Data Science

Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI) and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. These insights can be used to guide decision making and strategic planning.

The data science lifecycle involves various roles, tools, and processes, which enables analysts to glean actionable insights. Typically, a data science project undergoes the following stages:

• Data ingestion: The lifecycle begins with the data collection—both raw structured and unstructured data from all relevant sources using a variety of methods. These methods can include manual entry, web scraping, and real-time streaming data from systems and devices. Data sources can include structured data, such as customer data, along with unstructured data like log files, video, audio, pictures, the Internet of Things (IoT), social media, and more.
• Data storage and data processing: Since data can have different formats and structures, companies need to consider different storage systems based on the type of data that needs to be captured. Data management teams help to set standards around data storage and structure, which facilitate workflows around analytics, machine learning and deep learning models. This stage includes cleaning data, deduplicating, transforming and combining the data using ETL (extract, transform, load) jobs or other data integration technologies. This data preparation is essential for promoting data quality before loading into a data warehouse, data lake, or other repository.
• Data analysis: Here, data scientists conduct an exploratory data analysis to examine biases, patterns, ranges, and distributions of values within the data. This data analytics exploration drives hypothesis generation for a/b testing. It also allows analysts to determine the data’s relevance for use within modeling efforts for predictive analytics, machine learning, and/or deep learning. Depending on a model’s accuracy, organizations can become reliant on these insights for business decision making, allowing them to drive more scalability.
• Communicate: Finally, insights are presented as reports and other data visualizations that make the insights—and their impact on business—easier for business analysts and other decision-makers to understand. A data science programming language such as R or Python includes components for generating visualizations; alternately, data scientists can use dedicated visualization tools.

What Is Data Science Used For?

Analysis of Complex Data

Data science allows for quick and precise analysis. With various software tools and techniques at their disposal, data analysts can easily identify trends and detect patterns within even the largest and most complex datasets. This enables businesses to make better decisions, whether it’s regarding how to best segment customers or conducting a thorough market analysis.

Predictive Modeling

Data science can also be used for predictive modeling. In essence, by finding patterns in data through the use of machine learning, analysts can forecast possible future outcomes with some degree of accuracy. These models are especially useful in industries like insurance, marketing, healthcare and finance, where anticipating the likelihood of certain events happening is central to the success of the business.

Recommendation Generation

Some companies — like Netflix, Amazon and Spotify — rely on data science and big data to generate recommendations for their users based on their past behavior. It’s thanks to data science that users of these and similar platforms can be served up content that’s tailored to their preferences and interests.

Data Visualization

Data science is also used to create data visualizations — think graphs, charts, dashboards — and reporting, which helps non-technical business leaders and busy executives easily understand otherwise complex information about the state of their business.

Benefits of Data Science

Improved Decision Making

Being able to analyze and glean insights from massive amounts of data gives leaders an accurate understanding of past developments and concrete evidence for justifying their decisions moving forward. Companies can then make sound, data-driven decisions that are also more transparent to employees and other stakeholders.  

Increased Efficiency

By gathering historical data, businesses can pinpoint workflow inefficiencies and devise solutions to speed up production. They can also test different ideas and compile data to see what’s working and what’s not. With a data-first approach, companies can then design processes that maximize productivity and minimize unnecessary work and costs.  

Complex Data Interpretation

Data science allows for the handling of large volumes of complex data, which businesses can then use to build predictive models for anything from anticipating customer behavior to forecasting market trends. If other organizations can’t extract insights from complicated data, companies that do have the clear advantage of being the first ones to foresee upcoming events and prepare accordingly.    

Better Customer Experience

Collecting data on customer behavior allows companies to determine customer buying habits and product preferences. Teams can then leverage this data to design personalized customer experiences. For example, businesses can create marketing campaigns tailored toward certain demographics, offer product recommendations based on a customer’s past purchases and tweak products according to customer uses and feedback.  

Strengthened Cybersecurity

Data science tools give teams the capacity to monitor large volumes of data, which makes it easier to spot anomalies. For example, financial institutions can review transactional data to determine suspicious activity and fraud. Security teams can also gather data from network systems to detect unusual behavior and catch cyber attacks in their early stages.

Data Science Techniques

• Regression: Regression analysis allows you to predict an outcome based on multiple variables and how those variables affect each other. Linear regression is the most commonly used regression analysis technique. Regression is a type of supervised learning.
• Classification: Classification in data science refers to the process of predicting the category or label of different data points. Like regression, classification is a subcategory of supervised learning. It’s used for applications such as email spam filters and sentiment analysis.
• Clustering: Clustering, or cluster analysis, is a data science technique used in unsupervised learning. During cluster analysis, closely associated objects within a data set are grouped together, and then each group is assigned characteristics. Clustering is done to reveal patterns within data — typically with large, unstructured data sets.
• Anomaly Detection: Anomaly detection, sometimes called outlier detection, is a data science technique in which data points with relatively extreme values are identified. Anomaly detection is used in industries like finance and cybersecurity.

Human Augmentation

Human augmentation is the term for technologies that improve human capabilities. They primarily work to elevate human performance, health or quality of life. Popular examples of human augmentation technology are devices such as cochlear implants or robotic limbs. However, human augmentation also applies to how humans and machines can work together, which we can see in the growing applications and capabilities of artificial intelligence (AI). By combining the strengths of automation and artificial intelligence with expert human guidance, human workers, care givers, students, public servants and professionals in virtually any kind of work can work faster and smarter.

How Does Human Augmentation Work?

Human Augmentation works by enhancing human abilities through technology. Not only is human augmentation able to improve existing skills, but humans can also access new skills using technology.

There are three different types of human augmentation:

• Sensory. This technology is used mostly to restore abilities or compensate for impairments. Sensory augmentation is the enhancement of human senses by interpreting multisensory information.
• Action. Augmented actions focus on improving human’s physical abilities. Technology advancements have allowed people to have more precise functions from their artificial limbs, with robotics playing a large role. Augmented action technology can also improve human capabilities.
• Cognitive. This looks at how computers and technology can assist the cognitive process. Augmented cognition technology aims to help improve decision-making, memory and attention.

What Technologies are Used in Human Augmentation?

AI and machine learning (ML) is used to help improve human capabilities in various ways:

• What is AI analytics? Predictive AI analytics can predict trends and events that humans alone may not be able to. Virtual assistants also work to aid human workers by scheduling and setting reminders, offering insightful recommendations, and collecting and presenting relevant information. An example of this is agent assist in contact centers.
• What is agent assist? Agent assist technology provides live support for contact center agents, offering guidance, problem-solving pathways and presenting relevant information based on the issues and behaviors it detects.
• What is a chatbot? Chatbots can be used to help people find the information they need more quickly, and be presented with solutions and advice for queries and problems.

