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Act on insights closer to where your data is created with edge computing


4 min read

Executive Summary

We are in the midst of a new era of computing, edge computing. Like each era of computing before it, edge will impact every industry and force IT departments to adapt to new architectures, deployment models and business models. With edge IT, companies can increase the speed at which data is gathered and interpreted and enable decisions to be made in near- or real time, increasing revenue growth through enhanced user experiences and new services.

IDC's research on the value of edge confirms that transforming the user experience is a primary driver. More than half of organizations surveyed say that a higher quality experience for the end user is the top benefit of edge investment. ”
IDC White Paper
Sponsored by IBM, The Importance of Effective Operations in Unlocking Edge IT Value, January 2020.

Edge is not one of those technologies that you can choose to adopt for your business. Ignoring it will be disastrous for any long-term business strategy. Being an early adopter, on the other hand, will give you a strong competitive advantage similar to that of forward-thinking companies from previous eras of computing.

For example, some of today's most progressive organizations are already moving their enterprise applications to edge locations to get improved resiliency vs. lost connectivity to their central, public or private clouds. While this has redounded to their benefit, IBM believes that the value of edge can be extended even further in the following ways:

  • Better control over data and costs: Minimize data transport to central hubs and reduce vulnerabilities and costs.
  • Faster insights and actions: Tap into insights right at the source where data is created and processed.
  • Continuous operations: Run autonomously, even when disconnected, and reduce disruption and cost due to outages.

Learn how a single administrator can manage the scale, variability and rate of change of application environments across tens of thousands of endpoints simultaneously.

Discover the art of the possible in your industry

Edge computing takes data and application processing and extends it further out from your central datacenter or public cloud locations into your regional and branch offices, retail stores, manufacturing sites, distribution centers, and connected assets in the field, on the roads and anywhere in between.

Review the industry-specific pages to see how your enterprise can shift from the status quo to embracing the art of the possible.


4 min read

Edge computing for banking and financial services

Key use cases:

  • Banking / ATM security
  • Data privacy and residency guidelines


Banks may have cameras installed on hundreds of ATM machines located across many cities, positioned to protect the interests of customers and prevent bank card fraud attempts. However, recordings are typically used after a fraud has been reported and a customer has experienced a financial loss, not to mention that the monitoring is very resource intensive.

With edge computing, the video feed can be analyzed in real time with integrated AI image recognition software on the ATM machine itself, without relying on human intervention or the time and cost of transporting terabytes of video data to a centralized cloud for video analytics. The ATM which has been tampered with can be automatically shut off and the bank can take immediate action to contact local law enforcement.

Finally, ATMs require software updates. Updating every ATM can be a manual and slow effort, and those updates have to be performed by humans on a one by one basis. With edge computing, the management and updates to those ATMs can be automated and conducted by a single administrator in a fraction of the time.

The status quo:

  • Banks cannot analyze ATM video feeds in real time, and video analysis requires human intervention which is slower and expensive.
  • Inadequate monitoring and analytics put ATMs at risk and can delay alerts to law enforcement.
  • Updates to ATMs are manual, costly and timely

The art of the possible:

  • Video feeds can be analyzed in real time, without human intervention or the time and cost of transporting terabytes of video data to a centralized cloud for video analytics.
  • ATMs which have been tampered with can be automatically shut off before fraud can occur.
  • Banks can update their ATMs with a single administrator in near-real time, thus ensuring the latest software patches and updates have been made.

Learn how a single administrator can manage the scale, variability and rate of change of application environments across tens of thousands of endpoints simultaneously.


5 min read

Edge computing for retail

Key use cases:

  • Retail inventory management
  • Personalized client experiences
  • Kiosk management and end-user services


Imagine you’re the CIO of a major retail chain. Your goal is to have an efficient, secure and automated means to manage your inventory and delight your customers with personalized services. You need to do so with low latency and real time information updates. However, your enterprise is currently struggling to manage inventory and understand your customers’ needs due to a limited supply chain solution, and you need to ensure that the products are on the shelves when their consumers want them.

