There is no denying the fact that artificial intelligence has become important in the field of web design. A growing number of web developers are using data analytics, AI and other big data tools to make the most out of their strategy. In fact, e-commerce and SaaS platforms are part of the reason that the market for AI is projected to be worth $126 billion by 2025.
AI is Changing the Future of Web Design
We are at that time of year where we start to look towards the future. Many of us will have already started thinking about our resolutions for 2022, and it’s incredibly similar when it comes to the beautiful world of web design.
Yes, the world’s best designers have long been thinking about what progression looks like in 2022, from freelance and distinguished designers respected the world over to huge brands of website building platforms like Duda. The latter are continually looking for ways to simplify web design to make it easy for beginners to set up in just a few steps. They have discovered that learning AI is one of the best ways to improve the quality of their work.
A number of important trends are playing a role in the future of the web design sector. AI technology will be at the epicenter of these changes for the foreseeable future.
So, what are we expecting with AI-driven web design in 2022?
There are a lot of misconceptions about AI in web design. Once you have dispelled them, you will get a better sense of the future of AI in this profession.
Here are some of the biggest AI trends shaping the web design profession.
Funky Designs
We’re already welcoming bolder and brighter designs to websites these days, and that’s going to continue in 2022 and beyond. We’re seeing new fun and more festive designs that offer more interactivity without losing focus or user simplicity. Imagery and shapes are what many designers are using at the moment and more intriguing fonts and overall lighter elements.
This is one of the most important benefits of using AI in web design. Developers can use sophisticated AI tools like Canva or Befunky to create unique shapes that can be incorporated into their layouts.
Black and White
We’re stepping into the age before color TV for this next trend and the monochromatic glory of black and white. If you get it right, there’s nothing that quite matches a black and white color scheme more, and it allows designers to focus on simplicity, a key element of any user experience.
This is another area where AI can be useful. A number of tools can automatically transition a number of images into black and white.
Three-Dimensional Design
In gaming, we see a considerable rise in VR, and in web design, we’re welcoming the arrival of 3D in style. This is one of the most important applications of AI in modern web design. Virtual reality wouldn’t be possible without major advances in machine learning technology. The last few years have seen many websites develop three-dimensional elements, and in 2022 we’re going to see more simplistic elements added, such as shadows, animations, or layering.
It’s becoming more and more the time to start thinking about 3D, as they ultimately provide a more “real” experience for the user, which can be incredibly important for e-commerce. Users are more likely to get a fuller, more detailed image of a product, while they can also see all angles and ensure it’s the right product for them.
Scrolling Text
It’s believed that we are likely to see ‘the one-page website,’ with all the information you need on one page over the next few years. Of course, scrolling text will play a huge part in this, and it’s a trend we’re already seeing more and more of.
Scrolling Text is a great way to encourage engagement and interaction with a website, and if this is currently lacking on your site, it could certainly be worth considering as we move into 2022.
AI technology can help improve the presentation of scrolling text. It can help you change the speed of text, transparency and other important factors to improve the user experience. Also, conventional methods of creating scrolling text were very tedious and often entailed creating a lot of different frames in a tool like Canva. AI has made it a lot easier to create scrolling text without this level of time involvement.
No Image Homepages
That’s right. While we’re all so used to seeing imagery on a homepage, designers are going to be ditching it for the next 12 months, using powerful and oversized fonts, shapes, and animations to emphasize their brand values and purpose.
As seen with the black and white trend, minimal aesthetics will be popular in the coming months and years, and no image homepages really play into that movement. Just don’t leave your homepages completely blank…
AI technology can help with automated testing. It can create variations of a webpage with and without images and determine which one converts better after the split-testing experiment is finished.
Large Footers
Often an area of a site that gets forgotten about, 2022 will be the year of the footer. The footer is where lots of vital information can be found, from front contact information to quick links to get around the site.
The next year will bring in the era of the “mega footer,” a must-have element of design that will provide multiple points of information. The WWF is already using this and offers a good example of what one looks like. They’re most useful for more significant sites, with the ability to direct people to critical pages quickly and easily.
Again, AI technology is useful for conducting automated split-testing experiments and making sure the right footer is presented to improve the user experience.
AI Technology is Crucial for Modern Web Design
Web developers are more dependent on AI technology than ever. These trends illustrate some of the biggest trends in the web design profession.
Advances in artificial intelligence have been shaping the state of the Internet for years. One of the biggest changes has been in the arena of cybersecurity.
However, there are a lot of threats posed by AI as well. More hackers than ever are weaponizing AI technology to penetrate network defenses, create more terrifying malware and do better assessments of their targets.
AI has also led to new discussions about the role of passwords.
Password-based authentication features have remained a mainstay security framework for an extended period. However, the risks of using this authentication option are widely known, as hackers can steal, buy, or guess passwords to compromise networks and access sensitive data.
The evolution of AI in cybersecurity has led to some important questions about the role of passwords for cybersecurity. AI technology has led to a new generation of password cracker tools. This has made some people believe that passwordless security solutions might be preferable.
The introduction of passwordless authentication is thought to mitigate these risks. AI technology has made passwordless technology possible in recent years. Passwordless login dispenses passwords completely for advanced authentication, strengthening network security and eliminating all password-related attack vectors, such as phishing scams. However, despite these benefits, passwordless authentication has its fair share of risks and limitations, including the following:
1. Poor Identity Proofing
Identity proofing entails determining the real identity of users during account registration and provisioning to ensure that only authorized individuals can access organizational data. This is a crucial step in protecting businesses and organizations from severe cyber security threats associated with unauthorized access, such as identity fraud.
