Big data technology has led to some other major technological breakthroughs. We have talked in detail about applications of big data in marketing, financial management and even the criminal justice system. However, there are other benefits of big data that get less attention, even though they are also remarkable.
One of the newer applications of big data is with NFTs. The entire concept of NFTs is actually predicated on big data.
How Big Data is Creating a Booming Market for NFTs
Non-fungible tokens – or simply NFTs – have gained global recognition in recent years. They have been responsible for the significant changes across various sectors, including art and finance. As a result, many have posited the possibility of every part of society being influenced eventually by NFTs.
This might sound overblown, yet it is not. The past few years have demonstrated the potential effects that NFTs can have – being one of the most significant innovations – in sports, fashion, and tech, among others. Since NFTs became a norm in 2021, it has attracted all sorts of media hype and drama.
NFTs have been made possible due to advances in big data. Praphul Chandra, Founder and CEOof Koinearth wrote an article for The World Economic Forum titled If data is the new oil, then enterprise NFTs are the tankers. Here’s why. Chandra pointed out that there are many wonderful benefits of treating data as an asset, but there are logistical issues that make that difficult. NFTs have helped mitigate many of these challenges.
However, it can prove difficult to fully fathom the dramatic rise these tokens have enjoyed in recent times. To lend you a hand, this comprehensive guide will provide an insight into what NFTs are and how they work. Let’s dive in!
What are NFTs?
Non-fungible tokens are a unit of data on a blockchain network, which can offer stable proof of ownership when linked to a physical or digital asset. These tokens often contain data that can be connected to songs, images, avatars, and videos, among others.
Despite this, they can also help owners gain exclusive access to digital or live events, as well as being connected to physical assets, such as cars and more. Big data creates opportunities to make the most of NFTs. With this in mind, it is safe to affirm that NFTs can enable users not just to create items but buy and sell assets safely on blockchain technology.
However, it must be stressed that you can’t purchase intellectual property rights or copyright of the underlying asset, except stated otherwise. However, trading NFTs is not so straightforward. The next section will explain better.
Creating, Buying, Selling and Valuing NFTs with Data Science
As stated earlier, dabbling into NFTs is not so simple. Fortunately, data science has made it a lot easier to take advantage of them, as Dr. Stylianos Kampakis discusses in this post in The Data Scientist. To purchase one, cryptocurrencies are used to fund an NFT account while using a crypto wallet to safeguard the data when an NFT is bought.
You are also going to have to know how to value them. This is where data science becomes especially valuable.
There are various tools that can enable traders to make the most of NFT derivatives, including NFT Profit. The steps below will provide more insight into how to create and trade NFTs:
Acquire a crypto wallet
Crypto wallets are used to store digital assets. You can choose between a software or hardware wallet. However, while the former is more suitable for short-term trades, the latter is a safer means of storing and transferring valuable assets.
The cryptocurrency sector is benefiting tremendously from data analytics. One of the benefits is that analytics helps investors time their purchases and select the best cryptocurrencies.
You will come across several NFT marketplaces that allow the use of traditional methods of payment. These include MakersPlace and Nitty Gateway. A lot of data analytics tools help you assess the quality of different marketplaces. You should also look at reviews of them to find the best. On the other hand, OpenSea and SuperRare only support cryptocurrencies. ETH is the best crypto for NFT transactions since most NFTs are part of the Ethereum blockchain at a high level.
Choose a Marketplace
Before choosing a marketplace, consider whether you need to mint a single NFT or a collection of these tokens in a batch. OpenSea represents one of the best places for the latter. You can also consider Rarible and LooksRare as well. Besides this, you should know that minting is associated with initial costs, which could be in the form of a transaction fee in some marketplaces. You should also acquaint yourself with royalty splits.
Mint a new NFT
Ensure that you must hold the intellectual property rights and copyright for the item you wish to mint before picking an item. This process is crucial since you can easily end up having legal issues if you create tokens by using assets that are not yours. You can create an account at your preferred marketplace. Then, you can start minting.
It is worth noting that some NFTs might only be purchased on the open market. However, you can buy your favorite NFTs immediately or, in some cases, make a bid on your preferred NFTs and wait until the closing of the auction.
Value Your NFTs
Data analytics can be used to value NFTs. Kampakis and one of his students developed a hedonic regression method with one of his students to value CryptoPunk. He points out that the same data analytics approach can be very useful or valuing other NFTs.
Big Data Has Made NFTs Valuable New Commodities
NFTs have grown in recognition across the far reaches of society. Big data has played a huge role in this sudden new market. NFTs serve so many purposes as a crucial modern innovation. However, creating, buying, and selling these tokens are not so simple. You must acquaint yourself to prevent getting into trouble during NFT transactions. The good news is that data analytics makes all of these steps easier.
The automotive industry is struggling to meet demand as a growing supply chain shortage cripples the global economy. Chip shortages, among other components, have fueled a steep increase in car prices, as much as USD$900 above the manufacturer-suggested retail price (MSRP) for non-luxury cars and USD$1,300 above MSRP for luxury ones.
Market analysts predict the supply chain to normalize in the third quarter of 2022, which is only a few months away as of this publication. In light of this, industry experts are using analytics to streamline production and minimize waste to address these challenges. Here’s an in-depth look into analytics and its role in the automotive sector.
The Fundamentals
It’s important to know that analytics is integral to every facet of car production, not only in supply chain optimization (more on that later). Everything from knowing consumer trends to ensuring a steady stream of resources requires data—lots of it. The cars themselves are valuable sources of data, an estimated 25 GB that can help manufacturers understand trends more.
Each aspect of the automotive workflow has its respective form of analytics. However, making the most out of these analytic processes entails integrating them into cross-value chain analytics, narrowing them down to four fundamentals:
Customer Behavior – Gauging the potential of various customer groups, catering to new customers while keeping old ones, and expanding the overall customer experienceMarketing Management – Measuring the impact of marketing campaigns on sales and other trends and developing auto repair advertising ideas and other campaignsPredictive Quality – Identifying possible defects and other issues in manufacturing lots, examining the need to issue recalls, and assessing warranty claimsSupply Chain Optimization – Anticipating trends to adjust the supply of specific parts and accessories and evaluating custom orders
Each of these is crucial in its own right, but turning tons of raw materials into a reliable and tech-laden vehicle begins with the supply chain. Car brands can study the market for trends and tailor their marketing campaigns based on them, but they won’t matter without the means to make the car that will deliver to customers.
