Apigee Unveils Latest Version of Apigee Insights Analytics Platform

Michael Vizard
Apr. 10 2014, 09:00AM EDT

After acquiring InsightsOne earlier this year, Apigee is now ready to show the first fruits of that labor. Apigee today launched an upgrade to the Apigee Insights Big Data analytics platform, that is tightly integrated with the Apigee Edge API management platform.

Waqar Hasan, senior vice president for Big Data at Apigee, says Apigee Insights takes in data via the Apigee Edge platform and exposes the analytics results it generates back through Apigee Edge. Aimed at Fortune 2000 companies looking for a more efficient and cost effective way to analyze the massive amounts of data they collect, Hasan says Apigee Insights allows organizations to store that data anywhere they choose.

Hasan says that as a Big Data analytics platform, Apigee Insights is designed to bring a predictive analytics capability to large amounts of data that can be easily invoked via an API. Instead of trying to master traditional predictive analytics applications from companies such as SAS Institute or IBM, Hasan says Apigee Insights provides a simpler way to add a predictive analytics capability via an API that dynamically scales to meet the needs of the business.

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Use cases for Apigee Insights span everything from consumer data (demographics and social media usage), product usage data (how a customer interacts with a product), and API data (usage of app and information services). The end goal is to be able to apply predictive analytics in real time to enable businesses to dynamically adjust processes in response to specific events, says Hasan.

Unlike other approaches to text analytics, Hasan says that Apigee Insights is based on an approach that associates specific words with statistically significant events. In contrast, Hasan says other approaches rely on natural language approaches to try and infer the potential meaning of a specific event.

While interest in all things relating to Big Data is high, most businesses are still in the phase of trying to figure out what to actually do with it. Clearly, there is a need to find a way to efficiently analyze all that data. In the case of Apigee the argument is that it will be simpler for organizations to do that via an API that delivers analytics results versus putting people in front of a complex statistical analytics application that usually requires a data scientist to master.

There’s no doubt that developers are going to be increasingly asked to embed analytics capabilities within their applications. How they go about providing that capability will naturally vary widely. But applications that don’t have some form of analytics capability that can be invoked will, from a business perspective, be rendered nothing short of obsolete.

Michael Vizard

Comments

Comments(1)

Totally agree with the last two paragraphs. The biggest question for a business is: "What question do I want to predict answers to?" There's a trend with Prediction APIs, of services that abstract the complexity of creating Machine Learning models that make predictions. In the end, you don't need to hire a data scientist but to have your developers prepare the datasets to send to these services, and to have them integrate predictions into your app.