Machine Learning Bolsters AWS QuickSight BI Analytics Service

Amazon Web Services Inc. (AWS) has bolstered its QuickSight business intelligence service with machine learning designed to discern hard-to-find business insights undiscoverable with some BI dashboard solutions.

Machine learning-powered ML Insights was last week made generally available and added to Amazon QuickSight, a fully managed BI cloud service noted for its purportedly first-ever pay-per-session pricing model, introduced last June.

To help enterprise users -- even those with no technical expertise or ML experience -- get a handle on the tremendous amount of data being supplied for BI initiatives, machine learning is used for purposes such as discovering hidden trends and outliers in data, identifying key business drivers and performing what-if analysis and forecasting.

ML Insights
[Click on image for larger view.] ML Insights (source: AWS)

Targeting use cases such as sales reporting, Web analytics, financial planning and embedded analytics, ML Insights features include:

  • ML-powered anomaly detection to uncover hidden insights by continuously analyzing billions of data points.
  • ML-powered forecasting to predict growth and business trends with point-and-click simplicity.
  • Auto-narratives to tell customers the story of their dashboard using plain-language narratives.

The auto-narratives feature lets users create natural language summaries of data visualizations, which can be embedded into dashboards to highlight key insights that others may want to access without having to comb through a whole dashboard.

"With auto-narratives, Amazon QuickSight automatically interprets the charts and tables in your dashboard and provides a number of Suggested Insights in natural language," AWS said in a blog post announcing the ML Insights preview last year. "Depending on the shape and form of your data, you might get suggestions such as what the day-over-day changes look like, what was the highest sales date, what the growth rate is at and what the forecast looks like for the next seven days."

More detailed information is available in a March 14 blog post that digs into using the service in a variety of use cases, along with an embedded introductory video.

About the Author

David Ramel is an editor and writer for Converge360.


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