Poor identity proofing automatically introduces risks to passwordless authentication. To mitigate this risk, organizations should create accounts for real users through proper identity governance. Companies should also implement robust security controls and app attestation to verify and validate established identities and device integrity continuously.
AI technology might make it easier to help networks better recognize users. However, until that happens, this is a vulnerability that hackers may take advantage of with their own machine learning tools.
2. Non-Secure Identity Management
Identity and access management ensures that authorized users or specific identities can access certain information from the system. This prevents unauthorized users from gaining access to restricted resources. However, non-secure identity management is a significant passwordless risk since it exposes organizations to possible identity fraud. Weak authentication can also be easily subverted by malicious actors.
Organizations should use secure identity proofing to ensure that even authorized personnel only access what they are allowed to. Companies can also use passwordless multi-factor authentication, which uses robust authentication features, such as biometrics. Zero-trust cyber security principles should also be observed to ensure that access within the company’s network and outside is independently verified.
As we stated in the past, AI and data analytics technology are making zero-trust technology more reliable. However, companies will need to utilize it strategically.
3. Vulnerable End-User Authentication Devices
Theft of mobile devices is another common risk associated with passwordless authentication. If hackers and other malicious players get hold of unlocked user devices, they can intercept PINs, OTPs, and links generated from authentication apps or sent through SMS or email. SIM swapping is another possible risk associated with vulnerable devices.
Hackers can manipulate service providers into canceling and transferring crucial information from legitimate SIM cards. They can use AI technology to better understand the nature of the people they are impersonating or scamming. In addition, they can intercept sensitive communication and access any service that relies on SMS authentication. User-owned devices are also exposed to many other threats. Apart from possible theft, threats from insecure apps containing malware and infected websites are common.
These risks expose organizations to severe risks. Unfortunately, the mobile nature of these devices increases their vulnerability to theft and unauthorized access. Therefore, companies can reduce these risks by implementing a multi-factor cryptographic authentication and MDM (Mobile Device Management) solution.
AI Leaves Some Vulnerabilities with Passwordless Authentication
AI technology has led to some new questions about the state of cybersecurity. Passwords are a prime example.
Passwordless authentication relies on the ability of the authentication device to gather specific user attributes, such as fingerprint or facial features. AI technology has made passwordless authentication more effective, but some limitations still remain. While this authentication method is advanced, reliable, and comparably safe, you should be wary of the various risks it presents. Fortunately, unlike password-based authentication, vulnerabilities in passwordless authentication are very low.
Cloud technology is one of the most impactful developments of the 21st Century. Analysts found that global organizations spent $41.8 billion on cloud infrastructure in the first quarter of 2021 alone.
Many major companies have already moved to the cloud and discovered the benefits of doing so. However, some organizations are still unsure of what measures they should take to adapt to the cloud. They might be skeptical of some of the benefits as well. These companies will be more likely to adapt and start investing in cloud technology if they have a better understanding of the advantages that it offers.
Here is a quick question.
If you are asked to exchange your POS system with hundreds of new customers for your e-commerce store, would you say yes to the deal?
Probably not.
If there is one tool keeping your store up and running smoothly, it is your POS system. Bartering it for a few hundred new customers wouldn’t make a difference if you cannot deliver those customers a hassle-free shopping experience. Will it?
So, when the POS system is critical for your business growth and sustainability, why not take it to the next level?
A cloud-based POS system exactly helps you to achieve that.
Integrating inventory operations, marketing, sales, and customer management, brings the best of all worlds. But to know exactly how, you have to read on.
7 Benefits a cloud-based POS system can give your business in 2022
In a heavily disrupted world by the Covid-19, a cloud-based POS system is no longer just a growth hacking strategy. It is a foundational need irrespective of your business size.
Integrating a cloud-based system is the way how future businesses deliver their consumers the complete shopping experience without interacting in a physical environment.
To do so, it deploys multi-level automation to operations in an e-commerce store. What follows is you suddenly experience a spike in the productivity and efficiency of your employees.
The best part?
You have more creative time to innovate inside your organization.
However, to experience the full power of a cloud-based POS system, you need to ensure you choose the right tool. If your software is not robust, you end up doing more bad than good to your process. Lightspeed is a powerful tool to consider here.
In the following section, we give you a deep dive into the top benefits that a powerful cloud-based POS system can render to your business.
Spoiler alert? Lightspeed has each of the following features.
1. It gives you universal accessibility
Managing an e-commerce store is a complex process. This makes efficient team communication indispensable for smooth operations. A cloud-based POS system precisely addresses this need.
Centralizing your database, the tool not only helps you use data for more optimal business decisions but also improves the data transparency and visibility of your entire organization. No matter if you have a single point of sale or multiple, you can access your management floor remotely from anywhere.
Here are a few ways how this feature can transform your operations.
2. Builds your dream team
The cloud-based POS system can boost the productivity of your employees exponentially.
Unlike legacy-based systems, a cloud system enables your team real-time access to reports on what is going on your work floor. The best part?
They can update information related to their operations, schedules, and tasks whenever they want, from wherever they want.
Data management is one of the most crucial elements when it comes to running an e-commerce store.
Be it lead generation reports, SOPs (standard operating procedure), productivity reports, or monthly sales reports, it manages it all.
The best part?
You can update any information from anywhere without immediately notify each member. No matter how many reiterations you undergo, all of your team members will be kept on the same page.