Tackling The Issues
The lifeblood of any business or industry is its logistics, and car manufacturers are no different. Without the necessary components delivered to factories and service centers, manufacturing and repair activities can slow down, if not grind, to a stop.
Understanding how analytics will help curb the effects of supply chain issues in the automotive industry involves understanding the problems themselves. Industry experts have identified four critical issues that analytics need to address.
Supply Chain Visibility
A typical car needs an estimated 30,000 individual components (the exact number may vary depending on the make and model). It only takes one missing part for the whole vehicle to get stuck in production limbo.
Unfortunately, many experts agree that the automotive industry struggles with supply chain visibility. Difficulties in making orders and tracking their deliveries can hamper manufacturing. Resolving them becomes even more crucial for carmakers shifting to just-in-time manufacturing practices.
Recommendations include integrating suppliers into a common analytics platform to help identify delays and other problems. Analytics hardware and software that uses Internet of Things (IoT) technology can assist with real-time tracking.
Risk Management
The automotive industry faces numerous risks, from missed production goals to mishaps on the factory floor. Supply delays are no less significant for reasons explained earlier. Car makers should have contingencies, such as setting up secondary suppliers.
One standard method for mitigating risks is through a Failure Mode and Effect Analysis (FMEA). This method outlines how a business may suffer from failure and how it can affect the company in the short or long term. Businesses should conduct FMEA early in the design phase to have enough time to react to risks accordingly.
Quality Control
Regardless of the source of components, effective quality control is a must. Recalls are evidence that manufacturers may have overlooked something along the workflow and have to spend to replace the faulty parts.
Analytics can provide sufficient data to conduct internal and third-party audits of parts quality. Although internal quality control may be easy, getting suppliers to adhere to quality standards can be tricky. In this case, it all boils down to effective dialogue between the manufacturer and its suppliers.
External Factors
Risk factors like economic crashes, natural disasters, and—in this status quo—global health crises can hinder operations. While preventing them is beyond a manufacturer’s control, reducing their impact is well within.
Conclusion
As explained in this piece, analytics isn’t only integral for resolving the automotive industry’s many issues with its supply chain. It’s more or less being implemented significantly, helping manufacturers take careful steps as they build their products. It’s safe to say that analytics may hold the key to the more affordable and reliable cars of tomorrow.
Artificial intelligence technology has many implications for the fields of marketing and multimedia. Many marketers are using AI to create higher quality video content.
What Are the Benefits of AI Technology with Video Editing?
Video content is becoming more and more popular these days. According to Wyzowl, people now watch approximately 19 hours of online video per week. To create attractive and engaging videos, you need to have the right software at hand. However, editing videos is still a ton of work. The good news is that it can be done more efficiently with tools with sophisticated AI capabilities.
Video editing is much more efficient with AI. It used to take video editors between four or five hours to edit a single minute of footage. AI technology helps streamline the process and can save hundreds of hours of editing an hour long documentary. You can take advantage of powerful new features with AI. This includes using powerful filters and mockups that were previously unavailable to video editors before AI was available.You can automate many tasks with AI.
If you’re looking for the best video editing software, here are 5 different solutions you can try. They all use complex AI algorithms to edit videos more effectively.
While some of these video editors have a paid version with more features, there are plenty of great options that are completely free. So whether you’re a beginner or an experienced video creator, we have something for you! They all use machine learning technology to create quality video content.
In 2022, video editors are more popular than ever. Thanks to the advances in technology, you’re able to create truly stunning videos. From movies to TV shows, music videos to commercials, video editors are in high demand. And it’s no wonder why. A good video editor can take a boring concept and make it into something truly special. You can add effects, transitions, and voice-overs that bring the video to life and make it more engaging to your audience.
Best Free Video Editing Software for Desktop
If you’re looking to edit your promotional videos on a budget, check out our list of the 5 best free or partially free AI-based video editing software for 2022.
1. Adobe Premiere Rush
Adobe Premiere Rush is a great video editor app that has several advantages in today’s world of instant video sharing on social networks. It makes sense to include it as part of the Creative Cloud subscription ($52.99 per month), but a monthly fee of $9.99 seems excessive. If you believe that Rush may help solve your issues, try out the free Starter Plan, which provides three exports and 2 GB of server space.
Here are some of the selling points of this AI-based video editing tool.
Pros:
Simple and user-friendly interfaceExport in 1080pUnlimited desktop exportsChange video color, size, and positionAdd transitions, pan, zoom, and PiP effectsAutomatic reframingThe ability to change the aspect ratio of your video
Cons:
Lack of features in the free version of the softwareThe paid subscription is a bit pricey compared to other options
2. DaVinci Resolve
DaVinci Resolve is a powerful online video editor with a learning curve that can help you to create great clips by leveraging the power of artificial intelligence. The free version has no watermark and can export up to 1080p video. If you want to know how to edit videos, DaVinci can help you with that.
Pros:
A great set of features for experienced usersAdvanced color correction that is relatively easy to execute when compared to other alternatives.High-quality audio processing with FairlightExtensive resources to learn how to competently use the software as easy as possibleNo watermark on exported videos and audioFree or $299 one-time payment for the studio version
Cons:
The learning curve may be too steep for some usersThe free version lacks some advanced features that are only available in the paid versionWorking with multiple audio plugins with new windows for each can get a bit clutteringSome of the keyboard shortcuts for executing commonly used functions can be confusing, and it takes time to get used to them
3. Movavi Video Editor
Movavi Video Editor is a comprehensive AI-based video editing software solution that offers a wide range of features and tools for both novice and experienced users. The software is easy to use and navigate, which makes it simple to find the features you need. In addition, Movavi offers many tutorials and training resources to help you get the most out of the software.
