Cloud migration is a big decision for any organization – and one that should not be taken lightly. There are a lot of things to consider when making the move, and it’s important to do your research and plan carefully to avoid common mistakes.

1. Lack of knowledge

A lack of knowledge is one of the biggest cloud migration mistakes organizations can make. The first step in any migration is understanding exactly what you’re trying to achieve and how the cloud can help you get there. Without this essential foundation, it’s all too easy to make other costly mistakes further down the line.

Many companies dive into cloud migration because there is so much buzz around the cloud and CTOs/COOs are scared to get left behind. But going in without the necessary implications in mind can increase costs, make the transition less secure and set an organization up for failure.

When considering a move to the cloud, do your homework first. Research what other companies in your industry are doing and assess whether the cloud is right for your business. Make sure you have a clear idea of what you want to achieve with your migration, understand the security concerns associated with a cloud move and how things like radius security can help address them.

2. Lack of planning

Another common mistake organizations make when migrating to the cloud is failing to plan properly. Moving to the cloud is a big undertaking and needs to be approached strategically. Many organizations make the mistake of thinking they can just move their existing infrastructure and applications over to the cloud as-is. This is often not the case and can lead to all sorts of problems further down the line.

Instead, take the time to plan out your migration. Assess what needs to be moved to the cloud and what can stay on-premise. Work out how you’re going to move everything over and what changes need to be made to your applications and infrastructure. And, most importantly, make sure you have a solid backup and disaster recovery plan in place before you start the migration.

3. Inaccurate cost assessment

One of the main reasons organizations migrate to the cloud is to save on costs. And while the cloud can certainly help you reduce your IT expenditure, it’s important to assess costs properly before making the move.

Many organizations underestimate the costs associated with migrating to the cloud and end up spending more than they anticipated. To avoid this, it’s essential to understand the different pricing models cloud providers offer and how these will impact your bottom line. Additionally, don’t assume all cloud providers are created equal. They all have different pricing structures and features, so it’s important to compare them before making a decision.

4. Improper risk assessment

Another mistake organizations make when migrating to the cloud is failing to assess risk properly. Any time you move data off-premise, you’re introducing additional risk into your environment. This is why it’s so important to assess the risks associated with your migration before you start. Identify what sensitive data you’ll be moving to the cloud and put security measures in place to protect it. Work out how you’re going to manage access to your cloud environment and who will have responsibility for it. And make sure you have a solid disaster recovery plan in place in case something goes wrong.

5. Migrating all at once

Many organizations try to migrate everything to the cloud all at once and this is often a recipe for disaster. Not only is it incredibly complex and time-consuming, but it also increases the risk of errors and downtime. Instead, it’s usually best to take an incremental approach to migration. Start by moving over non-critical applications and data. This will give you a chance to iron out any problems and get used to the new environment before migrating to more critical workloads.

6. Improper testing

Another common mistake organizations make when migrating to the cloud is failing to test properly. It’s essential to test your applications and infrastructure thoroughly before and after migration to ensure everything is working as it should be. Many organizations make the mistake of assuming that their existing tests will be sufficient. However, it’s often necessary to create new tests or modify existing ones to account for the changes introduced by the migration. Additionally, don’t forget to test your backup and disaster recovery plan. This is one area where many organizations fail, and it can have disastrous consequences if something goes wrong.

7. Neglecting staff training

If you’re moving to a new cloud provider or using new cloud-based applications, it’s important to train your staff properly. Many organizations make the mistake of assuming the team will be able to figure things out for themselves, and this often leads to frustration and errors. Successful digital transformation of any kind depends on training, so take the time to train your staff on how to use the new applications and systems properly. This will help ensure a smooth transition and minimize the risk of errors. https://theconversation.com/beware-of-shadows-created-by-the-cloud-97141

8. Improper monitoring

It’s essential to monitor your cloud environment closely to ensure everything is running smoothly. Many organizations make the mistake of assuming their existing monitoring tools will be sufficient. However, it’s often necessary to modify or create new monitoring scripts and processes to account for the changes introduced by the migration. While doing all of this, it’s important to stay constantly monitoring your backup and disaster recovery plan. You want to be sure that nothing is going to go wrong and that you have a solid plan in place in case something does happen.

