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IoT initiatives and other emerging workloads force data center change

Internet of things projects and other new technology, such as analytics and machine learning, are tied to software-defined storage and public cloud use, changing the face of IT.

Transformation has been the main narrative in IT lately. With the rise of the digital economy, the connection between data and business results is stronger than ever. The competitive drive to use data more effectively to push business results, revenue and profitability has led to the popularity of newer workloads that are changing the nature of IT. These workloads include analytics, machine learning, blockchain and the internet of things. It's a wave of transformation substantially different from any we've experienced before and with serious business repercussions for those who don't adapt.

The internet of things offers an excellent illustration of how emergent workloads are forcing changes in data center architectures. A quarter of IT decision-makers indicated their organizations have IoT initiatives underway, according to Enterprise Strategy Group's latest IT spending intentions data. This means a quarter of companies already have programs in place to collect data to become more operationally efficient; better monitor products and services; deliver a superior customer experience; and even develop new products, services or business models.

Unlike other workload categories, it's important to note that IoT isn't a single thing. It's a collection of hundreds and even thousands of industry- or project-specific initiatives and workloads. This is an important distinction. For example, IoT projects may involve collecting and analyzing data from "smart" consumer products, or environmental monitoring as part of a smart city initiative, or factory floor data to improve collaboration among machines and people. However, in spite of this diversity, common trends have emerged in how IT organizations respond to the increased data storage and access demands IoT generates.

A quarter of companies already have programs in place to collect data to become more operationally efficient.

Evidence suggests a link between IoT and software-defined storage (SDS) and IaaS public cloud options. In the Enterprise Strategy Group's research, IT shops that indicated IoT as a top driver of data growth were more likely to be committed to SDS as a long-term strategy. Additionally, more than three-quarters of companies with IoT initiatives underway use IaaS. Where IoT was a top driver of data growth, there was a higher likelihood of using IaaS. SDS and IaaS offer several common benefits that make sense for IoT environments. These include the following:

  • Scale. IoT projects tend to generate tremendous amounts of data. Using analytics at the edge, data that's sent to and retained in the data center can be significantly reduced, but, in aggregate, IoT initiatives are designed to capture a huge amount of data that will scale over time. Here's where SDS offers benefits. Designed to be hardware-agnostic, SDS technologies aren't typically confined by arbitrary capacity limits and often support large capacities. IoT workloads also tend to generate unstructured data sets, ideal for the scalable file and object storage commonly offered as SDS technologies. Similar comments can be made for IaaS, with its ability to scale data capacity as needed and as quickly as required.
  • Agility and flexibility. Hardware flexibility, often an inherent component of SDS and IaaS architectures, contributes significantly to scalability. This flexibility is about more than just scalability, however, as SDS architectures typically deliver consistent data access while the underlying hardware changes, evolves and adapts. Hardware flexibility lets you integrate new hardware capabilities, such as faster memory and processing, without having to wait for a full system refresh. This approach helps reduce the overall cost of infrastructure. Furthermore, neither SDS nor IaaS generally requires generational forklift upgrades. This reduces both the infrastructure and management costs of storage, as well as the potential risk of a migration procedure. SDS architectures also tend to improve infrastructure agility, the speed at which new capacity can be deployed and provisioned. This in turn speeds up the delivery of new data-driven business efforts, such as IoT initiatives.
  • Pay per consumption. The flipside of infrastructure scalability is controlling the cost of that scale. Not surprisingly, the rate of data growth and the cost of storage infrastructure continue to be among the top three most commonly identified storage-related challenges. Cloud services, such as IaaS, are known for their pay-per consumption models. Many SDS products offer similar payment structures as well. By only paying for what you consume, you can reduce infrastructure costs. The benefits, however, extend further. Pay-per-consumption models also make it easier to adjust the pace of storage consumption if demand becomes less predictable. That can make it easier to consume infrastructure faster than anticipated while still optimizing costs. There are, of course, differences in the cost structures among various IaaS and SDS services and products. These added costs, such as for data egress common to IaaS, must be considered in any infrastructure decision.

These three capabilities should be part of any IT storage infrastructure modernization effort and not just for IoT initiatives. And while IaaS is often associated with these three capabilities -- and rightly so -- increased interest in SDS reminds us there are multiple on-premises storage products that deliver cloud-like benefits as well, such as greater agility, flexibility and scale.

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