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How AI is Changing the Face of Modern Web Design for Retailers

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AI-led visual search tool


If you wish to build a retail website that actually converts, you’ve got to move beyond the aesthetics and imagery. According to data, it takes only 50 seconds for a user to form an opinion about your website. Naturally, creating a lasting first impression should be every eCommerce website’s priority.

AI is Changing the Face of Modern Web Design for Retailers

To make a lasting first impression, you’ll want to tap into the true potential of your website, you need to provide an end-to-end simplified and personalized user experience. This is where AI is making its indelible mark. In this in-depth guide, we will deep dive to understand the tips, tricks, and benefits of integrating AI into your website. Let’s jump right in.

How Technology is Transforming the Retail Industry: A Data-Backed Perspective

First things first, it is critical to understand the increasing role of AI in today’s digitally-driven eCommerce landscape. Amidst the fears of physical shopping and social distancing, retailers have adapted to the new normal by integrating a host of intuitive technological features to better cater to their user base’s demands. These include:

  • The Powerful Visual Search Technology:

Wouldn’t life be simpler if you could simply shop using images from your web or smartphone? eBay’s powerful AI-led visual search tool allows you to do just that. Its “Image Search” feature allows a consumer to take a photo of an item they wish to purchase or take an existing image from their phone’s Camera Roll.

When they log onto the eBay website, the platform will show listings of visually similar items when compared to the uploaded image. In simpler words, the AI technology reduces the consumer’s effort (and time) to search for products using the right keywords and makes the buying process becomes seamless, quick, and efficient:

Image Search Feature

Pro tip: This tool can be integrated on the landing page to provide contextual product recommendations to the consumer and address complex user queries visually, within seconds. ASOS also uses visual search technology to make the consumer’s life easier:

ASOS also uses visual search technology

  • Chatbot technology:

    Chatbots, a.k.a., virtual assistants make use of complex natural language processing (NLP) systems to mimic human interactions and drive more meaningful conversations with users.

    These bots can literally take over a live agent’s workload and assist customers by helping them make purchases, by providing personalized product recommendations, by assisting them in making payments or completing an order purchase, and so on.

    Take a look at Nordstrom’s chatbot, which effectively asks users a series of questions about what they want and then generates custom-made ideas based on the answers:

Nordstrom's chatbot

If data is to be believed, chatbots will drive $112Bn in retail sales by 2023. So integrating this intuitive and intelligent technology into your website design is a must if you wish to retain the competitive edge while adding a ‘personal touch’ to your website.

  • Artificial design intelligence (ADI): While still in its nascent stages, the ADI technology can automatically build websites based on the latest UI/UX trends and double up as a virtual web designer for all intents and purposes. This technology leverages key data points such as website analytics, user preferences, product catalog, etc., to come up with a customized theme, content, and color palette. It also uses AI to produce functional and attractive web designs without human intervention. Here’s an example of an ADI design by the web development platform, Wix:

ADI design by WIX

All in all, this data-powered process makes web designing more accessible, quick, and affordable. However, the ability to customize the website might be restricted, making your overall design less unique than human-made custom designs. Additionally, it might lack a seasoned graphic designer’s personal and creative touch, so tread with caution.

Pro tip: If you have an e-commerce website that routinely receives thousands of Request for Quotes (RFQs), you can use the ADI technology to power and optimize your website, and drive data-driven interactions with customers. You can also use this technology if you have a product launch or a new event coming up, and need a microsite to be set up within minutes.

The learning: Considering how Covid-19 has accelerated digital adoption, particularly in the eCommerce sector, it makes sense for businesses to invest in the right mix of AI-driven tools to boost conversions through increased responsiveness, drive user happiness, and deliver a hyper-personalized, engaged, and rich experience to users at scale.

Moving on, let’s understand why AI needs to be integrated into your eCommerce website.

Top-5 Reasons Why AI is the Super-Fuel Retailers Need to Boost Growth

The smart consumers of today demand a customized, features-driven, and optimized shopping experience. This is where a slew of AI tools can lend a helping hand by:

  • Enhancing your website’s appearance, usability, user experience, as well as speed
  • Optimizing the search abilities–be it visual or voice search
  • Enhancing the quality of user interactions and accelerating the query resolution time
  • Providing a hyper-personalized user experience based on real-time consumer insights such as spending habits, user behavior, demographics, etc.
  • Targeting consumers through strategic digital campaigns and providing in-depth reports on the campaign’s performance
  • Acting as a web design diagnostic tool by way of running A/B tests, maintaining high design quality, etc.