9. Not making an exit plan

If something goes wrong during your migration, it’s important to have a rollback plan in place. This will allow you to quickly revert back to your on-premise environment if necessary. While it is unlikely you will need to use your rollback plan, it’s important to have one in place just in case. One example where a rollback plan could be a lifesaver is if, by chance, data is accidentally deleted during the migration process. If you have a rollback plan in place, you can quickly revert back to your on-premises environment and restore the missing data. https://growntechnology.com/hybrid-cloud-benefits-and-barriers/

10. Failing to update documentation

Finally, don’t forget to update your documentation after migrating to the cloud. This includes things like your network diagrams, application inventory and runbooks. It’s essential to keep your documentation up-to-date so you can quickly and easily troubleshoot any problems that may arise.

Updated documentation might also include things such as your cloud provider’s service level agreement, contact information and details of any support agreements you have in place. The cloud is the future. Migrating to the cloud can be complex and daunting – but you can ensure a smooth and successful transition by avoiding common mistakes.

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But issues with primitive public cloud platforms (mainly revolving around security, compliance and customizability) have since caused many businesses to opt for the hybrid cloud model: a combination of the public and private cloud models, which gives organizations the best of both worlds and makes for an efficient cloud strategy. https://growntechnology.com/5-defining-qualities-of-robots/

1. What is this?

2. Hybrid cloud benefits

First, the hybrid cloud optimizes the workload in both the private and public cloud infrastructures it comprises. It balances cost, security, speed, availability, and scalability efficiently.

2.1 Lower overall cost

It helps the enterprise optimize capital expenditure (CAPEX) and operational expenditures (OPEX) Infrastructure cost is one of the biggest challenges in any enterprise, and the hybrid cloud helps pacify this by bringing a balanced combination of public and private resources. This allows organizations to make a proper plan for workload distribution.

2.2 Better security

A combination of public and private clouds brings the best combination of security solutions. That’s because the public cloud, by nature, is configured with automated and highly efficient security systems. This reduces error, as human intervention is minimized, and is more cost effective than traditional cloud security measures. At the same time, the private cloud provides more customized security to protect organizations’ sensitive data. Bringing these benefits together, the hybrid cloud gives the enterprise the most bang for its buck in terms of security.

2.3 Low latency and high availability

Public cloud services rarely fail — but if/when they do, it can be detrimental to client organizations. Private cloud and local data center can provide the backup for public cloud downtime, but to really ensure airtight availability, organizations distribute their workload between the public and private clouds (i.e., the hybrid cloud). Ideally, store your critical data in the private cloud and/or your local data center so service continuity can be maintained even if there is any downtime in the public cloud infrastructure. The above factors can also apply to latency; using the hybrid cloud model can help reduce the time it takes for data to travel.

2.4 Scalability

In today’s competitive business environment, scaling up to catch the growing market demand is the key to success. And the hybrid cloud is the perfect solution. The private cloud does not scale up quickly, but the public cloud infrastructure is highly scalable. Since it combines the two models, the hybrid cloud allows the enterprise to scale up the public part of its cloud infrastructure whenever necessary and in a cost-effective way.

2.5 Ease of management

Hybrid cloud solutions are easy to manage because they provide efficient and reliable management solutions for the infrastructure as a whole. Public cloud solutions also provide lots of automation (sometimes AI-based), which is very helpful for managing the infrastructure.

2.6 Innovation and growth

Because the hybrid model is highly cost-effective, organizations can experiment with it without having to invest upfront. This creates a great opportunity to innovate and grow: With a hybrid cloud, you can take calculated risks, test new ideas and implement them.

3. Hybrid cloud challenges

3.1 Complex implementation

3.2 Security issues

3.3 Network latency

3.4 Management hurdles

4. How to develop a successful strategy

________________

𝐂𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐯𝐞 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬𝐞𝐬.

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The term “robot” is not easily defined, but its etymology is reasonably simple to track. It is not a very old word, having been implemented into English reasonably recently. It dates back to the early twentieth century when Polish playwrights. Karel Capek presented a unique and somewhat prophetic glimpse into the future. His groundbreaking play, “Rossum’s Universal Robots.” Capek chose the word “robot” based on its Old Church Slavonic origin, “rabota” – which basically translates to “slavery.”

