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WebFOCUS Data Science and Advanced Analytics

Data science, machine learning, and predictive analytics have entered the mainstream – for both specialists and now less data savvy users ('citizens') – as organizations look to data analytics for competitive differentiation, business efficiency and additional revenue.

Data Mining

Data Science's Early Days

Thirty years ago, the practice of data mining attempted to extract patterns and knowledge from large amounts of data – not too dissimilar from today's data science. But back then this was a specialist field.  Big data, data science, artificial intelligence (AI), and machine learning (ML) are all very popular terms right now, as they mostly pertain to the ability to generate real value from high volumes of data.

What Has Changed?

The proliferation, ubiquity, and increasing power of computer technology has dramatically increased the ability for data collection, storage, and manipulation. We can now process vast volumes of data, in near real time, and extract new patterns, predictions, recommendations, and automation to help business execute and innovate. We really are living in a new data age.

WebFOCUS Suite of Data Science Capabilities

The new WebFOCUS delivers a comprehensive suite of data science capabilities that reaches everyone – from the data scientist to the data analyst to the everyday worker.

How Does WebFOCUS Do It?

Statisticians and AI experts who code in R with libraries from CRAN, or code in Python with Scikit-Learn, Numpy, and other packages can seamlessly share their work across the entire WebFOCUS ecosystem. Results can be shared with business users, with full control over who can view and act on insights. Statistical models can be built directly from within WebFOCUS, with scoring routines embedded into WebFOCUS applications to combine predictive analytics and BI. 

Introduction to WebFOCUS Data Science

WebFOCUS RStat is a powerful user interface that integrates machine learning with WebFOCUS. This toolset addresses the main requirements of predictive analytics: data access and preparation, predictive model training, testing and evaluation, and model deployment. Business users can use RStat to build commonly used statistical and machine learning models.

With the RServe Adapter, data scientists working with the R programming language can integrate WebFOCUS applications with the full gamut of R’s public and custom libraries to provide advanced analytics, machine learning, AI, and statistical analysis. WebFOCUS can also be connected to the open-source RServe environment, allowing users to customize their analyses through R scripting.

The WebFOCUS Python Adapter attaches WebFOCUS to the Python environment for computing the values of virtual data fields. Data scientists can choose their preferred tool – R, Python, or both. The effect is the same from the end-user’s perspective; whether the deployed script was created using R or Python, values have been computed for a virtual field.

Conclusion

WebFOCUS Rstat and full integration with R and Python network systems, models, and statistical functions allow scientists and citizens to contribute directly to BI and analytics content. But that's not all WebFOCUS brings to the table. Information Builders' award-winning data access and cleansing tools ensure organizations are working with trusted data.

The result is a modern enterprise platform that offers a range of support for data scientists who can develop advanced analytics, AI, and machine learning; for citizen data scientists who can employ augmented models and statistics; and everyday users who can securely consume and benefit from analytics that incorporate predictions, recommendations, and even automation.