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11 LinkedIn Posting Tips to Get Viral – LinkedIn Marketing Tips

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how to increase linkedin post views


In this article, we will share six LinkedIn algorithm tips and insights and 11 actionable content tips. You can start following these tips on LinkedIn today to gain maximum engagement and opportunities. If you have a B2B company whose budget is meager and you are looking for all the possibilities to promote it on Linkedin?

11 LinkedIn Posting Tips to Get Viral on LinkedIn

We’re going to start with some algorithm tips first because algorithm tips are super important. They affect the total reach that you can have with your LinkedIn post.

Why is this algorithm so important?

LinkedIn has an algorithm that determines what kind of posts you want to see. It will make sure that you only see posts that are interesting for you. So we want to make sure that the algorithm identifies our post as enjoyable to be shown to way more people.

LinkedIn Algorithm Tips and Insights

 

 

[1.TIP] personal connections

The first tip, you should have a lot of personal connections on LinkedIn because whenever you post anything on LinkedIn, it will be visible to your contacts first.

If these people start engaging with your content, their network will get notified, and if they start engaging, they will notify their network. So, therefore, the bigger your first-degree network, the bigger the potential of reaching many people on LinkedIn, so that’s a crucial one.

[2.TIP] Interest Relevance

You will see the posts of those people with whom you are not connected, nor have you ever interacted with them. This is because you may have used such hashtags or have been part of Linkedin groups. Based on your specific interests, LinkedIn shows you posts that you are interested in seeing.

[3.TIP] Engagement Probability

LinkedIn will try to put posts in your feed based on things you have liked in the past as comments and likes, where it thinks you’re most likely to engage. So also interesting to think about the LinkedIn algorithm based on the actions you did in the past.

[4.TIP] The First 30-60 Minutes of Your Content

When you publish any content on Linkedin, it will be shown to your connection in the first 30 to 60 minutes. Based on this 30 to 60 minutes, LinkedIn will ensure that this content is eligible to be shown to thousands or just a handful of people.

How does this LinkedIn algorithm work in these first 60 Minutes?

# Linkedin Analyzing

Linkedin will first analyze based on a few simple things like do you use offensive words in content or use something that LinkedIn doesn’t want to push you there?

It’s just raw mechanical analytics that they do to determine whether we will show this to people. So if you’re not using offensive languages or things not allowed on LinkedIn, don’t worry about that one.

# Test Drive Your Content

Linkedin will drive your content. If the material is not good, Linkedin will stop this content from sharing with more people and show it to fewer people.

# How Will Those People Engage?

It will show it to some people and see how well they engage with this content. If you get many comments and likes, the algorithm will say okay, which is nice. We will start showing it to more people.

If in these first minutes, then LinkedIn will stop pushing it in the feed, not a lot of people will see it. So after these 30-60 minutes, they will analyze how did your post do?

Did a lot of people like it? Did a lot of people comment on it? Or did you get some negative signals?

For example, did people hide your post? Did people flag your post?

And the moment a “hide” happens, it limits your reach. So these first 30 to 60 Minutes are critical. If at some point you reach thousands of people that are liking and commenting on your post.

Yeah, you have a post that has the potential to go viral, and then also human editors will come in. They will analyze your post and see what is happening. Why is this going well?

If you’re gaming the system doing things that are not legit, LinkedIn will stop it. However, immediately your reach is discontinued and will not be shown to more people.

If you’re doing good things, then they will start pushing the content to even more people.

So these are the four stages that your LinkedIn post goes through to get these things and to know that these first 30 minutes are super important.

[5.TIP] Post on the Right Time

It’s essential to post on a moment where you know that you will get the maximum engagement and those first 30 to 60 minutes. So think about the moment that your audience might be online, the moments that they might check LinkedIn and might engage with your content.

They did some research, and in this blog post, they tell you the best moments to post on LinkedIn — try a quarter to eight, quarter to eleven, quarter to one, and a quarter to six. These can be magical times for your posting.

For me, my best moment to push content on LinkedIn is around noon between 12 and 1. This is just because of my personal experiences.

This is how my network reacts. So I would take these numbers that you see online with a grain of salt test and drive them with your audience.

See what works for your numbers would suggest that for B2B brands, Wednesday would be the best solution, followed by Tuesday. For B2C Brands, they would say Monday and Wednesday.

But again, please test this yourself; check — and ask yourself, “How does my audience resonate with my content?”

When is the best moment for me to push this content on LinkedIn?

[6.TIP] Don’t include links in your posts

The sixth tip is super important and is an early mistake because people include external links in their content. Whenever you create content on LinkedIn, never have a link to your blog or website in it.

Linkedin wants to be a content platform; they want you to produce content on the platform. However, the LinkedIn algorithm hates external website links, so it does not want you to go to another platform through a link.

If you want to do this, you can first place a link in the comment, and then people will see and click. But please do not share the link in a regular LinkedIn post. Those were my tips, my very simple tips to increase the way you post on LinkedIn if you’re thinking about the LinkedIn algorithm. If you’re already doing these things, you will see you will have an increase in reach.

11 Linkedin Posting Tips to Get Maximum Engagement on Linkedin

All these 11 tips you see here are things that we tested out with our Axis Web Art team.

Increase your social media engagement on Linkedin

 

1. Write in a Conversational Rhythm and Tone

Nobody likes boring content, so always try to write in a conversational rhythm and accent. For example, I have often seen people write on LinkedIn like a very business. But people don’t like this kind of content, and it doesn’t work very well on LinkedIn.

So it is better that you create a material with which people like to connect. Try to create a dialogue in your content, keep that interactive tone in your writing, and understand the harmony between long and short sentences.