1. Intelligence

2. Sense perception

The technology that empowers robot senses has fostered our ability to communicate electronically for many years. Electronic communication mechanisms, such as microphones and cameras, help transmit sensory data to computers within simulated nervous systems. Sense is useful, if not fundamental to robots’ interaction with live, natural environments. The human sensory system is broken down into vision, hearing, touch, smell and taste – all of which have been or are being implemented into robotic technology somehow. Vision and hearing are simulated by transmitting media to databases that compare the information to existing definitions and specifications.

3. Dexterity

Dexterity refers to the functionality of limbs, appendages and extremities, as well as the general range of motor skills and physical capability of a body. In robotics, dexterity is maximized where there is a balance between sophisticated hardware and high-level programming that incorporates environmental sensing capability. Many different organizations are achieving significant milestones in robotic dexterity and physical interactivity.

4. Power

Robots require an energy source, and there are many factors that go into deciding which form of power provides the most freedom and capability for a robotic body. There are many different ways to generate, transmit and store power. Generators, batteries and fuel cells give power that is locally stored but also temporary, while tethering to a power source naturally limits the device’s freedom and range of functions. One very notable exception would be the simple machine-based bipedal walking system that relies only on gravity to propel its walk cycle (developed at Japan’s Nagoya Institute of Technology). While this may not qualify as a stand-alone (no pun intended) robot, it could lead to innovations in how robot power could potentially be optimized, or possibly even generated.

5. Independence

Intelligence, sense, dexterity and power all converge to enable independence, which in turn could theoretically lead to a nearly personified individualization of robotic bodies. From its origin within a work of speculative fiction, the word “robot” has almost universally referred to artificially intelligent machinery with a certain degree of humanity to its design and concept (however distant).

Modern robots have already overcome many of the hardest challenges they faced up until just a few years ago. The robot race is running at an amazingly fast pace, and we can only wonder what machines could achieve in the upcoming future.

________________

𝐂𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐯𝐞 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬𝐞𝐬.

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When you think about network security, an air gap may not be the first thing that comes to mind. After all, it isn’t the most popular form of data protection, and it certainly isn’t the most convenient. But if you find out someday that your backups are corrupted, ransomed or lost, then you may realize that an air gap would have been a good idea.

1. What is an air gap?

An air gap is the lack of connection between a device and the rest of the network. If you take a device, disable its wireless connections (like Wi-Fi, cellular and Bluetooth) and unplug its wired connections (like Ethernet and Powerline), then you’ve air-gapped it. The device has no physical network connection and is not accessible over the network. It is completely separated, and as far as the network is concerned, the device does not exist.

2. Advantages and disadvantages of air gaps

Why would you want an air gap between a device and your network? The main reason is security. Almost all attack vectors depend on a network connection to spread and infect devices like PCs and servers. They can’t jump an air gap, so they can’t cause trouble. The problem is that there aren’t many things you can do with an air-gapped device. You can work offline if you have applications for word processing, spreadsheets and productivity installed on it. But almost every good use of a computer – the web, email, conference calls, collaboration, and software as a service — requires a network connection.

It’s a trade-off between security and usefulness. You wouldn’t air-gap your human resources system or manufacturing applications; you need them to be constantly online. That’s why almost anytime you hear about an air gap, it’s in the context of protecting your backup data.

3. What is an air gap backup?

The air gap backup is a way of putting your backup onto media that is physically disconnected from your network. The concept of the air gap has been around ever since administrators started worrying about viruses infecting their data, causing havoc like downtime, loss of data and loss of revenue. It has taken on new urgency in an era when they’re worrying more about ransomware, which causes so much more havoc.

Ransomware is usually executable, running as a process on an endpoint, like a computer, server, network switch, router, IoT device or smartphone. It scans the network looking for more endpoints that its payload can exploit. It figures out what’s running on them and delivers a payload that will encrypt every file and display a ransom notice.

Naturally, if you’re hit with ransomware, you’ll try to restore from your most recent, clean backup instead of paying the ransom. Unfortunately, the bad actors know that which is why the ransomware first scans the network looking for where you store your backups. Then, once it wipes out or otherwise infects the backups, it continues infecting all of your other endpoints. That brings us back to the air gap backup. Placing an air gap between your network and your backup device would be a good way to protect your data from ransomware, but how could you back up to a device that’s off the network? You’d have to keep connecting and disconnecting it every time you wanted to back up — which could be several times a day — and that would be a headache.