4. Keeps your store running
Your next customer might be lingering just around the corner, but if you fail to deliver what they are looking for, you lose money on the table.
Inventory management is no doubt critical for an e-commerce store. A cloud-based POS system ensures that you never miss out on keeping track of your inventory.
From inventory counts, reordering inventory, triggering promotional prices to labor usage percentages, you can do it all. It is ideal when you need either a single store location or multiple. Its centralized system keeps you updated.
The best part?
You can enjoy heavy automation on these tasks.
5. Easy maintenance
Updates and upgrades are an integral part of any software maintenance. After all, it is what keeps the application in top performance.
However, unlike on-premise POS systems, keeping your software fresh is much easier with a cloud-based POS system. Its centralized, cloud-based software keeps you ahead of the curve.
With this tool, automatic updates are a common phenomenon.
Updating a single device is sufficient to update the entire chain linked to the software. That is a huge time saver. It ensures there are no communication inconsistencies due to any workflow disruption.
Yet another distinguishable feature for cloud-based POS systems is that their upgrades occur at a much higher frequency than traditional legacy systems.
6. Enhanced security
There is so much sensitive information that your online store handles in a day.
From user transaction information, personal details to your payment system information, so much information needs protection.
In such an ecosystem, can you afford to take the security of such information lightly? No matter how many safety protocols you adhere to, digital products are always vulnerable to data breaches. One way or the other, hackers try to find a way to acquire unauthorized access. Hence, it is important for organizations to implement critical data security strategies to keep off cyber attacks.
With a cloud-based POS system, your data security has safety insurance. Unlike on-premise software, the security system of a cloud POS system is much more advanced and comprehensive.
From encryption technology to strong firewalls, cloud-based software takes multiple measures to safeguard your data from the hands of hackers. Yet that is not all a cloud-based POS does for your e-store.
There is more. You do not need to worry about losing your data with a cloud-based system. Unlike local servers, storing data in a cloud server is way safer.
Even if you lose data on your device, your central system always has a backup of the data. This means 360-degree security for all your data.
7. Enhanced customer success
All POS systems’ performance comes down to one core factor: usability. If your teams cannot use it, it does not matter. This makes customer support a key element to what you can achieve with the software in the long term.
A cloud POS system is way better than an on-premise POS system with post-sales support when it comes to customer support.
With on-premise software, you get into purchasing a one-off product.
Here, even if your software provides great post-sale service, the maximum support you get here comes in FAQ sections and DIY tutorials.
But with a cloud-based POS system, this support runs deeper.
Utilizing the SaaS-based model, a cloud-based POS has an ongoing journey with its customer. This ensures they give you high levels of attention to solve your problem day in, day out.
To do this, they install real-time customer support and deploy AI-enabled assistance. This ensures your questions are answered in the best possible way whenever you want, wherever you want.
Sometimes these support experiences also have bilingual experts who make it easier for you to do the job. Fix your problem.
8. Improved scalability
The cloud-based POS system is designed for scalability.
With a transparent, ongoing pricing system, a cloud-based POS system ensures you can customize your requirements as you scale.
The best part is it supports your cost optimization venture. This means you pay only for what you use.
Unlike legacy systems, cloud POS systems keep the pricing lean and recurring. This can help your company reallocate resources in more critical areas like inventory stocking. So if you are removing on a tight budget, a cloud POS is any day better than on-premise systems.
9. Reduced costs
If the benefits of a cloud-based POS system could not convince you to invest in the tool, its cost efficiency definitely will.
Its all-over costs are much cheaper than in-premise POS systems, from installation to maintenance. Here’s how?
Utilizing the democratizing SaaS model tends to make the product’s price point much cheaper and ongoing. In addition, this helps to personalize the demand of the product in real-time. The best part?
It supports your business to scale.
Yet again, the additional system setup costs for the POS system are significantly cheaper when it is cloud-based. After all, with the cloud, a huge part of the system is taken care of for you.
Again, when it comes to upgrades, the cloud-based software helps to stay ahead of the herd at zero to minimal costs.
Unlike traditional legacy service providers, cloud-based software updates automatically at no added costs, opposite to legacy software.
When you face an occasional technical malfunction in a legacy POS system, the issue is usually unique to your process. Unfortunately, this means the service provider may take longer to fix the problem while costing you some significant service charges down the line.
With cloud-based POS, it is never the case.
If there is a glitch, it is faced universally amongst its users and fixed by the provider at no extra cost from the users.
Cloud-Based Systems Offer Many Distinct Advantages
A cloud-based system can prove to be one of your best business decisions for the coming year.
Not only does it have the capability to transform your business into a growth hacking machine but helps you reallocate your resources for maximum ROI (return on investment).
Whatever tool you choose, ensure it can support your dynamic business needs. No matter where you are now, a startup or an enterprise, it must be able to support your ambitious goals.
Artificial intelligence has become a lot more important for many industries. There are a lot of companies that use AI technology to streamline certain functions, bolster productivity, fight cybersecurity threats and forecast trends.
The market for AI technology is going to continue to grow as more companies discover the benefits it provides. In November, Garter published a study that found companies around the world will spend $62 billion on AI technology. This is a great opportunity for software publishers that have a knack for creating quality AI programs.
Unfortunately, this can be difficult. Companies need to understand the needs of customers purchasing AI solutions. In order to meet their expectations, they must use the right software. Fortunately, a growing number of software publishers are creating great applications that help their customers capitalize off of the wonders of AI technology.
