Scale advanced analytics across the enterprise

Analyze real-time data and automate decisions to connect intelligence and action

Accelerate innovation and achieve a competitive advantage with data science and streaming analytics. Algorithms are only one piece of the advanced analytics puzzle. Being able to access, prepare, visualize, model, deploy, score, monitor, and retrain models within a fully auditable and governable framework is the end-to-end analytics lifecycle that is paramount to success. To outpace the competition, organizations need to score high-volume, streaming data directly within business systems and at the edge. These are all the ingredients needed for scalable, real-time, predictive analytics.

What is Advanced Analytics?

Going beyond business intelligence to find hidden patterns in data, advanced analytics includes techniques like cluster analysis, complex event processing, data mining, forecasting, graph/network analysis, machine learning, multivariate statistics, neural networks, optimization, predictive analytics, real-time or stream analytics, text mining, simulation, and visualization.

  • TIBCO Scored Highest for Production Refinement in Gartner’s Critical Capabilities for Data Science

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주요 이점

Simplify, Collaborate, and Do More

Big data technologies are complex. Simplifying the end-to-end analytics lifecycle within big data ecosystems like Spark and Hadoop allows you to use data science techniques at scale. Collaborating across data science, line of business, and IT teams on big data analytic projects increases efficiency and productivity for the entire organization.

Simplify, Collaborate, and Do More
Find Anomalies and Take Action

Find Anomalies and Take Action

Analyzing high-volume streaming data at the edge and directly within business systems allows you to find anomalies, make decisions, and take action at point of impact. With ever increasing volumes of data, being able to analyze, filter, summarize, and gain insight in real time reduces the need to move and store every bit of data collected.

Find Anomalies and Take Action

Analyzing high-volume streaming data at the edge and directly within business systems allows you to find anomalies, make decisions, and take action at point of impact. With ever increasing volumes of data, being able to analyze, filter, summarize, and gain insight in real time reduces the need to move and store every bit of data collected.

Find Anomalies and Take Action

Operationalize, Monitor, Manage, and Trust

Many organizations struggle to operationalize analytics. As data drifts and models decay, being able to retrain, refresh, and automatically deploy new analytic models at the edge or directly within business systems lets you understand and act on trustworthy results.

Operationalize, Monitor, Manage, and Trust