4. Types of air-gapped backup

That’s why most companies stop short of air-gapped backup; instead, they get as close as they can, balancing security with convenience. They have a few options based on factors like budget, risk tolerance and degree of automation.

5. Why is air gapping important?

The ransomware actors have made it a priority to destroy your backups or make them otherwise useless. They want to deprive you of your last line of defence. You’ve put plenty of other defences in place on your network before backups to avoid having a single point of failure. For instance, your servers have dual network cards, power supplies and disk arrays so your data remains safe and moving in case hardware goes down. And, you replicate among data centers for disaster recovery and business continuity.

But those defences do you little good in a ransomware attack. Most responsible businesses calculate how much a catastrophic outage costs them; it can range from thousands to hundreds of thousands of dollars per minute. When those minutes start adding up to days and weeks, the damage adds up very fast and can put you out of business altogether. Note also that regulations play a role in this. Sectors like banking, healthcare and government impose certification criteria or legal requirements that data be stored where it’s not network accessible. That’s often the starting point for a discussion about air-gapped data storage. Even if there is no regulatory requirement, if your business demands that level of protection, then air-gapping is important.  https://growntechnology.com/how-low-code-can-help-businesses-automate-iot-networks/

Creating and maintaining an air gap always involves some inconvenience, so it’s an anomaly in a discipline like IT, where the focus is on unrelenting automation and digital transformation. Air-gapping your backups may not be the easiest technique for IT administrators to implement or maintain. But it is a simple way to preserve them from the ravages of ransomware. https://theconversation.com/with-the-increase-in-remote-work-businesses-need-to-protect-themselves-against-cyberattacks-138255

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Implementing the Internet of things in growing organizations can provide unique insights if data is managed properly. And low-code platforms can allow organizations to quickly build the infrastructure needed to do just that.

1. How much data can the IoT collect?

In 2019, the estimated volume of data in zettabytes (a trillion gigabytes) from the IoT was 13.6. In 2025, it’s estimated to be 79.4. The average data companies manage, however, can vary. Usually, it’s anywhere from 47.81 terabytes (TB) for the average small business to 347.56TB for the average enterprise. In short, the IoT can provide a business with a lot more data—no matter your business’ size. This opens the door to deeper, more accurate customer and business insights. However, the sudden surge of all that extra data also presents a major challenge.

2. The challenge with implementing IoT networks

The IoT empowers organizations to increase productivity, streamline workflows and redefine how a business operates. The data streams it provides can move across a range of IT infrastructures. Innovation is essentially constant, with new apps and features added daily. When you start connecting more and more devices to the IoT, you face an increasingly vast data lake with streams flowing into it constantly. Thus, the challenge quickly shifts from capturing data to managing data. And that can create a major bottleneck for growing businesses. When people try to explain the benefits of the IoT, they often use the logistics metaphor: Sensors in refrigeration containers can track temperatures to ensure perishable goods stay within defined parameters. https://www.techopedia.com/2/28629/internet/social-media/7-sneaky-ways-hackers-can-get-your-facebook-password

3. More data, more problems?

Often one of the biggest pushbacks to connecting more systems to the IoT is whether anyone has the time to actually analyze that data. Depending on how you approach implementing IoT, it can be like turning on a firehose of data. And if you don’t have the right systems, that data ends up where most data goes to die: endless spreadsheets. When data is siloed in spreadsheets (or other platforms), it becomes increasingly difficult to manage. Reporting can’t happen in real time. Manual data entry errors are costly and put your business at increased risk. Moving data around requires either delegating it to a team member (who has to find time to do it) or outsourcing. Both involve more costs.

4. How can IoT networks and low-code support business functions?

Low-code platforms are Software as a Service (SaaS) interfaces designed to streamline the development of applications and integrations. In short, they’re an incredibly agile way to build apps. Rather than building up complex custom applications from scratch, you simply drag and drop bits of code or visual elements to create the solutions you need.  This drastically reduces the time and cost needed to build custom applications. Instead of spending seven figures on custom app development and waiting months to test and go live, you can design custom software solutions in days. Low-code platforms also present many benefits as a cost-reduction strategy. As a SaaS platform, costs scale with use—which makes them very affordable solutions for businesses with a limited IT budget. Plus, they’re designed for people who do not have a coding background. This means they’re easier to use and onboarding is much faster (and cheaper). https://growntechnology.com/why-network-analytics-are-vital-for-the-new-economy/

