Staying ahead of the technology curve means strengthening your competitive advantage. That is why we give you data-driven innovation insights. This time, you get to discover 5 hand-picked startups developing edge analytics startups.
Global Startup Heat Map: 5 Top Edge Analytics Startups
The 5 edge analytics startups you will explore below are chosen based on our data-driven startup scouting approach, taking into account factors such as location, founding year, and relevance of technology, among others. This analysis is based on the Big Data & Artificial Intelligence (AI)-powered StartUs Insights Discovery Platform, covering over 1.3 million startups & scaleups globally.
The Global Startup Heat Map below highlights the 5 startups & scaleups our Innovation Researchers curated for this report. Moreover, you get insights into regions that observe a high startup activity and the global geographic distribution of the 101 companies we analyzed for this specific topic.
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Sliceup detects Network Infrastructure Problems
The consistent uptime of commercial and enterprise networks is critical for day-to-day operations. Any interruptions in their services result in significant losses financially and potentially in a loss of customers. Startups are using edge computing and analytics to develop solutions to monitor and prevent IT problems before they can cause damage.
The US-based startup SliceUp creates an analytics system that uses an edge and cloud technology ecosystem. The edge computing systems use AI and Machine Learning to identify anomalies at the point where the raw data is generated. The benefit of the technology is that only important and relevant information is sent to the backend or stored in the cloud server and anomalous data is removed. This helps IT departments reduce the time, effort, and cost required for operational security.
Novi creates an Inspection Analytics Platform
The offsite processing of data is used as a cost-saving measure as on-premises hardware is not a requirement and in the case of remote applications, traditional analytic systems cannot be applied. Most applications or devices that require building models by processing large volumes of data, typically have the processing performed offsite. The problem with offloading the processing and analytics of data is the time delay between the data generation to the action performed from the processed data. With the advances in the Internet of Things (IoT), edge computing, and AI, startups are building solutions to perform all data processing and analytics at the edge.
The US-based startup Novi develops edge analytics solutions for inspection purposes in the infrastructure, manufacturing, space, and defense sectors. The edge analytics platform uses onboard multi-sensor data fusion and neural networks to provide insights in areas such as asphalt inspection and repair, power line damage, product flaw detection in manufacturing lines, and aerial imaging. Their solution benefits industries such as manufacturing, space, and defense, where solutions can combine the data capture and processing in a single package.
Arundo improves Operational Performance
The application of data analytics has been on an increasing trend in recent years with the IoT giving a significant boost to the use of analytics. Analytical systems primarily depend on a reliable network for communication for cloud analytics or other cloud-related services for modeling. This is a challenge when applied remotely, for example in ships in the middle of the ocean and remote offshore wind farms. Startups are developing edge analytics solutions to overcome these challenges.
The US-based startup Arundo provides its clients with an edge computing solution that easily connects to the IT system and provides analytical insights. Their product has been designed and built specifically for easy integration into systems in areas with low to no connectivity. The entire analytical process is performed from data collection to model building and providing recommendations or insights is done at the edge.
Industrial Analytics builds a Modular Edge Analytics Platform
The number of IoT-connected sensors used by Industry 4.0 is continuously on the rise with analytics software optimizing the value of data captured by these sensors. On a small scale, the time delay from the data capture from sensors to analyses and subsequent reporting often has no negative impact. However, when scaled to large applications the time delays can seriously impact the performance of equipment and production environments. Startups are developing edge analytics solutions to handle both the data capture and analytics at the source of the data generation.
German startup Industrial Analytics creates AI-based edge analytics modules for industrial plants. Their modules include physics and AI analytics for vibrational analysis, thermodynamics, and rotor dynamics. A module focused on IoT sensors to handle position determination, data integration, and safety. Finally, the modules are all controlled through an interactive user interface (UI) and a mobile application. The edge analytics platform serves to monitor and optimize industrial processes and rotating equipment such as compressors, turbines, and pumps.
Crosser develops Streaming Analytics Software
The developments in Industry 4.0 create a data-rich environment in smart factories with data being collected from nearly all connected machines and equipment. Having so much data is a great benefit but also creates many challenges in processing data from multiple streams before analysis. This is not a cost-effective analytics process when scaled to large factories or environments. Edge analytics solutions for equipment are in development by startups and can overcome these hurdles.
Swedish startup Crosser designs and develops Streaming Analytics and Integration software for any edge, on-premises, or cloud. The Crosser Platform enables real-time processing of streaming or batch data for Industrial IoT (IIoT), Data Transformation, Analytics, Automation, and Integration. Their software solutions are designed for Industry 4.0 applications such as condition monitoring and predictive maintenance by simplifying the data collection, processing, and visualization cycle.
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