Edge computing is a major driver and key enabler for digital transformation projects.
One of the guiding principles behind digital transformation initiatives is to achieve efficiency with business workflows. Any additional step in the processing of data and any delay in the processing of digital inputs is likely causing a negative impact. This challenge is amplified by an exploding volume of business inputs from new sources, including IoT devices.
Edge computing solutions that facilitate data processing at the origin of business data reduce the steps of data handling and, subsequently, increase workflow efficiency.
From Data Center, To Cloud, To Edge
Edge computing is the next big wave of technology architecture in information management, moving processing power away from centralized and cloud data centers and closer to the origins of physical data. With edge computing, each intelligent device — including smartphones, drones, sensors, robots and autonomous cars — shifts some of the data processing from the cloud to the edge. The cloud will continue to be used to manage IoT devices and to analyze large datasets in use cases where immediate action is not imperative.
However, we are not looking at a complete shift. In a similar way that cloud computing has not and will not fully replace centralized data centers, edge computing will augment rather than replace cloud computing. The new paradigm of “processing anywhere“ means that data will be processed where it originates and ingested into workflows aligning with business requirements.
Edge computing will forever alter how businesses interact with the physical world. Whether you consider it revolutionary or evolutionary, it is well on its way to mainstream adoption.
According to IT analyst firm Gartner, “Around 10% of enterprise-generated data is created and processed outside a traditional centralized data center or cloud. By 2025, Gartner predicts this figure will reach 75%.”
Edge Computing Is Going Mainstream
The explosive growth of data — in particular, inputs generated by IoT devices — puts big data discussions in a different light. Business needs are changing, starting with the accelerated pace of business workflows and the need for quick responses to new requirements.
Edge computing addresses the need for real data processing that can be performed where the data resides. It is driven by two factors: 1) the cost of time (latency), and 2) the cost of transport (bandwidth). Bandwidth and latency issues continue to be hurdles for the processing of transactional data and data analysis. According to an InformationWeek article, “Low latency and high-volume data processing are the two key ingredients to realize the promise of edge computing. … Edge computing can eliminate distance-induced latency and act as the facilitator for a new breed of strategic applications while also adapting to evolving IT infrastructures.”
I believe edge computing’s adoption will increase significantly as the business world becomes more networked and needs to adapt to digital transformation requirements.
Information Capture Moves To The Edge
Edge computing has a logical synergy with document and information capture. The dynamics in the capture market show similar trends. Document capture started as a largely centralized process. As capture use cases shifted from back-office-focused records management applications to customer-focused workflow automation, this drove the demand for decentralized capture operations. The underlying trend was to capture business documents at their “first point of contact” with an organization (e.g., a bank branch office rather than an operations center). Document scanner architecture has adapted to meet the need to process and analyze at paper-based data points of origin, enabling edge capture.
Once we zoom in to the processing and data analysis of information capture, edge capture becomes an integral part of edge computing, offering similar advantages.
Advantages Of Edge Capture
• Faster turnaround time: Capturing and processing data at the edge of an organization’s IT infrastructure eliminates delays in processing and offers instant insights from the data.
• Cost reduction: The reduction of data latency and bandwidth required reduces the cost of transmitting images and data as well as the unnecessary cost caused by delayed business workflows.
• Data privacy: Organizations can easily sift through sensitive information, handle it locally and deliver nonsensitive information directly to the cloud.
In digital transformation projects, organizations want to ensure that they take advantage of edge computing technology by following these simple steps for mission-critical workflows:
• Assess where data is being generated or is first received by your organization.
• Identify process steps that negatively impact the organization, caused by latency and bandwidth in your data-driven processes, and quantify the cost involved.
• Identify two workflows that present opportunities for edge capture based on their process characteristics and whether they involve the highest cost.
• Establish a pilot project to validate the quantitative and qualitative advantages of deploying an edge capture solution.
Following these guidelines will help organizations realize the full potential of edge capture.