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How AI Is Changing the Outlook of the Retail Market

How AI Is Changing the Outlook of the Retail Market

It is now visible that AI, that is, Artificial Intelligence, has revolutionized and brought changes in the world. The day it joined the tech world, it changed every sector and, no doubt, transformed retail. AI is experiencing improvements daily, expanding the chances of growth for every business utilizing AI.

A few decades ago, AI was not even in a scene; it was merely a dream, but it has materialized now. In the coming years, the AI bandwagon will most likely become a part of any retail business that doesn’t run well. No matter which part of the business, either its customer service or strategies, security or marketing, AI has revolutionized the way of doing business.

However, this blog will emphasize how AI has reshaped the retail market. We will also discuss the rise of AI, what it is, why businesses are ready to adopt this incredible technology, how AI transforms customer standards, and the future of this technology. Without further delay, let’s know this exciting technology and how it works.

The Rise of AI:

First of all, AI stands for Artificial Intelligence. Traveling back to history, the fathers of this technology, Minsky and McCarthy, foreshadowed how artificial intelligence would transform the world. This news took the whole industry by storm. As per them, AI would replace human Intelligence with machine intelligence.

The modern definition of AI is a machine’s ability (a computer itself or a robot controlled by a computer)to execute those functions only achievable with human Intelligence.

With time, AI started developing, and the world witnessed daily innovations. Today, dreams have turned into a reality where Apple Siri directs the user to purchase something, and self-driven cars are running on the roads, all thanks to AI and related technology. In the past years, every industry has encountered changes.

For instance: the excessive use of robots in the manufacturing industry or the advancement of healthcare by employing artificial Intelligence. These life-changing innovations were all possible because of AI.

Why are Retail Businesses embracing Artificial Intelligence?

As discussed earlier, AI has transformed almost every industry, and retail is not untouched. This is the sole reason it has become vital in the retail sector. From online giants like Amazon to their counterparts like Walmart and Target, retail businesses are switching to AI. The reasons for the same are:

  • It is Reliable: Nothing can win against AI machines when it comes to reliability. They are more reliable and efficient than humans. If you demand to work 24* 7, then they can do it. Unlike humans, these machines do not demand eating, sleeping, or refreshment time. Businesses don’t have to entertain the demands of work breaks or sick leaves with these machines, making them more efficient and reliable than humans. They can also process data in seconds, which requires enough time when done by humans.
  • AI has enhanced Customer Services Over the Years: AI has completely changed customer service over the years. Today, customer care is the marketing backbone of any business. Retail business owners are familiar with the paramount importance of customer care, so they are opting for AI over human Intelligence. Bots can handle chats, phone calls, and emails without taking breaks. They use sophisticated vocabulary developed through years and years of language data. Their valuable assistance builds trust that leads to the creation of loyal customers for the business. Nowadays, some ad targeting software uses AI to determine the target audiences effectively.
  • It Enhances Productivity: Experts recommend that AI operates as an aide-de-camp for employees. Under the roof of AI, employees can share their workload with bots and emphasize their tasks. This helps them become more efficient and show better performance results.

The Best Examples of Using AI in Retail:

Artificial Intelligence is omnipresent in today’s retail sector. Here we represent some examples of retailers that are efficiently using AI in their business functions:

Sephora Utilizes AI to suggest makeup. Famous makeup brand Sephora uses AI to recommend makeup for its customers. Finding the correct makeup for most females can be challenging because of their skin type and color complexion. Color IQ examines their face and advises foundations and concealers accordingly. LipIQ is also a component of the ColorIQ technology that scans the lips and proposes an appropriate lip color.

LOWES uses LoweBots to find Items. Finding an item in stores where multiple items are placed can be a back-breaking task for customers. This is where  LoweBots come in conveniently. In the LOWES stores, these bots wander around and give directions to customers. They will keep asking about what you’re trying to find and assist you based on the answers you provide them.

North Face brand advises Coats to CustomersNorth Face employs AI to offer coats to its customers. The customer has to tell the event details to the bot, which can figure out which coat will best suit the event.

Walmart employs AI to Monitor InventoryWalmart is one of the earliest retailers to use AI to operate its in-store inventory. The advanced bots can scan all the galleries to see the inventory level. They send notifications to the store’s depository to refill inventory whenever required.

How AI is Setting New Consumer Standards:

Artificial Intelligence plays an essential role in generating new consumer trends and standards. AI plays a vital role in setting new consumer trends and standards. Consumers do not need to struggle hard to find products anymore. Previously, consumers faced a dilemma in finding out what they were looking for.

Artificial Intelligence now maintains track of their last searches and recommends a product even when the customer isn’t asking for it. It just strikes the nail on the head, as customers now don’t have to see the unwanted ads anymore. Since AI-fetched ads are relevant, they become more engaging rather than annoying for customers.