Unfortunately, new AI software has the possibility of being infected with bugs, just like any other application. It requires extensive testing to ensure that it works appropriately.
Testing is Essential for Companies Creating AI Software Applications
Testing is an integral part of software development. Not only does it ensure the product is bug-free, but it also provides valuable information about how well the product solves the problem for which it was written. This is even more important when developing AI software applications, because they often use machine learning technology to improve their functions over time. They can get worse at performing certain tasks if the machine learning algorithms are not tested properly.
There are many types of testing, some more specialized than others, so this article will briefly overview different types.
Ad Hoc Testing
One of the fundamentals of software testing is ad hoc testing. This type of testing is done at any point in the development process when deemed necessary by either a developer or an analyst.
These tests are typically created on the spot to test hypotheses about certain parts of the code, such as whether or not one section can handle more users than another. This is one of the most important testing guidelines AI software developers need to follow. They are often documented after they have been performed for future reference.
Unit Test
The first test to perform on any code is a unit test. This type of test focuses on individual units within a program and verifies that they work as expected. The unit can be anything from a simple function to a complex class with many methods and properties.
These tests check that each piece works individually and then run through several usage scenarios to make sure everything still works properly when all elements are used together.
Unit testing is a great way to find bugs early in the software development cycle. In addition, it provides a growing suite of regression tests that can be run throughout development to make sure nothing was broken during implementation changes.
This is one of the most popular testing methods for companies creating AI applications. AI programs are usually built piece by piece, which means that it is necessary to test these elements independently.
Integration Test
The next level up from unit testing is integration testing. This type of test focuses on larger chunks of code, often individual classes or modules within classes, ensuring they all cooperate when used together as expected.
Integration testing typically occurs after basic unit tests have been completed successfully to make specific higher-level components still work correctly with each other. In addition, these tests check individual parts and how those parts fit together into the larger system to ensure everything works well at the interface between units (i.e., how the units talk to each other).
Functional Test
The next step up from unit and integration software testing is functional testing. While these tests are given the same name outside of software development, for our purposes, we will call them functional tests rather than system tests because this type focuses on how well a program satisfies its requirements rather than how well the program works in general.
Functional tests are typically created by business analysts or users who use the product as if they were its target audience to ensure it does what they expect. These test cases are incredibly valuable when used throughout development because they provide real-time feedback about whether or not the program meets the user’s needs. In addition, it gives a clear view of potential problems before the product reaches them.
If you are creating an AI application that relies heavily on machine learning technology, it is prudent to see how it performs over an extended period of time. The software needs to be trained with enough use.
Load Test
Another type of functional testing is load testing, which focuses on how well a program can perform with varying numbers of users or other amounts of work. This type of test simulates low-, medium- and high-load scenarios (determined by the analyst) to see which types cause bottlenecks in the system.
These tests are often run during development but may also be scheduled for times when it’s convenient for users who will ultimately use the product this way.
Smoke Test
A smoke test is a quick check that verifies whether or not an application starts up properly after being installed onto a machine, usually performed at pre-defined stages throughout development to make sure new additions to the code don’t break anything.
Acceptance Test
An acceptance test is a functional test created by the actual users of the program to ensure it meets their needs and can be used as one type of functional test on this list. Often, business analysts work with end-users to create these tests during the planning stages before writing any code.
Performing web scraping with Java is a great way to gather data that is useful for an acceptance test. What is web scraping and how can it help? By collecting data that shows user experience, business analysts can analyze real world scenarios. As a result, these tests are valuable because they are written based on what real users need rather than what developers think they need.
Sanity Test
Like the smoke test mentioned above, a sanity test is used to ensure an application starts up properly after being installed onto a machine. These tests are typically performed at pre-defined stages throughout development but tend to be less formal than smoke tests because they do not verify that all requirements are met. Instead, these tests check if anything breaks when upgrading versions.
Follow the Right AI Testing Strategies
Artificial intelligence software has evolved in recent years. It has helped many companies develop a competitive edge. Companies need to make sure the software is rigorously tested when creating these programs.
Although there are many different types of testing when you are creating AI software, these ten tend to be the most common and relevant for project stakeholders. Of course, depending on the type of product being created, the specific functions that need to be tested will likely change, but those listed here can serve as a good starting point from which to build more functional tests as necessary.
Savvy business owners recognize the importance of investing in big data technology. Companies that utilize big data strategically end up having a strong advantage against their competitors.
However, despite the benefits big data provides, companies that are using it are in the minority. Only 30% of companies have a well-defined data strategy. An even smaller number of companies have a data strategy that is supported by the company leadership.
In order to make sure that a data strategy is supported, companies need to appreciate the potential applications it provides. They will be more likely to invest in it.
There have been some significant innovations in the workforce that have changed the way they operate as a whole. Technological advancements like enhanced software tools and e-learning content to assist in training employees are among these innovations. Still, in the world of business, perhaps nothing has benefited the industry more than big data analytics. But how exactly do they help businesses? Furthermore, how does that help then translate to better service for their customers? And in what ways have they influenced businesses for the future?
It Helps Locate Target Demographics
Understanding your target audience is one of the essential parts of running a business and utilizing Big data analytics is a critical component in doing so. Statistics on social media usage, customer ages, genders, hobbies, careers, income, and lots more are used to determine a target audience accurately. Not only that, but an analysis of competitor data and demographics can prove helpful as well for comparing results and practices. This data collection can then be used for countless marketing strategies but ultimately is processed to make any necessary changes or adjustments.
