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DotData Updates Data Science Automation Platform

Version 1.4 adds new machine learning algorithms, AI-powered feature engineering from geo-temporal data, and enhances automated data preprocessing and data collection.

Note: TDWI’s editors carefully choose vendor-issued press releases about new or upgraded products and services. We have edited and/or condensed this release to highlight key features but make no claims as to the accuracy of the vendor's statements.

DotData, a company focused on delivering end-to-end data science automation and operationalization for the enterprise, has released Version 1.4 of its dotData Data Science Automation Platform. The update enhances the platform and provides users with deeper insights, increased flexibility, ease of use, and greater performance to meet their specific business goals.

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.  Version 1.4 enables users to automatically design such geo-temporal features with a few clicks.

New state-of-the-art machine learning algorithms.The update 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. In addition, 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. 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 functions 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. The new release 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.

DotData Platform accelerates the entire data science process, enabling companies to rapidly scale their AI/ML initiatives to drive transformative business changes. dotData Platform also democratizes the data science process by enabling more participants with different skill levels to effectively execute on projects, making it possible for enterprises to operationalize 10x more projects with transparent and actionable outcomes.

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