This information should come as no surprise, as according to the latest reports, the current global AI market is valued at over $136 billion, and is expected to grow by 120% every year moving forward.

We therefore find AI solutions permeating every sector – web design included. Web design requires a mix of skills that involve technical knowledge as well as analytical skills and creative thinking, all of which modern web designers believe AI can help with.

Let's explore this subject in more detail as we take a look at how artificial intelligence is revolutionizing web design in 2023.

The role of AI in new digital experiences

We know that in the modern, oversaturated online markets, the most successful brands are the ones that can deliver truly personalized experiences to their customers on a consistent basis. Consistency is the key to long-term growth and success, but that also means that in order to be consistent in personalization, you need to adapt your strategy to the new market trends and consumer demands.

This need has given rise to DXPs, or digital experience platforms, which allow companies to better understand their customers and optimize their experience online. These platforms will rely heavily on AI models and machine learning methodologies in the months and years to come, as companies need efficient and effective ways to collate fast amounts of data quickly.

For web designers, it will be easier to create digital experiences with AI that can leverage not just the consumer data and the market trends, but also the intricate needs of each client. This benefit will make web design more scalable, interactive, and personalized in every sector moving forward.

Artificial intelligence in creative design

Creativity is an innately human trait, and it's difficult to imagine that a machine would ever be able to match the creative processes and outcomes of human imagination. After all, AI algorithms don't truly understand or see the world around them, they're not self-aware, and they don't dwell on the why of things.

They simply deliver information based on the keywords and inputs given to them.

And yet, we are already witnessing the advancements made with creative AI algorithms that are able to do everything from writing stories to generating truly stunning images and even videos. While that doesn't mean that AI will be able to match the amazing website designs made by humans anytime soon, it does mean that it's already heavily involved in creative processes.

AI is great at design ideation, brainstorming, and even at creating first-draft designs that are so good, human developers can effectively work off of them to produce top-tier results.

How web designers integrate AI solutions

For a long time, we used to think that artificial intelligence would render human creatives obsolete, and that writers and designers would be replaced by AI. Clearly, that did not happen, nor is it likely to happen anytime in the foreseeable future.

Rather than thinking of AI as a job-killer, we need to think of it as a valuable tool we can use to empower ourselves and our teams to do better work. In fact, this opportunity could be the way to finally achieve that highly sought-after work-life balance – with the help of AI.

Modern web designers and forward-thinking companies in many sectors are embracing AI for this very reason. Many companies are already providing education software development for designers and entire teams to enable AI integration faster, and get web design agencies up to speed on artificial intelligence.

This integration is a great opportunity to combine human creativity with AI efficiency.

Leveraging AI-driven UX

Optimizing the user experience is one of the most important elements of successful web design, and that goes for websites and their applications, mobile apps, and integrations. Humans have gotten pretty good at this element, but artificial intelligence has the potential to take UX design to the next level.

For one, artificial intelligence can more easily deliver personalized user interfaces by leveraging dynamic content and various micro-adjustments based on the user's preferences, key data points, and online behavior.

AI can also be invaluable for websites with advanced visuals and features like 3D objects and augmented reality capabilities. For example, smart technology can help designers hollow out a 3D model when they're creating 3D web assets, making advanced design work more efficient and effective.

Aside from website personalization and advanced features, it's also important to note that AI can make meaningful and swift accessibility adjustments to websites and digital applications. Simply put, AI promotes accessibility and inclusivity online by ensuring website compliance and providing suggestions on how to improve these features.

Potential ethical issues

Before we wrap things up, let's consider the possible ethical issues that arise from using AI in web design. Because these AI tools work off of huge training data sets and don't actually understand the context of data or the world around them, there's a significant risk of inadvertently creating harmful content.

This lack of understanding means that AI can perpetuate racial or ethnic stereotypes, for example, or may even create offensive content by accident. That's why during development, businesses need to couple what the users want from an app with AI integrations, all the while keeping all ethical issues in mind. It's a complex, multi-faceted process that can't be overlooked.

Web designers need to stay on top of the latest developments in the field of intellectual law and copyrights, as well as to keep the AI under control.

The issues of copyright and intellectual property are for the policymakers and the governing bodies to address moving forward.

Over to you

Artificial intelligence is a powerful driving force in the web design industry, and it's important to be aware of its capabilities, as well as how web designers currently use the technology. It's also important to remember the potential ethical and legal issues that may arise as a result of this implementation, which need to be dealt with on a regulatory level in the months and years to come.

The post Revolutionizing Web Design With AI: Cutting-Edge Techniques and Creative Possibilities appeared first on Datafloq.

Here's why farmers are increasingly relying on computer-aided design to build their equipment.

The Dawn of Modern Agriculture

Farmers are no longer restricted to manual tools like rakes, shovels, and mule-drawn plows. Since the advent of the tractor in 1892, agriculture has been increasingly industrialized, relying on vehicles and heavy machinery to perform what would otherwise be back-breaking labor.

And for good reason – machines can plant vast numbers of seeds while barely disturbing the soil, fertilize entire fields in an afternoon, and harvest crops with little human input. Automated farm equipment frees up countless hours and may revolutionize the food and textile industry. There's no turning back.

A New Form of Farming

Agriculture and computers might not seem to mix at first glance. Though machinery now feels as familiar on a farmscape as cattle or sheep, software seems more at home in an air-conditioned office. But computers are at the heart of modern mechanical design.

Farmers and engineers have teamed up to produce cutting-edge equipment via CAD, and it's making agriculture easier for everyone involved.