Are you still skeptical of the benefits of using big data to target demographics more effectively? One study found that data-savvy companies were 23 times more likely to acquire new customers. This is largely due to the fact that they are able to use data analytics technology to get a better understanding of their target customers and adapt their marketing strategies accordingly.
Assists Advertisers
Like so many other departments, advertising is a vital aspect of marketing and business. Many companies fight tooth and nail to get a jump on the market and hold ranks throughout it. This is where Big data analytics comes into play. By using collected data and demographics, monitoring online transactions, and keeping tabs on customers’ direction, advertisers can throw together viable campaigns that will give their company an added push for success.
Lets Businesses Track Customer Moves
As any employee or business owner would tell you, customers keep their world spinning and the organization booming. With Big data, businesses can determine their customers’ habits and patterns and any potential trends that might emerge. This collected data is then used to ensure current customers continue to return and new ones follow suit. It is because of this that, Regardless of how long you’ve been in the game, keeping a tab on customer moves is vital to both aspiring and long-lived businesses.
There Are Many Other Profound Benefits of Big Data
While these are some of the most essential highlights of Big data, it is capable of much more. It is essential for all businesses that hope to reach high levels of success, retain their customer base, and aspire for future longevity. Is your business utilizing it to its fullest potential?
People keep receiving dismal news on internet security these days. In 2020, data breaches rose by almost 20% between January and September. Hence, users are increasingly aware of how vital data encryption is to protect their data. A significant development is making the web much safer in an encouraging sign.
When you visit most websites, you might notice a green lock just beside its address. It’s an indication that the page you’re on has reached you through HTTPS, the secure web protocol, different from the plain HTTP of old times.
However, unsecured connections, interactions, and sites continue to haunt users. Even companies issue policies instructing how they must deal with users’ data. Sadly, not everything works out with proper encryption, which is something that many entities might still disregard.
You are still under scrutiny
Despite the introduction of HTTPS, certain entities have the means to watch over your shoulder. The fact that you’ve visited a particular website isn’t hidden by HTTPS. However, it ensures that those prying on you find it harder to gain access to the information you read or post on the net. It’s also guaranteed that you get to see what its creators want you to when you are on a website.
With no encryption, repressive institutions or any malicious entity may replace the content on a certain webpage with their content. Furthermore, data encryption mistakes can simplify hackers’ attempts to steal confidential data. Thus, it is important to choose encryption and implement it successfully. If not, you leave your organization or your personal device susceptible to various attacks.
Enhanced security
The original HTTPS protocol was released in 1995. Web encryption has therefore been around for quite some time now. Companies now could protect your payment details when you made online payments and prove that the merchants you were purchasing from were genuine. The technology being used was Secure Socket Layer or SSL in short. However, SSL’s successor, Transport Layer Security (TLS), came to be used beyond just credit card payments years later.
It’s because most website owners saw no benefit in encrypting everything. However, with criminal elements using increasingly sophisticated technology to steal unencrypted passwords and states altering websites, wider use of encryption assumed greater significance.
With time, prominent sites like Facebook, the New York Times, Wikipedia, and others have opted for HTTPS. In late 2015, Google took the initiative and declared that it would favor sites with HTTPS protocol over those that still didn’t. Thus, it is obvious why providers go for approved encryption standards.
The challenges and the road ahead
Encryption is a critical component for safeguarding sensitive information. It includes many details that could be exploited. After all, data breaches are unforgiving: they either happen, or they do not. And once they do, it is difficult for companies to retain their reputation. Regaining customers’ trust will be a long journey. Some of them might even choose one’s competitors. Thus, if businesses want to compete in any industry, protecting their clients’ data should be a top priority.
The loopholes
It was found in 2014 that even the HTTPS protocol has certain limitations. The flaw called Heartbleed had made it vulnerable. Apart from this technical issue, another cause of worry is the protocol’s dependence on certificate authorities like Let’s Encrypt. Supposing hackers take control of Let’s Encrypt, they may hijack certificates or even issue false ones. People, therefore, aren’t as protected as they feel they are.
Considering that even HTTPS cannot cover everything, you will be wise to take some extra precautions. For instance, a Virtual Private Network refers to an application that encrypts your internet traffic and safeguards your connection to each website. Tools like Atlas VPN also hide your IP address, making you more anonymous online and making sure that none can track your location. It is one of the best solutions to data interception and theft due to the lack of encryption on websites and networks.
Conclusion
With cybercrime on the rise and states looking for more control over user data, encryption assumes greater significance. It will continue playing a role, and we already see users turning to technologies like end-to-end encryption. The latter means that not even the service providers can see the traffic you initiate. Thus, always pay attention to the encryption situation on each website you visit. Also, be sure to check how certain companies safeguard your data from breaches and other violations.
It is no secret that businesses that are looking to maximize profit in the near future are looking at the role AI can play to unlock potential profits. For businesses in industries that rely on Computer-Aided Design (CAD) the question can be asked, how is AI transforming their industry or supplementing current technology to help boost profit margins?
AI, CAD, and other Acronyms
Artificial intelligence (AI) is a specific discipline of computer science whose main aim is to o design computational systems that operate similar to human intelligence despite being made out of components that make up a computer rather than an organism. AI and its principles have been used sometimes to imitate and even surpass unique human characteristics including but certainly not limited to learning, prediction, perception, speech recognition, decision-making, translation, social intelligence, and motion.