You also need to understand what consumers are looking for, or what they would have bought if the right product was available. In-store intelligent video image recognition, AI and analytics can indicate where your inventory stands and what actions need to be taken, such as offering a flash sale on overstock items in a particular store for in-store shoppers or those nearby.

Another example? You work for a large supplier of retail kiosks deployed nationwide. Operating these kiosks is a manual process and the operations to maintain and manage them are costly.

Your current approach also lacks resiliency and security for transactions, and every software update requires manual installations which can drive up admin costs and limit the number of updates that can be performed. In addition, update cycles may be longer which can negatively affect performance and increase risk.

Edge computing can automate remote distribution and management of kiosk-based applications and help them continue to operate even when they aren’t connected or have poor network connectivity. Using edge IT also helps the kiosk applications stay current so that your end-users don’t have to interact with older versions that lack the right functionality or features.

With edge IT, you can automate software distribution to your kiosks coupled with edge servers in major venues like stadiums or public travel hubs to improve availability, decrease administrative costs and accelerate delivery of new features.

The status quo:

  • Retailers may struggle to manage inventory and are striving to ensure the products are on the shelves when their consumers want them.
  • Retail supply chain solutions do their best to manage inventory but lack visibility into real time consumer behavior – beyond just purchasing.
  • Maintaining and updating thousands of kiosks is a manual and inefficient process.

The art of the possible with edge computing:

  • Retailers know what their consumers are looking for and personalize their services and offers.
  • Video analytics and AI help you identify when inventory is running low, and an automated supply chain can restock inventory without pause.
  • Kiosks can be updated via autonomous management, allowing for improved availability, reduced operational costs and faster delivery of new and personalized features.

Learn how a single administrator can manage the scale, variability and rate of change of application environments across tens of thousands of endpoints simultaneously.


4 min read

Edge computing for automotive

Key use cases:

  • Improved driver safety and experience
  • Cost savings for the services provider


Between 2018 and 2023, the volume of connected car sales is growing at a compound annual growth rate of 16.8 percent and will reach some 76.3 million units by 2023.* And as the number of vehicles that include connected devices increases, so will the amount of unstructured data that will need to run through those powerful analytics to produce actionable outcomes.

Without an edge computing solution, it’s more difficult to keep the firmware up to date and ensure vehicle data is sent back to the vendor in a timely and uninterrupted manner. Lack of timely updates may also increase security vulnerabilities. In addition, latency becomes an issue, and being able to quickly deliver and receive critical, possibly lifesaving information is put at risk.

With edge computing, connected cars can provide insights into real time and location-based weather data that could improve driver awareness and take precautions to prevent accidents in hazardous conditions.

Drivers can also be alerted to take necessary steps based on manufacturer safety recalls in real time (e.g. automatically route to the closest dealer). Auto manufacturers can also deliver infotainment and third party in-vehicle applications that improve their user experiences.

Private data becomes more vulnerable when transported over longer distances. An edge IT solution can prioritize what data needs to be processed by the vehicle’s onboard computing system, and what data should be relayed back to data centers for analysis. This precise and faster exchange of data can drive greater operational efficiency for your enterprise, and a more secure, seamless and enjoyable experience for your end-users.

The status quo:

  • Distance and latency cause disruption to service or delay critical infotainment and firmware updates.
  • Longer distances for data exchange can increase mobile connectivity costs and expose sensitive user data.

The art of the possible with edge computing:

  • Faster updates and low latency create a safer and more enjoyable driving experience.
  • Limit unnecessary data backhaul to reduce costs and limit the risk of jeopardizing client privacy.

Learn how a single administrator can manage the scale, variability and rate of change of application environments across tens of thousands of endpoints simultaneously.


4 min read

Edge computing for communications service providers

Key use cases:

  • AI-powered automation to reduce telco operations costs
  • 5G and edge computing for public safety and health


5G presents a revolutionary time for communications service providers (CSPs) to increase earnings, accelerate service innovation and reduce operational costs. However, many years into the software network “revolution” (NFV, SDN and Telco Cloud), CSPs have yet to truly achieve this potential.

With the transition to 5G underway, many CSPs are now able to fully embrace a cloud centric infrastructure migration. The edge network itself is potentially multitiered and may be composed of regional data centers, micro-data centers or edge clouds.