For example, by using computer software, designers can create a model for a tiller. Instead of building the machine and testing its efficacy under different soil conditions – for example, seeing if it will fail when tilling compacted soil – engineers can evaluate and tweak the model on the computer first. This saves time, money, and materials, and it extends the life of the machine. It also minimizes the risk of equipment failures that could cause serious injuries.

Farmers can work with a CAD engineer to customize their current equipment, too, to make it more streamlined and efficient. This can be simpler than creating something from scratch. Or, they can approach an engineer with a brand-new design concept in mind and the CAD software can bring it to life. The beauty of CAD is the ability to create customized pieces and make equipment that perfectly suits a farmer's needs.

Why Is CAD Becoming so Popular?

Drawing a design by hand can take hours. Though the art of drafting blueprints isn't dead, it's certainly fallen by the wayside thanks to computers. It's easy for people to collaborate on a CAD design and visualize exactly what they're building. Engineers can design a tractor tire and view it from every angle, including what it looks like inside.

Plus, a computer model can be saved and easily shared, unlike a physical blueprint. Erasing a single mistake is as simple as hitting “undo.” Meanwhile, hours' worth of drawing on paper can be undone with a single cup of spilled coffee. CAD is just more convenient in every way.

In addition to the ease of design, CAD models allow people to nip equipment failures in the bud. Engineers can identify impact responses, temperature distribution, fatigue, and any stress points a machine might have before they build a prototype. They can also use the model as a proof of concept when applying for funding to build the equipment.

Creating Customized Farm Equipment

The image of the quaint pastoral scene isn't a relic of the past, but it's getting an upgrade. Today, farmers can take advantage of equipment like tractors, combine harvesters, and automatic irrigation systems to grow large swaths of crops. Engineers are more and more frequently designing and iterating on this machinery using computers.

CAD is here to stay, and it's paving the way for a new generation of farmers to feed the growing population. And with more mouths to feed than ever before, farmers certainly have their work cut out for them.

The post CAD Could Customize Farm Equipment appeared first on Datafloq.

Consider Using Simulations in the Early Stages

If people make configuration mistakes in a data center, the ramifications can be extremely costly. There's no way to predict the future and know for certain that a data center will be fit for its purpose in every way. However, 3D simulations can reveal many aspects early on that people may not otherwise notice until much later.

People can make various tweaks to the models to best represent the actual environmental conditions of the operational data center. For example, they can specify certain variables for the ambient temperature and supply air, plus change physical dimensions in each simulation to see the effects.

Many simulation products for data centers work in the cloud. It then becomes easier for decision-makers to reach collaborative choices about how to proceed with a certain facility. Plus, people can try different configurations one after another, helping them efficiently compare the various results.

Such simulations are also valuable when tight budgets are a concern. A data center client may want to know the differences associated with choosing different sizes of servers or other details that will ultimately impact how much they spend on the project. Simulation software shows them those specifics quickly, keeping everyone well informed about the possibilities.

Prioritize the Data Center's Primary Components

Since data center configuration is such a lengthy and painstaking process, it's often challenging to figure out the most productive way to dive into the task. One recommended best practice is to focus on the main parts of the data center and spend time assessing what each one needs.

The Facility's Space

Begin by thinking about the minimum number of square feet or meters needed to house all the IT equipment. If a client is moving into a bigger data center, looking at how much space the old one had can provide useful information.

People must also consider how much space the data center must have now and for any future needs. That's one of the most practical ways to make the facility as future-proof as possible and ready to scale.

Power Requirements

Data centers collectively use more electricity than some countries. The overall consumption is getting less intensive due to technology improvements that promote better efficiency. However, there's still a long way to go. That's because it's not just about the electricity required to run the equipment. Data centers also use electricity to keep assets from overheating. Exploring sustainable options, such as clean energy, is a smart decision.

Electrical wire insulation must also be a part of assessments of power requirements. Insulation safeguards copper and steel from environmental elements, extending the life span of those materials. Insulation also reduces electric shock risks.

Security

Data centers are vital for keeping modern society running, and cybercriminals know it. The facilities are common hacking targets, which means data center configuration must encompass how to keep the assets and data safe. However, security risks can come from inside the facility, too. One study found that 67% of respondents mentioned misconfiguration as their biggest cloud security concern.

Having a strong infrastructure against cyber threats is essential, but security extends to physical controls, too. People must secure the facility's perimeter and its contents through various means. That could include installing cameras within and outside the data center, using biometrics for access control, and planning only single entry points into an area that allows one person to enter at a time.

Hardware and Software

Data center hardware breaks down into several key types. There is the core computing equipment, such as the computers, servers, and racks, plus the networking equipment, including routers, switches, firewalls, and cables. It's also necessary to stock the data center with essential storage equipment, like hard drives and backup resources.

People must not overlook the software that accompanies those assets. Data center configuration requires ensuring that the hardware and software will work smoothly together and that the collective tech assets are fit for their purpose.

Disaster Avoidance and Recovery

A 2022 study found that 80% of data center employees had dealt with outages over the past three years. The research also indicated that more than 60% of them resulted in losses of at least $100,000. It's not realistic for people to hope they can prevent all such service interruptions. However, making the right data center configuration choices can make outages less likely.

A good starting point is to scrutinize the data center's operations and aim to reduce management errors that could cause disruptions. Proper planning regarding things such as when to perform software updates or other types of maintenance can keep things running smoothly, too.

A critical part of data center configuration involves having the technology and infrastructure in place so the facility can recover from issues as speedily and fully as possible. Disaster recovery planning often requires working with third-party providers, including to determine what solutions they offer and which are most suitable for the data center's needs.

Explore Sustainable Solutions for Data Center Configuration

An earlier section touched on how incredibly power-intensive data centers are. As decision-makers think about the future, many realize they must make the facilities both sustainable and profitable.