Human Augmentation Examples

Replicating Human Ability

• Naked Prosthetics: A company that creates custom hand prosthetics for individuals who have had their fingers amputated. They are one of the first finger prosthetics manufacturers to provide their users with extremely high levels of dexterity.
• eSight: A wearable device similar to glasses that provides legally blind individuals with the ability to see their environment. The device has cameras on the front that take in the environment in near-eye quality and display it on a screen that sits right in front of the wearer’s eyes.
• MotionSavvy: A platform that translates sign language into speech and speech into sign language, acting as a personal translator for deaf people. While these products are currently geared towards businesses with deaf employees, they could eventually expand into apps on smartphones, making sign language communicable for anyone.
• Cochlear Implants: Cochlear is one of the first companies to develop such a product that restores hearing without requiring an external hearing device to be worn.
• Bioprinting: The process of creating organic tissues (organs, bones, skin, etc.) using 3D printing techniques. While this technology is still in its earliest stages, it has the potential to completely redefine the medical industry and how we typically think of healthcare.

Supplementing Human Ability

• Exoskeletons: Wearable, mechanical devices that can be worn on the outside of the body. They typically provide the wearer with artificial strength and endurance. The Sarcos Guardian is an example of an industrial exoskeleton that allows a human worker to lift up to 200 pounds, perform precise operations with heavy machinery, and handle repetitive motions without strain.
• Neuralink: Another project by Elon Musk with the ultimate goal of creating a brain-computer interface (BCI). If successful, the project would allow individuals to interact with a computer on a neural level. While this project is still in its infancy, Musk has a track record of making impossible ideas a reality.
• Waverly Labs: has created a set of earbuds that can translate conversations in real-time, completely sidestepping the need to learn foreign languages. While the languages it can translate are limited, and it isn’t completely reliable, it’s a strong step forward.
• Google Glass: By now, everyone has at least heard of this technology, even if you’re not entirely sure what it does. While the device has had a rocky start (mostly due to a premature announcement), vision augmentation is becoming increasingly popular.
• HoloLens 2: is a mixed reality headset from Microsoft that allows people to visualize and manipulate objects in holographic form. The device has many commercial and industrial uses, such as 3D computer-aided design and design collaboration, employee training and virtual instruction, and gaming.

Exceeding Human Ability

• Zapata Flyboard Air: A turbine-powered hoverboard. The driver stands on top of it like a skateboard or surfboard and can fly up to 500 feet in the air. While the device is available for purchase, quotes seem to hover around a quarter of a million dollars. That said, the company is working to make the product more practical and affordable.
• Invisibility Cloak: The idea of turning invisible certainly captures people’s imaginations, and while we don’t quite have the technology ready yet, we are getting closer. Various researchers have already come up with ways to make certain surfaces and objects invisible, and they’re trying to apply the same concepts to human cloaking.
• Artificial Blood Cell: While still theoretical, research by Robert Freitas Jr. has explored the possibility of creating artificial blood cells. This idea was born from research into mammals—whales, dolphins, etc.—who can hold their breath underwater for long periods of time. The assumption is that their blood cells are better at storing oxygen, which we might be able to recreate.
• Nanobot: Even though the word rings of science fiction, these have a very high potential of becoming a normal part of the medical industry. According to Wikipedia, a nanobot is “a robot that allows precise interactions with nanoscale objects, or can manipulate with nanoscale resolution.” These bots can be deployed into the human body to perform specific tasks that do things the human immune system can’t on its own, such as targeting and attacking certain diseases and cancers the human immune system struggles with.
• Synthetic Memory Chip: It’s no secret that hard drives are much better at retaining their memory than we are. They are also able to access that memory with greater speed and accuracy. This idea motivated neuroscientist Theodore Berger to explore synthetic memory chips that can be installed in the human brain. While still conceptual, the project could allow people to have “perfect” memories that never forget information.

 

Autonomous Vehicles

Autonomous vehicles (AVs) use technology to partially or entirely replace the human driver in navigating a vehicle from an origin to a destination while avoiding road hazards and responding to traffic conditions.

Autonomous vehicle, automobile that employs driver assistance technologies to remove the need for a human operator. There are six stages of automation in automobiles, ranging from fully unassisted manual driving at stage 0 to fully automated self-driving cars at stage 5.

Though the terms self-driving or automated are commonly used interchangeably with autonomous, cars currently on the market are not capable of acting fully autonomously and cannot be operated without the intervention of a human driver. The industry standard is to use the term automated.

What are the 6 Levels of Autonomous Vehicles?

There are six different levels of automation and, as the levels increase, the extent of the driverless car’s independence regarding operation control increases.

At level 0, the car has no control over its operation and the human driver does all of the driving.

At level 1, the vehicle’s ADAS (advanced driver assistance system) has the ability to support the driver with either steering or accelerating and braking.

At level 2, the ADAS can oversee steering and accelerating and braking in some conditions, although the human driver is required to continue paying complete attention to the driving environment throughout the journey, while also performing the remainder of the necessary tasks.

At level 3, the ADS (advanced driving system) can perform all parts of the driving task in some conditions, but the human driver is required to be able to regain control when requested to do so by the ADS. In the remaining conditions, the human driver executes the necessary tasks.

At level 4, the vehicle’s ADS is able to perform all driving tasks independently in certain conditions in which human attention is not required.

Finally, level 5 involves full automation whereby the vehicle’s ADS is able to perform all tasks in all conditions, and no driving assistance is required from the human driver. This full automation will be enabled by the application of 5G technology, which will allow vehicles to communicate not just with one another, but also with traffic lights, signage and even the roads themselves.

Advantages

Autonomous vehicle technology may be able to provide certain advantages compared to human-driven vehicles. One such potential advantage is that they could provide increased safety on the road – vehicle crashes cause many deaths every year, and automated vehicles could potentially decrease the number of casualties as the software used in them is likely to make fewer errors in comparison to humans. A decrease in the number of accidents could also reduce traffic congestion, which is a further potential advantage posed by autonomous vehicles. Autonomous driving can also achieve this by the removal of human behaviours that cause blockages on the road, specifically stop-and-go traffic.

Another possible advantage of automated driving is that people who are not able to drive – due to factors like age and disabilities – could be able to use automated cars as more convenient transport systems.

Additional advantages that come with an autonomous car are elimination of driving fatigue and being able to sleep during overnight journeys.

