5G and IoT – how to deal with data expansion as you scale

With all the talk of self-driving cars and automated drone deliveries, it is easy to forget that the Internet of Things has been around for years. Even before the term “Internet of Things” was first created in 1999, enterprising computer science students had automated processes to improve their experience. One of the first “internet-connected” devices was the Coca Cola machine at Carnegie Mellon University in the 1970s, where students could check how many bottles were in the machine and how cold they were before walking down to buy a bottle.

What has changed over time is how many devices are connected and how much data they create. For example, Traxens uses data from sensors within shipping containers to provide insight into where each container is located, alongside other data like temperature and humidity levels. This insight into data has helped Traxens deliver more accurate data to its clients in the shipping and rail sectors, as well as flagging opportunities to improve efficiency and save costs across different locations.

So, what will change in the future with the advent of 5G? How will this make a big difference to companies and increase the adoption of IoT? And how will companies use 5G and IoT together to scale up their businesses?

The potential for 5G – more connections, more data

With low latency and higher bandwidth, 5G, the next generation of mobile data infrastructure, will provide a fillip to applications benefiting from IoT data. According to Gartner, 66% of organisations plan to deploy 5G by 2020. Meanwhile, 59% say they will include IoT communications in the use-case for 5G.

IHS Markit estimates the number of IoT devices will rise from 27 billion in 2017 to 73 billion in 2025. Popular use cases will include environmental monitoring, smart metering, inventory intelligence, and operator productivity, says Capgemini. Use cases for 5G and IoT are continually expanding – as part of the UK government’s nationally coordinated trials, for example, the 5G RuralFirst programme has put 5G collars on cows to control a robotic milking system.

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No matter what their purpose, applications of IoT over 5G will all have one thing in common: lots of data. Research conducted by IDC estimates that 90 zettabytes (1 billion terabytes) of data will be created on IoT devices by 2025. This amounts to roughly half of the 175 zettabytes currently created by all computing worldwide.

The challenges for IoT analytics at this scale

It is not just the volume of data that should concern organisations hoping to reap the benefits of combining IoT and 5G technologies. To succeed, they will need to manage two issues: how to create a data architecture that can manage both the speed at which data will arrive from IoT devices and how to integrate that data with other data sources across the enterprise and from outside the organisation.

Take a use case in logistics, for example. Sensors in a shipping container may tell a retailer that their fruit has exceeded the temperature threshold due to a refrigeration unit malfunction. Traditionally, this would not be detected and the order would likely be spoiled. With IoT, this sensor report can be detected and investigated in real time rather than finding out when a delivery is made and potentially affecting a service level agreement. The problem can then either be solved in advance, or another delivery made to meet the SLA.

Additionally, companies can use data from elsewhere in their supply chain to find alternative supplies for specific requirements. Thinking more broadly, this information can be used alongside data from stores, social media and weather forecasts to understand overall demand signals, while data from marketing can help to understand the impact of promoting alternatives.

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To achieve this kind of response, companies will need to share, replicate and integrate data across organisational boundaries in near real time, as well as store data for the longer term to support machine learning and historical analysis.

To meet the challenge of reacting at speed to 5G data from an army of IoT devices, organisations cannot rely on existing data networks or architecture. Data will arrive too rapidly, be managed over diffuse geographies, and stored in on-premise, cloud and hybrid applications. Most legacy applications are not designed with geographic distribution in mind, and many enterprise data strategies do not take the technical challenges presented by IoT and 5G into account. Organisations require an enterprise data layer – a highly distributed, always available data store that supports master and operational data to provide real-time data availability and functionality to all consumers and endpoints.

Data and context

The speed at which organisations can integrate and analyse data will be vital because context is so important. For example, knowing a vehicle is stationary may not mean very much – unless you know it was travelling at 50 mph two seconds earlier. There will be a certain amount of data that can be processed at the edge in real time, but this is not suitable for other use cases. For example, getting contextual analysis in a matter of seconds will also be vital if organisations are to benefit from IoT, yet this has to be processed centrally in order to provide the right results to the business as a whole.

Similarly, in the consumer setting, knowing that a customer has walked into a store is one thing, but to make the information useful, the retailer also needs to know all their online purchases, website clickstreams, service centre calls and so on. Building this Single Customer View is not something that can be achieved at the edge, however much it helps to have data close to the customer. This is why the distribution of data is so important – data might be created in multiple places, but it needs to be managed and used in the right places where it can provide the most value back to the business.

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In both industrial and consumer settings, organisations need to prepare their data architecture to cope with the scale of data from IoT devices created and distributed across 5G networks. Teams have to plan ahead around consolidating and analysing this data so that they can make the most of their results, even as they spread it across multiple locations for availability and resiliency.

Alongside this, organisations will have to manage greater complexity at scale. As they grow the volume of data they manage over time, they will have to embed this data within new business processes that integrate IoT applications with more traditional applications such as ERP, HR, finance and CRM.

Businesses which succeed at exploiting IoT and 5G to their fullest extent will see the data they create as an opportunity to enhance customer experience and retaining customers by anticipating their wants and needs will be at the heart of new business models.

IoT and 5G will increase demand for distributed data

IoT has already provided its worth in improving business efficiency in specific use cases. The opportunity around IoT, when combined with 5G, should support more future growth and wider adoption. It should support organisational transformations that put customers at the centre of new business models. And it should support smarter use of data at scale.

However, only those building a fit-for-purpose enterprise data architecture will succeed, based on a more distributed approach to data. The remainder will see this influx of data as a constraint, not an enabler, and lose ground in the market as they struggle to use data effectively.

Written by Patrick Callaghan, strategic business advisor, DataStax

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