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dotData Enhances Data Science Automation Platform

March 12, 2019

dotData released Version 1.4 of its dotData Data Science Automation Platform. This latest update adds significant enhancements to the platform and provides users with deeper insights, increased flexibility, ease-of-use, and greater performance to meet their specific business goals.

dotData will be showcasing its dotData Enterprise Version 1.4 and dotDataPy Version 1.0 at the Gartner Data and Analytics Summit from March 18-21 in Orlando, Florida, and will also present a session, "Why Do 85% of Enterprise Data Science Projects Fail?", on Wednesday, March 20.

dotData's AI-powered Data Science Automation Platform completely automates the entire data science process, from data collection through production-ready models, including feature engineering.

"One of the most exciting enhancements in Version 1.4 is the support of AI-powered feature engineering for geo-temporal data which are very rich but difficult-to-analyze data sources, providing deeper insights over a geographical spectrum," said Ryohei Fujimaki, PhD, dotData's CEO. "We've also made significant enhancements in the machine learning and data preparation components of the platform, giving enterprises the freedom to solve more data science challenges, faster."

Key updates of the dotData Platform Version 1.4 include:

Feature Engineering from Geo-Temporal Data

The ability to leverage geo-temporal data such as GPS data, census information, and data from mobile devices, is growing in importance across many industries, including financial services, retail, and healthcare.

For example, for a retail store, geo-temporal patterns, such as, "whether there is a sporting event within three miles of the store during the next week," are often very important to enable the store to optimize its inventory. dotData Version 1.4 enables users to automatically design such geo-temporal features with a few clicks.

New State-of-the-Art Machine Learning Algorithms

dotData Version 1.4 now supports more state-of-the-art machine learning algorithms, including Gradient Boosting (XGBoost, LightGBM), Random Forest, and others. The Platform automatically tunes the hyperparameters of these algorithms to achieve the best performances in various statistical metrics.

dotData users can automatically take advantage of these highly-accurate ML algorithms, in addition to previous white-box algorithms, to improve model accuracy.

Enhanced Automatic Data Preprocessing

dotData Version 1.4 significantly enhances data preprocessing on both source data and features, including data integration, source data cleansing, and feature outlier filters, in addition to preprocessing functionalities supported in previous versions such as missing value imputation and data normalization.

This data preprocessing is fully automated, expanding the range of automation and further freeing up data scientists to focus on the highest value projects with the biggest impact.

Drag-and-Drop Data Collection

dotData Version 1.4 supports drag-and-drop data collection from CSV files in addition to existing JDBC data connectors. This enables users to import their locally-customized data quickly without handling SQL or interacting with databases.

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