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How to Design a Foolproof IoT Cybersecurity Strategy – ReadWrite

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How to Design a Foolproof IoT Cybersecurity Strategy - ReadWrite


Technology has been advancing at a dizzying speed in the last decade. Here is how to design a foolproof IoT cybersecurity strategy.

One area that has been growing fast is the Internet of Things (IoT). IoT simply means a network of connected hardware devices that can communicate via an internet connection. The IoT network simplifies many processes and tasks by reducing human participation.

The Rise of IoT in Enterprise

While IoT has been a common concept in homes, enterprise use is only beginning to rise.  And with that comes the need for improved IoT cybersecurity.

McKinsey’s report shows that the adoption of IoT technology on an enterprise level has increased from 13% in 2014 to 25% in 2019. The same report forecasts that the number of connected devices is expected to shoot to 43 billion by 2023. That’s three times the number of connected devices there were connected in 2018.

It’s inevitable. IoT is becoming a prominent ingredient in enterprise operations. But there’s a caveat that comes with the rise of IoT — increased cybersecurity risks.

And that’s why you must design a foolproof IoT cybersecurity strategy.  

The Challenges of IoT Cybersecurity

IoT cybersecurity is a nightmare for most CISOs and CIOs. Unlike traditional IT cybersecurity, which is straightforward (more or less), securing an IoT environment is fraught with many challenges.

One of the biggest problems with IoT is that each device comes with its own software and firmware. In most cases, updating these is difficult. And as you know, software updates are a part of maintaining good cybersecurity hygiene. This poses a massive problem with IoT as every new line of code or functionality added could introduce new attack vectors. And conducting and monitoring updates at scale is next to impossible.

Another challenge is that most IoT devices don’t support third-party endpoint security solutions. One reason for this is regulations surrounding the devices (like FDA regulations for medical devices). As a result, enterprises end up focusing their security on the communication channels between devices and networks.

On an enterprise level, the number of connected devices is just too massive to keep track of. You can end up wasting valuable time and resources playing cat and mouse just to keep all your devices updated. That on its own can leave you open to attacks from other directions.

The Need for Enterprise Level IoT Cybersecurity Solutions

The need for innovation and efficiency are driving the growth of IoT adoption at an enterprise level. Business growth is virtually impossible today without keeping pace with current technology trends.

When it comes to cybersecurity, the more devices you have in your network, the more vulnerable you are. And because enterprises can deploy IoT devices and services at scale, they run a higher risk of being vulnerable to external threats.

That’s why, when adopting IoT in your business, you must be prepared to beef up your cybersecurity.

Because of the large number of devices in the network, IoT cybersecurity must be taken seriously. This is because a single infected device can infect and compromise the entire network. As a result, malicious agents can gain access to sensitive data or have control of your operations.  

4 Must-Haves for Foolproof IoT Cybersecurity

Because there are many entry points that malicious actors can take advantage of, IoT cybersecurity requires a multi-layered and scalable security solution. Here are some of the major components to consider as you build your IoT cybersecurity strategy.

  1. Block Attackers with Next-Generation Firewalls

A firewall is a network security device that monitors network traffic. It can permit or block data packets from accessing your devices based on a set of security rules and protocols. As the name suggests, its purpose is to establish a barrier between your internal network and external sources. Doing this prevents hackers and other cyber threats from gaining access to your network.

While there are many different types of firewalls, for your IoT cybersecurity strategy to be effective, you must employ next-generation firewalls (NGFW). Basic firewalls only look at packet headers, while NGFW includes deep packet inspection. This allows for the examination of the data within the packet itself. As a result, users can more effectively identify, categorize, or stop packets with malicious data.

Next-gen firewalls are a vital part of any IoT cybersecurity strategy as they can monitor traffic between multiple devices effectively. As a result, only verified traffic is allowed access to your network.

  1. Secure Data with Encryption

Another layer of security you need to consider for your IoT cybersecurity strategy is encryption.

A study by ZScaler shows that over 91.5% of enterprise transactions occur over plain text channels. That means only 8.5% of transactions are encrypted. This is worrisome as this means hackers have a huge opportunity to access enterprise systems and wreak havoc. For example, they could launch a distributed denial of service (DDoS) attack that could cripple your business.