Pros:
Intuitive interfaceThe ability to add transitions, titles, and stickersBuilt-in library of stock backgrounds, music, soundsHollywood-style effects and filtersFree or $59.95 one-time payment for the paid version
Cons:
The free version has a watermark which can be annoyingThe software lacks some professional features for trimming clips
4. Avidemux
Avidemux is a great AI-driven video editing app for both beginners and experienced users. It’s a free program that can be used on different operating systems. It has a simple interface that is easy to learn and use. Avidemux also has many features, including the ability to cut and join video files, change the video codec, add watermarks and subtitles, and more.
In addition, Avidemux is highly customizable, allowing users to change the interface layout, keyboard shortcuts, and many other settings. Here are the pros and cons of this AI video editing tool.
Pros:
Simple interface that is easy to learnA wide range of featuresFree or $19.95 one-time payment for the paid version
Cons:
The free version can only export up to 720p video, which may not be enough for some usersIntricate and confusing cut featuresBatch process unavailable
5. Blender
Blender is a powerful video editing software that has been gaining popularity in recent years. While it lacks some of the bells and whistles of more expensive editors, it more than makes up for it in terms of features and flexibility. One of the things that set Blender apart is its node-based interface, which allows for complex effects and compositing. This makes it ideal for artists and graphic designers who want to add an extra level of polish to their work. In addition, the software is also very capable when it comes to more traditional forms of video editing, such as trimming and cutting.
Cons:
A powerful set of features for experienced usersNo watermark on exported videos and audioFree or $129 one-time payment for the paid version
Cons:
The learning curve may be too steep for some usersThe free version lacks some advanced features that are only available in the paid version
6. VSDC Free Video Editor
VSDC Free Video Editor is a great choice for anyone looking for a powerful yet easy-to-use video editor that uses the best AI algorithms to create quality videos. The app is packed with features, yet it’s still easy to use. It supports many popular video formats, and it can be used to create videos of any length. In addition, VSDC offers a variety of ways to edit your videos, including trimmed sequences, picture-in-picture arrangements, and split-screen effects. Whether you want to create a short video for social media or a longer one for YouTube, VSDC Free Video Editor is worth checking out.
Pros:
A powerful set of features for experienced users4K and HD SupportBuilt-in DVD burning toolColor blending and filtersSpecific multimedia devices creationDesktop video captureNon-linear video editingNo watermark on exported videos and audioFree or $19.99 one-time payment for the paid version
Cons:
The learning curve may be too steep for some usersFor professional tools like motion tracking, stabilization, and beat syncing you need to switch to VSDC ProAudio waveform and hardware acceleration features aren’t available in the free version
AI-Based Tools Make Video Editing Easier than Ever
AI technology has made video editing easier than ever. However, you have to use the right AI-based video editing tools.
We all work in different areas and need video editors for specific tasks. To pick the software that is right for you, you need to take the time to explore the available alternatives. When choosing, always listen to your feelings, because the right video editor can only be found through trial and error. Good luck in mastering the digital world and finding a tool that will help you comfortably solve your problems!
Data scientists need to have a number of different skills. In addition to understanding the logistics of networking and a detailed knowledge of statistics, they must possess solid programming skills.
When you are developing big data applications, you need to know how to create code effectively. You will need to start by learning the right programming languages. There are a lot of important practices that you need to follow if you want to make sure that your program can properly carry out data analytics or data mining tasks.
Common Programming Mistakes Data Developers Must Avoid
By now, you probably know that coding involves extensive work. It will be even more intensive when you are creating big data applications, because they tend to require a lot more code and greater complexity.
Sadly, complex applications are more likely to have bugs that have to be resolved. You will have to find ways to effectively debug issues when creating software. To make coding more straightforward and effective, you must start by learning the best practices. This entails being aware of some of the biggest mistakes that can cause your code to fail.
This article outlines some common coding errors that programmers creating big data programs need to avoid.
Failing to Back Up Code
One of the most common programming errors is failing to create a backup for your work. Building code is hard work, and you don’t want to risk losing all your information because of a system failure or power outage. Therefore, you will need to spend some time backing up your code as you continue with work.
The purpose of creating backups for your work is that if you lose or damage a file or if problems happen, your backups will survive, and you continue to work uninterrupted. This is more important than ever, since many developers are increasingly dealing with ransomware attacks, so backing up your essential work is critical. Applications that handle data mining and data analytics tasks are even more likely to be targeted by hackers, because they often have access to very valuable data.
One of the most serious errors in programming is using bad names for your variables. The variable name should represent the kind of information contained in the variable. Of course, they are referred to as variables because the data contained within them can change. However, the core operations of the variables remain the same.
Some budding programmers make the mistake of using names that are either too short or cannot communicate their use in the code. When naming them, you may assume that you understand their use. However, if you return to your code after a few months, you may not recall what the variables were for. Using a lousy name also makes sharing your work or collaborating with larger team members cumbersome.
Another mistake that many programmers developing data science applications make is that they don’t provide important information about the function served by the variable. Unfortunately, they may also include this information in a difficult way to read and understand. The best variable names are neither too long nor too short. Anyone going through your code should understand what your variables represent.
The name should designate the data that your variable represents. Also, your code will probably be read more times than written. So instead of taking the most straightforward approach to writing code, you should focus on how easy it will be for other people to read it.
Most experts recommend using simple and concise code names. The name should be written in English but shouldn’t comprise special characters.
Improper Use of Comments
Data science applications are very complex. Therefore, they are more likely to have cumbersome code that can be difficult to follow. Therefore, it is imperative that developers creating big data applications use plenty of comments to understand the code and make sure other programmers can pick up on it as well.
Comments are excellent reminders of the function performed by a piece of code. In programming, a comment implies an annotation or explanation in the source code intended to make the code easier to comprehend. Unfortunately, compilers and interpreters generally ignore comments, but they serve an essential function.
All programs should contain comments that make it easy to describe the purpose of the code. Users need to be able to use a previously created program as easily as possible. However, there are limitations on the number of comments you can have in your code.
Having too many comments means you will have difficulty changing the comments every time you alter the variables. Only use comments in situations where the code is not self-explanatory. If you use the correct naming convention, your work should have very few comments.