Digital Twins

A digital twin is a virtual representation of an object or system designed to reflect a physical object accurately. It spans the object's lifecycle, is updated from real-time data and uses simulation, machine learning and reasoning to help make decisions.

It is composed of the following three elements:

• A physical entity in real space.
• The digital twin in software form.
• Data that links the first two elements together.

A digital twin functions as a proxy for the current state of the thing it represents. It also is unique to the thing represented, not simply generic to the category.

While many digital twins have a 2D or 3D computer-aided design (CAD) image associated with them, visual representation is not a prerequisite. For example, the digital representation, or digital model, could consist of a database, a set of equations or a spreadsheet.

The data link, often but not necessarily two-way, is what differentiates digital twins from similar concepts. This link makes it possible for users to investigate the state of the object or process by querying the data, and for actions communicated through the digital twin to take effect in its physical counterpart.

The Digital Twin Consortium, an industry association working to build the market and recommend standards, adds an important phrase to the basic definition: "synchronized at a specified frequency and fidelity."

These qualifiers refer to three key aspects of the technology:

• Synchronization ensures the digital twin and the represented entity mirror each other as closely as possible.
• The frequency, or speed, at which data gets updated in a digital twin can vary enormously, from seconds to weeks to on demand, depending on the purpose.
• Fidelity is the degree of precision and accuracy of the virtual representation and the synchronization mechanism.

Types of digital twins

Several ways of categorizing digital twins exist, but the following four categories, organized in a hierarchy, are the most common:

• Component twins (also referred to as part twins): The most basic level; it's not for simple parts like screws but for things like mechanical subassemblies.
• Asset twins (product twins): Two or more components whose interaction is represented in the digital twin.
• System twins (unit twins): Assets assembled into a complete, functioning unit.
• Process twins: Systems working together to serve a larger goal.

Some observers employ less abstract categories for digital twins and name them by the type of physical object they represent. Examples are infrastructure twins for highways and buildings, and network twins for processes such as supply chains that involve entities operating in a network.

Benefits of digital twins

Because they're virtual, digital twins can reduce the cost and risk of having to work on the physical things they represent. Further benefits include the following:

• Improved operational efficiency from having more timely data and faster, more effective production.
• More effective and less expensive R&D and reduced time to market because physical prototypes, which can be expensive and hard to modify, are replaced with virtual prototypes, which are more flexible and produce more data.
• Better product quality as digital twins help identify defects earlier in the production process.
• Longer equipment uptime from predictive maintenance enabled by analyzing individual digital twins instead of having to shut down all the equipment to isolate a problem.
• More accurate and efficient remote monitoring of facilities and equipment through integration with their digital twins.
• Improved product end-of-life processes, such as refurbishment and recycling, thanks to more accurate information about the age and contents of a product.

Challenges of digital twins

Organizations looking to develop digital twins face other daunting hurdles. Here are six of the biggest digital twin challenges:

• Data management. Data cleansing is often needed to make data from a CAD model or IoT sensor usable in a digital twin. A data lake might need to be established to manage the digital twin data and perform analytics on it. Deciding who owns the data is another problem.

• Data security. Digital twin data is timely and mission critical, but it also travels through several networks and software applications, which makes securing it at every stage challenging.

• IoT development. As the preferred data source for most of the real-time and historical data about an entity or process, IoT sensors are usually a basic requirement of digital twins. Implementing IoT presents its own challenges in network infrastructure and storage capacity, device and data security, and device management.

• System integration. Digital twins often begin life in CAD software but get more use in PLM, where they're used in post-sale services, such as performance monitoring and equipment maintenance. Numerous CAD and PLM software vendors have one-to-one integrations, but it isn't always adequate and smaller vendors might have no built-in integration.

• Supplier collaboration. The numerous participants in a supply chain must be willing to share information from their own production processes to ensure that the information in a digital twin is complete.

• Complexity. The data collected in the different software applications used by a manufacturer and its suppliers is not only voluminous, it changes often. Last-minute design changes, for example, must make it into the final version of the twin so the customer and manufacturer have the most current information.

Metaverse

The metaverse refers to the convergence of physical and virtual space accessed through computers and enabled by immersive technologies such as virtual reality, augmented reality and mixed reality. Described by proponents as the next iteration of the internet, this 3D virtual world is envisioned as a persistent, collective, shared space where digital facsimiles of ourselves, or avatars, move freely from one experience to another, taking our identities and monetary assets with us.

Visions of a parallel digital universe where humans can experience life in ways both akin to and not possible in the real world aren't new -- they predate the internet. But the concept of a blended physical and digital reality became more tangible in recent decades as technological advances -- from the near-universal adoption of mobile phones and rollout of high-speed internet to popular games such as Pokémon Go -- made the metaverse seem less far-fetched.

Why metaverse technology is still important for businesses?

Although the vision of a rapid gestation of fully-realized virtual worlds where humans work, shop and socialize from the comfort of their couches has dimmed, the metaverse isn't dead. Components of it are gaining traction as graphics and capabilities for virtual and augmented reality, bolstered by AI, rapidly improve. The development of new technology such as eye tracking, which uses sensors to monitor and record eye movements, promises to make visual experiences more engaging.

In the area known as the industrial metaverse, epitomized by the Nvidia Omniverse platform, companies are building digital twins to design and monitor physical objects. Businesses are also using virtual reality (VR) to train employees and applying augmented reality (AR) to overlay information on real-world objects, helping their employees work better.

In e-commerce, customers are clamoring for virtual products that "tie back to the physical world," according to a June 2023 McKinsey report, which stated that the market for metaverse commerce alone -- "from home and food to fitness and apparel" -- could drive "$5 trillion in value creation by 2030." A report from data-gathering company Statista pegs the metaverse market at  $74.4 billion in 2024 and predicts that by 2030, at an annual growth of rate of 38%, it will reach $507.8 billion with over 2.6 billion users.

A short history of the metaverse

The recent hype around the metaverse belies a history that dates back to the previous century when the name was introduced into the lexicon, albeit in a fictional setting.

Author Neal Stephenson coined the word metaverse in his 1992 dystopian sci-fi novel Snow Crash to describe a virtualized environment where people gained status based in part on the technical skill of their avatars. In addition to popularizing the concept of digital avatars, the novel's depiction of a networked 3D world is said to have influenced real-life web programs, including Google Earth and NASA World Wind.

Another novel that popularized the metaverse was Ernest Cline's Ready Player One, published in 2011 and later made into a movie by Steven Spielberg. It depicted a future where people escape real-world problems by entering The Oasis, a virtual world accessed using a VR headset and haptic gloves that provide tactile sensations. Such haptic feedback also became a key metaverse building block.

