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3 Reasons Why Voice Recognition Will Change Our Lives – ReadWrite



Amazon Alexa fail screenshot

The idea of talking to a machine a few decades ago would have belonged to the realm of science fiction, if not idiocy. And yet, after years of rigorous multidisciplinary research and development, here we are. A future where millions of users command and interact with a growing number of devices using nothing but their voice. All thanks to cutting-edge voice recognition technology.

3 Reasons Why Voice Recognition Will Change Our Lives

Voice recognition has seen a great deal of development in recent years. Smartphones and smart speakers housing AI-based virtual assistants have become a common sight in every household. I distinctly remember trying out the Google Assistant and Microsoft’s Cortana back in the day when they were initially rolled out.

Apart from a few oddly surprising but clearly pre-programmed jokes, the speech recognition itself was fairly inaccurate and frustrating. The conversations weren’t organic. One would have to speak with deliberation, adding long pauses and clear punctuations.

All in all, it was a gimmicky-looking feature at best. Enough to stimulate your typical tech nerd but nowhere even close to becoming an everyday technology.

Source: Twitter


Fast forward to today, and cutting-edge algorithms can beat any of us at language processing.

Add to that the program’s ability to draw from various sources and languages across the internet, and the conversation is really over. However, it is impossible to ignore the breathtaking rate at which language recognition has evolved in the past few decades.

And much like smartphones and the internet, voice recognition is quickly becoming an essential and indispensable part of our lives. Therefore, it might be worth investigating what a world populated with sophisticated voice recognition algorithms might look like; and how they’ll change our lives.

Voice Recognition can Trigger a Multi-Industry Disruption, Empowering SMBs and Tech Giants Alike

The most significant benefit of being able to talk to your devices is that it is much more convenient than using a computer or a smartphone. Given this advantage, we expect voice recognition to improve the communication between brands and customers in the coming future. This will allow for a greater degree of personalization and increased customer satisfaction.

And this is already happening. Google reports that around 58% of the people who own voice-activated speakers use them to create and manage shopping lists. And around 62% of the same are willing to buy products via their speaker.

Industries like e-commerce stand to benefit the most from such a transition. Especially when smart speakers can help with reducing buying resistance for the customers. In light of such positive trends, it is obvious that voice search is changing the way we shop.

But beyond just shopping, sectors like healthcare and education also benefit immensely from voice recognition technology. Alexa is helping out senior dementia patients, while Google Assistant can help enhance learning for young kids. These are just some select examples, but they indicate a growing trend most industries are catching up on.

How Voice Recognition will take over the consumer market

Apple, Google, and Amazon (along with a few other smaller players) have been relentlessly pouring millions into research and development for application and home-based virtual assistants. It’s no secret that voice will be a massive business opportunity for these giants, hence the aggressive competition.

We don’t know how Big Tech will monetize voice recognition, but their involvement will benefit the industry for sure. As tech companies continue to develop voice technology further, we can expect voice-based devices to get better over time. We’ll see a significant improvement in the pricing, functionality, and even accessibility of these devices. With the improvement curve being an exponential one, following the development curve closely.

Such rapid growth will likely sprout multiple opportunities for voice recognition, many even within previously untapped niches and industries. Smaller software and app development companies will be incentivized to build on the shoulders of these tech giants. They’ll develop their products and service based on the underlying voice recognition architecture built by bigger companies. Allowing for drastic improvements in areas like customer experience and usability for their products.

It’s a win-win for both. Smaller players get access to sophisticated voice recognition technology, something they couldn’t develop on their own. And larger players get an ever-increasing market share by opening up their technology. This will inevitably boost voice adoption and allow the technology to penetrate deeper within the consumer market.

To conclude, voice recognition will soon be empowering a growing number of industries. The estimated market size for voice recognition-based tech and devices is expected to reach around $27.16 Billion by 2026. With such promising figures and groundbreaking implications, voice recognition is sure to take over the consumer market in the coming years.


Rethinking our Privacy in the Age of Virtual Assistants

Voice recognition has a ton of potential. It can significantly enhance business operations, improve consumer-device interaction, and carry the machine-human relationship to a whole new level.

However, behind all the hype lies a glaring but ugly problem, most of us are too afraid to admit. I’m talking about privacy.

Around 50% of smart speaker users fear that voice technology places their privacy at risk. On one level, there is always the risk of cyber-attacks and data breaches. And brands are trying to address this by developing devices that do much of the voice recognition locally. This is to prevent any data from being transferred over to the cloud. But on a deeper level, we face the problem of accidentally installing corporate spyware right into our homes.

It’s no secret that tech companies are behind every bit of data and personal information we produce. While harvesting data to roll out personalized ads may not be unethical in itself, there is certainly a line that needs to be drawn here. To be clear, smart speakers aren’t recording your everyday conversations (yet). But they still need to be ‘awake’ looking out for the trigger words that wake them up.

Again, the problem here isn’t that amazon employees are listening to your dinner table conversations; but instead of ‘privacy expectation.’

What is Privacy Expectation?

Privacy expectations, in simple terms, is the level of privacy consumers can expect from companies while using their products and services.

And new technologies, especially something like smart speakers and virtual assistants, generally tend to lower down our privacy expectations. In other words, they make us comfortable with sharing a growing amount of data as we get familiar with the technology.

