Data and analytics are the heartbeat of any business looking to drive revenue and innovate – especially during volatile and uncertain times. Businesses have gradually transitioned away from traditional data centers in favor of cloud data storage and analytics solutions to access data and unlock business insights more easily. We expect more enterprises will transition to the cloud in 2023, as Gartner is projecting that enterprise cloud spending will be nearly $600 billion by next year. That number will continue to climb as data generation continues to explode.
However, for many businesses, relying on the cloud for their data and operational analytics needs is challenging. Distributed workforces located in hard-to-reach areas often suffer from latency due to slow Internet connections. Organizations that rely on real-time insights can’t afford to have a lag between the data that’s being generated and the subsequent analysis.
For maximum output, the flexibility and reliability of cloud services need to be met with the power of edge computing. Here we’ll look at the benefits of bridging cloud and edge for analytics, real-life examples of distributed use cases, and best practices for implementing edge technology.
Operational Analytics is Everywhere
Operational analytics refers to the real-time analysis of information on the internal functions and processes of a business. Examples include: how many orders, calls or service logs are being processed, how much revenue is generated at any given moment, or the current status of shipments or inventories. Industries of all kinds deploy operational analytics, ranging from retailers who use insights to target customers while they’re inside a store, and manufacturers who analyze IoT sensor data to identify and resolve potential problems with production-line equipment before they happen.
Data is being produced at an explosive rate and is growing increasingly complicated to process for analysis. This is particularly true for industries within which data collection is in areas with poor Internet connectivity. Offshore oil platforms are an example. These operations require intense analysis of things such as equipment status, GPS location data, current oil prices and more. Any delay in processing and transmitting data from the oil platform to its headquarters could result in poor maintenance of equipment, support teams troubleshooting issues too slowly, or a miscommunication about the cost of oil.
Edge Analytics Use Cases
To process data in a variety of environments, many businesses have shifted operations to the edge. Edge computing allows businesses to generate, collect, store, and process data locally without relying on Internet connectivity. Rather than worrying about being able to connect to a cloud service provider, users of edge computing can enjoy around-the-clock availability of data and systems, allowing for real-time analysis of operations.
Edge computing is becoming increasingly popular, with Gartner estimating that by 2025, more than 50% of enterprise-critical data will be created and processed outside the data center or cloud. This represents a huge opportunity for businesses to capitalize on edge and merge it with their current cloud architecture to ensure continuity with their analytical efforts.
Bridging the cloud to the edge for analytics also offers unprecedented speed in data processing. Without needing a connection, intelligent decisions can be made on the fly based on data that’s instantly generated and available. Additionally, edge analytics can operate on time series, spatial, and other IoT specific data more securely, without worrying about being targeted by external threat actors.
Another use case of analytics on the edge that’s not often discussed is emergency response management in buildings with smart sensors. Devices like fire and smoke sensors can communicate with a dashboard on the edge in the event of an emergency and can automatically trigger a call to a local emergency response team.
The use cases for cloud integration into the edge are numerous, requiring businesses to take careful steps through implementation to reap the benefits.
Bringing in the Edge
Businesses looking to integrate their cloud solutions into the edge for operational analysis must consider many factors. These include having a crystal-clear picture of where data resides, which data points are relevant, when the data expires, and which data needs to be aggregated. Also, constantly moving data to the edge can be costly and there is the potential for network bandwidth, storage and latency issues.
Building the bridge from cloud to edge for operational analytics can take a lot of work, but will up-level analysis of operations, increase security and provide better real-time insights. Take a deeper look into how Actian is modernizing edge application data processing to make data analytics a breeze here.