Social media users are also experiencing the power of AI. Have you often seen a product on a social media platform when you have searched for it on Google? Possibly, every time. These targeted advertisements suggest to customers what they want. If many customers like the product, then it becomes a new trend. AI also identifies customer behavior towards a particular product and aids companies in knowing whether it’ll sell.

The Future of AI in Retail:

No doubt, the future of AI is shiny and glittery in the retail world. Although this technology offers so many benefits, it is still in its beginning stage. We are the first generation experiencing the magic of AI. The reality is that AI has just started to bloom, and in the coming years or decades, we will be able to see its full potential.

Even when it is in its infancy, many retail companies are leveraging its benefits. They now create their strategies based on consumer behavior. While AI keeps advancing, we can expect it to automate more and more operations in the retail industry in the coming years.

Data and Automation Assist with Sustainability

How Can Data and Automation Assist with Sustainability in Your Business

The entire world is facing the inevitable digital transformation, which has not just changed the daily lives of ordinary men but has also changed the overall look of business operations in different industries. Technological progress and the introduction of innovative technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and automation are supporting company leaders to operate at better efficiency than ever before. They are capable of generating more revenue and delivering better services without any compromise. Hence making the globe a better place in the process. But the question is-How?

Why is sustainability a better method?

For many years, organizations of all sizes have acknowledged the intrinsic value of social, environmental, and governance (ESG) ambitions regarding customer retention and seamless operation at every step. Sustainability plans are smart business steps that can support company longevity and keep customers returning.

However, many company leaders acknowledge the importance of sustainable initiatives, but only one-fourth have embraced sustainability as part of their business model, as per the International Institute for Management Development (IMD).

To get the most excellent prospect for long-term business success, the Switzerland-based company stimulates executives and company policymakers to follow local laws and rules and take a more proactive direction to sustainability.

Data and automation technologies support by offering tools to established companies and startups to meet their sustainability goals.

Smashing barriers and executing green initiatives:

Ideally, an organization’s sustainable ambitions should be genuine and environmentally oriented instead of being focused on making profits.

Today’s tech-familiar consumers are using their spending power to support environmentally aware companies and are even willing to invest a few extra bucks in sustainable products and brands.

Future-focused companies maintain transparency by revealing their sustainability goals and ambitions and promoting customer feedback.

However, feedback would only be so effective if it has the capability to churn some sense from it, and automation can become a complete game-changer in this matter.

Automation software can support this by reducing data interpretation burdens, allowing companies to accelerate their green initiatives and save money along with time.

For instance, by using automation software, companies can swiftly and easily track energy use, the amount of waste produced each day, consumer habits, carbon footprint, and many other things in order to streamline operations. Based on the amount of data collected, it could take months for humans worker to organize and analyze the relevant information properly. Technology makes things much faster with greater accuracy.

Data-powered insights to disclose optimization:

When we talk about a company’s sustainability goals, waste minimization must be at the forefront of the conversation. For a valid reason-It’s challenging to know the exact numbers in industrial waste production. Waste generation is a significant global problem and is expected to grow with time.

In addition, solid waste management is indeed a wasteful process in its own way, causing approx 1.6 billion tons of greenhouse gas emissions into the atmosphere in 2016 alone, as per data shared by the World Bank.

Manufacturing may profit from the data-automation-sustainability interplay in a massively wasteful industry, beginning with conservative inventory management. Extra inventory can block the supply chain and landfills. Nevertheless, using data-based insights and intelligent automation, businesses can thrive in the balance between large and less stock, greatly reducing waste, emissions, and overall environmental impact.

Improved efficiency of operations:

Waste arrives in different ways, and many businesses are guilty of wasting time. The saying “time is money” gets into the process here-ineffective and inefficient operations and redundancies can heavily disrupt the day-to-day processes while wasting time and money of the company.

The good part is that automation can bridge some gaps, boosting the efficiency of processes at each and every level of the supply chain.

Human error leads to inefficiency and wastes the time and money of the company, and now company owners across industries are noticing this. Companies can now reduce workplace stress and monotonies through workflow automation. It allows employees to concentrate on meaningful work that boosts efficiency and makes fewer errors.

Companies ready to embrace and enforce workflow automation into their sustainability program should start small and know the operations wherein automation will provide them with the best result. The adoption will help achieve financial goals, environmental goals, or another plan altogether.

Measuring Weighing cost vs. benefit:

For small business owners, executing sustainability initiatives may appear more like a pipe dream than an achievable goal, as the technology implementation is costly. However, businesses that have already adopted technology to drive sustainability must hire skilled employees who can potentially use these resources and streamline operations for enhanced economic and environmental benefit.

As companies can utilize automation and data analytics to improve efficiency, alter energy use, reduce waste and otherwise support using sustainability, the expense of financing in automation is worth it. By empowering company leaders to see the big and better picture regarding carbon footprint, data and automation can support optimizing operations and enhance a company’s bottom line.