Telecoms are transforming their access and core networks to host application workloads using telco cloud technologies to reap the benefits of higher capacity and low-latency applications enabled by 5G.

Think about this from a compelling use case perspective:

  • Drones as first responders: 5G autonomous networks can significantly boost the power to analyze what drones are seeing and hearing in real time and make decisions on how to respond. This is critical in emergency situations, as drones are able to provide further reaching eyes and ears and in a faster timeframe than humans in manned vehicles.
  • Public health and safety: Think for a moment about the variables that could affect the life of a loved one as they are traveling from home to hospital in an ambulance, and how critical it is to transmit patient data in real time. That’s the power of 5G slicing—being able make split-second decisions that can be the difference between life and death

The convergence of 5G and edge computing will drive business in every industry and change how work gets done and how businesses operate. CSPs can deliver new connected experiences with data regardless of whether it’s running in a centralized on-premises, public or private cloud data center to the network core out to the edge.

The status quo:

  • CSPs lack intelligent automation and face high operational costs.
  • End users lack the ability to quickly receive and transmit critical information.

The art of the possible with 5G enabled edge computing and telco cloud:

  • CSPs can offload data by including telco cloud compute capabilities, located in network edge locations.
  • End users can receive and transmit critical information in real time.

IBM’s platform for telco cloud , with industry-leading open source technology for flexible orchestration and service assurance, can help you rapidly develop, deploy, and scale new services and even reduce response times.


4 min read

Edge computing for manufacturing

Key use cases:

  • Worker safety
  • Production optimization
  • Reduced operational costs


From mines to factory assembly lines, there’s a common priority – worker safety. The question is, how does worker safety translate when you’re using powerful computing hardware (often times hosted in unfavorable physical conditions) to power, analyze and inform your decisions and actions.

Many of today’s progressive manufacturing companies are tackling this by investing in edge IT to handle the most pressing worker safety issues. For example, you need a fast response time to handle an onsite accident or the means to prevent it from happening in the first place. You also need to improve the quality and speed of a factory assembly line.

Manufacturing companies utilize data to optimize their operations, improve worker safety, reduce energy consumption and increase productivity. Through the use of intelligent, autonomous machinery, human workers can avoid hazardous and high-risk conditions.

The machinery is equipped with sensors and AI and ML capabilities that can detect and avoid hazards in real time and prevent humans from entering unsafe environments. Because these enterprises use edge computing to run and analyze data right on the machinery itself, the information needed to make real time decisions is consistently available, and without having to connect to a cloud or a remote data center.

Predictive analytics can also offer timely insights into the condition of factory equipment to detect potential failures, minimize loss and downtime and help to maintain quality.

The status quo:

  • Worker safety is jeopardized due to a lack of real time insights.
  • Production quality and operational costs are marginalized.

The art of the possible with edge computing:

  • Machinery is equipped with sensors and AI and ML capabilities that can detect and avoid hazards in real time and prevent humans from entering unsafe environments.
  • Utilize data to optimize operations, reduce energy consumption and increase productivity.

Learn how a single administrator can manage the scale, variability and rate of change of application environments across tens of thousands of endpoints simultaneously.


2 min read


Act on insights closer to where data is created with edge computing.

IBM’s edge and telco network cloud solutions run on Red Hat Open Stack and Red Hat OpenShift, the leading open hybrid multicloud platform that runs anywhere — from any data center to multiple clouds to the edge.

With IBM you can:

  • Employ autonomous management to orchestrate the scale, variability and rate of change in edge environments – running anywhere.
  • Implement edge-enabled industry solutions, built on IBM expertise.
  • Modernize networks so that telecom service providers can deliver new services at the edge.

In addition, IBM offers extensive industry expertise and a robust ecosystem of telco operators, networking and IT providers—including equipment manufacturers, independent software vendors, and systems integrators.

Make the art of the possible a reality today.

Edge computing can also help your business establish a safe return to work. Watch the video below that shows how edge computing is used to identify elevated body temperatures of individuals, then explore other use cases such as face mask detection, social distancing scoring, and crowd density measurement.