How then is AI being used in CAD, is likely the next logical question needing to be answered? In the past traditional CAD software has been described as parametric. Meaning that through features and the constraints of the software repetitive changes to the model can in theory be automated. This is typically time-consuming and relies on a lot of previous errors and learning from mistakes to be realized in the professional environment.
One use case of AI in CAD applications is to drastically speed up the parametric process drastically increasing workflow and production speeds. AI is capable of doing this by performing computational tasks much faster than a human, the human operator could make a change to the model, realize it will cause some kind of issue then change the model. The AI-enabled software package can run a similar process but exponentially faster. For every error encountered by the human operator, an AI module could have done thousands. This strictly forms part of the AI discipline of Machine Learning, but it is not hard to see the benefits of such technology for both CAD and Building Information Modelling (BIM) software packages. While it is important to know the distinction between BIM solutions like revit vs autocad both are seeing the impact of AI integration.
Several specialized engineering disciplines need to operate and collaborate on the same model. This is turn has always meant that errors could and would crop up during the design phase. AI can predict when a clash might occur and provide a solution right away, simplifying the design phase and allowing different specializations to collaborate more effectively. Similar principles can be used in the testing of prototypes with AI algorithms being used to test a prototype even before a prototype is manufactured.
Conclusion
AI has become somewhat of a buzzword, especially among marketers and sales teams wanting to flog products. Their software “solution” has AI stamped all over it and when asked to explain how it is used it is portrayed as some kind of techno-magic. This detracts from the real innovations AI is bringing to several industries including businesses reliant on efficient and accurate CAD modeling.
Today we’re announcing a public preview for the BigQuery native JSON data type, a capability which brings support for storing and analyzing semi-structured data in BigQuery.
With this new JSON storage type and advanced JSON features like JSON dot notation support, adaptable data type changes, and new JSON functions, semi-structured data in BigQuery is now intuitive to use and query in its native format.
You can enroll in the feature preview by signing up here.
The challenge with changing data
Building a data pipeline involves many decisions. Where will my data be ingested from? Does my application require data to be loaded as a batch job or real-time streaming ingest? How should my tables be structured? Many of these decisions are often made up front before a data pipeline is built, meaning table or data type changes down the road can unfortunately be complex and/or costly.
To handle such events, customers have traditionally had to build complex change-handling automation, pause data ingest to allow for manual intervention, or write unplanned data to a catch-all String field which later has to be parsed in a post-process manner.
These approaches all add cost, complexity, and slow down your ability to make data driven insights.
Native JSON to the rescue
JSON is a widely used format that allows for semi-structured data, because it does not require a schema. This offers you added flexibility to store and query data that doesn’t always adhere to fixed schemas and data types. By ingesting semi-structured data as a JSON data type, BigQuery allows each JSON field to be encoded and processed independently. You can then query the values of fields within the JSON data individually via dot notation, which makes JSON queries easy to use. This new JSON functionality is also cost efficient compared to previous methods of extracting JSON elements from String fields, which requires processing entire blocks of data.
Thanks to BigQuery’s native JSON support, customers can now write to BigQuery without worrying about future changes to their data. Customers like DeNA, a mobile gaming and e-commerce services provider, sees value in leveraging this new capability as it provides faster time to value.
“Agility is key to our business. We believe Native JSON functionality will enable us to handle changes in data models more quickly and shorten the lead time to pull insights from our data.”—Ryoji Hasegawa, Data Engineer, DeNA Co Ltd.
JSON in action
The best way to learn is often by doing, so let’s see native JSON in action. Suppose we have two ingestion pipelines, one performing batch ingest and the other performing real-time streaming ingest, both of which ingest application login events into BigQuery for further analysis. By leveraging the native JSON feature, we can now embrace upstream data evolution and changes to our application.
Batch ingesting JSON as a CSV
JSON types are currently supported via batch load jobs of CSV-formatted files. So as an example, let’s create a new table called json_example.batch_events and then ingest this correctly escaped login_events.csv file into BigQuery with the below bq commands. You’ll notice the batch_events table has both structured columns as well as a labels field which uses the new JSON type for our semi-structured fields. In this example some application values will remain highly structured such as event creationTime, event ID, event name, etc. so we’ll define this table as storing both structured data as well as semi-structured data.
We’ll look at how to run queries using the new JSON functions a bit later in this blog, but first let’s also explore how we might stream semi-structured real-time events into BigQuery using the JSON type too.
Real-Time Streaming JSON Events
Now let’s walk through an example of how to stream the same semi-structured application login events into BigQuery. We’ll first create a new table called json_example.streaming_events which leverages the same combination of structured and semi-structured columns. However, instead of using the bq command line, we’ll create this table by running the SQL Data definition language (DDL) statement:
BigQuery supports two forms of real-time ingestion: the BigQuery Storage Write API and the legacy streaming API. The Storage Write API provides a unified data-ingestion API to write data into BigQuery via gRPC and provides advanced features like exactly-once delivery semantics, stream-level transactions, support for multiple workers, and is generally recommended over the legacy streaming API. However because the legacy streaming API is still in use by some customers, let’s walk through both examples: ingesting JSON data through the Storage Write API and ingesting JSON data through the legacy insertAll streaming API.
JSON via the Storage Write API
To ingest data via the Storage Write API, we’ll stream data as protocol buffers. For a quick refresher on working with protocol buffers, here’s a great tutorial.
We’ll first define our message format for writing into the json_example.streaming_events table using a .proto file in proto2. You can copy the file from here, then run the following command within a Linux environment to update your protocol buffer definition:
We’ll then use this sample Python code to stream both structured and semi-structured data into the streaming_events table. This code streams a batch of row data by appending proto2 serialized bytes to the serialzed_rows repeated field like the example below. Of particular note is the labels field which was defined within our table to be JSON.