One way you can prevent malicious actors from gaining access to your network is to secure your data with encryption. This must be both for your software and hardware. But more importantly, you must use encrypted VPN solutions to ensure the safe transmission of data between your devices.

  1. Identity and Access Management

Initially designed for users, identity and access management (IAM) security solutions were designed for users. IAM ensures that only authorized people have access to systems and information they need to do their job. It also ensures that only authorized users have access to critical data.

But with the proliferation of IoT, IAM (which is sound management) is becoming another layer of security that can be applied to devices.

Just like human beings, digital devices have identities. And IAM tools have evolved to the point of being able to manage hundreds of thousands of devices and their users. With products like A3 from AeroHive, for example, IAM can identify each device in your network and grants them specific access controls.

When it comes to enterprise IoT, managing all your connected devices’ digital identity is critical to safeguarding your network infrastructure. More important is to ensure that each device only has the required access levels to your data. 

  1. Network Segmentation

Network access control (NAC) has been a critical part of cybersecurity since the birth of networks. And to this day, it remains an integral part of most cybersecurity strategies – especially IoT cybersecurity. 

The good thing about traditional network endpoints is that they usually run endpoint protection services. However, with IoT, this is not the case. And that’s where network segmentation comes in.

Using NGFWs to segment your IoT network from the rest of your network is advisable as it keeps potential threats confined within a controlled environment. For example, if an attacker manages to gain access to a device in your segmented IoT network, the threat is confined to that part of your network alone.   

Putting It All Together – Designing an IoT Cybersecurity Strategy

Now that you’ve seen your best options for IoT cybersecurity let’s quickly dive into designing your strategy. However, note that this is not a guide set in stone as every business’s cybersecurity needs are never the same. 

That being said, here are a few guidelines to help you design your enterprise IoT cybersecurity strategy: 

Determine what You Need to Protect

With your security protocols and guidelines in place, the next step to foolproof  IoT cybersecurity is to determine what you need to protect. This involves conducting an audit on:

Your Processes 

Understanding the most critical processes in your organization is essential as it enables you to know where to focus your efforts. Most cyberattacks target processes that can cripple your business, so be sure to have a clear picture of these.  Know what they  are — know how to protect.

Your Devices 

From data storage devices to devices that facilitate your processes, you must know every device in your network and where it fits in your operations. Remember, you’re only as secure as your most vulnerable device. And because all your data is stored and transmitted by your devices, you must invest more effort and time in ensuring your security is foolproof here.

Your Personnel

One aspect of cybersecurity many organizations overlook is their staff. You must ensure that your employees are up-to-date with the latest cybersecurity protocols and safety measures. Failure to do this could make your employees unknowingly compromise your security. For example, one employee could give another a password just to speed up an aspect of your process. While this may seem as harmless as playing a game during work hours — this is a severe breach of security protocol.

Having a clear view of how your devices and their users are connected is crucial to understanding your network’s most vulnerable points. As a result, you can plan on which security solutions you can implement at each point.

Consider Compliance

Sure, compliance is not really a security issue, but they do go hand in hand. That’s why as you plan your IoT cybersecurity strategy, you must do so with compliance in mind.

Incompliance is a serious issue that must be addressed as you map out your cybersecurity plan. Failure to comply could lead to you being slapped with hefty fines.

So what exactly does compliance mean in cybersecurity?

Cybersecurity compliance involves meeting various controls enacted by a regulatory authority, law, or industry group. These controls are put in place to protect the confidentiality, integrity, and availability of data that your business works with. Compliance requirements are different for each industry or sector, and that’s why you must always be careful to know your industry’s specificities.

To ensure that you’re compliant, always have a compliance program that runs in conjunction with your cybersecurity strategy.

Know and Anticipate Your Threats

To ensure that you design a robust IoT cybersecurity strategy, you need to know and understand the security risks you face. To do this, start by evaluating your business by asking questions like:

  • What is your product?
  • Who are your customers?

While these may seem like simple questions, the answers will help you answer two fundamental questions:

This will help you narrow down the types of attacks that will most likely be targeted at your business.