Fiction aside, the foundational technologies supporting an actual metaverse date back to the 1960s. The metaverse's legacy includes two other hype waves that are all but forgotten -- the first one in the early 2000s when use of the pioneering Second Life virtual community plateaued after initial growth, and the second in 2010 when the first VR headsets proved not to be the gateway to the metaverse that inventors anticipated. Both busts led to significant technological advances, though.

What does the metaverse consist of?

Today’s metaverse consists of ten layers, which fall into four categories: content and experiences, platforms, infrastructure and hardware, and enablers. Here are some examples of each:

Content and experiences

• content—developed by users, creators, and developers—enriches metaverse experiences
• applications tied to specific metaverse use cases, such as learning or events
• virtual worlds where groups can gather, interact, and create

Platforms

• platforms that facilitate access and discovery of content, experiences, and apps
• platforms designed for creators of 3-D experiences

Infrastructure and hardware

• people interface with the metaverse via devices, operating systems (OS), and accessories
• the metaverse is powered with underlying infrastructure such as cloud computing, semiconductors, networks, and more

Enablers

• security, privacy, and governance are critical for the metaverse to function well and fairly
• tools and apps that manage digital identity
• tools to access the metaverse economy via payments and monetization

Is the metaverse just a fad?

It’s not uncommon to hear the metaverse described as a flash in the pan, soon to burn out. While we don’t know how this technology will evolve, we do track great interest and involvement from customers—which indicates a fundamental change to the way people use the technology of the internet.

The change is already upon us. According to a recent McKinsey survey, more than 20 percent of the population, on a net basis, say they will spend more time exercising, working, reading, and shopping online in the future. And 10 percent of the population has already tried AR or metaverse dating, and a majority enjoyed it more than the real-life alternative.

Here are six reasons the metaverse is here to stay:

• Constant technological improvements—for instance, in computing power and large-data processes such as graphics rendering—allow ever-larger virtual worlds to exist. As discussed, the rapid adoption of 5G is enabling people to access these worlds via their mobile devices more easily. Finally, production costs of AR and VR hardware are declining, and new devices such as haptic gloves and bodysuits are increasingly coming on to the market.
• Tech companies have made huge investments to build the metaverse. Companies big and small are increasingly keen to participate.
• Gaming in the metaverse is already mainstream but expanding use cases are making the metaverse more accessible. Immersive retail, sports, and educational experiences are becoming available, as well as corporate applications like employee trainings and team collaboration. In South Korea, the city of Seoul even announced the creation of a virtual Mayor’s Office.
• Online commerce is already mainstream. Commerce in the metaverse is much the same as the buying and selling people are already used to doing—except it’s with cryptocurrency. And as cryptocurrencies grow more common, the barrier to entry will become lower and lower.
• Gen Z consumers are coming into financial maturity. They are more familiar and comfortable with virtual worlds, transactions, and goods than millennials or previous generations tend to be.
• A shift toward influencer marketing bodes well for the metaverse. A significant share of innovative and engaging experiences will probably come from these creator-users.

DevOps

DevOps combines development (Dev) and operations (Ops) to increase the efficiency, speed, and security of software development and delivery compared to traditional processes. A more nimble software development lifecycle results in a competitive advantage for businesses and their customers.

DevOps can be best explained as people working together to conceive, build and deliver secure software at top speed. DevOps practices enable software development (dev) and operations (ops) teams to accelerate delivery through automation, collaboration, fast feedback, and iterative improvement.Stemming from an Agile approach to software development, a DevOps process expands on the cross-functional approach of building and shipping applications in a faster and more iterative manner.

In adopting a DevOps development process, you are making a decision to improve the flow and value delivery of your application by encouraging a more collaborative environment at all stages of the development cycle.DevOps represents a change in mindset for IT culture. In building on top of Agile, lean practices, and systems theory, DevOps focuses on incremental development and rapid delivery of software. Success relies on the ability to create a culture of accountability, improved collaboration, empathy, and joint responsibility for business outcomes.

Although DevOps isn't a technology, DevOps environments apply common methodologies. These include the following:

• Continuous integration and continuous delivery (CI/CD) or continuous deployment tools, with an emphasis on task automation.
• Systems and tools that support DevOps adoption, including software development, real-time monitoring, incident management, resource provisioning, configuration management and collaboration platforms.
• Cloud computing, microservices and containers implemented concurrently with DevOps methodologies.

Why is DevOps important?

At its core, DevOps and DevOps practices are shown to improve software quality and development project outcomes for the enterprise. Such improvements take several forms, including the following:

• Collaboration and communication. DevOps eliminates many of the traditional organizational silos that can inhibit creativity and workflows. DevOps practices bring together developers, IT operations, business leaders and application stakeholders to ensure that the software being built is designed, developed, tested, deployed and managed in a way that is best for the business and users.
• Development outcomes. DevOps adopts a cyclical process of ongoing, iterative development. Traditional development methodologies, such as Waterfall development, codify requirements and outcomes months or years in advance of the actual development process. DevOps projects typically start small with minimal features, then systematically refine and add functionality throughout the project's lifecycle. This enables the business be more responsive to changing markets, user demands and competitive pressures.
• Product quality. The cyclical, iterative nature of DevOps ensures that products are tested continuously as existing defects are remediated and new issues are identified. Much of this is handled before each release, resulting in frequent releases that enable DevOps to deliver software with fewer bugs and better availability compared to software created with traditional paradigms.
• Deployment management. DevOps integrates software development and IT operations tasks, often enabling developers to provision, deploy and manage each software release with little, if any, intervention from IT. This frees IT staff for more strategic tasks. Deployment can take place in local infrastructure or public cloud resources, depending on the project's unique goals.

What are the benefits of DevOps?

DevOps has been widely embraced by developers and organizations because of the many improvements that it brings over traditional development paradigms. DevOps benefits include the following:

• Fewer silos and increased communications between IT groups.
• Faster time to market for software, enhancing revenue and competitive opportunities for the business.
• Rapid improvement based on user and stakeholder feedback.
• More testing results in better software quality, better deployment practices and less downtime.
• Improvement to the entire software delivery pipeline through builds, repository use, validations and deployment.
• Less menial work across the DevOps pipeline, thanks to automation.
• Streamlined development processes through increased responsibility and code ownership in development.
• Broader roles and skills.

What are the challenges of DevOps?