Succeeding in those aims often means thinking outside the box. Some companies have experimented with building data centers under the ocean to reduce cooling costs while being kinder to the environment. Others power their data centers with 100% renewable energy.

There's no universally best way to make a data center more sustainable. However, when people take the time to become familiar with the options, they'll be in good positions to make informed and relevant choices.

Excellent Data Center Configuration Supports Reliability

Putting time, thought, and effort into the aspects explored here will help those tasked with configuring data centers have the best possible outcomes. However, anyone who is new to that responsibility or has doubts about how to proceed with their specific case should consider getting advice from more experienced parties who are willing to share what they know and provide suggestions.

The post The Complete Guide to Data Center Configuration appeared first on Datafloq.

Take for example an application such as Amazon. Is Amazon considered a data product? The short answer is no. Even though the Amazon website can not work effectively without its data, the main goal of Amazon is to sell products to customers. But take, for example, the product recommendation algorithm used on the Amazon e-commerce website. This algorithm takes in the user's data and processes it for better future decisions (suggesting products for the customer to buy).

This article will state the different types of data products and some examples for each type. Moving on, we will list 4 tips to look out for when creating your own data product.

Types and Real-Life Examples of Data Products

In general, data products can be divided into 3 categories or types:

1. Data-enhanced products

This category includes data products that are created to boost a company's sales. The most well-known product in this category is recommendation algorithms. Recommender algorithms are specifically built to suggest future products to customers based on their previous experiences (products they bought, liked, or products that people with similar interests have bought).

2. Data as insight products

Not directly used to generate revenue, data as insight products are used to boost sales, discover new markets, and so on. Examples of data as insight products include social media apps, which collect data on users for future insights on each customer. Such data includes what the user likes, dislikes, etc.

3. Data as a service products

Source

These are products that customers can subscribe to using an API (application programming interface). Such services are usually integrated into the customer's main product. Some examples of data as a service products include weather forecasting, GPS, etc.

What to do when designing your own data product

1. Identify the correct market

Before creating any product or service, a given market demand must be present. Such demand will be the main reason for creating any product in the first place. Some questions to ask yourself when creating your own data product are: Who would benefit from my product or service? Are customers willing to pay value X to buy my product? Does the profit of creating and selling such a product out way the costs and the risks?

2. Pick high-quality data

As data can be thought of as the main building block of any data product, utilizing the highest grade data set possible would surely make the biggest difference. So why pick high-quality data?

To answer this question we must state what high-quality data means in the first place. High-quality data is data that ensures accuracy and consistency. Meaning that each data point collected is accurate (with a low degree of error) when compared to the actual real value. Moreover, for data to be of high quality, data should be collected at a consistent rate, with no empty time gaps. Lastly, high-quality data should be new, making sure that the data is up to date.

How to make sure that your data is of high quality? By running strict guidelines on each data point collected. Such guidelines include data cleansing, the review and correction of each data point in a given data set. The data cleansing process will remove inaccurate, incomplete, or misleading records. All in all, data cleansing would decrease the cost and time of creating your data product.

3. Open feedback channels

Source

Taking in your customer's feedback on any project, product, or service is of absolute importance. Some feedback forms would include customer reviews, surveys, questionnaires, etc. By analyzing such data, a better understanding of a given product's weaknesses and strengths can be achieved, allowing for improvements. This can be done by removing or improving all the weak points of a given product and focusing on a given product's strengths.

4. Repeat till perfection

You will unlikely get the final product right the first time. You need to focus on perfecting your product in all ways possible. By utilizing different methods such as the evaluate and iterate approach, feedback channels, and more, you can test how well your data product is performing, allowing you to keep on improving and optimizing it.

So what are some common ways to improve your data product? As stated earlier, you may find a way to improve what you are feeding into your system. This means you can feed your data product better quality data. This quality improvement is sure to improve your data product's accuracy and performance.

Moreover, in the case of higher system loads, you can boost the system's scalability allowing for even more customers and better load performance.

Conclusion

In this article, we started by defining what exactly is a data product. We also stated, that even if some products do process data in a statistical way, it does not mean that they fall under the data product category. Moving on, we stated the 3 most common data product types which are data enhanced products, data as insights, and data as a service.

We also gave some real-life examples of data products such as recommendation systems, weather apps, and global position systems. Lastly, we focused on 4 tips to follow when creating your own data product.

I hope that after reading this short article, you have a better grasp of what a data product is, and what to look out for when creating your own unique data product.

The post Top 4 Considerations for Designing a Data Product appeared first on Datafloq.

With 3D printing, it may be possible to custom-create ergonomic equipment and tools that make working in manufacturing facilities much more comfortable and less risky.

The New Focus on Ergonomics in Equipment Design

Without the right tools, workers may be required to move in a way that is awkward, painful, or puts significant strain on muscles, tendons, and joints.

Cumulative strain can lead to repetitive stress injuries over time, but even a single bad motion can cause serious injury. Awkward and difficult motions may also put workers at greater risk of a slip, fall, or struck-by incident, amplifying the dangers that already exist in most manufacturing facilities.

Musculoskeletal (MSK) injuries are expensive to both employers and employees and the price of treating MSKs has steadily risen over the past few decades.

Workers often need more than a week off work to recover from an MSK, and MSKs like carpal tunnel syndrome may require as many as 25 days on average for recovery. Long-term damage may remain after this recovery as well, potentially making workers more prone to future injuries.

Preventing MSKs With Ergonomic Working Practices and Equipment

The right tool can make daily work both more efficient and more comfortable. Many facilities that need workers to move barrels without a forklift, for example, will rely on something like a lifting drum transporter that workers can use to lift and wheel barrels around the facility.

