As the IoT data-enabled services continue to expand, the market is approaching the stage of data democratization, where real-time analytics is very sought after.
Currently, data integration, real-time stream processing, and analytics services are falling under the umbrella of data management services within the IoT value chain, where each component has also seen economic growth. ABI Research estimates that IoT data management services are a rapidly growing market, with expected growth from $10.1 billion in 2020 to $42.9 billion by 2026.
The emergence of stream processing and analytics solutions provided the impetus for data-enabled decision making and insights for enterprises.
Ultimately, there are two supplier groups dominating the discussion of stream processing and analytics: cloud-first and edge-first. Azure, AWS, IBM Microsoft, Cloudera, Informatica, Software AG Apama, and TIBCO are centering their data management services around the cloud-centric approach, which complements their already existing product and capabilities within full-stack end-to-end IoT portfolios.
Other vendors, such as ScaleOut, Crosser, SWIM.ai., and ClearBlade, are serving the real-time streaming analytics market by bringing an edge-centric approach and teaming up with cloud vendors to deliver industry-specific data management applications.
“Edge vendors are expanding their Streaming as a Service (SaaS) offering toward hardware-agnostic systems. The embedded event streaming and ingestion capabilities packaged as purpose-built, off-the-shelf SaaS has a pre-built application logic and an industry-specific “way” to handle data,” says Kateryna Dubrova, Research Analyst at ABI Research. “Effectively, everyone is attempting to move from a service market to productized out-of-the-box SaaS in relation to streaming technologies.”
Interestingly, data management and streaming services have been deeply impacted by Apache Project open source technologies, such as Spark and Flink. IBM, Confluent, Azure, and AWS have already adopted open-source technology into their IoT portfolio. “The adoption is largely driven by the cost incentive to reduce the capital investment for the end-users as well as appeal to enterprises with existing open-source legacy infrastructure. In contrast, companies like Striim, SWIM.ai Guavus are offering streaming analytics services only reliant on proprietary technology,” Dubrova explains.
Additionally, by focusing on data management and streaming technologies, cloud vendors are ceding the advanced analytics market to other suppliers. “Hence, the advanced analytics market is an excellent example of the “coopetition” in the IoT ecosystem, where cloud vendors are partnering with advanced analytics suppliers. This coopetition enables them to promote an end-to-end IoT technology stack; for example, Azure and AWS have partnered with Seeq to leverage its advanced analytics capabilities,” says Dubrova.
At the same time, other vendors, such as Oracle, Cisco, and Huawei, are pushing intelligence and analytics closer to the devices, expanding their edge portfolio. Such divergent analytics strategies represent the reality and challenges for serving a very diverse IoT ecosystem with IoT data-enabled services.
“Essentially, the vendors are branching into industry-driven solutions, and holistic-horizontal platforms with vertical enabled applications, all and all, the competitive market landscape remains in a state of flux. Subsequently, it is vital to understand each vendor offering and placement in the IoT analytics value chain to gain insight into the competitive landscape for real-time streaming and analytics solutions,” Dubrova concludes.