These are just some of the important use-cases of how AI can drive user engagement and improve the UI/UX of your overall website design.

The learning: With more and more customers becoming avid online shoppers, owing to the global pandemic, the time is ripe for eCommerce brands to integrate AI into their web designing strategy and provide unique experience to customers while boosting their digital footprint.

How AI can be Implemented into Retail Websites: Useful Strategies & Hacks

In this section, we will highlight key strategies to embrace while implementing AI into your retail website:

#1. Use AI to enable shoppers to discover complimentary products–from size and color to shape and fabric, there are many factors to consider while delivering contextual search capabilities. For example, Pinterest allows users to choose an item in any photograph online. It then showcases identical items using image recognition software:

Pinterest using image recognition software

You can also integrate AI-powered quizzes into your website and provide tailored recommendations based on the user’s inputs as ThirdLove demonstrates below:

ThirdLove product image

product recommendations based on the user's inputs

#2. Use AI tools to drive informed decision-making on key areas of focus such as design, content, structure, layout, graphics, etc. By empowering a model that analyzes existing websites and encourages real-time suggestions, AI can enable brands to deliver an improved user experience and target the right users at the right time with the right messaging.

#3. Use Artificial Design Intelligence to create smarter websites within minutes, without the hassle of coding. Here’s an example of AI that’s integrated into web designing to provide a clean, functional, and seamless browsing experience by Wix. The platform asks the users a series of questions to understand their website requirements and suggests layouts as well as designs accordingly:

AI to design smaller websites

#4. Engage in effective, reliable, and quick quality checks. AI tools can be used to address tedious and repetitive tasks of quality assurance checks. As you can imagine, this leaves your developers with saved time to focus on other areas such as enhancing the existing features or taking care of bugs. Additionally, AI tools can bolster your website’s security, and uphold the privacy and sensitivity of your consumer’s data–one of the biggest concerns among users today.

#5. Personalize the online experience using real-time user data. AI can analyze content as well as consumer preferences to personalize the website experience from start to finish. For instance, Sephora’s Virtual Artist makes use of augmented reality and allows consumers to virtually ‘try on’ products, thereby enabling an informed purchase experience:

Sephora's Virtual Artist makes use of augmented reality and allows consumers to virtually 'try on' products
This functionality can be integrated into your website’s high-performing product pages to enhance the customer experience, connect better with users, and drive brand loyalty. Today, customer loyalty is no longer restricted to loyalty points and rewards, it is all about driving an innovative and convenience-led experience for the customer.

The learning: AI has doubled up as a crucial web designing tool, assisting eCommerce businesses to leverage technology and improve the user experience via real-time and accurate user data, via contextual product recommendation, via convenience-led voice and search capabilities, and so on. It also does much of the heavy-lifting with respect to key stages and elements of the web designing process such as coding, privacy and security, user experience, quality analysis, among others.

Top Factors to Pay Attention to When Setting Up AI-Powered Chatbots

When you are setting up your own AI-powered chatbot, here are a few important best practices to keep in mind:

  • Think about the end goal that the chatbot should achieve–it could be better user engagement, gathering real-time user feedback, analyzing user preferences, providing personalized product recommendations, and so on.
  • Strategize and conceptualize your chatbot’s tone of voice as well as personality. Customers do not want to speak to robot-sounding dull bots that answer in one-word or singular sentences. It helps to lend your bot a warm, friendly tone and a quirky/funny personality if your brand positioning allows it. Plus, your chatbot should be well-trained to drive meaningful conversations with the user, drive contextual recommendations, and self-learn with every interaction:

Product recommendation through chatbot

  • Keep a backup ready by enabling chat routing to the next available live agent, if the chatbot is unable to effectively address the user’s query. Making the customer wait and then providing incorrect solutions is a complete no-no.
  • Use a variety of formats to deliver the message to the user instead of using plain text. You can introduce new products via images or videos as shown below:

Sephora uses different ways to introduce new products

You can use GIFs, emojis, etc., and drive engagement by literally speaking in the customer’s language. You can include quizzes to gather user feedback or collect user information on product preferences. The idea is to play with a variety of formats and provide diversity to the onlooker.