Using one of these tools will likely be much safer than manually lifting or rolling a barrel to its destination.

Other ergonomic manufacturing tools include lift aids, transportation devices, and adjustable-height platforms. These tools make moving goods easier and can reduce the amount of stooping, stretching, and bending that workers will need to perform on a daily basis.

Using 3D Printing to Create Ergonomic Equipment

Sourcing the right equipment can be both expensive and time-consuming, however. The purchase of a tool that will be used infrequently may also be hard to justify.

3D printing may help. With a 3D printer, it's possible to economically custom-manufacture ergonomic tools and manufacturing equipment that workers can use to make their movements less painful or repetitive.

Additive manufacturing techniques, like 3D printing, are often much more cost-effective than conventional, subtractive manufacturing techniques when manufacturing volume is low for example, when a business is prototyping a new product or manufacturing just a few items.

The low cost of 3D printing makes the technology useful when businesses need unusual, personalized, or custom designs like ergonomic tool grips that can modify existing equipment or be used in future tools.

If a business needs to print just a few copies of a single tool or component, conventional manufacturing methods may be cost-prohibitive. The same job may be practical with 3D printing, however.

Businesses can use 3D printing to create ergonomic lift aids and transportation devices as needed, providing workers with supportive equipment without having to pay as much as these aids would traditionally require.

The same businesses can also use 3D printers to create replacement parts or components, potentially making those ergonomic tools easier to repair or maintain.

Custom orders are also much more practical with 3D printing, meaning businesses may be able to tweak designs and adjust the functionality of ergonomic equipment as needed for their particular needs.

3D printing can also help reduce a company's carbon footprint. Shipping can generate significant emissions, but 3D printing allows a company to manufacture components or tools closer to where they are needed helping to minimize shipping emissions and the overall carbon cost of a new tool.

How 3D Printing Can Help Make Manufacturing Jobs More Ergonomic

With ergonomic equipment, businesses can help reduce the risk of injuries due to strain or repetitive motion. Manufacturing ergonomic equipment can be uneconomical, however. 3D printing may be one solution. With a 3D printer, businesses can create ergonomic tools or components that may make work much safer.

The post 3D Printing for Ergonomic Design in Manufacturing Facilities appeared first on Datafloq.

CAD involves using computers to help create, analyze and modify digital designs before making a product. CAM then uses these models to control manufacturing processes and machines to deliver end products that meet the design's specifications. Both innovations have brought substantial benefits to the manufacturing industry, but they can do more with AI.

Here's how AI is improving CAD and CAM processes in modern manufacturing.

Increased Productivity

The first and most significant way AI improves 3D modeling is by making the process more efficient. Some CAD solutions now include AI design assistants that predict what the user is likely to do next. These tools analyze how people tend to handle designs, then use that information to suggest next steps or automatically add changes.

Some AI design assistants use as many as 15 million CAD sketches as their base dataset to influence their predictions. They can make remarkably accurate predictions with that much information. This improves productivity in the design process by letting users add multiple elements with a single button press instead of drawing them manually.

These AI assistants can go further to automate design choices, too. For example, AI can look at how elements were applied in past designs to align geometries and automatically apply them to new creations. Traditionally, this process would require slow, careful adjustments to ensure everything lines up correctly, but AI can do it in mere seconds.

These features let engineers create new designs in far less time. They can then focus on other tasks or increase their output, and manufacturers can shorten new products‘ time to market.

Optimized Design Choices

Similarly, AI features in CAD software can make suggestions to improve 3D model designs. AI algorithms can analyze past creations and various physics considerations to determine how different choices influence results. They can then help engineers create more effective, affordable or resilient designs.

This may look like an AI assistant in simpler systems, suggesting that engineers choose a different shape or material to support the intended weight. Some solutions can go further than that, automating almost all the design processes. Systems can form a recommended shape based on users' preset parameters.

These suggestions and automated designs will further improve efficiency and create better end products. Since AI is typically better than humans at making connections between data points, it may find flaws or opportunities human engineers miss. Manufacturers can then design and produce optimized products, potentially increasing sales and preventing recalls.

Novel Designs

AI in 3D modeling can go a step further by creating entirely new, unique designs. With enough data at their disposal, AI systems could generate new shapes and configurations that offer advantages over what engineers use today. Product innovation would accelerate as a result.

New CAM technologies provide the flexibility to create novel structures and designs. For example, 3D printing has made stretchable electronics a reality, opening the door to a vast range of new product designs and functions. AI can take advantage of these opportunities to create innovative, unique models that human engineers wouldn't have imagined.

AI can analyze where previous designs fall short and assess 3D printers' near-limitless range of motion to find new solutions to persistent problems. They could then reveal how manufacturers can rethink product designs to address these issues. After AI in CAD designs these models, AI programs could guide CAM systems to create them with minimal errors.

Decreased Waste

Implementing AI in CAD and CAM processes will also decrease waste. AI-assisted CAD models can account for production processes, creating designs that will produce less refuse when machined. Other AI solutions can then take these models and guide CAM systems to machine these designs in the most efficient, least wasteful way possible.

Letting machines handle manufacturing processes instead of humans already reduces waste. Automation can achieve levels of consistency that would be impossible for a human, and AI can take this even further. As these systems produce more parts, AI algorithms can notice areas to improve and adjust the machining process accordingly.

Similarly, if a mistake does arise, AI systems can notice and respond to it in real-time. They can then produce a usable product despite early mistakes or poor-quality materials, where a traditional system would've created a defect. AI in CAM reduces overall waste by cutting down on the number of problems.

