While Cloud computing can handle the data deluge of global digitalization, enterprises need to bridge its shortcomings with edge computing: study

The ability of organizations to realize business value from data depends on their capacity to collect, process, store and analyze it at the edge, research is showing.

As networks become increasingly congested with huge volumes of data generated from user and IoT devices, IT leaders are recognizing that analyzing real-time data nearer to the edge yields greater efficiencies and insights, which results in improved business outcomes.

According to a global study of 2,400 IT decision-makers (ITDMs), 70% in Asia-Pacific (APAC) markets, including Australia, China, Hong Kong, India, Japan, Singapore and South Korea; are already actively using edge technologies to deliver new outcomes, with 6% planning to do so in the next year.

However, there has been growing recognition among ITDMs in the region (84%) of the urgency around the need to implement integrated systems to handle data at the edge. 

Moreover, the maturity of a company’s deployment at the edge is strongly correlated with its ability to derive value from the data collected from devices. Globally, 78% of ITDMs in production deployment with edge technologies said they were in a position to use this data to improve business decisions or processes. That compared with just 42% of ITDMs in APAC who were only at the pilot stage, and 31% who were planning pilots in the next year. 

The Cloud-Edge continuum

According to Partha Narasimhan, CTO and HPE Senior Fellow, Aruba, which commissioned the study: “This research suggests that the vast majority of IT leaders are already embracing the edge or are preparing to. Developing an edge strategy against the backdrop of existing cloud implementations is becoming a necessity as the number of connected devices increases and it becomes impractical to transfer vast volumes of data to a cloud or data center environment, especially as organizations undergo digital transformation to advance their business objectives and address customer needs.”

Despite the economic impact of COVID-19 pandemic, the global interconnection bandwidth has a projected 47% compound annual growth rate by 2023, with APAC leading the adoption of private connectivity services, said Justin Chiah, Senior Director (South East Asia, Taiwan and Hong Kong/Macau), Aruba. “This elevated appetite for edge connectivity services, enabled by increasingly-prevalent IoT infrastructure, continues to transform the new hybrid workplace model as businesses move workloads to the edge.”

Chiah added that this trend drives a whole slew of opportunities for innovation and new developments across sectors such as education, financial services, government, healthcare, manufacturing and retail.

For example, deploying edge networking infrastructure can streamline previous industrial processes which required heavy human interaction. “This allows businesses to continuously improve their supply chains and achieve success safely and more efficiently,” he said.

Drowning in data

The benefits of edge technologies are becoming increasingly important as ITDMs in the region grapple with the growing amounts of data generated within their networks and look towards the cost and latency advantages of storing and processing it at the edge. The study revealed:

  • 37% of ITDMs across the APAC markets surveyed said there was too much data for their systems to handle and 30% stated that they cannot process the data quickly enough to take action.
  • A quarter also highlighted problems with budget (26%), a lack of skills (27%), and an inability to collect data from so many different sources (25%) .
  • In Singapore, 36% of ITDMs cited complying with data regulation as a common challenge in creating value from data.
  • 61% of Singaporean ITDMs (compared to 54% in APAC region) recognized ‘much faster data processing’ and the ‘ability to recognize serious issues faster’ as a result of optimizing machine learning and A) on their networks.

ITDMs in APAC cited a variety of benefits from capturing and analyzing data at the edge, from operational efficiencies to the opportunity to create new products, services and revenue streams. 

  • 54% of ITDMs highlighted ‘improving operational efficiency and costs’ as one of the biggest benefits of capturing and acting upon data from user devices, and 49% cited ‘increasing workforce productivity’.
  • In parallel, 43% of respondents believed the data gave them deeper customer insights, 41% cited the opportunity to create ‘new differentiated products, services, revenue streams and business models’ and 41% highlighted the potential for personalized service delivery.

Cost, skills and security concerns

While ITDMs in the survey showed a growing interest in processing and analyzing data at the edge across APAC, they were also concerned about various barriers to adoption.

  • 34% of ITDMs in APAC pointed to a lack of expertise, skill or understanding with regard to edge technologies, as well as difficulty integrating with legacy technologies as top concerns. The overwhelming majority (95%) thought they were missing at least some skills needed to help their organization unlock the value of data. Globally, that rose to 98% and 99% of ITDMs in the government and hotels/hospitality sectors respectively.
  • AI and Machine Learning skills (48%), analytical skills (45%) and risk management/information security skills (41%) ranked highest in terms of areas of expertise that companies were lacking.
  • In Singapore, cost of implementation also presented a barrier to adoption of edge computing to ITDMs (40%).

Overall, there were mixed feelings about the security implications of the edge in the region. While 59% of ITDMs said that connecting IoT or user devices at the edge had made or would make their businesses more vulnerable, 48% identified improved security as one of the biggest benefits of capturing data from user devices.

Bridging the edge to the Cloud

As businesses continue to increase their dependence upon data, it is critical that it is analyzed and processed at the source of collection reliably and securely. Traditional network architectures and operational processes built to support the cloud and mobility era need to adapt to these new requirements.

As organizations move ahead to build an edge infrastructure, there are a few key concepts to keep in mind for a successful implementation, according to Aruba:

  • Unify – The Edge incorporates all network domains including wired, wireless and SD-WAN; and all locations including Campus, Branch, Data Center and Remote Worker environments. Network operations teams should only consider solutions that can manage all domains and locations from a cloud-native, single pane of glass that can centralize and correlate all cross-domain events and operations.
  • Automate – Network uptime and performance are critical at the Edge. Network operations teams should only consider solutions that provide reliable, highly accurate and specific AI-powered insights, and automation that can resolve issues more quickly, before they impact the business or users.
  • Protect – The proliferation of IoT devices generating the data that fuels new business outcomes also presents new security challenges. Network operations teams should consider solutions that use AI to detect, classify and continuously monitor these devices and work seamlessly with access control solutions to automatically place devices in centralized policies that ensure they remain secure and only communicate with predetermined resources.

Chiah concluded: “Harnessing insights at the edge is an opportunity for enterprises to revolutionize their approach to data and unlock its value as a business asset. Organizations that can process, store and analyze data at the edge will be able to use that data first to optimize their existing business model, and over time, will develop innovative products, services and experiences that will not only augment, but transform their offerings for customers and employees.”

Edge computing aside, data management considerations encompass a range of other considerations from data fabrics, analytics process automation to unified cloud data platforms and data monetization.