TIBCO Software announced Thursday a series of innovations to its Predict portfolio of products that are intended to reduce the time it takes to gain insights from data. The changes include updates to TIBCO Spotfire, TIBCO WebFOCUS, TIBCO Data Science and TIBCO Streaming.
The TIBCO Spotfire Mods framework, used by developers to modify Spotfire with new visualizations, has been updated to allow modification of Spotfire with data science algorithms.
“The value of the Mods framework to developers is that it unlocks the creativity of the visual analytics developer community to create and share these modifications with anyone,” said Mark Palmer, senior vice president of engineering for TIBCO. “New Balance, [at] one of our breakout sessions [during] TIBCO Now, said, they grab new Mods every week so they can look at their data in new, creative ways.”
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Another update, Spotfire Data Functions, will extend the Mods framework further by adding data functions, AI and data science capabilities into Spotfire. Data Functions, for example, would allow a Python developer to share a unique forecasting algorithm or any predictive model that can be added to the Spotfire engine, Palmer said.
These updates are available today.
Spotfire and TIBCO ModelOps, which is used to move AI models into production, will be integrated “soon,” the company said, to allow non-programmers to augment decision-making with AI using no-code interfaces.
“By modifying Spotfire with data science algorithms, business users can more easily leverage data science while they’re exploring data,” Palmer said. “For example, a sales operations analyst could use a data science mod to predict sales for coming quarters based on an algorithm created by a data scientist that compares current conditions to historical trends. The mod in this case would be a Python predictive model that generated a forecast line to make it easy to see prediction.”
TIBCO WebFOCUS gets new cloud-native capabilities that add containerized deployment options and a managed cloud offering to support enterprise-wide scaling. It also is receiving enhanced authoring and assembly capabilities like filtering, styling, reporting and app development in a single hub. The changes are designed to help organizations find new ways to use AI and machine learning to augment data preparation, content creation and collaboration, the company said.
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TIBCO Data Science is getting access to a range of new cloud data sources as well as TIBCO Data Virtualization integration, Spotfire interoperability, AutoML augmentations, native Spark pipelines and improvements to the user experience.
TIBCO Streaming will be updated “soon,” the company said, with Dynamic Learning, a capability that allows users to continuously learn and readjust predictions based on real-time streaming data, not just historical, stored data, Palmer said.
TIBCO Streaming currently supports over 150 connections to various forms of streaming data, from IoT sensor streams (using protocols like MQTT or OSI PI), to messaging middleware (TIBCO TCI, Apache Kafka, JMS), to market data (e.g., Bloomberg, FIX).
“From a business perspective, this in-built connectivity means customers simply drag an icon on their development canvas and start consuming any real-time data they choose,” Palmer said. “Without this connectivity, it can take weeks, months, or more to handle high volumes of real-time data effectively.”