Repetitive Code
Another frequent programming error is repetition. One of the core philosophies of effective programming is to not repeat yourself. You may need to revise your work multiple times to ensure that you have not repeated code. As a rule, copy and pasted code are likely repeated. You want to practice using functions and loops as you generate code.
This can be a very costly problem when you are creating a program that has to process lots of data. Your program can crash if there are lots of repetitive issues.
To avoid repetition, do not reuse code by copying and pasting some existing fragments. It is much safer to put the code in a method. This way, you can always call it if it is required the second time.
Being Inconsistent with Code Formatting
When creating new code for a data science application, consistency implies settling for a style and sticking with it throughout. The first level of consistency is the individual degree. This essential consistency means doing what you prefer and staying true to it.
On the other hand, collective consistency means doing your work in a way that can be easily understood by others when working in teams. If other developers are to go through your code, they should be able to understand the work wherever you touch code, respect and remain consistent with the style.
This article summarizes a few mistakes to avoid when programming or coding. For example, stay away from functions that are too big and name your code appropriately. Research more on how to avoid coding errors online.
Enterprise cloud adoption increased dramatically during the COVID-19 pandemic — now, it’s the rule rather than the exception. In fact, 9 in 10 companies currently use the cloud in some capacity, according to a recent report from O’Reilly.
Although digital transformation initiatives were already well underway in many industries, the global health crisis introduced two new factors that forced almost all organisations to move operations online. First, that’s where their customers went. Amid stay-at-home mandates and store closings, customers had to rely almost solely on digital services to shop, receive support, partake in personalised experiences, and otherwise interact with companies.
Second, the near-universal shift to remote work made the continued use of on-premises hardware and computing resources highly impractical. To ensure newly distributed teams could work together effectively, migrating to the cloud was the only option for many companies. And although current adoption statistics are a testament to the private sector’s success in this endeavour, most companies encountered some obstacles on their journey to the cloud.
Barriers to success in cloud adoption
There are several different types of cloud platforms and a variety of cloud service models. To keep things simple, I tend to think of cloud resources in terms of two components: back end and front end. The former is the infrastructure layer. Outside of the physical servers and data centres that every cloud provider is comprised of, the infrastructure layer encompasses everything related to information architecture, including data access and security, data storage systems, computational resources, availability, and service-level agreements. The front end is the presentation layer or application interface, including the end-user profile, authentication, authorisation, use cases, user experiences, developer experiences, workflows, and so on.
Not long ago, companies would typically migrate to the cloud in long, drawn-out stages, taking plenty of time to design and implement the back end and then doing the same with the front end. In my experience working with enterprise customers, the pandemic changed that. What used to be a gradual process is now a rapid undertaking with aggressive timelines, and front-end and back-end systems are frequently implemented in tandem where end users are brought in earlier to participate in more frequent iterations.
Moreover, the pandemic introduced new cost considerations associated with building, maintaining, and operating these front-end and back-end systems. Organisations are searching for more cost savings wherever possible, and though a cloud migration can result in a lower total cost of ownership over the long run, it does require an upfront investment. For those facing potential labour and capital constraints, cost can be an important factor to consider.
Aggressive timelines and cost considerations aren’t roadblocks themselves, but they can certainly create challenges during cloud deployments. What are some other obstacles to a successful cloud integration?
Attempting to ‘lift and shift’ architecture
When trying to meet cloud migration deadlines, organisations often are prone to provision their cloud resources as exact replicas of their on-premises setups without considering native cloud services that can offset a lot of the maintenance or performance overhead. Without considering how to use available cloud-native services and reworking different components of their workflows, companies end up bringing along all of their inefficiencies to the cloud. Instead, organisations should view cloud migration as an opportunity to consider a better architecture that might save on costs, improve performance, and result in a better experience for end users.
Focusing on infrastructure rather than user needs
When data leaders move to the cloud, it’s easy to get caught up in the features and capabilities of various cloud services without thinking about the day-to-day workflow of data scientists and data engineers. Rather than optimising for developer productivity and quick iterations, leaders commonly focus on developing a robust and scalable back-end system. Additionally, data professionals want to get the cloud architecture perfect before bringing users into the cloud environment. But the longer the cloud environment goes untested by end users, the less useful it will be for them. The recommendation is to bring a minimal amount of data, development environments, and automation tools to the initial cloud environment, then introduce users and iterate based on their needs.
Failing to make production data accessible in the cloud
Data professionals often enable many different cloud-native services to help users perform distributed computations, build and store container images, create data pipelines, and more. However, until some or all of an organisation’s production data is available in the cloud environment, it’s not immediately useful. Company leaders should work with their data engineering and data science teams to figure out which data subsets would be useful for them to have access to in the cloud, migrate that data, and let them get hands-on with the cloud services. Otherwise, leaders might find that almost all production workloads are staying on-premises due to data gravity.
A smoother cloud transition
Although obstacles abound, there are plenty of steps that data leaders can take to ensure their cloud deployment is as smooth as possible. Furthermore, taking these steps will help maximise the long-term return on investment of cloud adoption:
1. Centralise new data and computational resources.
Many organisations make too many or too few computational and data analytics resources available — and solutions end up being decentralised and poorly documented. As a result, adoption across the enterprise is slow, users do most of their work in silos or on laptops, and onboarding new data engineers and data scientists is a messy process. Leaders can avoid this scenario by focusing on the core data sets and computational needs for the most common use cases and workflows and centralise the solutions for these. Centralising resources won’t solve every problem, but it will allow companies to focus on the biggest challenges and bottlenecks and help most people move forward.
2. Involve users early.
Oftentimes, months or even years of infrastructure management and deployment work happens before users are told that the cloud environment is ready for use. Unfortunately, that generally leads to cloud environments that simply aren’t that useful. To overcome this waste of resources, data leaders should design for the end-user experience, workflow, and use cases; onboard end users as soon as possible in the process; and then iterate with them to solve the biggest challenges in priority order. They should avoid delaying production usage in the name of designing the perfect architecture or the ideal workflow. Instead, leaders can involve key stakeholders and representative users as early as possible to get real-world feedback on where improvements should be made.