5. Challenges with IoT networks and low-code

While many of the mainstream low-code applications are built to support the IoT, there are still potential challenges. For one, the IoT is complex. And even though users can build custom applications with only a little background knowledge of code, that doesn’t mean it’s necessarily easy to do so. You’re potentially looking at an intricate web of disparate systems, IoT endpoints and platforms. Plus, you need to know the best way to organize data streams and present them in meaningful ways.   Additionally, applications are increasingly complex: Advancements happen every day. IT teams, with their background in code, are better equipped to set up the necessary infrastructure businesses need to gain meaningful insights from these new technologies. As a result, low code isn’t positioned to replace software developers. Instead, it’s a tool that can help them scale their efforts. Other team members can write the basic business logic needed to run the automation and they can highlight the relevant data points. However, they still need to collaborate with IT to build the necessary infrastructure to support IoT effectively.

Despite the challenges it comes with, low-code platforms have the power to amplify the work developers do. And in the end, it can help them build the systems businesses need to capitalize on all the benefits the IoT and automation offer. It’s important for businesses—especially those thinking the IoT is out-of-reach due to data complexities—to realize low code is the ladder that will help grasp its full potential.

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The world is becoming more data-dependent, which means intolerance for network downtime – or even noticeable lag-is on the rise. Unlike in earlier periods of the digital age. The poor network performance is not just an annoyance. It is now a threat to our productivity, our lifestyles and perhaps even our lives.

At the same time, network infrastructure is evolving into a multi-party construct in which one data stream interacts with dozens of independent providers, any one of which could become the weak link between application and user. This is forcing digital organizations to become more proactive in their network monitoring and management, driving demand for increasingly sophisticated and intelligent analytics engines.

1. Operational insight

It’s a basic tenet of networking that you cannot manage what you cannot see or understand. This is why many organizations are turning to new generations of artificial intelligence (AI)-powered analytics. Which can not only crunch performance data faster. More accurate than current software but can also dynamically adjust its focus to detect anomalies and data patterns that would otherwise remain hidden.

According to 360 Market Updates, the global market for network analytics is on pace to more than double, to $2.7 billion. By 2026 – a compound annual growth rate of 16.4%. The key takeaway is that modern analytics does more than just monitor bit rates and throughput.  It encompasses a wide range of metrics to ensure that networks are not just functional but optimized. As well, intelligent analytics can evolve dynamically, just as data patterns do, meaning they can keep pace with new deployments and new use cases without programmers’ or network operators’ direct control.

Intelligent analytics’ ability to evolve will prove crucial as organizations undergo the digital transformation that will place much of the enterprise data ecosystem under intelligent control, says Enterprise Networking Planet’s Michael Sumatra. In a digital economy, we can expect the pace of business to accelerate rapidly—even as profit margins become narrower and opportunities emerge from highly targeted, segmented markets. This means that network resource usage, load balancing and a host of other functions must jump to near-real-time to ensure data and services can be leveraged for maximum benefit. With 5G networks and the Internet of Things (IoT) connecting everything from cars to health monitoring devices, performance degradation will become much more severe than a few seconds of lag as you’re streaming the latest cat video. https://theconversation.com/with-the-increase-in-remote-work-businesses-need-to-protect-themselves-against-cyberattacks-138255

2. Honest networking

AI does not improve network performance on a purely operational level. It can also delve into traffic patterns and other data sets to ensure. Networks are used for their intended purpose and to protect against hacking and data theft. Security firm Cylynx recently outlined a number of ways in which financial institutions, insurance companies and other organizations are using intelligent analytics to combat theft, fraud and abuse of their networks—a problem estimated to cost upwards of $5 trillion per year, nearly 6% of the global GDP.

Through massive data gathering and high-speed intelligent analytics. Organizations can spot the patterns revealing all manner of scams.  Including fraud rings conducting identity theft. Forgery and other crimes, as well as attempts to create fake IDs, take over accounts and submit false information to acquire funds. In addition, many of these patterns also contain digital clues allowing investigators to track down the perpetrators. https://growntechnology.com/is-your-organization-aware-of-these-6-key-public-cloud-risks/

While AI is being introduced to enterprise data environments in a number of settings. Its deployment is still in the very initial stages. As yet, most use cases are still in the test phase—because no one is quite sure what AI will do given the opportunity. However, it does seem likely that the more AI infiltrates the digital world, the more it will be relied upon to maintain the myriad of intricate balances necessary for a smooth-functioning environment. And nowhere will this be more profound than the network.