However, DevOps challenges abound. The DevOps paradigm poses its own complexities and changes that can be difficult to implement and manage across a busy organization. Common DevOps challenges include the following:

• Organizational and IT departmental changes, including new skills and job roles, which can be disruptive to development teams and the business.
• Expensive tools and platforms, including training and support to use them effectively.
• Development and IT tool proliferation.
• Unnecessary, fragile, poorly implemented and maintained, or unsafe automation.
• Logistics and workload difficulties scaling DevOps across multiple projects and teams.
• Riskier deployment due to a fail-fast mentality and job generalization vs. specialization where access to production systems is handled by less IT-savvy personnel.
• Regulatory compliance, especially when role separation is required.
• New bottlenecks such as automated testing or repository utilization.

In short, DevOps doesn't solve every business problem, or benefit every software development project in the same way.

Full Stack Development

Full stack developers must have knowledge of an entire technology stack, i.e., the set of technologies that are used to build an end-to-end application quickly and efficiently. For example, if they want to build an application using the MEAN stack, they should know how to work with MongoDB, Express, Angular and Node.

Full stack developers should be able to judge whether the selected technologies are the right choice for their project during the early phases. Some responsibilities of a full stack developer are to:

• Help in choosing the right technologies for the project development and testing both on the front end and the back end.
• Write clean code across the stack by following the best practices of the tools used.
• Be up to date with the latest technologies and tools to make the best technology usage decisions.

Technology Related to Full Stack Development

Front-end Development

It is the visible part of website or web application which is responsible for user experience. The user directly interacts with the front end portion of the web application or website.

Front-end Technologies:

The front end portion is built by using some languages which are discussed below:

• HTML: HTML stands for Hyper Text Markup Language. It is used to design the front end portion of web pages using markup language. HTML is the combination of Hypertext and Markup language. Hypertext defines the link between the web pages. The markup language is used to define the text documentation within tag which defines the structure of web pages.
• CSS: Cascading Style Sheets, fondly referred to as CSS, is a simply designed language intended to simplify the process of making web pages presentable. CSS allows you to apply styles to web pages. More importantly, CSS enables you to do this independent of the HTML that makes up each web page.
• JavaScript: JavaScript is a famous scripting language used to create the magic on the sites to make the site interactive for the user. It is used to enhancing the functionality of a website to running cool games and web-based software.

Front End Libraries and Frameworks

• AngularJS: AngularJs is a JavaScript open source front-end framework that is mainly used to develop single page web applications(SPAs). It is a continuously growing and expanding framework which provides better ways for developing web applications. It changes the static HTML to dynamic HTML. It is an open source project which can be freely used and changed by anyone. It extends HTML attributes with Directives, and data is bound with HTML.
• React.js: React is a declarative, efficient, and flexible JavaScript library for building user interfaces. ReactJS is an open-source, component-based front end library responsible only for the view layer of the application. It is maintained by Facebook.
• Bootstrap: Bootstrap is a free and open-source tool collection for creating responsive websites and web applications. It is the most popular HTML, CSS, and JavaScript framework for developing responsive, mobile-first web sites.
• jQuery: jQuery is an open source JavaScript library that simplifies the interactions between an HTML/CSS document, or more precisely the Document Object Model (DOM), and JavaScript. Elaborating the terms, jQuery simplifies HTML document traversing and manipulation, browser event handling, DOM animations, Ajax interactions, and cross-browser JavaScript development.
• SASS: It is the most reliable, mature and robust CSS extension language. It is used to extend the functionality of an existing CSS of a site including everything from variables, inheritance, and nesting with ease.

Back-end Technologies

It refers to the server-side development of web application or website with a primary focus on how the website works. It is responsible for managing the database through queries and APIs by client-side commands. This type of website mainly consists of three parts front end, back end, and database.

The back end portion is built by using some libraries, frameworks, and languages which are discussed below:

• PHP: PHP is a server-side scripting language designed specifically for web development. Since, PHP code executed on server side so it is called server side scripting language.
• C++ It is a general purpose programming language and widely used now a days for competitive programming. It is also used as backend language.
• Java: Java is one of the most popular and widely used programming language and platform. It is highly scalable. Java components are easily available.
• Python: Python is a programming language that lets you work quickly and integrate systems more efficiently.
• Node.js: Node.js is an open source and cross-platform runtime environment for executing JavaScript code outside of a browser. You need to remember that NodeJS is not a framework and it’s not a programming language. Most of the people are confused and understand it’s a framework or a programming language. We often use Node.js for building back-end services like APIs like Web App or Mobile App. It’s used in production by large companies such as Paypal, Uber, Netflix, Walmart and so on.

Front end vs back end vs full stack

Applications that require higher scalability and more complex workflows require broader skill sets and collaboration across teams. For example, the front end may be handled by the UI team, and the back end by another team. In some organizations, individuals will be required to work on both the front-end and back-end implementation of a feature. This is where full stack developers would come into play.

Front-end developers

These developers handle the UI of a web application (or website)—for example, visual effects, frames, navigation, and forms. They focus mainly on user experience and use HTML, CSS, and JavaScript as programming languages.

Back-end developers

They deal with the business logic, security, performance, scalability, and handling request-response of the application. They create or use frameworks to design the core application workflows and use technologies like JavaScript, Python, Java, and .NET.

Full stack developers

They are responsible for coding end-to-end workflows by using both front-end and back-end technologies. MERN stack and MEAN stack are examples of JavaScript-based technology stacks that full stack developers can use to build end-to-end applications.

Cyber Security

Cyber security is the practice of defending computers, servers, mobile devices, electronic systems, networks, and data from malicious attacks. It's also known as information technology security or electronic information security. The term applies in a variety of contexts, from business to mobile computing, and can be divided into a few common categories.

• Network security is the practice of securing a computer network from intruders, whether targeted attackers or opportunistic malware.
• Application security focuses on keeping software and devices free of threats. A compromised application could provide access to the data its designed to protect. Successful security begins in the design stage, well before a program or device is deployed.
• Information security protects the integrity and privacy of data, both in storage and in transit.
• Operational security includes the processes and decisions for handling and protecting data assets. The permissions users have when accessing a network and the procedures that determine how and where data may be stored or shared all fall under this umbrella.
• Disaster recovery and business continuity define how an organization responds to a cyber-security incident or any other event that causes the loss of operations or data. Disaster recovery policies dictate how the organization restores its operations and information to return to the same operating capacity as before the event. Business continuity is the plan the organization falls back on while trying to operate without certain resources.
• End-user education addresses the most unpredictable cyber-security factor: people. Anyone can accidentally introduce a virus to an otherwise secure system by failing to follow good security practices. Teaching users to delete suspicious email attachments, not plug in unidentified USB drives, and various other important lessons is vital for the security of any organization.