  • Ensure that your bot is capable of providing multilingual support if you wish to cater to a global audience.
  • Integrate your bot with the right messaging channels that your customers frequent such as WhatsApp, Facebook, etc.:

Integrate your bot with the right messaging channels such as whatsapp, facebook etc.

Closing Thoughts

There’s no doubt that AI is disrupting nearly every industry, area, or domain–with eCommerce web designing being no exception. eCommerce brands are beginning to understand the vital role AI plays in augmenting the human effort of designing by taking over routine and repetitive tasks and driving a seamless, personalized and consistent browsing experience. That said, you should brainstorm to understand the key gaps within your website and then strategize on how AI technology can help address the specific pain point. Implementing any and every new AI-driven technology, without strategic thinking, will only make the browsing experience chaotic or worse, senseless.

Article Image Credit: from the author; thank you!

Top Image Credit: kaboompics; pexels; thank you!

Srushti Shah

Srushti Shah is an ambitious, passionate and out of the box thinking woman having vast exposure in Digital Marketing. She is working as a Digital Marketer and Content writer at Acquire. Her key focus is to serve her clients with the latest innovation in her field leading to fast and effective results. Working beyond expectations and delivering the best possible results is her professional motto. Other than work, she loves traveling, exploring new things and spending quality time with family. Reach out to Srushti Shah on Twitter or LinkedIn

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How Preql is Transforming Data Transformation

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How Preql is Transforming Data Transformation


More than one million small businesses use ecommerce platform Shopify to reach a global audience of consumers. That includes direct-to-consumer (DTC) all-stars like Allbirds, Rothy’s and Beefcake Swimwear.

But online sellers like these are also ingesting data from platforms like Google Analytics, Klaviyo, Attentive and Facebook Ads, which quickly complicates weekly reporting.

That’s where data transformation comes in.

dbt and Preql 

As the name implies, data transformation tools help convert data from its raw format to clean, usable data that enables analytics and reporting. Centralizing and storing data is easier than it’s ever been, but creating reporting-ready datasets requires aligning on business definitions, designing output tables, and encoding logic into a series of interdependent SQL scripts, or “transformations.” Businesses are making significant investments in data infrastructure tooling, such as ingestion tools, data storage, and visualization/BI without having the internal expertise to transform their data effectively. But they quickly learn if you can’t effectively structure your data for reporting, they won’t get value from the data they’re storing—or the investment they’ve made.

The space includes two major players: dbt and startups.

Founded in 2016, dbt “built the primary tool in the analytics engineering toolbox,” as the company says, and it is now used by more than 9,000 companies—and it is backed by more than $414 million.

But dbt is a tool for developers at companies with established analytics engineering teams.

Preql, on the other hand, is a startup  building no-code data transformation tool that targets business users who might not have expertise in programming languages but who nevertheless need trusted, accessible data.  

Preql’s goal is to automate the hardest, most time-intensive steps in the data transformation process so businesses can be up and running within days as opposed to the six- to 12-month window for other tools. 

“We built Preql because the transformation layer is the most critical part of the data stack, but the resources and talent required to manage it make reliable reporting and analytics inaccessible for companies without large data functions,” said Gabi Steele, co-founder and co-CEO of Preql.

The startup is therefore positioning itself as an alternative to hiring full analytics engineering teams solely to model and manage business definitions—especially among early-stage companies that are first building out their data capabilities. 

In other words, Preql is the buffer between the engineering team and the people who actually need to use the data.

“Data teams tend to be highly reactive. The business is constantly asking for data to guide decision making, but in the current transformation ecosystem, even small changes to data models require time and expertise. If business users can truly manage their own metrics, data talent will be able to step out of the constant back and forth of fulfilling reporting requests and focus on more sophisticated analyses,” said Leah Weiss, co-founder and co-CEO of Preql.

But that’s not to say dbt and Preql are bitter rivals. In fact, they are part of the same data transformation community—and there’s a forthcoming integration.

“One way to think about it is we want to help the organizations get up and running really quickly and get the time to value from the data they’re already collecting and storing without having to have the specialized talent that’s really well versed in dbt,” Steele added. “But as these companies become more sophisticated, we will be outputting dbt, so they can leverage it if that’s the tool that they’re most comfortable with.”