Ongoing Improvements

As in other industrial processes, AI in 3D modeling can enable ongoing improvements. The more facilities use these tools, the more data these systems can learn from. Over time, they'll recognize patterns of successes and failures and suggest changes to expand the former and minimize the latter.

Using AI in both CAD and CAM can combine the design and manufacturing processes, paving the way for operational improvements. Data from both sides will reveal how the production side can adapt to better serve the designs engineers want to produce. AI can recognize these areas of improvement and tailor them as new factors emerge.

Similarly, AI on the CAD side can adjust design recommendations to fit CAM systems' capabilities better. Since these predictions grow more accurate with more data, AI will provide more value to manufacturers the longer they use them. After a few years, 3D modeling and production processes will be far more efficient and accurate than they started.

AI Is the Future of 3D Modeling

AI in 3D modeling is still a relatively new practice. Despite its novelty, the technology is already growing across CAD and CAM software solutions. As more companies realize its benefits, more systems will offer it. It will likely become the industry standard before long.

CAD and CAM technologies have become as popular as they are for their efficiency, accuracy and reliability. AI can improve on each of these benefits, pushing results higher than previously possible. These improvements could change the industry for the better.

The post How Can AI Improve CAD/CAM? appeared first on Datafloq.

Generally speaking, IoT devices in consumer and commercial markets don't consume much power. That could change as users relegate more demanding tasks to the IoT, and even minimal consumption adds up when there are hundreds of gadgets in one area.

Consumption isn't the only issue, either. As the IoT grows, users will expect more consistent, flexible performance, raising power supply demands. It‘s impossible to have one without the other.

Why IoT Power Supplies Must Improve

While the IoT doesn't seem to have many glaring power supply issues on the surface, there's trouble on the horizon. IoT devices are more susceptible to breakdown than traditional electronics, largely from power-related concerns. These gadgets are typically always on, tucked into heat-trapping confined spaces and lack consistent quality standards.

These issues have done little to slow IoT growth up to this point, but that could change. As additional consumers and businesses come to rely on the IoT, disruptions or outages will have more severe consequences. With the advent of smart cities and widespread edge computing, power interruptions could affect critical infrastructure and even put people at risk.

Today's IoT power supply standards may be sufficient for current use cases but not for the future. End-users expect more out of their devices, asking for less power consumption, more diverse features and more consistent service. Power integrations will have to improve to provide for these demands.

In light of these developments, here's how device manufacturers can optimize power supplies to improve design.

Embrace Flexibility

The first thing IoT manufacturers can do to optimize power supplies is to make them more versatile. IoT use cases are already diverse, and they'll likely continue in this direction with more users. Power supplies must be flexible enough to support various features and end uses to sustain further growth.

One of the key areas in which power supplies must be flexible is wireless protocols. There are already 21 separate connectivity standards IoT devices could use, all with varying power needs. Power supplies that can adapt to meet a wider range of protocols will meet more users' preferences, ultimately leading to higher sales.

Highly specialized devices could also serve an increasingly diverse market, but versatile device designs are more appealing. First, creating a single power supply that can meet multiple demands is most cost-effective for the manufacturer. Second, versatility is more helpful to consumers, letting them accomplish numerous tasks with one device.

Use Switching Power Supplies

Many IoT systems today use linear power supplies, which are relatively simple and inexpensive. While linear regulation is sufficient for many IoT applications today, it's starting to show signs of aging. If device manufacturers switched power supplies, they could create smaller, more efficient and flexible gadgets.

Switching power supplies offer more efficiency and smaller sizes than linear regulation, making them ideal for IoT power integration. They would let manufacturers fit more features and components in the same space. This could create higher-functioning technologies while maintaining the unobtrusive form factors users demand.

Since switching power regulation lets devices handle a wider range of energy levels, it also improves versatility. The only downside to this type of regulation is that it's more expensive, as it typically requires more components. As technology advances and more manufacturers use these power supplies, though, they'll become more affordable.

Isolate High- and Low-Voltage Components

As IoT devices grow smaller and more complex, manufacturers will have to put additional thought into component placing. High- and low-voltage circuit parts that are too close could interfere with one another. This connection poses several risks, from component failure to electrical shock and fire hazards.

Power supply isolation is essential to prevent these issues while maintaining a compact design. As devices get smaller, this isolation will have to rely on methods other than physical distance. Insulating components with nonconductive materials is a straightforward, affordable solution but may create heat-related problems in more power-hungry use cases.

Magnetic isolation removes the need for insulators but makes components more sensitive to electromagnetic interference. Capacitors, which don't take up much space and block DC flow while enabling AC, are an ideal solution. More IoT power supply designs should include these components to allow smaller device sizes while isolating high- and low-voltage circuits.

Consider Alternative Power Sources

If the IoT is to expand and reach its full potential, its power integrations must move past traditional sources. Devices can serve more varied purposes if they don't need AC power or batteries to perform. IoT power supplies that can work with a wider range of energy sources will become increasingly in-demand.

Solar is perhaps the most promising alternative energy source for the IoT. Amorphous silicon panels can generate electricity in light as low as 200 lux, potentially powering devices from even indoor lights. Consequently, power supplies that can support amorphous silicon photovoltaics could enable a suite of battery-free, sustainable IoT devices.

Kinetic power generation is a promising alternative power source for wearable devices. IoT in industrial machines or vehicle engines could use thermoelectric power. These options let IoT device manufacturers create more sustainable, versatile designs, and all need flexible, reliable power supplies.

Optimizing IoT Power Supply Will Improve IoT Devices

People who think about expanding the IoT's capabilities often focus on its features. The inner workings of IoT devices, especially power supply, must also grow to meet these demands.