2. Use Simple Words

You do not need to be cute and clever when creating your content; use simple words. You should try to be clear and straightforward. Write content as if a ten-year-old would be able to understand it.

It’s essential to use these simple words. Make it super easy for your reader to follow along and understand the things that you’re saying.

3. Use The Power of White Space

If you want to post a lot of content on LinkedIn and you will put all these sentences underneath each other. It will feel like a hefty chunk of text. So what you want is that between each sentence, you will put some white space.

Press the enter button two times on your keyboard. Include some white space in there. That makes it a lot easier for your reader to read it; though it feels lighter and definitely on mobile, it will make a huge difference.

This white space can also increase your users’ desire to read, so it is imperative to use white space to improve readability.

4. Start With An Attention Grabber

When you want to write a successful LinkedIn post, always start with an attention grabber. Because if you ride a long post, only the first two sentences will be shown, then there will be the read more button.

If you cannot get your audience’s attention in the first two sentences, people will not read the post that you are writing. And they will scroll to the next Linkedin post.

So make sure that you have something that grabs the attention, that they are super interested and that they want to read the rest of the post.

5. Use Emojis For a Fluent Reading Experience

Use emojis for a fluent reading experience. So we use it in two different ways. One in our attention grabber at the end, we include an emoji.

Why is this?

If people scroll through their feed and see an Emoji, they might stop for half a second to read the first two sentences. Then, if they think it’s interesting, press the read more button and read our entire post. So that’s something that works for us.

The other way we use it is to use it as a clever way of bringing some extra structure within your LinkedIn post. For example, if I use lists (like five things). I will put a finger emoji that points to this find different things to add some extra structure within my LinkedIn post.

6. Give Away Stuff on Linkedin

We often leave stuff on LinkedIn, and if anyone wants to get it, they need to put a comment at the bottom of my post. This strategy works very well because if you miss the beginning of this article, we have talked about the algorithm that explains the importance of these 30-60 minutes.

If you adopt this marketing strategy, then people are going to respond in the comments. Because only if they put something in the comments will they get the lead magnets you have for them. The algorithm identifies this post as something super valuable, and you will keep on ranking; you will keep on being displayed within the feed.

7. Related Hashtags and Tag

Use related hashtags and tag people to increase engagement. For example, if people are signing up for LinkedIn now, they will be asked to select a few hashtags. As a result, people are posting content within these hashtags.

Now, whenever you post content within these hashtags, people who are already posting content within this hashtag, or will in the future, will get a notification, and it will also show up within their feed.

So these hashtags allow you to reach people you’re not yet connected with, and at this point, I don’t think LinkedIn got the maximum potential they have for hashtags yet. But I think the hashtags in the second half of 2021 and 2022 will play a bigger role than today.

8. Get Personal

People on LinkedIn are also just people, and people like people. So be personal; try to talk about stories that revolve around yourself. Try to talk about personal things.

Don’t always try to be a brand that no one can relate to, be that guy they can relate to. Share personal experience and be human on LinkedIn.

9. Plant Your Seeds

It is essential to connect with influential people and to connect with the people you see on Linkedin. And in the beginning, you will not get clients out of it. But connecting with those will allow you to make a relationship with them.

To make sure that they see what you’re posting. You’re seeing what they’re posting and start engaging with their content. Start commenting on their posts, liking their posts, engaging with them. And you will see the seeds you are planting now; they will be good trees in a few years, so invest in them.

10. Learn How to Tell a Story on Linkedin

People love to read stories. They hate self-promotion.

So if you want to be a thoughtful leader and include some exciting things on LinkedIn, if you know that people will read, then writing a story is a skill you need to master.

And there are only five simple steps that you need to follow to write a story using the Hero’s Journey Framework. If you do social media jobs or want to learn SEO techniques, writing a story is an important part.

Here you talk about a particular challenge, the problem that your hero has.

    • 2nd. Phase:- Identify the problem

You will identify the problem, such as what issues have brought the challenge. How did it affect your life and your business?

    • 3rd. Phase:- Guidance Phase

It’s the phase where your hero meets his guide. How did he realize he had this problem? How did he realize that he needed help? Who helped him? What was the reason that he could now find help? And we’ll talk about that magical moment, the moment when he met his guide.

    • 4th. Phase:- Solution Phase

In this phase, he will solve his problem. The problem is now resolved. What does it look like right now? What does the solution look like? How did it affect the business?

    • 5th. Phase:- Life Experience

Now your life has changed, and you have become a different personality. You have become stronger and more capable than before. So at the end of this post, you will talk about your own experiences. You will talk about what happened and how other people can do this same transformation to be where you are right now.

This simple thing is a framework that’s also being used in many movies, a lot in TV series, but it works. People love stories. So use this simple framework to write successful LinkedIn posts, and you will be amazed at the power of storytelling.

11. Try to Ask For Engagement

In the end, you can ask like: Okay, if you like this post, let me know, or I now share 10 tips. What would tip number 11 be? Then, ask for people to engage in your content, and they might do so. So this might be the easiest thing, but it might make a world of difference in engagement rates on your LinkedIn post.

Manoj Babal

Digital Marketing Expert

Manoj Babal is a Digital Marketing Expert at Axis Web Art, using his vast business and personal experiences to help digital entrepreneurs build bulletproof businesses and reach the freedom they desire. He has published many articles on different websites. He loves to write about Digital marketing, social media, SEO, Tech, Business, Travel, Relationships, Auto, Health, Education, Lifestyle, Fashion, Sport, Home Improvement, Entertainment, etc.

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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.

Featured Image Credit: Provided by the Author; Thank you!

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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