3. Focus on workflows first.
Rather than aiming for a completely robust, scalable, and redundant system on the first iteration, companies should determine the core data sets (or subsets) and the smallest viable set of tools that will allow data engineers and data scientists to perform, say, 80% of their work. They can then gradually gather feedback and identify the next set of solutions, shortening feedback loops as efficiently as possible with each iteration. If a company deals with production data sets and workloads, then it shouldn’t take any shortcuts when it comes to acceptable and standard levels of security, performance, scalability, or other capabilities. Data leaders can purchase an off-the-shelf solution or partner with someone to provide one in order to avoid gaps in capability.
No going back
Cloud technology used to be a differentiator — but now, it’s a staple. The only way for companies to gain a competitive edge is by equipping their data teams with the tools they need to do their best work. Even the most expensive, secure, and scalable solution out there won’t get used unless it actually empowers end users.
Kristopher Overholt works with scalable data science workflows and enterprise architecture as a senior sales engineer at Coiled, whose mission is to provide accessibility to scalable computing for everyone.
Artificial intelligence is driving a lot of changes in modern business. Companies are using AI to better understand their customers, recognize ways to manage finances more efficiently and tackle other issues. Since AI has proven to be so valuable, an estimated 37% of companies report using it. The actual number could be higher, since some companies don’t realize the different forms of AI they might be using.
AI technology has been helpful for businesses in different industries for years. It is becoming even more valuable for companies as ongoing economic issues create new challenges.
The benefits of AI stem from the need to manage close relationships with business stakeholders, which is a difficult task. Businesses do not exist on islands. All companies require complex relationships with various suppliers and service providers to develop the products and services they offer to clients and customers — but those relationships always carry some risk. Since the War in Ukraine, Covid-19 crisis and other problems have worsened these risks, AI is becoming more important for companies that want to mitigate them.
Here are some of the risks that organizations face in dealing with suppliers, and what they can do to mitigate those risks with artificial intelligence.
Failure or Delay Risk
Failure to deliver goods is one of the most common risks businesses have suffered over the past two years. This risk is best defined as a complete supply or service failure, which can be permanent or temporary.
There can be many localized or widespread reasons for a supplier to fail to provide goods or services. For example, poor management might cause their business to collapse, eliminating their products from the supply chain. The availability of materials can cause failures, as suppliers cannot manufacture products when they lack the resources to do so. Finally, unexpected or unavoidable events, like the blockage of a major trade route or unprecedented and severe storms, can cause catastrophic delays that shut down manufacturing or prevent trade from coming or going to a region.
This is one area that can be partially resolved with AI. You can use predictive analytics tools to anticipate different events that could occur. Cloud-based applications can also help.
Google Cloud author Matt A.V. Chaban addressed this in a recent article. Hans Thalbauer, Google Cloud’s managing director for supply chain and logistics stated that companies are using end-to-end data to better manage risks at different junctions in the supply chain to avoid breakdowns.
Brand Reputation Risk
Suppliers have to be true to their mission and think about their reputation. Fortunately, AI technology can make this easier.
There are a few ways that a company’s brand can be negatively impacted by a member of its supply chain. If a supplier maintains poor practices that result in frequent product recalls, the business selling those products might be viewed by consumers as equally negligent and untrustworthy. Likewise, if a supplier publishes messaging that contradicts a brand’s marketing messages, consumers might become confused or disheartened by the inconsistency of the partnership. Because the internet reveals more about supplier relationships and social media provides consumers with louder voices, businesses need to be especially careful about the brand reputation risks they face in their supply chains.
How can AI help with brand reputation management? You can leverage machine learning to drive automation and data mining tools to continue researching members of your supply chain and statements your own customers are making. This will help you identify issues that have to be rectified.
Competitive Advantage Risk
Businesses that rely on the uniqueness of their intellectual property face risks in working with suppliers, who might sell that IP, counterfeit goods or otherwise dilute the market with similar products.
Saturated markets require companies to develop some kind of unique selling proposition to provide them with a competitive advantage. Unfortunately, the power of that competitive advantage can wane if a business opts to work with an untrustworthy supplier. In other countries, rules about intellectual property are less rigid, and suppliers might be interested in generating additional revenue by working with a business’s competitors, offering information about secret or special IP. Though the supply chain itself might be unharmed by this risk, this supplier behavior could undermine a business’s strategy and cause it to fail.
AI technology can help suppliers improve their competitive risk in numerous ways. They can save money through automation, identify more cost-effective ways to transport goods and improve value in other ways with artificial intelligence.
Price and Cost Risk
This risk involves unexpectedly high prices for suppliers or services. In some cases, business leaders do not adequately budget for the goods and services they expect from their suppliers; in other cases, suppliers take advantage of a lack of contract or “non-firm” prices to raise their costs and earn more income from business clients. This is one of the easiest risks to avoid, as business leaders can and should perform due diligence to understand reasonable rates amongst suppliers in their market.
AI technology can also help in this regard. Machine learning tools have made it a lot easier to conduct cost-benefit analyses to recognize opportunities and risks.
Quality Risk
Cutting corners can cut costs but doing so can also result in poor product or service quality that is unattractive to consumers. Businesses need to find a balance between affordability and quality when considering which suppliers to partner with.
Some suppliers maintain a consistent level of high or low quality, but with other suppliers, quality can rise and fall over time. Some factors that can influence quality include material and labor cost in the supplier’s region, shipping times and costs and the complexity of the product or service required. Business leaders who recognize a dip in quality might try to address the issue with their current supplier before looking for a new supplier relationship.
Fortunately, AI can help identify any of these issues.
The Best Risk Mitigation Strategy Requires AI Technology
AI technology has made it a lot easier for suppliers to manage their risks. Undoubtedly, the best way to mitigate the risks associated with suppliers is with a robust supplier risk management system. The right AI tools and procedures help business leaders perform more diligent research and assess supplier options more accurately to develop a supply chain that is less likely to suffer from delays, failures, low quality, undue costs and other threats. Risk management software developed for the supply chain help business leaders build and maintain strong relationships with top-tier suppliers, which should result in a stable and lucrative supply chain into the future.