A Closer Look at Preql

The startup raised a $7 million seed round in May, led by Bessemer Venture Partners, with participation from Felicis.

Preql collects business context and metric definitions and then abstracts away the data transformation process. It helps organizations get up and running with a central source of truth for reporting without having a data team or writing SQL.

Preql reads in data from the warehouse and writes back clean, reporting-ready schemas. It partners with data ingestion tools that move data from source applications into the warehouse such as Airbyte and Fivetran and cloud data warehouses like Snowflake, Redshift and BigQuery. For businesses who consume data in BI tools, it also partners with Looker, Tableau and Sigma Computing. 

DTC Target

Preql is initially focused on the DTC market in part because the metrics, such as cost of customer acquisition (CAC), conversion rate and life-time value (LTV), are standardized. They also tend to have lean operations.

“We’ve found that these companies are working really hard to download data from disparate sources—third-party platforms that they use, Shopify, their paid marketing platforms—in order to get a sense of even basic business health and performance,” Weiss said. 

They also tend to use manual reporting processes, which means “it’s often an operations person who’s downloading data from a bunch of sources, consolidating that in spreadsheets, making a bunch of manual interventions and then outputting weekly reporting or quarterly reporting,” she added. 

But much of what these companies want to measure about performance is consistent and a lot of the data sources are structured the same way.

“With Preql, we were able to make some assumptions about what we wanted to measure with the flexibility to customize a few of those definitions that are specific to our business,” added Cynthia Plotch, co-founder at Stix, a women’s health essentials ecommerce site. “Preql gave us clean, usable data for reporting.  We were up and running with weekly reporting within days, saving us months of effort if we had to invest in data engineering teams.”

Data Transformation in 2027

Steele and Weiss believe the next five years will be about “delivering on the promise of the modern data stack.”

In other words, answering questions like: Now that we have scalable storage and ingestion, how can we make sure we can actually leverage data for decision making? And how can we build trust in reporting so we can build workflows around it and act on it? 

This is because a lot of companies struggle to move on to predictive analytics and machine learning because they never solved the fundamental issue of creating trusted, accessible data. 

 What’s more, Preql believes the next phase of tools will go beyond building infrastructure to deliver more value as data talent sits closer and closer to the business.

“Data analytics will only get more complicated because the number of data sources is growing, along with their complexity, and the need is becoming more acute for real time results. And the more data you have, the more granular the questions become and even more is expected of it,” Amit Karp, partner at Bessemer Venture Partners added. “I think we’re in the very early innings of what’s going to be a very long wave—five, ten or even 20 years down the road.  It’s a giant market.”

Rekha Ravindra

Rekha has 20+ years of experience leading high-growth B2B tech companies and has built deep expertise in data infrastructure – helping to take often very complex technology and ideas and make them understandable for broader business and tech audiences.

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Can Traditional Companies Act Like Start-Ups?

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Demos_Parneros_ Traditional Companies and Start Ups.jpg


Much has been made about the culture clash between older, slower, more traditional companies and younger, more dynamic, faster-moving tech start-ups. Each has advantages and disadvantages, but, generally speaking, it is very hard to reconcile the two approaches, as they are naturally in opposition to each other.

The general motto among start-ups of “move fast and break things” has led to very quick yet massive successes, with some companies, Google and Amazon being the most obvious examples, growing larger than traditional competitors who have been around for decades and decades. But it has also led to a lot of unconsidered damage to traditional industries like transportation and publishing, their ‘disruption’ doing as much harm as good. And, more often than not, start-ups can see millions or even billions in investment being wasted on bad ideas and unproven tech (Theranos, anyone?). “Fake it till you make it” means that, eventually, you actually do need to make it.

Image Credits: Pexels

Meanwhile, traditional companies, while providing more useful and regular forms of employment, great institutional knowledge, and decades of business experience, have their own problems. Because they often resemble large, inefficient bureaucracies, they are slow to move and respond to change. Old companies can be blind to, and even fearful of, innovation and new technology. This can leave them dead in the water when the future finally arrives. Kodak, for example, went from venerated, dominant business to almost nothing in just a few years because it refused to accept the revolution of digital photography.

But is there a way to integrate the two approaches? To take the best from both cultures and business plans and use those aspects to move into the future? To get big, old businesses to work, at least in some ways, like small, agile, young start-ups? Yes, but it isn’t easy.