Once executed, we can see our table now has ingested a few rows from the Storage Write API!
Preview of the json_example.streaming_events table in BigQuery after data ingestion.
JSON via the legacy insertAll streaming API
And lastly, let’s explore streaming data to the same streaming_events table with the legacy insertAll API. With the insertAll API approach, we’ll ingest a set of JSON events stored within a local file in real-time to our same streaming_events table. The events will will be structured like the below, with the labels field being highly variable and semi-structured:
Example JSON events to be ingested into BigQuery, with the field labels as a highly variable input which is constantly being updated.
Now run the following Python code which reads data from the local JSON events file and streams it into BigQuery.
Now that our JSON events have successfully been ingested into BigQuery (through batch ingest, the Storage Write API, the legacy streaming API, or even all three) we’re ready to query our semi-structured data in BigQuery!
Preview of the json_example.streaming_events table in BigQuery highlighting the semi-structured nature of the labels JSON field.
Querying your new JSON data
With the introduction of the native JSON type, we’re also introducing new JSON functions to easily and efficiently query data in its native format.
For instance, we can get a count of the events we ingested which encountered a login authentication failure by filtering on the labels.property field of the JSON value using dot notation:
We can also perform aggregations by averaging event threats caused by login failures within our data set by natively casting a threatRating field within labels as a FLOAT:
Native JSON with existing tables
What if you have existing tables, can you take advantage of the Native JSON type without rewriting all your data? Yes!
BigQuery makes operations like modifying existing table schemas a snap though DDL statements like the below which adds a new JSON column titled newJSONField to an existing table:
From here, you can decide on how you want to leverage your newJSON column by either converting existing data (perhaps existing JSON data stored as a String) into the newJSON field or by ingesting net new data into this column.
To convert existing data into JSON, you can leverage an UPDATE DML statement to update your existing through either the PARSE_JSON function, which converts a String into a JSON type, or by using the TO_JSON function, which converts any data type into a JSON type. Here are examples of each below:
Converting a String into JSON:
Converting existing data stored as a nested and repeated STRUCT, like the example here, into JSON:
How can you get started with native JSON in BigQuery?
Data comes in all forms, shapes, sizes, and never stops evolving. If you’d like to support your data and its future evolution with the BigQuery native JSON preview feature, please complete the sign up form here.
Big data has become a very important part of modern business. Companies are using big data technology to improve their human resources, financial management and marketing strategies. Digital marketing, in particular, is very dependent on big data. Companies are expected to spend over $5 billion on big data marketing services in 2026.
One of the most important big data applications in marketing is with SEO. Companies that guide their SEO strategies with data analytics and AI technology have an incredible edge over their competitors.
Local SEO Strategies Must Utilize Data
There are over 30.2 million small businesses in the US, so it’s critical that you make yours stand out. Getting local traffic from those who can actually visit your venue is one of the best ways that you can do this. Using data-driven marketing strategies that enable you to reach local audiences helps immensely.
In order to make this happen, you’ll need to invest in a top-notch local SEO strategy. You need to know which data-driven SEO tools and resources to utilize and how to use them to your advantage. Read on to learn some marketing-related local business ideas to generate more interest in your services.
1. Use Data Mining Tools to Discover the Best Performing Local Keywords
One of the best local SEO ideas is to use local keywords in your blog posts, HTML, and descriptions. These keywords often include the area or city that you’re in along with something related to your website. An example of this keyword type would be “Chinese restaurant in Fort Lauderdale.’
Another local keyword strategy is to capitalize on ‘near me’ searches. Many mobile users will make queries like ‘Chinese restaurant near me’ so that they can find immediate services. Directing these users to your site leads to instant sales conversions.
Unfortunately, finding the best keywords for a local business can be difficult. The good news is that big data tools have made it much easier. Tools like Ahrefs and SEMRush rely extensively on data analytics. They are able to access databases of keywords, which can be pulled from Google Keyword Planner. They can also use data mining tools to see which keywords similar websites are ranking for, which helps them identify keywords they might not have considered in the past. This is a good option to prevent tunnel vision by looking for keywords with the same keywords for seed ideas. You can also use data analytics to find the best long-tail keywords.
2. Claim Your GMB Listing and Use AI to Optimize Your Listing
Google My Business is a database where small businesses claim their address and business type. You go to the site, type in the name of your business and the address, enter other relevant information, and verify your location. This is how you get your site at the top of Google when nearby consumers make ‘near me’ searches.
GMB is also helpful because it lets people get directions to your business. It automatically directs users to Google Maps. No additional assistance is necessary.
You will get more value out of your Google My Business listing if you use big data and AI to optimize it. This involves using tools like Grammarly that use AI algorithms to identify grammatical and spelling errors. You can also use data analytics tools to see commonalities between different listings that are ranking on the front page of Google, such as the length of descriptions and amount of images.
3. Encourage Online Reviews
Reviews are important for making a business stand out and putting it above the competition. They link to your page and improve off-page SEO by directing inbound traffic. They also let other consumers know what you’re all about so that they trust your services above others.
Because local consumers tend to trust reviews, this is critical for drawing in a nearby audience. Additionally, people can leave reviews directly on your Google My Business page. This feedback shows up when people Google and click on your business so that they can make a more informed decision about whether to utilize your services.
