Put DCIM into Google and you’re likely to get one of two results, Digital Camera Images or Data Centre Infrastructure Management – we’re concerned with the latter.
Described as a fairly new and emerging technology, DCIM is the convergence of IT and building facilities functions within a data centre. Leveraging such innovations as automation, zero-touch provisioning and big data, it has quickly found itself at the heart of edge analytics, due to its ability to ensure business continuity within a facility.
“Autonomous vehicles, smart cities, telemedicine, content delivery, and augmented and virtual reality are some of the biggest trends in edge computing that data centre managers need to support,” says James Cerwinski, VP of product management and marketing at Sunbird.
“DCIM software allows data centre managers to maintain uptime and business continuity to provide these innovative services.”
As for the wider “analytics at the edge” conversation, this varies according to industry and vertical.
For example in manufacturing, transportation, oil and gas or utilities, there lie “great use cases for management and control as they look to modernise infrastructure by implementing predictive maintenance solutions or real-time optimisation, and we can expect more possibilities to come with 5G and cheaper bandwidth,” says Rob Lamb, client principal and CTO at Dell Technologies.
Overall, however, there are a few main trends driving the transformation of the edge analytics space. The first is the huge amounts of data being migrated to the cloud every hour due to the growing number of digitisation initiatives across the industry.
“This is because of the powerful processing, analytics, and delivery capabilities cloud offers – equalling cost savings for many who traditionally relied on data centres,” says Janet Liao, principle product marketing manager at Talend.
Additionally, as cloud use continues to grow, there are a growing number of use cases with strict latency, bandwidth, privacy and resiliency requirements.
“Here, edge analytics provides a more effective solution by analysing data in real-time, as if data can be processed at the edge it will minimise latency and bandwidth compared to when it is transferred to a data centre,” continues Liao.
“What’s more, data breaches are less controllable when data leaves the edge to a centralised cloud data store. By embracing edge analytics, businesses can have more visibility and control over the security of the data.”
It appears that customers, be they enterprise, wholesale or anything else in between, are all wanting the same things from their edge providers – and that’s service unification and Cerwinski.
According to Daniel Yu, director of Azure Marketing, there is a specific need to consolidate the tools and services of IoT with big data.
“For this the combination of edge analytics and big data analytics in the cloud enable users to process the data locally for low latency, but also build machine learning model in the cloud, and then score them in real time for predictive maintenance, for example,” Yu says.
In addition, he says that customers require a “new level of agility that enables them to quickly deal with abrupt business disruptions, all while using the freshest insights to guide their decision”.
“Additionally, customers are looking to maximise their current investments and optimise their business processes by intelligently using their data. A unified analytics platform enables organisations to reduce project development time.”
Things differ slightly for DCIM edge customers. Cerwinski instead points to uptime, efficiency and a saving of resources as the key needs.
“All data centre managers demand that DCIM software helps them increase uptime, improve capacity planning and utilisation, and boost people productivity. In short, customers want to be able to do more with less resources, and hence they need a DCIM product that is fast, easy, and complete,” explains Cerwinski.
Even though these demands remain the same for both enterprise and wholesale clients in the DCIM space, there is a difference between the importance placed on these needs according to their market.
“Different segments might place greater emphasis on one need or another,” continues Cerwinski. “For example, an owner operator of a data centre has to cover every aspect of data centre management from full power chain management to asset management in the rack. While for the operator of a colocation facility, needs typically are focused on the critical infrastructure with less emphasis on asset management.”
The general feeling amongst the industry is that the cloud and data centre community has been largely unaffected by the global Covid-19 pandemic. This has been spurred on by the fact that most companies have been forced to accelerate their digital transformation journeys and remote working has quickly seen communications services in high demand – edge analytics has experienced the same.
“Based on my discussions with customers, Covid-19 hasn’t necessarily had a huge impact on the macro trends driving edge analytics. However, it certainly has seen companies expand their bandwidth, get more comfortable working remotely, and start to get to grips with sending data through the network,” says Alan Jacobson, chief data and analytics officer at Alteryx.
Interestingly however, Jacobson doesn’t expect this upwards trend to continue as those with use for edge analytics will already have used it.
“As bandwidth continues to expand and network traffic speeds increase, the demand for edge analytics will likely continue to drop. Those that will embrace edge analytics will already have specific use cases that need it to deliver performance at speed.”
Similarly, the specific requirements for DCIM have also seen an increase in demand, particularly as zero-touch provisioning and remote building management has become even more necessary.
“The Covid-19 pandemic caused a spike in the utilisation of data centre resources, and data centre professionals need to ensure that mission-critical systems are running, power capacity is available for those systems, and redundancy is clear,” adds Cerwinski.
“To make this even more challenging, public health guidelines and shelter-in-place orders have required data centre managers to reduce the number of people in the data centre to the bare minimum. The need for DCIM software has grown considerably as the ability to remotely manage and monitor data centre infrastructure has become more important than ever.”
Examining automation and zero-touch provisioning as something akin to Network Function Virtualisation (NFV) an area of telecoms network management, similarly “data centre professionals demand that DCIM software automate their most common manual and time-consuming tasks,” Cerwinski says.
“DCIM delivers on this requirement via integrating with the systems customers already enter data into, such as VMware, ServiceNow, Cherwell, Jira, and BMC. We call this automation via integration in which the DCIM zero-configuration analytics dashboard becomes the single pane of glass for all data centre key performance indicators.”
The use of zero-touch provisioning and automation extends beyond just DCIM specifically and is central to the general edge analytics space.
“The real advantage of zero-touch is that you can reduce the effort of facilitating data collected from the edge,” says Lamb.
“You can send a ‘dumb’ device out by courier and connect it to a 4G or 5G network, where it can remotely obtain all its configuration information. Until you get to zero-touch, edge computing will always have a relative cost attached because of the need for a skilled engineer to deploy and configure it.”
Talend’s Liao adds that automation is equally as important for onboarding and security, through the elimination of human error.
“Zero touch provisioning enables easier onboarding of IoT devices onto an IoT cloud platform as it enables automatic provisioning and configuration,” she says.
“This prevents developer error during the provisioning and configuration process, and provides a more secure interaction between the device and platform as the security framework had already been established on both ends during the pre-production stage.”
As the 5G race continues – as well the prevalence of content on demand, latency-sensitive gaming and complex cloud environments – the importance of edge analytics has only started to be felt. As networks continue to scale and numbers continue to rise the need for greater granularity, visibility and efficiencies will become even more mission critical in the near future.
