and AI process – feature engineering and operationalization. Learn more at, and join us on Twitter and LinkedIn.