Just a few years ago, 5G was introduced as a technology that could change the world - telcos like Nokia, Verizon and Huawei presented demonstrations of how they would make 5G a reality.
In just a few short years (and thousands of work hours, testing and trials behind the scenes), 5G became more than an idea. Now it is a full-blown technology available to businesses and consumers worldwide.
One of the most commonly discussed business applications of 5G is how it supports and enables the Internet of Things (IoT). IDC predicts there could be 55.7 billion connected devices worldwide, collecting and distributing all kinds of data back to IT systems in the cloud or on the edge.
5G and IoT are all lucrative technologies, but they are only useful if the data they collect can be stored, managed, used, and analysed to provide meaningful insights. That means there is an expectation for the cloud and the edge - as well as all of the supporting technologies - to help businesses make sense of this data.
Cloudera's Australia and New Zealand country manager Nick Hoskins believes 5G will drive innovation, and there are two key forces at play: Increasing data volumes and network speeds.
“When we talk to customers, we tune in to the fact that they want to collect all this incremental data, but also drive insights from that data faster and increasingly in real time as it's moving. That's becoming important for companies to stay competitive and to improve customer service.
“When we think about the edge now and increasingly into the future, companies want to be able to ingest large volumes of data from thousands of devices and move it through the data lifecycle, all with insights in real time at every touchpoint. 5G services will support that.
He says that traditional business intelligence insights often don't match organisations' business needs, which is why streaming analytics will be so important in providing insights before any ‘mishaps' happen.
He points to large retail chains as an example. Retail firms are, of course, able to collect data about customers and purchasing habits from POS terminals. But there is also more of a focus on understanding a customer's online behaviour and IoT across a connected supply chain.
“Retailers are thinking about processes and production cycles to support and understand customer demands in real time. There are two ways to collect that data - traditional at-rest sources like pulling data and analytics from something like a data warehouse, but increasingly they are collecting data in motion through sources like IoT partner systems.
Whether data is in motion or at rest, it needs to be directed somewhere. Multicloud and hybrid cloud infrastructure both provide an extensible means of storing and analysing data, especially as on-premise infrastructure may not be flexible enough to be able to cope with workload spikes.
Organisations requiring lightning-fast insights may consider the edge, however, the edge does come with challenges such as infrastructure costs and limitations.
Hoskins believes that the edge will become smart and autonomous as technologies such as machine learning weave their way through edge solutions - this is becoming more common in verticals such as healthcare, and even fighting financial crime.
“Increasingly banks and credit card companies rely on streamed data and machine learning for real-time customer marketing, fraud detection and anti-money laundering (AML) activities,” explains Hoskins.
“Often pulling and integrating data from massive numbers of devices at the edge, these capabilities help uncover new suspected fraud patterns (and develop preventive triggers to identify fraud incidents), predict customer needs and determine in real time which offers to give each customer; and send alerts to customers in real time about potential fraud to improve customer experience and reduce customer complaints.
For the majority of organisations though, hybrid and multicloud are where data storage and analytics fit best.
“I think in both cases (the edge and cloud), businesses need a data management platform that can help them connect all the dots together, make it work seamlessly across multiple clouds, even data centers and indeed the edge. That's what Cloudera does, and that's what we're there to help with.
Cloudera Data Platform is a big data analytics platform that can process and analyse all of the insights brought in from IoT and connected devices. Within that is a capability called Cloudera DataFlow, which is a scalable real-time analytics capability that delivers insights and actionable intelligence. What's also important is being able to track data provenance and streaming data lineage, and managing and monitoring edge applications.
“Many organisations in the past have been relying on data, making its way into a data warehouse or a data lake before meaningful analysis and analytics can occur,” Hoskins explains.
“Some companies have gone out and adopted separate tools for driving real time insights when it comes to streaming data. The disadvantage with that is it creates yet another silo and new problems in figuring out how to integrate those components together."
"Companies need a platform that provides that ingestion transformation querying and predictive capabilities. We bring those together in an end-to-end data platform that supports multi- and hybrid cloud.