Global companies spent over $2.83 billion on marketing analytics in 2020. This figure certainly increased in light of the pandemic, as digitization accelerated.
Marketing has always been about numbers. Now, those numbers are highly refined, narrowed by algorithms and databases, and processed by people with advanced degrees. Indeed, data and marketing are a match made in heaven, taking much of the guesswork out of a profession that once was as much about luck as it was about creativity.
In this article, we look at how data impacts marketing. We also review what it takes for a business’ marketing division to find real success with their data implementation efforts.
Technique Matters:
Proper data analysis is very method dependent. Businesses that wish to use data in their marketing efforts first need to consider what data analysis techniques are right for them, and how they can use them to improve outcomes.
Some degrees specialize in data-driven marketing. To truly master this art form, consider pursuing an advanced degree in data analysis, or investing in staff with the appropriate background.
Knowing Your Audience
The best thing about data in marketing is that it helps you understand who your audience is. This is critical not only in how you describe your product but also in how it is framed. If your audience consists primarily of middle-aged business people, you’ll probably want to reach for a more formal tone. If your audience is millennials, humor might be more appropriate.
Data reveals the story that marketers need to tell.
Knowing What Features to Emphasize
Your product can probably do a lot more things than you are going to want to fit into a Tweet or a short ad. A successful marketing campaign knows how to emphasize the features that will appeal to the largest number of people.
Of course, not every marketing campaign is about casting the widest possible net. Numbers can also help you narrow the focus of your messaging by zeroing in on what features your best customers respond the most to. Not only does this maximize the impact of your ad efforts, but it also helps attract ideal customers: people who stick around, spend lots of money, and offer referrals.
How to Market Your Product
Data can also make your outreach efforts significantly more impactful. Most social media sites feature their own ad analytic software that helps you see who your demographic is and when they are most likely to be online.
Using this information, you can create targeted ads that only show up during the peak web traffic periods. Not only does this boost ad engagement but it also makes sure you aren’t wasting money.
The Necessity Of Making it A Data-Driven Culture
It’s important to understand that half measures will never produce any of the results listed above. Companies all too often invest heavily in data infrastructure, buying tools and software subscriptions that never get used, or worse yet, get used poorly. Superficial data implementation can lead to:
High rates of turnover: Employees who have little or no tech experience are often very discouraged when they are told they need to master a new software tool. It’s important to allocate a significant amount of time (months) to training. No one should be expected to master the tech overnight. True data implementation is a long-term investment and should be treated as such.Wasted Tech: On the flip side, some people will just ignore new software entirely. The average American worker has a company-provided tech stack filled with tools they don’t understand and never use. Why? Usually, it comes down to company leadership. If management isn’t taking data implementation seriously, the staff won’t either.Half-baked conclusions: Finally, poor data implementation also just produces bad results. Unless the training is significant and the tools are on point, the conclusions generated by a data implementation strategy are not going to produce the results you are looking for.
A true data-driven culture stems from the top down. Management must take the adaptions seriously, work towards understanding and implementing themselves, and check in regularly with the rest of the staff, not to breathe down their necks, but to address concerns and see how they can help smooth the transition along.
The current world is undergoing a rapid transformation as a direct result of the many scientific breakthroughs and technological advancements enabling the production of an abundance of intelligent gadgets, appliances, and systems.
Such intelligent devices, gadgets, and systems encompass automation, smart sensor networks, communication systems, and various other gadgets. Examples are robotics, smart cars, intelligent transport systems, and home robotization.
The management of parking lots is not a simple job for businesses and other organizations since there are many moving factors, such as the flow of traffic and the number of available places.
It is an activity that needs human effort, takes a lot of time, and is wasteful overall. Utilizing a parking management system may assist a company in lowering the administrative expenses associated with parking and lessening their parking space’s influence on the neighbourhood in which they are located.
Smart Parking is a low-barrier solution because it can be installed quickly and easily, is scalable, and is economical. It is the ideal solution for contemporary cities that wish to leverage the power of the internet of things while providing possible advantages to their residents.
Deep Learning Technology has started being used increasingly in managing parking areas. Learn more here.
What is Deep Learning Technology
Deep learning is a kind of machine learning that involves teaching machines to understand in the same way people do naturally, via observation and imitation of others.
Deep learning is one of the most important technologies underpinning autonomous vehicles since it allows them to detect things like stop signs and tell a human from a signpost.
It is essential to implement voice control in consumer electronics like mobile phones and tablets, televisions, and hands-free speakers. Recently, many emphases have been focused on deep learning, and for a good reason.
It means accomplishing goals that could not have been done in the past. A computer model may learn to execute categorization tasks directly from pictures, text, or voice using a technique known as “deep learning.”
Models trained using deep learning have the potential to attain an accuracy that is comparable to or even superior to that of humans.
And What is Parking Management System
Innovative technologies that answer problems in the parking business are collectively referred to as park management systems.
The fundamental concept that underpins any parking management system is easy to understand. People, businesses, and other organizations may benefit from this approach to better manage parking areas.
Car Parking Problems That the Modern Drivers Are Facing
Nearly two-thirds of U.S. drivers described experiencing worried when searching for parking, as per the research. The research also found that almost 42% of Americans had missed a meeting, 34% had aborted a visit due to parking challenges, and 23% had been the victim of bad driving.
Secured parking has become more sought after owing to two primary trends: development and a rise in automobile ownership. Drivers are having difficulty finding a parking space due to an increase in automobiles on the road.
Drivers in highly crowded locations lose a lot of money, production, and time due to the inefficiency of the existing parking structure. This irritates motorists and adds to the volume of traffic, increasing commuting times by 35 percent.
As a result, typical parking systems aren’t up to the task of making parking easier for drivers while lowering the amount of time they spend searching for a spot.
This demonstrates the logic for using cutting-edge technology in urban transportation to modernize the system and alleviate the difficulties experienced by drivers.
Ways Through Which the Deep Learning Technology Helps Parking Management Systems
Here are some of the most prominent ways through which the Deep Learning Technology helps parking management systems improve:
Reduces Search Traffic on Streets
The hunt for parking spots is responsible for over a third of all of the congestion that can be seen in metropolitan areas. Cities can better manage and reduce the amount of parking search traffic on the roads thanks to parking management solutions.
