In our most recent blog post, “Why IoT Analytics is more challenging than Traditional Analytics”, Vitria’s CTO and Co-Founder, Dale Skeen, detailed 8 key points on why the challenges of IoT Analytics are unprecedented. These challenges require new thinking on software architecture and platforms. In in this blog we discuss how our new VIA IoT Analytics Platform can meet each of those challenges head on and help our customers gain value quickly with IoT Analytics.
- ELASTIC SCALABILITY. First and foremost, IoT provides an unprecedented volume and velocity of data, far beyond what is seen in traditional analytics. VIA’s Open IoT Data Lake provides the open scalable data services required to handle this data and service the complete analytics life-cycle. The Data Lake is complemented by an Elastic Query Service that supports access by IoT applications, self-service analytics, and 3rd party data consumers.
- FAST DATA INGESTION. The variety of data in IoT is another significant challenge – structured data, unstructured data, data from 3rd party vendors, and all arriving from a variety of physical sources and methods. VIA features Fast Data Ingestion and integration to address this wide array of data types and data sources.
- REAL-TIME ANALYTICS. Many of the high value use cases in IoT are time-sensitive. VIA’s Real-Time Analytics provide continuous real-time contextual awareness and situational intelligence using descriptive analytics, multidimensional analysis, pattern matching, and more. This continuous real-time context is the nimble posture required for the time-sensitive decision-making inherent in IoT use cases.
- COMPREHENSIVE ADVANCED ANALYTICS. The complexity of IoT means that advanced analytics are required to make sense of all the data. Simple or traditional methods will not suffice in the IoT era. VIA features a comprehensive set of advanced analytics running in real-time (streaming) or batch mode. VIA’s analytics includes all the analytics need for IoT – including descriptive, predictive, and prescriptive analytics.
- INTELLIGENT & AUTOMATED ACTIONS. IoT use cases often involve scenarios where timely action must be executed or the value disappears quickly. In IoT, situations and context are continuously evolving and timely action is mandatory. VIA includes an Intelligent Actions and Automation capability that enables the fast – and often automated – action required to capture value in IoT.
- SELF-SERVICE ANALYTICS. Operational analytics in IoT demands “heat of the moment” processing and action that is not common for enterprise BI requirements. VIA includes a robust set of self-service analytics tools that remove the dependency on scarce IT and technical resources.
- VISUAL MODEL-BASED DEVELOPMENT ENVIRONMENT. Developing solutions for IoT analytics is often slow and difficult because specialized and deep IT skills are required. Furthermore, IoT Analytics often requires multiple types of analytics running over complex Big Data tools. VIA addresses these challenges head-on with an innovative visual modeling environment. VIA’s visual environment streamlines and simplifies this work and dramatically reduces the need to master exotic Big Data technology.
- PRODUCTION-GRADE ENVIRONMENT. Traditional enterprise analytics development is often “lab” oriented – meaning that it can be done in offline environments where failure is not catastrophic. This luxury does not exist in IoT environments, where a much higher degree of resiliency is required. VIA offers an integrated, holistic “solution” lifecycle management foundation that is robust and secure to meet the demands of 24 x 7 IoT-oriented production environments.
As we said in our original blog on the challenges, IoT Analytics is quantitatively and qualitatively different from traditional enterprise analytics. We have designed VIA explicitly to address these unprecedented requirements. Learn more how VIA can help you succeed with IoT Analytics.