________________

𝐂𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐯𝐞 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬𝐞𝐬.

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Storing data in the cloud is now a necessity for any enterprise that wants to keep up with the latest technological advancements. Hybrid and public cloud structures are becoming more and more common among companies and larger corporations. In fact, a whopping 72% percent of large enterprises and 53% of medium-sized ones use a cloud solution for their data storage needs, according to a 2021 survey. https://www.imperva.com/blog/top-10-cloud-security-concerns/

1. Shared access

Infrastructure as a service (IaaS) solutions allows data to be stored on the same hardware. By contrast, software as a service (SaaS) solutions forces customers to share the same application. Which means data is usually stored in shared databases.

Today, the risk of your data being accessed by another customer.  Who shares the same tables is close to zero – at least in the case of the major cloud providers such as Microsoft or Google. However, multitenancy risks can become an issue with smaller cloud providers; and exposure must be taken into proper account.

Adequately separating customers’ virtual machines is essential to prevent any chance of a tenant inadvertently accessing another customer’s data. Additionally, one tenant’s excess traffic may hamper other users’ performance; so it is also critical to ensure a proper workflow. Most of these potential problems can be safely prevented during the configuration phase by taking the right precautions at a hypervisor level.

2. Lack of control over data

On the other side of the spectrum, larger cloud services such as Dropbox or Google Drive may expose enterprises to a different type of risk. Since, with public cloud solutions, data is stored outside the company’s IT environment, privacy issues are mostly linked with the risk of sensitive data ending up in the hands of unauthorized personnel. That’s why newer cloud services frequently encourage customers to back up their data. However, privacy can be at stake when third-party file-sharing services are involved – since tighter security settings, which are normally employed to safeguard the most sensitive data, are now beyond the control of the enterprise.

There are steps that can be taken though. Data loss prevention (DLP) can prevent users from transferring data outside of the business. Security policies can dictate that staff are not allowed to use File Sharing sites such as Dropbox. Cloud Access Security Brokers (CASB) can prevent users from using unauthorized SaaS services.

3. Bring your own device (BYOD) issues

Up to 70% of companies ensured that employees are happier. More satisfied and can roam freely—working from home or on the go—with BYOD strategies, consequently reducing downtime and inefficiency. For obvious reasons, smart working became the norm during the COVID-19 pandemic.  BYODs became an even more necessary asset for many employees who were forced to work remotely. However, even if BYODs may have higher specs than those provided by the company, employees’ devices may lack security and adequate protection. What’s more, a data breach on an employee’s device can be almost impossible to contain since external devices cannot be tracked or monitored without specific tools. And, even if the employee’s device is secure, it can still be lost or end up in the wrong hands—meaning anyone outside the workplace environment can breach the company’s network with obvious consequences.

4. Virtual exploits

Some exploits only exist because of the cloud’s virtual nature, in addition to the traditional issues physical machines pose. Most consumers are not aware of these vulnerabilities. The public cloud, they’re even less in control of security. Less experienced remote workers can be easily predated by malicious cyber actors. According to recent reports from the US Cybersecurity and Infrastructure Security Agency (CISA). https://growntechnology.com/10-big-data-dos-and-donts/

5. (Lack of) ownership

Many public cloud providers have clauses in their contracts that explicitly state a customer is not the only owner of the data since the vendor owns the data. Providers often keep the right to “monitor the use” of data and content shared and transmitted for legal reasons. For example, if a customer uses a cloud provider’s services for illegal purposes – such as child pornography—the cloud provider can blow the whistle and alert the authorities.

And while denouncing a hideous crime may seem a perfectly legit choice, even in such cases more than a few questions may be raised about the potential privacy risks of the data held by the provider. Data is often an asset that can be mined and researched to provide cloud vendors with more revenue opportunities.