This software also assures parking safety, but its most significant contribution to reducing traffic congestion is that it makes the parking experience quicker, more pleasant, and less burdensome overall.
There will be fewer automobiles driving in circles around the neighbourhood looking for a parking place due to the use of intelligent parking technology.
This finally makes traffic flow smoother and reduces the amount of search traffic on the streets to the greatest extent feasible.
Eliminate Parking Stress
Most people want to avoid travelling to the more crowded parts of the city because they do not want to find themselves caught up in the parking problem that causes tension and anxiety among drivers.
It is frustrating to know that you will have to spend a lot of time looking for parking spots but that you will ultimately have to park your vehicle in a location that is quite a distance from where you need to be.
In addition, it is a source of frustration to have to drive up and down the same street many times to locate a parking spot, but to no avail.
The goal of developing Deep Learning Technology is to remove the stress associated with parking for motorists.
The users of the smart parking apps are informed about the available parking spots in the region they want to visit.
The uncertainty and stress involved with locating a suitable parking place close to the intended location are reduced as a result.
Minimize the Personal Carbon Footprint
One additional advantage of smart parking is that it helps cut down on the pollution caused by automobiles by reducing traffic and the need for drivers to move about as much as they do now.
When drivers have to go from one location to another for parking, this increases the individual environmental footprint.
In the United States, the average time spent searching for parking by car is about twenty minutes, which wastes fuel and time and contributes to increased congestion in metropolitan areas.
The longer it takes to find a parking spot, the greater the carbon footprint left behind; however, this time may be cut down significantly with the assistance of an intelligent parking solution.
There is a negative effect on the environment caused by the carbon dioxide emissions produced by all types of fuel, including diesel, gasoline, and fossil fuel.
The fact that the emissions from personal carbon footprints caused by automobiles do not immediately affect human existence is another unfavorable aspect of these emissions. Still, they might be a component in the progression of climate change.
When intelligent parking solutions are incorporated into metropolitan areas, there is a subsequent reduction in a person’s environmental footprint, particularly the amount of carbon dioxide released.
A Time and Cost-Efficient Solution
Intelligent parking technology may result in time and financial savings for motorists. When the drivers reach the crowded parking lot, they spend several minutes looking for a location to park their vehicles.
Since of this, eventually, they are wasting their time, which causes them to get upset because the drivers cannot arrive at the required place on time.
Similarly, travelling more kilometres in search of parking causes an increase in wasted gasoline, which in turn causes an increase in the amount of money spent by the drivers of the automobile’s fuel.
A system based on the Internet of Things (IoT), Smart Parking is outfitted with sensors that convey data to apps regarding empty parking places.
Instead of spending time and fuel driving around in circles looking for a parking spot, the drivers may use this application to guide them to the now available places.
Helps Consume Less Fuel
Deep Learning Technology is a consequence of human inventions and technology that allows easy access to parking places and helps save precious resources, including fuel, time, and space.
Deep Learning Technology is an outcome of human innovations and advanced technology. Deep Learning Technology provides easy access to the parking spots. When Deep Learning Technology is implemented in metropolitan areas, cars are directed directly to vacant parking places.
This makes the most efficient use of limited space. Because of this, there is no longer a need to drive additional kilometres to locate available parking places.
Because of this, using the Deep Learning Technology results in less wasted gasoline, which eventually saves the drivers money and makes the parking experience more pleasant for them.
Now Businesses Have More Commercial Parking for Rent!
Now you know how Deep Learning Technology has impacted the parking management system. In the world that we are living today, such innovations have made major advancements to accommodate people and businesses.
One major advantage for businesses is freeing up many spaces for a commercial parking lot for rent. Businesses have used data algorithms to ensure they stay ahead of the curve and generate sustainable profits.
Data science is both a rewarding and challenging profession. One study found that 44% of companies that hire data scientists say the departments are seriously understaffed. Fortunately, data scientists can make due with fewer staff if they use their resources more efficiently, which involves leveraging the right tools.
There are a lot of important queries that you need to run as a data scientist. You need to utilize the best tools to handle these tasks.
One of the most valuable tools available is OLAP. This tool can be great for handing SQL queries and other data queries. Every data scientist needs to understand the benefits that this technology offers.
Using OLAP Tools Properly
Online analytical processing is a computer method that enables users to retrieve and query data rapidly and carefully in order to study it from a variety of angles. Trend analysis, financial reporting, and sales forecasting are frequently aided by OLAP business intelligence queries. (see more).
A user can ask for data to be examined so that they can see a spreadsheet with all of an industry’s beach ball products that are sold in Florida in July, compare revenue statistics with all those for almost the same items in September, and compare other demand for a product in Florida during the same time period.
Several or more cubes are used to separate OLAP databases. The cubes are structured in such a manner that it is simple to create and read reports. Online Analytical Processing (OLAP) is a term that refers to the process of analyzing data online.
Data processing and analysis are usually done with a simple spreadsheet, which has data values organized in a row and column structure. For two-dimensional data, this is ideal. OLAP, on the other hand, comprises multidimensional data, which is typically collected from a separate and unrelated source. Using a spreadsheet isn’t the best solution.
What is the mechanism behind it?
The data is processed and modified after it has been extracted. Data is fed into an Analytical server (or OLAP cube), which calculates information ahead of time for later analysis. A data warehouse extracts data from a variety of sources and formats, including text files, excel sheets, multimedia files, and so on.
Types:
HOLAP stands for Hybrid Online Analytical Processing. The consolidated totals are saved in a data model in the HOLAP technique, while the particular data is maintained in a relational database. This provides both the ROLAP model’s data efficiency and the MOLAP model’s performance.
OLAP on the desktop (DOLAP): In Desktop OLAP, a person receives a dataset from the system and analyzes it locally or on their desktop.
Portable OLAP: Mobile OLAP allows users to use their device to access and evaluate OLAP data.
