Internet of Things (IoT) analytics refers to the collection and analysis of data stemming from a large number of heterogeneous Internet connected objects. IoT analytics is an integral element of the vast majority of IoT applications, which process data in order to offer data-intensive services or to drive actuation and control decisions. The Velocity of IoT data streams is usually the attribute that differentiates IoT analytics systems from the majority of conventional BigData systems, which handle large volumes of transactional data. Therefore, IoT analytics systems are usually supported by middleware frameworks for streaming data (such as the open source Apache Storm, Spark and Flink frameworks), rather than the popular MapReduce BigData processing framework. Given their Big Data nature, IoT analytics systems are usually integrated with Cloud computing infrastructures, in order to take advantage of the scalability, storage capacity and processing performance of the Cloud.
By 2022, 75% of all databases will be deployed or migrated to a cloud platform, with only 5% ever considered for repatriation to on-premises, according to Gartner, Inc. This trend will largely be due to databases used for analytics, and the SaaS model. "According to inquiries with Gartner clients, organizations are developing and deploying new applications in the cloud and moving existing assets at an increasing rate, and we believe this will continue to increase," said Donald Feinberg, distinguished research vice president at Gartner. "We also believe this begins with systems for data management solutions for analytics (DMSA) use cases -- such as data warehousing, data lakes and other use cases where data is used for analytics, artificial intelligence (AI) and machine learning (ML). Increasingly, operational systems are also moving to the cloud, especially with conversion to the SaaS application model."
In this age of data economy, data analytics is recognized as a key differentiator for companies trying to gain a sustainable competitive advantage and outperform their peers. However, the complexity of establishing an analytical architecture due to a wide array of disparate technical capabilities offered by a plethora of vendors makes the deployment of an on-premise solution a daunting task. For this reason, the Salesforce Analytics Cloud has captured the imagination from of both IT and business communities. The Salesforce Analytics Cloud represents the rethinking of analytics for the business user. The Analytics Cloud is a cloud-based platform designed for the business user to have access to analytics "on the go," providing answers to questions instantly on any device.
The need for cloud strategies to address information complexities and provide broader delivery options are some of the key drivers for organizations when considering expanding to the cloud. Overall, organizations require a way to access diverse and complex data sets and transform the stored data into valuable insight for business, the survey said.
AI tools can be highly complex, which means they require staff with deep AI, machine learning (ML) and data science skills. Cloud environments have also become more complex, requiring staff who understand the latest trends in AI, ML and analytics in "native" cloud environments, in addition to third-party tools. And AI and analytics involve a plethora of servers, storage, networking, integration and security options, with their associated business and risk implications.