Types of cybersecurity threats

Phishing

Phishing is the practice of sending fraudulent emails that resemble emails from reputable sources. The aim is to steal sensitive data like credit card numbers and login information. It’s the most common type of cyber attack. You can help protect yourself through education or a technology solution that filters malicious emails.

Social engineering

Social engineering is a tactic that adversaries use to trick you into revealing sensitive information. They can solicit a monetary payment or gain access to your confidential data. Social engineering can be combined with any of the threats listed above to make you more likely to click on links, download malware, or trust a malicious source.

Ransomware

Ransomware is a type of malicious software. It is designed to extort money by blocking access to files or the computer system until the ransom is paid. Paying the ransom does not guarantee that the files will be recovered or the system restored.

Malware

Malware is a type of software designed to gain unauthorized access or to cause damage to a computer. 

Cyber safety tips - protect yourself against cyberattacks

How can businesses and individuals guard against cyber threats? Here are our top cyber safety tips:

• Update your software and operating system: This means you benefit from the latest security patches.
• Use anti-virus software: Security solutions like Kaspersky Total Security will detect and removes threats. Keep your software updated for the best level of protection.
• Use strong passwords: Ensure your passwords are not easily guessable.
• Do not open email attachments from unknown senders: These could be infected with malware.
• Do not click on links in emails from unknown senders or unfamiliar websites: This is a common way that malware is spread.
• Avoid using unsecure WiFi networks in public places: Unsecure networks leave you vulnerable to man-in-the-middle attacks.

5G

5G is the fifth generation of cellular technology. It is designed to increase speed, reduce latency, and improve flexibility of wireless services.

5G technology has a theoretical peak speed of 20 Gbps, while the peak speed of 4G is only 1 Gbps. 5G also promises lower latency, which can improve the performance of business applications as well as other digital experiences (such as online gaming, videoconferencing, and self-driving cars). 

While earlier generations of cellular technology (such as 4G LTE) focused on ensuring connectivity, 5G takes connectivity to the next level by delivering connected experiences from the cloud to clients. 5G networks are virtualized and software-driven, and they exploit cloud technologies.

The 5G network will also simplify mobility, with seamless open roaming capabilities between cellular and Wi-Fi access. Mobile users can stay connected as they move between outdoor wireless connections and wireless networks inside buildings without user intervention or the need for users to reauthenticate. 

The new Wi-Fi 6 wireless standard (also known as 802.11ax) shares traits with 5G, including improved performance. Wi-Fi 6 radios can be placed where users need them to provide better geographical coverage and lower cost. Underlying these Wi-Fi 6 radios is a software-based network with advanced automation.

5G technology should improve connectivity in underserved rural areas and in cities where demand can outstrip today's capacity with 4G technology. New 5G networks will also have a dense, distributed-access architecture and move data processing closer to the edge and the users to enable faster data processing.

What makes 5G different?

5G runs on the same radio frequencies that are currently being used for your smartphone, on Wi-Fi networks and in satellite communications, but it enables technology to go a lot further. 

Beyond being able to download a full-length HD movie to your phone in seconds (even from a crowded stadium), 5G is really about connecting things everywhere – reliably, without lag – so people can measure, understand and manage things in real time. 

This has enormous potential – and together, we will take it to the next level. 

5G evolution

Things have changed a lot since the first generation of mobile technology.

• The 1G era was defined by briefcase-sized phones and short conversations between a relatively small number of professional people.
• In the lead up to 2G, the demand for mobile services grew and never slowed down.
• Phones that could fit in your pocket, SMS  and mobile internet access were hallmarks of the 3G world.
• Thanks to 4G, we have smartphones, app stores and YouTube.
• Now, 5G is completely reshaping both our professional and personal lives by enabling new use cases like connective vehicles, Augmented Reality and enhanced video and gaming.

When will 5G be available and how will it expand?

5G service is already available in some areas in various countries. These early-generation 5G services are called 5G non-standalone (5G NSA). This technology is a 5G radio that builds on existing 4G LTE network infrastructure. 5G NSA will be faster than 4G LTE. But the high-speed, low-latency 5G technology the industry has focused on is 5G standalone (5G SA). It should start becoming available by 2020 and be commonly available by 2022.

What is the real-world impact of 5G technology?

5G technology will not only usher in a new era of improved network performance and speed but also new connected experiences for users.

In healthcare, 5G technology and Wi-Fi 6 connectivity will enable patients to be monitored via connected devices that constantly deliver data on key health indicators, such as heart rate and blood pressure. In the auto industry, 5G combined with ML-driven algorithms will provide information on traffic, accidents, and more; vehicles will be able to share information with other vehicles and entities on roadways, such as traffic lights. These are just two industry applications of 5G technology that can enable better, safer experiences for users.

Internet of Things (IoT)

The internet of things, or IoT, is a network of interrelated devices that connect and exchange data with other IoT devices and the cloud. IoT devices are typically embedded with technology such as sensors and software and can include mechanical and digital machines and consumer objects.

Increasingly, organizations in a variety of industries are using IoT to operate more efficiently, deliver enhanced customer service, improve decision-making and increase the value of the business.

With IoT, data is transferable over a network without requiring human-to-human or human-to-computer interactions.

A thing in the internet of things can be a person with a heart monitor implant, a farm animal with a biochip transponder, an automobile that has built-in sensors to alert the driver when tire pressure is low, or any other natural or man-made object that can be assigned an Internet Protocol address and is able to transfer data over a network.

How does IoT work?

An IoT ecosystem consists of web-enabled smart devices that use embedded systems -- such as processors, sensors and communication hardware -- to collect, send and act on data they acquire from their environments.

IoT devices share the sensor data they collect by connecting to an IoT gateway, which acts as a central hub where IoT devices can send data. Before the data is shared, it can also be sent to an edge device where that data is analyzed locally. Analyzing data locally reduces the volume of data sent to the cloud, which minimizes bandwidth consumption.

Sometimes, these devices communicate with other related devices and act on the information they get from one another. The devices do most of the work without human intervention, although people can interact with the devices -- for example, to set them up, give them instructions or access the data.

The connectivity, networking and communication protocols used with these web-enabled devices largely depend on the specific IoT applications deployed.

IoT can also use artificial intelligence and machine learning to aid in making data collection processes easier and more dynamic.

Why is Internet of Things (IoT) so important?

Over the past few years, IoT has become one of the most important technologies of the 21st century. Now that we can connect everyday objects—kitchen appliances, cars, thermostats, baby monitors—to the internet via embedded devices, seamless communication is possible between people, processes, and things.

By means of low-cost computing, the cloud, big data, analytics, and mobile technologies, physical things can share and collect data with minimal human intervention. In this hyperconnected world, digital systems can record, monitor, and adjust each interaction between connected things. The physical world meets the digital world—and they cooperate.

