Business analytics and analytic thinking is changing rapidly in today's business world. The rapid growth in big data technologies like Hadoop and broad adoption of wireless device and sensors all connected to the network has created a new environment. It also creates a significant set of challenges for organizations seeking to gain business value from analytics initiatives.
The traditional role of analytics was primarily focused on answering "what happened" or perhaps "why it happened" in certain advanced cases. Historical and descriptive analytics served an important role, but their utility was limited to understanding events and issues after the fact.
The emergence of "Data Lakes" as part of the movement towards increased usage of unstructured data enhanced the baseline capabilities of historical, descriptive, and diagnostic analytics, but many kinds of business issues and challenges remained unsolved. For example, knowing why a machine broke down, or why a sales did not occur in the past is no doubt helpful, it is not nearly as profitable or useful as knowing in real-time and preventing the problem in the first place.
Chief among these unsolved challenges are the major class of problems associated with the capture and use of real-time data. As the business world enters the major adoption phase of Internet of Things (IoT) solutions and platforms, real-time streaming analytics will come to center stage. In IoT, the volume, variety, and velocity of data means that data must be analyzed in real-time or its value will quickly perish.
IoT applications like utility power management, manufacturing floor status, and in-store one-to-one marketing all require analytics – and action – in real-time or near real-time for the value to be captured.
While analyzing data in real-time is a major step forward, by itself it still does not fully address the challenges of IoT. Analytic thinking and taking appropriate action requires having an understanding of the current context AND the ability to make predictions about the near future. This is the crucial link needed for IoT Analytics and is at the heart of the differentiation of Vitria's IoT Analytics Platform.
In IoT, as data flows in real time in high volume, managers need real-time predictions about what the incoming data means for current operations. Doing this requires predictive models that can be updated in real-time. In addition, the platform or solution must do prescriptive analytics to recommend the next best action to capture value before it disappears. And finally, the system requires an intelligent actions capability to take the required action.
Vitria's IoT Analytics Platform is the only platform that meets all these demanding requirements for IoT Analytics.