Innovation Without Disruption

As stated, one of the greatest fears of traditional companies is having their business, or their entire sector, undercut by a growing start-up. While independent start-ups are expected to disrupt, be change agents, or however you want to put it, more traditional companies are prone to be much more risk averse. Naturally, one of the smartest things that an old company can do to avoid being left behind is to lead the disruption themselves.

Demos_Parneros_ Traditional Companies and Start Ups_3.jpg
Image Credits: Pexels

Many traditional businesses are currently investing in, and should continue to invest in, the digital transformation of their business model, from top to bottom. This, however, is a slow process, especially in sizable companies. The use of machine learning, predictive analysis, AI, and other cutting edge digital tools allows old business models to become more efficient, and respond to changes in supply and demand, and market tumult, in better and smarter ways. But it isn’t as easy as flipping a switch.

A New Business to Try New Things

Quite a few traditional businesses are spinning out new sectors, tech labs, and other separate silos to do the work of digital innovation for them. This isn’t uncommon. Businesses have, basically forever, had subsidiaries. The problem is that old businesses have trouble actually committing to the idea.

Often, the business that is spun-out is, essentially, a temporary one. The leaders of the core business get cold feet, limit the new project’s mandate, and pull it back in as soon as possible. Such hesitance is limiting in today’s digital world, where the next revolutionary innovation is always just around the corner.

Demos_Parneros_ Traditional Companies and Start Ups_3.jpg
Image Credits: Pexels

Furthermore, spin-outs with good ideas and potential for growth are frequently allowed to die on the vine, just as often they go to seed. Or, to make things clearer, the core business doesn’t invest in the digital spin-out’s success. The great advance of digital companies is their ability to scale with almost lightning speed. But core business have to be ready with resources and support for the scale-up to even happen, let alone work. Otherwise, a grand opportunity will go to waste.

If a business spin-out does well enough, it should be allowed to grow and change as it needs to, provided that it remains successful and worthwhile. Whether the goal is for the new business to simply make money in an area the core business isn’t directly addressing, or developing digital innovations for the core business to take up, if it works it works. Don’t get in the way of success just because it is new, or comes in an unfamiliar form. At the same time, core businesses must be careful of how they measure success for these new experiments. Measuring the new company or spin-out with the same metrics as the core business can sometimes choke the momentum and not give an accurate picture. Afterall, newer, smaller businesses, or initiatives shouldn’t be expected to be profitable immediately.

Cultural Change, From the Executive Level On Down

All the innovation in the world won’t mean anything if the people running the business itself refuse to change. Older companies, and older executives, can become set in their ways, dismissive of new technologies and ways of doing business, and ignore the automation and efficiencies of advanced digital tools. We saw this at the beginning of the widespread use of the internet twenty years ago, and we’re seeing it now.

More important than this, is the need for people in positions of real power in companies to implement the changes needed for innovation and advancement, and do so thoroughly and effectively. There must be a willingness to let the start-up culture infiltrate and influence the way business is done at every level, or it won’t be effective enough to help.

Demos_Parneros_ Traditional Companies and Start Ups_3.jpg
Image Credits: Pexels

It is painfully common for large, traditional companies to put money into research and development of new ideas and new technologies, only for executives and other decision makers to ignore what’s in front of them, either because of cost, or risk, or something as simple as a fear of the future.

But the future of business is changing in a digital world. Things move and change with an almost frightening speed. The Covid-19 pandemic is absolute proof of that; it wasn’t just companies with digital tools at the ready that were able to survive. While they had an advantage, it was the companies that were able to acknowledge the rapidly changing situation, and react to it quickly and efficiently, that kept things going and in some cases, even improved their bottom lines.

But It’s More Than Just a Cultural Change

One of the biggest advantages of tech start up culture is that it is forward-facing. It is an attitude towards business and technology that is not just looking towards the future (every business does that), but is actively trying to grapple with it, and even to shape it, if possible. Traditional, legacy businesses need to admit that the world is not static, and they have a responsibility in influencing how their industry develops.

Part of that responsibility is letting innovators be innovators. If a large company spins out a business unit to study and improve its digital technology, that company can’t then balk when those innovators recommend widespread change, or create a new idea that could shake the company, or its whole industry, to its core.

