2019 release of WPS Analytics version 4.1




04 Apr 2019




We are delighted to announce the 2019 release of WPS Analytics. There are many new features for the Workbench and the analytics engine for both coders and workflow users.

Last year, Forrester recognised our achievements and we have continued to build on those end-to-end analytics features, to establish our position as a leading player in data science and PAML. WPS Analytics version 4.1 further empowers users wherever they sit in the data landscape to prepare, wrangle, explore, mine and model data with the choice of intuitive visual ‘drag-and-drop’ or traditional coding tools. Advanced users benefit from huge productivity gains in improved visualisation, decision tree, predictive modelling and machine learning capabilities.

We love to receive feedback from users about our software and we use this to help us understand the most desirable features to add. For version 4.1 we have added many SAS language features and enhanced our drag-and-drop workflow to make it even easier to integrate open source languages with the powerful SAS language to get the best value from your analytics processes.

Which feature best suits you?

Would you like to discuss requirements or arrange a demo?

New features

When writing and running SAS language programs:

  • Point-and-click data profiling available from the WPS Server Explorer.
  • A new Run Step option available in the Script editor.
  • Option to clear the log each time a program or step is run.
  • Open encrypted datasets from the WPS Server Explorer, Project Explorer and File Explorer.

When creating drag-and-drop workflows:

  • Comments can now be added to a Workflow that helps project collaboration.
  • Importing data has been made easier.
  • Richer data prep blocks and code blocks.
  • More blocks for model training, scoring, data export and chart building.

Machine Learning: We have added innovative machine learning procedures for decision forests, neural networks and more.

Statistics: There are new and enhanced statistics, time series and graphing procedures.

ODS: Output formatting and reporting features have been significantly enhanced.

Hub: Our centralised data management and production deployment service is being adopted by our larger customers to keep tabs on data and reduce end-user provisioning and administration overhead.

We will continue to build further valuable analytics and machine learning features in 2019 and we look forward to hearing your ideas for future features.

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