You can also determine the kind of threats you’re most likely to face by studying your competitors. Take note of their risk profiles or the most common breaches in your industry.

Knowing the threats you’re likely to face will help you understand the kind of security measures you must put in place. After all, knowing your enemy is half the battle won (so they say).

Once you’ve determined all these factors, the next step is the most critical – selecting your cybersecurity framework. 

Select an Appropriate Cybersecurity Framework

Now that we’ve laid the groundwork, it’s time to get practical by selecting and implementing your preferred cybersecurity framework. In essence, a cybersecurity framework is a set of policies and procedures recommended by leading cybersecurity organizations. These frameworks enhance cybersecurity strategies in enterprise environments. A cybersecurity framework must be documented for both knowledge and implementation procedures.

Different industries have different cybersecurity frameworks designed and developed to reduce the risk and impact of your network’s vulnerabilities.

While cybersecurity frameworks are never the same, they all must address five critical functions of cybersecurity.

  1. Identify. Your framework must help you identify the existing cyber touchpoints within your business environment.
  2. Protect. This function addresses how you take care of access control, data security, and other proactive tasks to ensure your network is secure.
  3. Detect: Here, your framework addresses how you will identify any potential breaches. This is usually done by monitoring logs and intrusion detection procedures at network and device level.
  4. Respond. How do you respond when a breach is detected? You must have a procedure for understanding the breach and fixing the vulnerability.
  5. Recover. This stage of your framework deals with creating a recovery plan, designing a disaster recovery system, and backup plans.

With a cybersecurity framework covering these five areas, your enterprise IoT cybersecurity strategy will be robust enough to handle (almost) anything. 

As I said, there are myriad different types of cybersecurity frameworks you can adopt. However, most of them fit in one of three categories, according to cybersecurity expert Frank Kim. Let’s take a cursory look at them, so you have a better understanding of frameworks and how they fit in your cybersecurity strategy:

Control frameworks are the foundation of your cybersecurity. They help you:

  • Identify a baseline set of controls
  • Assess the state of technical capabilities (and inefficiencies)
  • Prioritize the implementation of controls 
  • Develop an initial roadmap your security team should follow

Examples of control frameworks include NIST 800-53 and CIS Controls (CSC).

Program frameworks are designed to help you develop a proactive cybersecurity strategy that enables you to identify, detect, and respond to threats. This is achieved by helping you:

  • Assess the state of your security program
  • Build a more comprehensive security program
  • Measure your program’s maturity and compare it to industry benchmarks
  • Simplify communications between your security team and business leaders

Examples of program frameworks include ISO 27001 and NIST CSF, among others.

The risk framework allows you to prioritize security activities and ensure that the security team manages your cybersecurity program well. You can use this framework to:

  • Define key processes and steps for assessing and managing risk
  • Properly structure your risk management program
  • Identify, measure, and quantify risks 

Examples of risk frameworks include ISO 27005 and FAIR.

For an exhaustive list of examples of the different types of cybersecurity frameworks you can implement in your business, check out this article.

It’s Time to Take IoT Cybersecurity Seriously

The rapid digital transformation that has been brought about by COVID-19 and the fast adoption of remote work has led to many organizations’ cybersecurity being stretched to its limits. Throw in IoT into the mix, and cybersecurity has become a nightmare for most organizations.

But this shouldn’t be the case for your business.

The key to winning cyber wars is to be proactive and anticipate cyberattacks before they happen. And this is when a cybersecurity strategy comes to play. 

As you adopt IoT in your business’s infrastructure and processes, make sure to design and implement a robust security strategy. This will help mitigate the risk of you falling prey to malicious agents who thrive on taking advantage of vulnerabilities in an enterprise’s IT infrastructure.

So, it’s time to take your IoT cybersecurity seriously.

Neal Taparia

Entrepreneur & Investor

Neal Taparia is the co-founder of Imagine Easy Solutions, a portfolio of online educational services that reached over 30 million students yearly. Neal sold the business to Chegg (NYSE: CHGG), where he stayed there as an executive for three years. He’s now pursuing a new initiative, Solitaired, which ties classical games with memory and attention training.

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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.

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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.

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.

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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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