Demos_Parneros_ Traditional Companies and Start Ups_3.jpg
Image Credits: Pexels

Conclusion

To put it as simply as possible, for an older, more traditional company to reap the benefits of adopting a start-up model, it has to actually adopt it. It can’t just make superficial changes, it needs to truly invest. But that kind of investment carries risk, which can make more traditional companies nervous. The work of transformation must actually be done.

That means supporting digital innovations and changes when they make things more efficient. It means letting spin-out businesses actually try new things, and grow to scale when they hit upon something new and successful. It means executives getting out of the way so the forces of change can actually, you know, change things. Otherwise, the ‘traditional’ company will just be the ‘old’ company, sitting around waiting for some new tech upstart to disrupt it into obsolescence.

Demos Parneros

Demos Parneros

CEO | President | Board Director

Demos Parneros is an experienced and innovative retail and e-commerce leader, helping Staples grow from a startup to a Fortune 100 company, serving as President of North American Retail and E-commerce businesses. He subsequently took on the role of CEO at Barnes & Noble, leading a focused transformation plan, which eventually led to the sale of the company. In addition to previously serving on several high-profile company boards, Demos now leads CityPark LLC, where he has invested in 15 companies, including several leading-edge retail tech startups.

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Understanding Edge Computing and Why it Matters to Businesses Today

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Hady Shaikh


The edge computing market is expected to reach $274 billion by 2025, focusing on segments like the internet of things, public cloud services, and patents and standards.

Most of this contribution is backed by enterprises shifting their data centers to the cloud. This has enabled enterprises to move beyond cloud systems to edge computing systems and extract the maximum potential from their computing resources.

This blog will provide a closer understanding of edge computing and how it helps businesses in the technology sector.

Understanding edge computing

From a technical standpoint, edge computing is a distributed computing framework that bridges the gap between enterprise applications and data sources, including IoT devices or local edge servers.

For an easier understanding, edge computing helps businesses recreate experiences for people and profitability through improved response time and bandwidth availability.

Why does edge computing matter for businesses?

When we talk about the most significant industry zones worldwide, for instance, the GCC region, which is heavily focused on the focus areas like cloud services, the transition from cloud technology to edge computing is now more prominent than ever for enterprises to leverage the potential of the technology.

And with only 3% of businesses at an advanced stage in digital transformation initiatives, the potential of edge computing is up for grabs.

It doesn’t matter if you’re running a mobile app development company, a grocery store next door, or a next-gen enterprise. You need to understand how cloud edge helps businesses and invest in this open-source technology.

Predictive maintenance

Edge computing is primarily sought in industries where value-added assets have a massive impact on the business in case of losses.

The technology has enabled reports delivery systems to send and receive documentation in seconds, usually taking days to weeks.

Consider the example of the oil and gas industry, where some enterprises utilize edge computing. The predictive maintenance allowed them to proactively manage their pipeline and locate the underlying issues to prevent any accumulated problems.

Support for remote operations

The pandemic has forced businesses to opt for remote operations, or a hybrid work model at the least, with the workforce, spread across different geographical boundaries.

This drastic shift has brought in the use of edge apps that would permit employees to secure access to their organization’s official servers and systems.

Edge computing helps remote operations and hybrid teams by reducing the amount of data volume commuting via networks, providing computing density and adaptability, limiting data redundancy, and helping users comply with compliance and regulatory guidelines.

Faster response time

Businesses can enjoy lower latency by deploying computational processes near edge devices. For instance, employees typically experience delays when corresponding with their colleagues on another floor due to a server connected in any part of the world.

While an edge computing application would route data transfer across the office premises, lower the delays, and considerably save bandwidth at the same time.

You can quickly scale this example of in-office communication to the fact that around 50% of data created by businesses worldwide gets created outside the cloud. Putting it simply, edge computing allows instant transmission of data.

Robust data security

According to Statista, by 2025, global data production is expected to exceed 180 zettabytes. However, the data security concerns will equally increase proportionately.

And with businesses producing and relying on data more than ever, edge computing is a solid prospect to process large amounts of data sets more efficiently and securely when done near the data source.

When businesses take the cloud as their sole savior for data storage in a single centralized location, it opens up risks for hacking and phishing activities.

On the other hand, an edge-computing architecture puts an extra layer of security as it doesn’t depend on a single point of storage or application. In fact, it is distributed to different devices.