What technologies have made IoT possible?

While the idea of IoT has been in existence for a long time, a collection of recent advances in a number of different technologies has made it practical.

• Access to low-cost, low-power sensor technology. Affordable and reliable sensors are making IoT technology possible for more manufacturers.
• Connectivity. A host of network protocols for the internet has made it easy to connect sensors to the cloud and to other “things” for efficient data transfer.
• Cloud computing platforms. The increase in the availability of cloud platforms enables both businesses and consumers to access the infrastructure they need to scale up without actually having to manage it all.
• Machine learning and analytics. With advances in machine learning and analytics, along with access to varied and vast amounts of data stored in the cloud, businesses can gather insights faster and more easily. The emergence of these allied technologies continues to push the boundaries of IoT and the data produced by IoT also feeds these technologies.
• Conversational artificial intelligence (AI). Advances in neural networks have brought natural-language processing (NLP) to IoT devices (such as digital personal assistants Alexa, Cortana, and Siri) and made them appealing, affordable, and viable for home use.

What are the pros and cons of IoT?

Some of the advantages of IoT include the following:

• Enables access to information from anywhere at any time on any device.
• Improves communication between connected electronic devices.
• Enables the transfer of data packets over a connected network, which can save time and money.
• Collects large amounts of data from multiple devices, aiding both users and manufacturers.
• Analyzes data at the edge, reducing the amount of data that needs to be sent to the cloud.
• Automates tasks to improve the quality of a business's services and reduces the need for human intervention.
• Enables healthcare patients to be cared for continually and more effectively.

Some disadvantages of IoT include the following:

• Increases the attack surface as the number of connected devices grows. As more information is shared between devices, the potential for a hacker to steal confidential information increases.
• Makes device management challenging as the number of IoT devices increases. Organizations might eventually have to deal with a massive number of IoT devices, and collecting and managing the data from all those devices could be challenging.
• Has the potential to corrupt other connected devices if there's a bug in the system.
• Increases compatibility issues between devices, as there's no international standard of compatibility for IoT. This makes it difficult for devices from different manufacturers to communicate with each other.

Blockchain

Blockchain technology is an advanced database mechanism that allows transparent information sharing within a business network. A blockchain database stores data in blocks that are linked together in a chain. The data is chronologically consistent because you cannot delete or modify the chain without consensus from the network. As a result, you can use blockchain technology to create an unalterable or immutable ledger for tracking orders, payments, accounts, and other transactions. The system has built-in mechanisms that prevent unauthorized transaction entries and create consistency in the shared view of these transactions.

Why is blockchain important?

Business runs on information. The faster information is received and the more accurate it is, the better. Blockchain is ideal for delivering that information because it provides immediate, shared, and observable information that is stored on an immutable ledger that only permissioned network members can access. A blockchain network can track orders, payments, accounts, production and much more. And because members share a single view of the truth, you can see all details of a transaction end to end, giving you greater confidence, and new efficiencies and opportunities.

How do different industries use blockchain?

Blockchain is an emerging technology that is being adopted in innovative manner by various industries. We describe some use cases in different industries in the following subsections:

Energy

Energy companies use blockchain technology to create peer-to-peer energy trading platforms and streamline access to renewable energy. For example, consider these uses:

• Blockchain-based energy companies have created a trading platform for the sale of electricity between individuals. Homeowners with solar panels use this platform to sell their excess solar energy to neighbors. The process is largely automated: smart meters create transactions, and blockchain records them.
• With blockchain-based crowd funding initiatives, users can sponsor and own solar panels in communities that lack energy access. Sponsors might also receive rent for these communities once the solar panels are constructed.

Finance

Traditional financial systems, like banks and stock exchanges, use blockchain services to manage online payments, accounts, and market trading. For example, Singapore Exchange Limited, an investment holding company that provides financial trading services throughout Asia, uses blockchain technology to build a more efficient interbank payment account. By adopting blockchain, they solved several challenges, including batch processing and manual reconciliation of several thousand financial transactions.

Media and entertainment

Companies in media and entertainment use blockchain systems to manage copyright data. Copyright verification is critical for the fair compensation of artists. It takes multiple transactions to record the sale or transfer of copyright content. Sony Music Entertainment Japan uses blockchain services to make digital rights management more efficient. They have successfully used blockchain strategy to improve productivity and reduce costs in copyright processing.

Retail

Retail companies use blockchain to track the movement of goods between suppliers and buyers. For example, Amazon retail has filed a patent for a distributed ledger technology system that will use blockchain technology to verify that all goods sold on the platform are authentic. Amazon sellers can map their global supply chains by allowing participants such as manufacturers, couriers, distributors, end users, and secondary users to add events to the ledger after registering with a certificate authority. 

Benefits of Blockchain

Having a cryptographically secure permanent record comes with perks:

More Security

Cryptography and hashing algorithms ensure that only authorized users are able to unlock information meant for them, and that the data stored on the blockchain cannot be manipulated in any form. Consensus mechanisms, such as proof of work or proof of stake, further enhance security by requiring network participants to agree on the validity of transactions before they are added to the blockchain. Additionally, blockchains operate on a distributed system, where data is stored across multiple nodes rather than one central location — reducing the risk of a single point of failure.

Improved Accuracy

By providing a fully transparent, single-source-of-truth ledger, where transactions are recorded in a chronological and immutable manner, the potential for error or discrepancy drops when compared to centralized databases or manual record-keeping processes. Transactions are objectively authorized by a consensus algorithm and, unless a blockchain is made private, all transactions can be independently verified by users.

Higher Efficiency

Aside from saving paper, blockchain enables reliable cross-team communication, reduces bottlenecks and errors while streamlining overall operations. By eliminating intermediaries and automating verification processes — done via smart contracts — blockchain enjoys reduced transaction costs, timely processing times and optimized data integrity.

Challenges of Blockchain

Although this emerging technology may be tamper proof, it isn’t faultless. Below are some of the biggest obstacles blockchain faces today.

Transaction Limitations

As blockchain networks grow in popularity and usage, they face bottlenecks in processing transactions quickly and cost-effectively. This limitation hampers the widespread adoption of blockchain for mainstream applications, as networks struggle to handle high throughput volumes, leading to congestion and increased transaction fees.

Energy Consumption

The computational power required for certain functions — like Bitcoin’s proof-of-work consensus mechanism — consumes vast amounts of electricity, raising concerns around environmental impact and high operating costs. Addressing this challenge requires exploring alternative consensus mechanisms, such as proof of stake, which consume significantly less energy while maintaining network security and decentralization.