6. Availability risks

So, other than the usual connection failures and downtime the ISP causes.  There’s also a risk of losing access to your services when the cloud provider goes down. Many cloud providers have been targeted by distributed denial of service (DDoS) attacks in the last two years. The amount of these attacks has steadily increased over the course of 2021.  Redundancy and fault tolerance are not under your IT team’s control anymore, which means a customer must rely on the vendor’s promise to back up its data regularly to prevent data losses. However, these contingency plans are often opaque and do not explicitly define who is responsible in case of damage or service interruptions.

Public cloud storage services can offer great value to enterprises and usually do a much better job securing data than an enterprise can on its own. However, any smart business owner must know the risks this solution might present and what measures they can take to mitigate these risks, besides what the vendor alone provides. Security always has been a concern when adopting new technologies. However, with the advent of cloud computing, organizations must take extra precautions to protect sensitive information stored online.

Big data is used and applied across multiple business domains as data analytics, artificial intelligence and machine learning continue to become part of the mainstream. Big data analytics can extract the real value out of this wealth of data, and this data can be structured, unstructured or semi-structured.

1. Do know the purpose and the starting point

The purpose of data collection and identifying the starting point is very crucial for the success of any big data project. To start with, the objective should be to identify the most promising use cases for the business. It will help the organization identify the components for those use cases.

After this, proper planning should be done to apply Big data techniques to these use cases and extract valuable insight for business growth. The priority of execution should depend upon the factors like:

• Cost of implementation.
• Impact on the business.
• Length of time required to launch.
• Speed of implementation.

2. Do evaluate data licenses properly

Data is the fuel for any big data and analytics projects. So, it is very important to protect your data from misuse. Proper licensing terms and conditions should be in place before granting data access to any vendor or third-party user. The data license should clearly mention the following basic points. There will be lots of other critical parameters also in the license agreement.

3. Do allow data democratization

Data democratization can be defined as a continuous process, where everyone in an organization is able to access the data. The people in an organization should be comfortable working with the data and expressing their opinion confidently.

Data democratization helps organizations to become more agile and take data-informed business decisions. This can be achieved by establishing a proper process. First, the data should be accessible to all the layers, irrespective of organizational structure. Second, a single source of truth (referred to as “the Golden Source”) should be established after validating the data. Third, everyone should be allowed to check the data and give their input. Fourth, new ideas can be tested by taking calculated risks. If the new idea is successful, then the organizations can move forward, otherwise, it can be considered a lesson learnt.

4. Do build a collaborative culture

In the game of big data, mutual collaboration among different departments and groups in an organization is very important. A big data initiative can only be successful when a proper organizational culture is built across all the layers, irrespective of their roles and responsibilities. The management of an organization should have a clear vision for the future and they must encourage new ideas. All the employees and their departments should be allowed to find opportunities and build proof of concepts to validate them. There should not be any politics to blame and stop the game. It is always a learning process, which must be accepted equally for both success and failure.

5. Do evaluate big data infrastructure

The infrastructure part of any big data project is equally important. The volume of data is measured in petabytes, which are processed to extract insight. Because of this, both the storage and the processing infrastructure have to be evaluated properly. Data centers are used for storage purposes so must be evaluated in terms of cost components, management, backup, reliability, security, scalability and many other factors.  Similarly, the processing of big data and the related technology infrastructure has to be checked carefully, before finalizing the deal. Cloud services are generally very flexible in terms of usage and cost. Established cloud vendors include heavy hitters like AWS, Azure and GCP but there are many more on the market as well.

6. Don’t get lost in the sea of data

Good data governance is very important for the success of big data projects. A proper data collection strategy should be planned before implementation. In general, there is a common tendency to collect every piece of legacy data of a business. But, all this data may not be a good fit for current business scenarios. So it is important to identify the business use cases first and determine where the data will be applied. Once the data strategy is well-defined and directly connects to the target business application, the next step of implementation may be planned. After this new data can be augmented to improve the model and its efficiency.

7. Don’t forget about open source

The usefulness of the tech you are considering should be evaluated based on the size of the project and the organizational budget. Lots of open-source platforms are available for free to run pilot projects. Small and mid-size organizations can explore those open-source solutions to start their big data journey. So, the organizational focus should be on the output and the ROI.

8. Don’t start without proper planning

It is a very dangerous trend to start all your big data projects in one go. This approach will likely only lead to partial success or total failure. Organizations should plan properly before starting their big data initiatives rather than going all in or taking a leap of faith. It is always recommended to start with a simple, small and measurable application. Once the pilot is successful, then it can be implemented in large-scale applications. It is key to take the time to develop a plan and to select the pilot project carefully.