In case of a hack or phishing attempt, a single compromised component of the network can be disconnected from the rest of the network, preventing a complete shutdown.

Convenient IoT adoption

Global IoT spending is expected to surpass $410 billion by 2025. For businesses, especially in the manufacturing sector, who rely on connected technology, the internet of things is at the thickest of things in the global industry today.

Such organizations are on the constant hunt to up their computational potential and probe into IoT through a more dedicated data center.

The adoption of edge computing makes the subsequent adoption of enterprise IoT quite cheap and puts little stress on the network’s bandwidth.

Businesses with computational prowess can leverage the IoT market without adding any major infrastructure expenses.

Lower IT costs

The global IT spending on devices, enterprise software, and communication services rose from $4.21 trillion to $4.43 trillion in 2022. While a considerable share of the global spending accounts for cloud solutions, obviously as the pandemic has only pushed the remote operations and hybrid working model further up.

When users keep the data physically closer to the network’s edge, the cost of sending the data to the cloud reduces. Consequently, it encourages businesses to save on IT expenses.

Besides cutting costs, edge computing also contributes to helping businesses increase their ROI through enhanced data transmission speed and improved networks needed to experiment with new models.

How is edge computing different from cloud computing?

Although edge computing and cloud computing are each other’s counterparts for data storage and distribution, there are some key differences regarding the user’s context.

Deployment

Edge computing deploys resources at the point where data generates. In contrast, cloud computing deploys resources at global locations.

Centralization/decentralization

Edge computing operates in a decentralized fashion, while cloud computing is centralized.

Architecture

Edge is made on a stable architecture, and cloud resources are made on loose-coupled components.

Response time

Edge-based resources respond instantaneously, and cloud resources have a higher response time.

Bandwidth

Edge computing requires lower bandwidth, while the cloud counterpart consumes a higher bandwidth.

Although, the above difference makes edge computing a clear winner in all aspects for any business. But there’s a catch!

Suppose your business resides at multiple physical locations, and you need a lower latency network to promptly cater to your customers who are away from your on-prem location. In that case, edge computing is the right choice for you.

Top edge computing use cases

Although there are numerous examples of edge computing use cases, I’ll talk about a few that I find the most interesting.

Autonomous vehicles

Autonomous flocking of truck convoys is the easiest example we can come for autonomous vehicles. With the entire fleet traveling close while saving fuel expenses and limiting congestion, edge computing has the power to eliminate the needs of all the drivers except the one in the front vehicle.

The idea being the trucks will be able to communicate with the others via low latency.

Remote monitoring of oil and gas industry assets

Oil and gas accidents have proved catastrophic throughout the industry’s history. This requires extreme vigilance when monitoring the assets.

Although oil and gas assets are placed at remote locations, the edge computing technology facilitates real-time analytics with processing closer to the asset, indicating less dependency on high-quality connectivity to a centralized cloud.

Smart grid

Edge computing is on course to elevate the adoption of smart grids, enabling enterprises to handle their energy consumption better.

Modern factories, plants, and office buildings use edge platform-connected sensors and IoT devices to observe energy usage and examine their consumption in real-time.

The data from real-time analytics will aid energy management companies in creating suitable, efficient workarounds. For example, watching where high energy consumption machinery runs during off-peak hours for electricity demand.

Cloud gaming

Cloud gaming, seemingly the next-big-thing in the gaming business like Google Stadia, PlayStation Now, etc., dramatically leans on latency.

Moreover, cloud gaming companies are on the quest to build edge servers as close to gamers as possible to reduce latency and provide a fully immersive, glitch-less experience.

Final thoughts

This concludes our discussion on understanding edge computing and how it matters for enterprises worldwide.

Now that you understand the benefits of edge computing and its applications in different industries and use cases, it is evident that it’s a great value proposition for businesses that want to acquire competitive advantages and lead their spaces from the front line.

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Hady Shaikh

Hady Shaikh is a professional product strategist with experience of over 10 years of working with businesses in mobile app development, product marketing, and enterprise solutions spaces. His C-suite leadership and expertise spans over helping clients in the MENA and US region build top-tier digital products and acquire tech consultancy. Currently working as the Principal Product Strategist at TekRevol, a US-based custom software development company, Hady’s vision is to establish a robust digital foothold in the GCC region by helping clients with their product strategy and development.

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