4. Looking for Area-Specific SEO Companies that Know How to Use Big Data
While SEO can be challenging, professional assistance can make it easier. Experts have the knowledge and tools to make local growth possible. You want to make sure that work with companies that can leverage big data effectively.
This website, for example, offers top services to a local Jacksonville market. You can also purchase local SEO packages from the same provider regardless of your location- it isn’t limited to a single city. This gets you the professional assistance that you need for a top-notch data-driven SEO campaign.
Using Big Data for More than Local Business Ideas and SEO
Big data is an effective part of modern SEO, but it needs to be used wisely. Creating an SEO campaign can be a challenge, but there are tons of local business ideas that can get your business more visibility. Now that you know some of the top local SEO tips in a world governed by big data, it’s time to get started. Head over to the ‘marketing’ tab on our home page for more ideas on how you can get the word of your local business out there.
More businesses are becoming reliant on big data than ever these days. Big data has been especially important for implementing modern marketing strategies. The marketing analytics market is projected to be worth $5.3 billion by 2026 as more marketers discover the benefits of big data technology.
We have talked about the merits of data analytics for social media marketing and other forms of Web 2.0 marketing in the past. However, marketers are also leveraging big data for newer marketing strategies as well.
Big Data Sets the Stage for Modern Web3 Marketing Strategies
The internet is undoubtedly the most noteworthy technological development in human history. As our lives become more digital and rely more on big data than ever, many web users are dissatisfied with the “monitoring system,” and the big tech companies control all of the cards.
However, we are beginning to notice the emergence of a new web known as Web 3.0 A shift in technology and power is promoting web3 or Web 3.0. As this new technology becomes mainstream, companies will need to scale their data resources in order to make the most of it.
A Brief History of Web 1.0 and Web 2.0 and the Evolution of Data-Driven Web Marketing
Web 1.0 was the period of decentralized, open protocols when most internet activities visited individual static web pages.
Web 2.0 is the period of centralization. A large portion of communication and commerce occurs on closed platforms in Web 2.0. A few super-powerful corporations like Google, Facebook, and Amazon control these platforms.
All of the entities that control these mediums rely heavily on big data. Marketers have found ways to harness the data on these platforms to make the most of their promotional efforts. Data analytics has been very helpful with social media marketing.
Whereas Web3 is a new concept in the making. Web3 may not have a definite description, but it is the next step in the World Wide Web’s evolution. The goal of Web 3.0 is to free the world from monopolistic control.
Web3 technology is even intertwined with big data technology. Savvy marketers are going to need to find ways to incorporate it into their strategies.
So, what exactly is Web3?
According to Web 3 marketing agency EWR Digital, Web3 is the third generation of internet access for websites and apps to provide data-driven and machine-based insight into information. Web 3.0’s ultimate objective is to make smarter, interactive, and accessible websites.
It is a global online environment built on the blockchain at its most fundamental level. Users will own sites and apps developed on Web3 rather than a central gatekeeper. Users would earn their ownership interest by contributing to the development and maintenance of such services.
Web3 will make the internet more balanced by allowing individuals to be independent.
True independence includes controlling one’s time and knowledge and managing who benefits from it. It relates freedom to human rights, such as fair use, ownership, and actual pay based on time-per-use.
Features of Web3 Marketing
Artificial intelligence, semantic web, and omnipresent characteristics are incorporated into Web 3.0. A website that employs AI should sort through and provide the information that it believes a particular user would find helpful. Because the results include websites for which people vote, social bookmarking as a search engine can outperform Google.
Humans, on the other contrary, influence these creatures. Outcomes. AI might distinguish between accurate and fraudulent results, resulting in products comparable to social bookmarking and social media but excluding the negative feedback.
Also, if you are a content creator, Web 3.0 is simple to understand. It simply implies that you get compensated for publishing content for others to consume. Semantic web’s core purpose is to separate and store information so that the system can learn what certain data means.
In short, a website should be able to understand the terms used in search queries in the same way that one can, allowing it to create and distribute better content. This method will also use AI; the semantic web will educate a computer on what the data means, and AI will then utilize the data.
Benefits of Web3 Marketing
1. Control of Information or Data
Users will reclaim total ownership and control of their data and the protection provided by encryption. Permission/need or on a case-by-case basis, information might then be supplied.
2. Accessibility of Data
One of the most vital advantages of Web 3.0 is the shift to access data from any location, which is mainly driven by the widespread use of a smartphone. The goal is to enable the users to get as much information as possible from anywhere globally.
3. Removal of the Central Point of Control
Ethereum provides a secure platform with completely encrypted files and unbreakable rules. As a result, the intermediates are no longer needed in the equation. Users’ data will no longer be under Apple’s or Google’s control.
4. The Blockchain with No Permissions
Anyone may generate a blockchain address and communicate with the network. It’s impossible to overstate the power to access permissionless blockchains. Users will not be limited by their wealth, location, gender, or various other demographic and societal criteria.
5. Uninterrupted Service
Account termination and distributed service denial are drastically decreased. The service impact will be minimal because there will be no single point of failure. The data will be kept on remote nodes to provide resilience, with several backups in place to prevent lockout or server failure.
How can a Web3 Marketing Agency Help You?
The goal of a Web 3 marketing agency is to help your company attain online industry leadership. True dedication to accomplishing this purpose has catapulted hundreds of individuals and their companies to online domination.
Your digital marketing agency must be able to put your strategy into action in a matter of weeks, not months. It should continue to bring value to your company in the long run. Also, the digital marketing agency should be able to develop your company.