9. Don’t neglect security

Data security is another important aspect of big data projects. In any big data scenario, petabytes of data are pulled from different source systems and then it is processed. The processed data is the input to the analytical model. The output of analytics is a valuable insight into the business. Once raw data has been refined, and meaningful information has been mined from that raw data, then the Confidentiality, Integrity and Availability (CIA) of that information becomes critical. When the data has critical business information, it becomes valuable to the organization. So, this data must be secured from external threats. Data security must be planned as a part of the big data implementation life cycle. https://growntechnology.com/edge-data-centers-the-key-to-digital-transformation/

10. Don’t focus on isolated business units

In today’s complex business scenario, focusing on a single business unit is not going to help. Organizations should take a top-level view of the business as a whole, and think in terms of a global perspective. The best approach should be to take small steps at a time and keep a global view. The focus should be holistic in terms of business units. It will have a positive impact and better ROI. https://www.mastersindatascience.org/learning/what-is-an-information-system/

There is no specific success path for big data implementation. But, it is a combination of planning, strategy, approach and various other factors which leads to success. Each organization has a specific goal to achieve, so the strategy should be planned accordingly, the pilot project must be chosen with care, and the resulting information must be protected and treated properly.

Edge data centers are the foundation of the next frontier of IT. But not all edge data centers have data gravity: the secret to getting the most bang for your buck and securing the future. Forward-thinking organizations should look for edge data centers with this X factor.

1. What is data gravity?

If you haven’t heard of data gravity yet, chances are you’ve heard of gravity. It’s a force of attraction that draws objects toward each other. When we add the word “data” to the mix, the definition doesn’t change much.

Data gravity is the phenomenon the IT infrastructure industry is experiencing where certain data center hubs seem to have created an ecosystem that continues to draw in value and deliver more and more opportunities to tenants on an ongoing basis without much effort. The ecosystem is self-bettering—a flywheel creating its own momentum.

Right now, the edge is rich in data gravity, but not all edge data centers have this X factor. Data gravity requires the right beginnings.

2. Why data gravity is essential to edge data centers

Consider a data center in a tertiary market compared to an edge data center in an up-and-coming market in the center of the United States. Sure, the former is a data center, but its connectivity ecosystem potential is underwhelming. Tenants will benefit in some ways but they’re cornered when it comes to spurring their expansion long-term and opening up more tools for digital transformation and evolution. On the other hand, the geographically central edge data center in an up-and-coming market changes the game. This facility still services the edge (as opposed to major hubs like Ashburn or Silicon Valley), but because it’s a midway point for east-to-west and north-to-south routes, companies colocated here will have much more to choose from. These types of long-term benefits draw high-value companies in and launch a data gravity-driven data centre ecosystem.

3. Recapping the Edge Exodus

In 2020 alone, research from McKinsey revealed that out of 2,395 surveyed participants across a full range of regions, industries, company sizes, functional specialities and tenures, half said their organizations had adopted AI in at least one function. In 2021, Ericsson forecasts proposed that in 2022, 5G subscriptions would pass one billion (a milestone reached two years faster than 4G did post-introduction. https://growntechnology.com/the-6-types-of-information-system-and-their-applications/

This skyrocketing demand for next-gen applications isn’t news to most. In reality, the story of IT’s move to the edge of the network is well-worn. It’s common knowledge now that when the world calls for low-latency applications and highly available workloads, the solution moves data capture, computing, and storage closer to the point of generation. This allows for quicker data transfer, more agile and mobile networking, and less latency and jitter for end-user experiences—all of which are paramount for making 5G use cases, some IoT applications and other similar opportunities functional. https://www.techopedia.com/10-biggest-data-breaches-of-all-time-and-how-to-prevent-them/2/34863

When organizations think of 5G, IoT, and AI, they have to be thinking of the edge. And if they’re thinking of the edge, they need to think of data gravity. This is the secret to getting the most bang for your buck and securing the future of IT—not just squirrelling it away in some endpoint facility. To find an edge data centre with data gravity, connectivity potential is a great indicator. Centralized data centre locations, an on-site or nearby IX and news of a growing ecosystem are great data gravity gauges. These are the facilities that forward-thinking organizations should be entrusted with their IT.