Web OLAP (WOLAP) is an OLAP system that can be accessed through a web browser. The WOLAP architecture is three-tiered. There are three parts to it: a client, software, and server software.
SOLAP (Spatial OLAP) was developed to make it easier to manage both temporal and non-spatial data in a Gis Mapping (GIS).
ROLAP (Relational OLAP): ROLAP is an extended RDBMS that performs typical relational operations using multidimensional data mapping.
A single OLAP cube cannot have a significant number of dimensions.OLAP necessitates data organization into something like a snowflake schema. These schemas are difficult to set up and maintain.Any change to an OLAP cube necessitates a complete upgrade of the cube.An OLAP system cannot access transactional data.
The Benefits of OLAP
There are also benefits of using OLAP, which include:
OLAP is a business platform that encompasses strategy, planning, monitoring, and analysis for many types of businesses.In an OLAP cube, data and calculations are consistent. This is a significant advantage.Search the OLAP data for broad or particular terms with ease.Create and analyze “What if” scenarios quickly.Corporate simulation models, and performance reporting tools all use OLAP as a foundation.It’s useful for looking at time series.Users can slice up cube data using a variety of metrics, filters, and dimensions.With OLAP, finding clusters and anomalies is simple.
The online analytical processing tool, also known as the OLAP, is a technology which helps the researchers and surveyors to look into their business from the various overviews.
Businesses have been using websites to reach customers for nearly 30 years. The first websites predated modern analytics technology. Google Analytics wasn’t launched until 2005. However, advances in analytics over the past decade have made it easier for companies to create quality websites. This has in turn increased the demands of customers using modern websites.
If you are trying to create a great website, then you have to leverage analytics tools to improve the quality of your site by assessing the right metrics. Keep reading to learn more about using analytics to optimize your website.
Analytics is Crucial for Optimizing Websites
Your website is only as brilliant as the amount of effort you put into it. A well-built site will automatically respond to your visitors by tailoring content for their needs. More thoughtful websites make users happier, generate more leads and conversions, and increase the odds that they will return.
A more imaginative visitor experience takes time to accomplish. However, making investments will give you the best return based on your goals and resources is best.
Web optimization positively impacts your revenue, whether you profit from advertising or sales via content distribution. You can use sophisticated digital and behavior analytics technology.
Analytics technology can measure the effectiveness of your website’s elements, reveal weak areas, and show you what needs improvement. You can track every performance component, including network bandwidth usage, user interaction patterns, and system response times.
One of the best ways to use analytics in website optimization is to test different value propositions. This will help you choose the best for your website.
A value proposition is a message that your site sends to visitors. It’s the promise of what you’ll provide, including customer service and products or services.
Your site should include offers highlighting your product or service and highlight what makes it distinguishable from other options available. It’s also vital that the value proposition is tailored to your audience and does not come across as generic or insincere.
You want to use Google Analytics or another website analytics tool to split-test different value propositions. You can create a Conversion Goal in Google Analytics and test the effectiveness of each value proposition to see which ones work the best.
Optimize your visual creatives
You will also want to use analytics technology to test different visual creatives. You can evaluate many different images, banners and buttons by setting up conversion goals in Google Analytics or creating heat maps with analytics tools like Crazy Egg.
Visuals are a super important part of any website. It’s a great way to bring your website to life and help you stand out in the crowd. Consider investing in photography and video production.
Visual content can build awareness, increase engagement and generate sales. It’s also great to share personal stories and experiences with your target audience. Remember that maintaining clear and consistent visual elements, including color, graphics, and contrast, is essential.
Therefore, you should use analytics tools to carefully optimize all of your visual creatives.
Focus on mobile responsiveness
Mobile devices account for a significant portion of all web traffic. To meet the growing needs of your customers, make sure your website is mobile-friendly.
Your site should also be compatible with multiple mobile devices, including smartphones, tablets, and small handhelds. Remember to include instructions and information about any mobile device-specific features.
Analytics technology helps you see how customers respond to different elements, so you can tweak them accordingly. You can also use AI applications that make websites more responsive.
Prioritize accessibility
Analytics technology can also help make your website more accessible to people with disabilities. Tom Widerøe has an entire article on Medium on this topic titled Can we use web analytics to improve accessibility?
Web standards allow people with disabilities to access websites, including those with impaired vision and hearing loss. For example, you can include alternate content such as text descriptions or transcripts for visual media, braille transcripts for audio content, and text-to-speech technologies for video and audio content.
Analytics technology helps identify the areas where people with disabilities will struggle to access your content. However, you will need to know what changes to make.
Alternatively, you can also use screen readers to provide access for blind users to multimedia content. If you aren’t already implementing these principles, it may be an excellent time to review your site’s accessibility and make any necessary updates.
Identify key integrations
You can also use analytics technology to identify outdated plugins or other issues. Then you can take measures to update your site.
One of the quickest ways to improve your site’s functionality is by integrating it with other web and mobile applications. Integrate your website with email marketing solutions, digital dashboards, and lead management services.
Site owners can use plugins that add to their existing customer platforms, such as shopping carts, and eCommerce platforms, such as Shopify. You can also integrate your website with social media platforms like YouTube, Facebook, and Twitter.
Test different calls to action
A clear CTA is vital for creating a memorable experience for your website visitors. Chances are, if your site lacks one, you may be missing the opportunity to keep your website visitors engaged with the content.
Compel users to take action and increase the chances of making a purchase, subscribing to your email list, or downloading your content.
Again, you will want to use analytics tools to test different calls to action. Setting up conversion goals in Google Analytics will help a lot.
Update regularly
Your website must reflect the same quality, functionality, and aesthetic style you display in your marketing materials. If not, then you’re quickly losing traffic to competitors.
You may find it easier to implement updates after the initial launch, so there is less risk of messing up or forgetting about them later. You can also use the timed release of new features as a way to ensure a consistent level of improvement throughout the life cycle of your website and content.
Analytics Technology is Essential for Website Optimization
With a more imaginative visitor experience, you’re more likely to generate better results. With transparent analytics and progress tracking, you can measure the success of your efforts and closely monitor website performance. Using effective web design, you can quickly build a more substantial online presence while retaining the interest of your online customers.