Better IoT power supply design paves the way for more versatile, safe and reliable devices. The sooner manufacturers can implement these power supply changes, the faster the IoT can expand. Preparation for success in tomorrow's markets starts today.

The post How to Optimize IoT Power Supply to Improve Device Design appeared first on Datafloq.

Creating a website has always been based on certain actionable inputs-

Competitor reference

Target audiences

Software capabilities and integration

Computer language and programming

Budget and Timelines

In other words, every time someone has designed and developed a website, the above factors have been taken into consideration. However, while the above was influenced by subjective tastes and preferences, Big Data has changed the ecosystem completely.

In this informative piece of content, we are going to examine and discuss the following points-

1. What is Big Data Analytics
2. Top 5 Ways Big Data Analytics is influencing UI and UX development
3. Advantages of factoring in Big Data for the design and development process
4. The Final Analysis

If you are someone who is starting a new website design and development project, you might want to stick around till the end of the article.

Big Data Analytics: What is it and why has it become so important?

Data, Big Data, Big Data Analytics

It seems that the hold and grip data has over decision-makers, policies and overall growth is increasing day by day.

The past few years have seen data become one of the strongest watchwords to be used in business organizations.

It rose to almost magical proportions thanks to its ability to show patterns, trends, conjectures, and figures that would help decision-makers in a host of areas.

The end goal of big data was to ensure that businesses are investing in areas and policies that reduce costs and ensure the biggest possible impacts.

Big Data Analytics is the process of evaluating big data to suit a businesses' interests. It seeks to accumulate and analyze huge volumes of data to arrive at an informed decision. According to experts, the application of big data analytics can help in-

1. Creating a competitive advantage over the competitors. Since a business already knows what works and what doesn't they can align their investments and efforts accordingly.
2. It can help in formulating and executing the best marketing plans and strategies. This can help in better reach, engagement, and branding opportunities.
3. Present potential areas and opportunities for maximizing revenue generation. Big Data Analytics can show customer segmentation and territorial and market differences.
4. Organizations looking to improve their internal operations and streamline efficiency can also benefit from big data analytics. It can pinpoint areas that need improvement.

The rise of organizations and professionals vouching for big data analytics continues to increase unabated. No matter what size an organization, it has in place some or the other way of relying on and benefitting from big data.

List of the Top 5 Ways Big Data Analytics is Influencing UI and UX Development

In this section, we are going to examine the top five ways big data analytics is influencing UI and UX development.

1. Cuts down the Timelines of UI and UX Development Projects-

All of us remember how designing a new website meant constant back and forth between the client and designers. Subjective preferences were highlighted every single time there was a difference of opinion. This created a lot of differences in project completion.

Needless to say, all these issues would delay the design and development project by a long time. By taking help from big data analytics UI and UX designers and developers already know about everything that is trending. They can easily integrate the same in the UI and UX.

In other words, subjective considerations have been replaced by objective data and facts. This reduces friction between the client and agency and allows for the completion of the website in a shorter time span.

2. Helps in Securing Automatic Updates and Bettering the UX-

With the rise of big data analytics, manual updates have become a thing of the past. With every possible update to the programming language, the design and development team does not adopt a manual approach to selecting updates that audiences will like and rejecting the ones they will not.

With the help of big data analytics, updates that are shown to be preferred by the audiences or website visitors will get automatically applied, whereas the rest will not be. UI and UX developers do not have to physically visit the website and fix updates.

This will not only reduce the dependence on the UI and UX agency but also ensure a smoother and more streamlined approach to updates. One area where this will prove to be highly beneficial is in the field of website security and best practices.

3. Charting out and Learning from the Heat Map Analysis

For a very long time, website owners were unable to track real performance and data from their websites. While they could see how many times a specific CTA had been clicked, they could not get an idea about hover functions, scrolling behavior, and so on.

In the last few years, big data analytics has been able to effectively break down the heat map analysis. It can specifically show which areas and pages of a website are attracting more visitors and encouraging them to stay for a longer period.

This can be used as a model of learning and other areas of the website can be altered or modified accordingly. Using big data analytics in this area can help to constantly improve a brand website and improve its digital performance in terms of traffic, CTRs, and more.

4. Creating more Attentive and Personalized User Experiences-

With the coming of big data analytics another exciting new piece of tech that is influencing UI and UX development is Artificial Intelligence. AI has been able to play a major role in personalizing the user experience for better engagement and conversions.

For example, every time you leave a music streaming platform and revisit it again, it shows you all your favorite artists, the genre of music, language, and more. Big Data Analytics together with AI becomes an instrumental force to boost website and app engagement.

In other words, it means that one website effectively appears in one million (or more) different forms to the different visitors and users. This is something that helps in creating better experiences for the users and allows the website to increase metrics, traffic and engagement.

5. Provides and Better Model of Testing than Customer Feedback Forms

If you have ever been involved in a website design and development project, you must have heard about A/B testing. You would have also heard about how customer feedback forms were rolled out every time a new website was launched.

Not anymore. With big data informing us at every step of the way, the need for customer feedback forms is a thing of the past. There are so many testing tools that are inbuilt in nature, which can help in trapping all the feedback necessary for improving a site's performance.

This not only reduces dependence on customers and their subjective feedback but also for quickening the pace of any and all alterations and modifications that need to be made. This is a fast, simple, and effective alternative that delivers more precise results at all times.