At the risk of sounding like a conspiracy theorist, I’m not arguing that voice recognition (in its current form at least) is a major threat to our privacy. In fact, most companies in the business are reasonably compliant to privacy laws; and whatever audio samples that companies do record are primarily used for training the algorithms.

But the problem here is that voice recognition is significantly different from other data-harvesting technologies because it shifts the privacy paradigm much further towards the companies and away from the consumers. For example, with social media, tech companies only had access to the data that users were willing to publish online.

While with something like smart speakers, if companies choose to track us in the future, the option of choosing the information we wish to share (and not share) is simply absent. We run the risk of opening our entire lives to algorithms that’ll scan every word we utter with the sole intent of getting us to buy a pair of socks or something.

A new Privacy Paradigm

Bear in mind, though, that this is entirely a hypothetical possibility. But the fact that corporations haven’t done it yet, isn’t sufficient evidence to believe that they won’t do it in the future. After all, Companies need to make money from the smart speakers they sell, especially when they are practically giving them away for free. And the way they’ll make up for it is by locking the users in their ecosystems and targeting them with voice-based ads.

With every breakthrough device/technology, consumers are willing to trade in a bit more of their information and privacy in exchange for a better experience and service. Voice technology takes it to a whole new level because the data we are talking about here isn’t digital but biometric. No other consumer technology in the past has opened up such large volumes of biometric data to algorithms, and the implications of doing so can go way beyond our wildest speculations.

Is this alarming? Yes. Does that mean that voice recognition will spell an end to our privacy? Most likely not. The takeaway here is that voice recognition will trigger a new conversation around privacy. Consumers will have to be ever more vigilant about who collects their data and how it is used. And businesses will have to be more transparent and consumer-centric with their privacy policy. Ultimately, wide-scale adoption of voice technology will certainly shift our current privacy paradigm.

How Voice Recognition Will Change Who We Are.

The interesting thing about conversing with a smart speaker is that it’s not the same as how we usually communicate. Everyday conversations are full of several subtleties like body language cues, facial expressions, and interpersonal chemistry—stuff that’s simply absent in a smart speaker.

More than that, smart speakers are designed to be ‘smart’; coupled with the internet, they are practically omniscient and considerably more engaging and entertaining than other humans. Is it plausible then that interacting with my amazon echo could change the way I communicate?

Could it inflate my expectations when it comes to conversations? Leaving me dissatisfied and thus, less interested in talking to other people. I know that sounds far-fetched, but if video games can change our brains and texting can change our language, why is it a stretch to assume that voice recognition can change how we communicate?

Voice-based virtual assistants are expected to do well in areas like education and even therapy. These are places that were traditionally thought to be the forte of humans. But it isn’t just the assistant’s universality that is fascinating here, but also its personality. Alexa isn’t just an assistant. It’s no longer just a tool, but with every patch and update, as its engineers strive to make its responses look more organic, Alexa slowly gains personhood.

That opens a brand new can of worms. We pass on a whole lot of power over to our assistants (and by extension, the companies that control them) the day we start treating them like people and not machines. And although that makes a great science-fiction plot, we must admit that such a future is much closer than it appears.

Voice recognition and AI

Consider the example of Replika. Replika is a chatbot app marketed as ‘the companion who cares.’ I discovered Replika from the Youtube comment section of a music video. It turns out when users expressed that they felt lonely, Replica recommended them a music video to make them feel better. And it seems like it worked. The comments section is teaming with warm messages and love letters directed to Replika as people express their gratitude towards the AI.

And Replika is just a chatbot, a good one I bet, but still a chatbot. Imagine then what fully flushed out voice assistants—programmed with a natural-sounding human voice and empathy—could do.

If virtual assistants and chatbots can promote music to their loyal users, they can just as easily promote products or ideas. Don’t get me wrong, It’s fine as long as our Personal AIs tell us silly jokes and offer trivial recommendations. But the fun quickly ends the day they start telling us what is true or who to vote for. Bear in mind that the distance between the two is alarmingly short and constantly shrinking. Again, if social media can impact our political views, why can’t voice assistants?

I admit these are some extreme examples. And I do not wish to indicate that the voice assistants are inherently bad or evil. My only claim here is that voice recognition can fundamentally change not just the digital-human interface but also our everyday lives.

As we get comfortable with using voice just as much as we use our screens, we might also end up getting comfortable with trusting algorithms just as much as we trust people. Voice recognition is the humanization of our machines at its finest.

In Conclusion

Voice recognition is an upcoming groundbreaking technology with far-reaching implications, especially in contrast with its initial use cases. And while it can be alarming to witness Tech giants racing for an oligopoly within the sector, the presence of reliable free and open-source voice recognition alternatives is certainly assuring.

In the coming years, we can expect voice-powered devices to improve our business as well as personal experiences. Having meaningful conversations with our computers will no longer be science fiction. Wide-scale voice adoption will compel us to think deeply about areas like privacy and the influence of technology on our lives.

It’s an exciting future full of ecstatic adventures and unexpected dangers. And although we may not know where voice recognition might take us, we can at least know for sure that it’ll change the world as we know it today.

Image Credit: jessica lewis; pexels; thank you!


How Preql is Transforming Data Transformation



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?



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.

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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.

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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.

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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.

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Image Credits: Pexels


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



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.


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


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


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.


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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