Scalability Issues

As it is now, every node of a blockchain network stores a copy of the entire data chain and processes every transaction. This requires a certain level of computational power, resulting in slow, congested networks and lagged processing times especially during high-traffic periods. Scalability issues arise due to limitations in block size, block processing times and resource-intensive consensus mechanisms. 

Regulation Concerns

Governments and regulators are still working to make sense of blockchain — more specifically, how certain laws should be updated to properly address decentralization. While some governments are actively spearheading its adoption and others elect to wait-and-see, lingering regulatory and legal concerns hinder blockchain’s market appeal, stalling its technical development.

What are the types of blockchain networks?

There are four main types of decentralized or distributed networks in the blockchain:

Public blockchain networks

Public blockchains are permissionless and allow everyone to join them. All members of the blockchain have equal rights to read, edit, and validate the blockchain. People primarily use public blockchains to exchange and mine cryptocurrencies like Bitcoin, Ethereum, and Litecoin. 

Private blockchain networks

A single organization controls private blockchains, also called managed blockchains. The authority determines who can be a member and what rights they have in the network. Private blockchains are only partially decentralized because they have access restrictions. Ripple, a digital currency exchange network for businesses, is an example of a private blockchain.

Hybrid blockchain networks

Hybrid blockchains combine elements from both private and public networks. Companies can set up private, permission-based systems alongside a public system. In this way, they control access to specific data stored in the blockchain while keeping the rest of the data public. They use smart contracts to allow public members to check if private transactions have been completed. For example, hybrid blockchains can grant public access to digital currency while keeping bank-owned currency private.

Consortium blockchain networks

A group of organizations governs consortium blockchain networks. Preselected organizations share the responsibility of maintaining the blockchain and determining data access rights. Industries in which many organizations have common goals and benefit from shared responsibility often prefer consortium blockchain networks. For example, the Global Shipping Business Network Consortium is a not-for-profit blockchain consortium that aims to digitize the shipping industry and increase collaboration between maritime industry operators.

Virtual reality and Augmented reality

We spend a lot of time looking at screens these days. Computers, smartphones, and televisions have all become a big part of our lives; they're how we get a lot of our news, use social media, watch movies, and much more. Virtual reality (VR) and augmented reality (AR) are two technologies that are changing the way we use screens, creating new and exciting interactive experiences.

Virtual reality uses a headset to place you in a computer-generated world that you can explore. Augmented reality, on the other hand, is a bit different. Instead of transporting you to a virtual world, it takes digital images and layers them on the real world around you through the use of either a clear visor or smartphone.

With virtual reality, you could explore an underwater environment. With augmented reality, you could see fish swimming through the world around you.

Virtual reality

Virtual reality immerses you in a virtual world through the use of a headset with some type of screen displaying a virtual environment. These headsets also use a technology called head tracking, which allows you to look around the environment by physically moving your head. The display will follow whichever direction you move, giving you a 360-degree view of the virtual environment.

Augmented reality

Augmented reality allows you to see the world around you with digital images layered on top of it. There are currently a couple of AR headsets available, including the Microsoft HoloLens and the Magic Leap. However, they are currently more expensive than VR headsets, and are marketed primarily to businesses.

Augmented reality can also be used on devices like smartphones and laptops without the use of a headset. There are a variety of apps that use AR, including some that allow you to translate text using your camera, identify stars in the sky, and even see how your garden would look with different plants. You may have even previously used AR without realizing it, while playing a game like Pokemon Go or using filters on Snapchat.

The differences between AR and VR

While both technologies involve simulated reality, AR and VR rely on different underlying components and generally serve different audiences.

In virtual reality, the user almost always wears an eye-covering headset and headphones to completely replace the real world with the virtual one. The idea of VR is to eliminate the real world as much as possible and insulate the user from it. Once inside, the VR universe can be coded to provide just about anything, ranging from a light saber battle with Darth Vader to a realistic (yet wholly invented) recreation of earth. While VR has some business applications in product design, training, architecture and retail, today the majority of VR applications are built around entertainment, especially gaming.

Augmented reality, on the other hand, integrates the simulated world with the real one. In most applications the user relies on a smartphone or tablet screen to accomplish this, aiming the phone’s camera at a point of interest, and generating a live-streaming video of that scene on the screen. The screen is then overlaid with helpful information, which includes implementations such as repair instructions, navigation information or diagnostic data.

However, AR can also be used in entertainment applications. The mobile game Pokemon Go, in which players attempt to capture virtual creatures while moving around in the real world, is a classic example.

Examples of Augmented Reality and Virtual Reality

Augmented reality entails abundant — and growing — use cases. Here are some actual applications you can engage with today.

• Ikea Place is a mobile app that allows you to envision Ikea furniture in your own home, by overlaying a 3D representation of the piece atop a live video stream of your room.
• YouCam Makeup lets users virtually try on real-life cosmetics via a living selfie.
• Repair technicians can don a headset that walks them through the steps of fixing or maintaining a broken piece of equipment, diagramming exactly where each part goes and the order in which to do things.
• Various sports are relying on augmented reality to provide real-time statistics and improve physical training for athletes.

Beyond gaming and other entertainment cases, some business examples of virtual reality include:

• Architects are using VR to design homes — and let clients “walk through” before the foundation has ever been laid.
• Automobiles and other vehicles are increasingly being designed in VR.
• Firefighters, soldiers and other workers in hazardous environments are using VR to train without putting themselves at risk.

Challenges for Business and Technology

Technology challenges

• Limited mobile processing capability – Mobile handsets have limited processing power, but tethering a user to a desktop or server isn’t realistic. Either mobile processing power will have to expand, or the work will have to be offloaded to the cloud.
• Limited mobile bandwidth – While cloud-based processing offers a compelling potential solution to the mobile processing bottleneck, mobile phone bandwidth is still too slow in most places to offer the necessary real-time video processing. This will likely change as mobile bandwidth improves.
• Complex development – Designing an AR or VR application is costly and complicated. Development tools will need to become more user-friendly to make these technologies accessible to programmers.

Business challenges

• VR hardware’s inconvenience – Putting on a virtual reality headset and clearing a room often detracts from the user experience. VR input devices, in the form of modified gaming controllers, can also often be unintuitive, with a steep learning curve.
• Building a business model – Outside of video gaming, many AR and VR applications remain in early stages of development with unproven viability in the business world.
• Security and privacy issues – The backlash over the original Google Glass proved that the mainstream remains skeptical about the proliferation of cameras and their privacy implications. How are video feeds secured, and are copies stored somewhere?