Advantages of Factoring in Big Data for the UI and UX Process

In this article, we have seen how big data can be a great way to boost and optimize the UI and UX of a website. In this section, we are going to list down some of the major advantages of factoring in big data analytics for all your future website design and development projects-

1. Data-driven design approach- using big data analytics help in creating the entire process of the design and development in a very streamlined manner. There are no errors in the process as everything is informed by data. This improves efficiency and reduces errors.
2. More leads, traffic, and sales– if you know where your traffic is on the website, you can tailor-make their experience to boost conversions. Adding a simple search box or changing the CTA can help you get qualified and verifiable leads that can help in sales and revenue generation.
3. Technologically advanced website– everyone wants to have a website that is run on the latest and most superior technology currently available. We also want to have the latest updates, interfaces, templates, and so on. With big data informing your decisions, you will always have a tech-advanced website.
4. The Best ROIs among competitors– big data analytics delivers the best ROIs in terms of investments, lower extended and delayed timelines, and faster turnaround times. This not only helps give a competitive edge but also ensures that the business is heads and shoulders over its competitors.
5. Attractive and engaging website- last but by no means the least, you should not think that all the talk about data makes for an unattractive and uneventful website. That is not the case. Big data helps in creating a website that has the latest design interfaces that arrest the attention of the visitors.

The Final Analysis

While other aspects of software development have seen big data analytics playing a more permeable role, website UI and UX development are still hesitant of embracing it with open arms.

Some of the leading UI and UX agencies of our times have streamlined their processes and are now depending on big data to inform them at every step of the way.

From impressing consumers thanks to the personalized nature of experiences to showing exactly which areas need improvement on a website, big data analytics has come a long way.

In the coming years, the incorporation of big data analytics into the design and development process is expected to rise in a major way. If you have any doubts that you would want us to clear, or discuss any of the points in greater details, do let us know in the comments section below.

The post How Big Data Analytics Is Influencing UI and UX Development appeared first on Datafloq.

Before going into the various ways in which you can improve your user experience vs your user interface design, there are a few differences that are worth mentioning first. Here are five ways to improve your UX vs UI design.

The differences between UX and UI design

UX refers to user experience, whereas UI is the user interface. They both sound the same, right? Well they do work closely together in order to make a good product, however, the roles they have are fairly different.

The main difference between the two is that UX design is about the overall feeling towards the experience, whilst the UI focuses on the interface's functionality and look. Dain Miller, a web developer, sums up the difference between UX and UI by saying UI is the saddle, the stirrups and the reins. UX is the feeling you get being able to ride the horse.

As UX focuses on a human approach to the product design, UI focuses on a human approach to the overall aesthetic experience of the product. The user experience design comes first and then it‘s followed up with user interface design. There's also a difference in that UX can work with any type of product design, but for UI is digital products only.

Both are needed and cannot work without the other, so with that said, it's worth considering some of the ways in which you can improve the design of each. By focusing on improving both, you're likely to create a much more polished and well-rounded product.

1. Competitor analysis vs customer analysis

Competitor analysis is important for user experience because it's good to look at how your competitors are drawing in customers and making their product experience unique. As a UX designer, there's a lot to be learnt from your competition that can help towards tailoring the experience that the customer has with the product.

There are plenty of competitor analysis tools that can be used such as Semrush or SpyFu as examples. These are great platforms for assessing your competitor's tactics and the methods they've used in their design process. By being able to track competitors and their tactics, you can adapt your own efforts to somewhat mirror theirs.

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Alternatively, UI designers will use customer analysis as part of their influence over the product. The user interface is being used by the customer after all, so it's clear that the analysis done should be through the eyes of a customer.

What is it that they could benefit from when it comes to the functionality of the product? Is there anything that they're missing when it comes to features in other similar products? Both are important to have in order to create a product that's better than your competitors and has everything the customer needs so that they don't end up looking elsewhere.

With so much competition out there, it's essential that these questions are asked and addressed in order to enhance your product and make it more favorable. For your customers, there are also focus groups you can conduct, getting feedback from existing customers and doing the relevant marketing that will help you discover improvements to make.

You may discover habits and preferences for your customers when it comes to developing your product. This is important information to know not only to help better the product's design for the user but also when it comes to marketing.

2. Product structure and strategy vs design research

Product strategy is helping to achieve an overall vision for the company's goals and objectives. It's an important part of the process because a strategy is useful for keeping everyone focused and on track, heading in the right direction. Both new and existing products require product strategies and a structure that will hopefully result in the best possible outcome.

A product structure and strategy is a roadmap for all those involved in the UX area of the product team. It's all about providing value for the users and to ensure that the product itself will do well on the market. So with that being said, the strategy should be focused on who the product is for and why people would spend their money in order to use said product.

For UI designers, design research is necessary to iron out any problems by enriching the knowledge of your team and can help to make user-centred products that are more effective.

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There are a few stages to this design research that are needed, including the primary research gathered through interviews, surveys and perhaps even focus groups and usability testing. From there, more research can be done to validate the design ideas that the individuals have come up with and running experiments to see if it works.

Research is certainly an important part of the design process because it can help to give the product more opportunity to fulfill the user's needs and wants from the product.

3. Content development vs branding and graphic development

Content is everywhere and it's a very influential aspect of the user experience overall. If the content provided isn't engaging or doesn't provide value to the user, then they're not going to get much usage out of the product. With 70% of marketers spending their money and time in content marketing, it's essential that the content itself is high-quality and valuable.

The development of content for user experience is key and so there are plenty of ways in which it can be developed to help better the customer's overall feelings towards the product. It's essential that there's communication between the UX designers and those responsible for the creation of the content.

UX designers, even though they might not be content creators, can still contribute their ideas to what should be created and what perhaps is already in existence and has been done before.

With UI, the branding and graphic development is an important stage of the product development. It's important that the branding of the product is aligned with all the other products or services you have, as well as your overall brand image for the business.

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Graphic development is critical because the world of graphic design is constantly evolving and if it's not the very best it could be, then it may feel a little outdated to your customers. It's necessary for the graphics to be refined and tweaked until they have that wow factor needed for the users.

4. Prototyping and wireframing vs UI prototyping

Techniques like prototyping and wireframing for user experience have helped to transform the design process that many follow in today's world. With digital prototypes, it can show those on the team a realistic version of the concept and this is something that's also useful to have for stakeholders.

With prototyping it not only helps save time and money but it gets everyone involved in a collaborative way. Wireframing is good for testing out functionality, which is important when it comes to user experience. A product that doesn't function correctly, isn't going to get a great response from the users.

On the flip side, UI prototyping can help the designer bring together visuals, navigational and interaction elements in order to see how the design behaves and feels for the user. This can be helpful in correcting any mistakes or troubleshooting issues within the design of the interface itself.

Both elements on either side are an important stage of the design process because they help to minimize the amount of problems that occur after launching the product and going live. The fewer issues that users face, the better experience and satisfaction they'll have with the product.

5. Coordination with developers vs implementation with developers

Collaboration is critical between designers and developers because the work doesn't simply end when handing over the baton. The developer should be seen as the designer's user, so it's important to coordinate with the developer to ensure that all the user experience that has been created and developed, is executed successfully.

There are some adaptations that might need to be made and so organizing this between the designer and developer is important. Some elements of the user experience design might not work in the way the designer thought when it comes to the development stage. So that collaborative element is necessary to keep things moving in the right direction.

For UI designers, implementation of the various elements is critical and when it comes to conversing with the developers, it has to be monitored. The reality of design is that it's never done, regardless of whether it's done by UX or UI designer. With that said, there's always progress that can be made even when it's in the hands of a developer.

For a UX designer, it's their job to consider the user's journey from start to finish and to figure out how to make the best experience possible. It's then the job of the UI designer to bring it to life and to fulfil the expectations that the UX designer sets. So both are needed in the process of making a product as they complement one another.

The post 5 ways to Improve Your UX vs Your UI Design appeared first on Datafloq.

UX design for AI interfaces is a challenge even for experienced UX designers.

We need updated guidance on designing interactions with AI services that provide meaningful experiences, keeping the user in control and respecting users' values, goals, and attention.
Microsoft

Let's take a deeper look at 4 key AI UX design principles that will supercharge your AI customer experience.

Principle 1: Setting the Right User Expectations

We are in an age of transition. While many are excited about the potential of AI, others are still somewhat hesitant to trust it completely. In some cases, hesitation is warranted. After all, AI is not natural intelligence.

This UX design principle ensures that we are informing the users what AI is capable of and where it may fall short. Anyone who has spent much time with a chatbot or a voice assistant is very familiar with how far AI still has to go. Telling the users explicitly about the errors that AI can make, helps to build a direct bond of trust with them and helps to enhance their experience.

Thus, designers must assist users to make wise decisions about their reliance on AI with their intuitive designs.

For instance, PowerPoint's QuickStarter, a feature that helps to build presentation outlines, has an AI-enabled design that explicitly sets the user expectations by providing explanatory text and suggested topics.

Principle 2: Setting the Right Context

While machine learning has some powerful applications, it has not been perfected in every instance. At many times, AI fails to set the context right and provides information that is culturally, socially or emotionally imperfect for a particular user. This can lead to cases of unhappy user experiences. So, setting the context right is a key UX design principle in the age of AI that needs to be implemented for sure.

AI-enabled UX designs that make inferences about people based on their needs, tastes, etc. to make proactive suggestions, should be designed in a way to make socially and culturally acceptable suggestions.

For example, Acronyms, a feature in Word has an AI-driven UX design that displays the meanings of the abbreviations relative to the currently open document.

Principle 3: Educating the Users

AI is infused in more and more of our daily lives. People will have an easier time adjusting to this change in how we interact with machines if we can give them some insight into how AI and machine learning work. This does not mean that design needs to teach users about data science and how the tech works. Instead, the right UX for AI can help them with a glimpse behind the curtain.

While it is not necessary to explain how it all works, there is no reason users can't see some of the data that is being used by the algorithm to come to conclusions and recommendations. This is especially important at this point when AI has some growing edges. Users will be more tolerant of errors if they understand just a bit about the process. As AI improves, this principle of design may also help users make better decisions about how much trust to place in the results.

Principle 4: Encouraging User Feedback

Creating helpful feedback channels is the next principle of UX design. Our greatest resource for improving both UX and the AI behind it is user feedback. User reporting can serve three essential functions.

• One, if properly used, can help the AI learn. Well-developed AI can learn from its mistakes.
• Two, if appropriately shared, can help programmers and engineers fine-tune the software for better results.
• Three, if well implemented, it can assist UX designers in a better understanding of how people interact across various user interfaces.

Ideas in Excel is a great example of the application of this AI-driven UX design principle. It provides visual graphs and trends to summarize users' data using AI design. Along with it, it encourages users to provide feedback by asking, Is this helpful?

Conclusion

As businesses move fast towards embracing AI to power up the customer touch-points, the need for well-designed user experiences has intensified too. Though AI primarily works behind the scenes, the right UX for AI interfaces ensures a seamless, intuitive, and immersive customer experience.

To come up with a brilliant AI UX design, both innovation & creativity are required. Keeping in mind the above UX design principles to drive AI customer experience can definitely give your businesses a competitive edge.

The post Supercharge AI Customer Experience with these 4 UX Design Principles appeared first on Datafloq.