Big Data be Integrated into Your Business to Improve Output

How can Big Data be Integrated into Your Business to Improve Output?

Nowadays, information usage is soaring. This information, dubbed Big data, has expanded too large and complicated for typical data processing methods.

Companies are potentially utilizing Big data to enhance customer service, boost profit, cut expenditures, and update existing operations. This shows that the impact of Big Data on businesses is enormous and will remain impactful in the coming years.

But do you know from where these affecting Big Data come?

Big data is generated mainly by three sources:

Business:

Companies produce massive amounts of data on a daily basis. Some examples include financial data like invoices, billing and transaction data, and internal and external documents like business letters, reports, production plans, and so on. Big data generation is vital for enterprises transitioning from analog to digital workflows.

Communication:

Communication is the data that one generates as an individual. Social media blogging and microblogging are all vital communication data sources. A new photo, a search query, and a text message contribute to the growing volume of big data.

IoT:

Sensors integrated with IoT system produces IoT data. Smart devices use sensors to gather data and upload it to the Internet—for example, CCTV records, automated vacuum cleaners, weather station data, and other sensor-generated data. Overall, big data can be called massive data collections obtained from different sources. It can be utilized to find patterns, links, or trends to analyze and anticipate them.

Big data can be used to enhance security measures. Businesses and individuals use free VPNs and proxies to protect their data. They both depend on big data because it supports strengthening the technology.

Now, let’s get into the details of how businesses can potentially use big data to improve their operations and boost productivity.

How do businesses use big data?

Big data applications have multiple uses. Also, we can easily see various businesses employ the technology for different objectives. Insights collected are often used to make products and services more efficient, relevant, and adaptive for individuals who use them.

The applications of big data are:

Catching security defects:

With things getting online, data breaches and theft are among the most common problems as digital systems are getting complicated. Big data can be used to find out potential security troubles and analyze trends—for instance, predictive analytics catch illegal trading and deceitful transactions in the banking industry. Comprehending the “normal” trends permits banks to discover uncommon behavior quickly.

Comprehending more about customers:

This is one of the most critical and typical big data applications. Companies extract vast amounts of data to analyze how their customers behave and their choices. This enables them to predict the goods that customers desire and target customers with more relevant and personalized marketing.

One of the best examples is Spotify. The company also utilizes artificial intelligence and machine learning algorithms to motivate customers to continue connecting with the service. Spotify finds related music to design a “taste profile” as you listen and save your favorite tracks. Using this information, Spotify can suggest customers new songs based on their earlier choices.

Product invention:

Comprehensive data collection and client demand analysis can also be used to forecast future trends. Companies can utilize big data analytics to transform collected insights into new goods and services. It allows them to predict what their clients need. The corporation can offer data-driven proof for production based on customer demand, popularity, and interest. Instead of waiting for clients to tell their needs, you can fulfill their demands beforehand. Besides this, being more innovative than competitors is also a plus point for businesses.

Create marketing strategies:

Well, we are pretty familiar with the fact that a small marketing blunder can cost a lot to a company. A marketing that does not resonate with the target demographic might end up creating disaster. However, the availability of more specific data makes marketing more secure but complex.

This lets you gather information on how people respond to your advertising and allows you to create more personalized campaigns. This increased focus allows the marketing team to make a more precise approach, turn more effective, and reduce cost load.

Do you think big data is a big risk game in a business?

Till now, it’s very clear that big data provides enormous opportunities. Businesses flourishing in different sectors can take advantage of the available data. However, it could not be a smooth journey as various challenges are involved with this analytics method.

The accuracy concern:

This will also allow you to start combining data streamlining from a vast range of sources and formats. The challenge then comes to knowing which information is valuable and reliable and how to crack that information meaningfully. However, “cleaning” of data is a part of the big data sector; it is not without complication.

The price barrier:

Welcoming and adopting the world of big data carries several drawbacks. There are many aspects to be considered here- the hardware and the software. One must consider data storage and systems for managing enormous amounts of data. Furthermore, data science is increasing rapidly, and those who understand it are in high demand. The fee for recruits or freelancers can be high. Lastly, developing a big data solution that meets your company’s needs demands significant time and money.

The security challenge:

The challenge of safely storing such a large amount of data generated from collecting such a large amount. Therefore, Cybersecurity is another essential concern as data privacy and GDPR grow more vital.

The bottom line

We can easily conclude that Big data is fetching enormous benefits to many companies belonging to different sectors. Therefore, companies may thrive in the digital economy by effectively analyzing and managing flooding data. There may be many hindrances in integrating big data into business infrastructure. Still, the initial investment overcomes the rewards and advantages offered by big data and its potential application in the business. Therefore, spending time deciding whether to go for big data or not will surely land you at a loss.

Big Data is Changing the Outlook of the Renewable Energy Sector

How Big Data is Changing the Outlook of the Renewable Energy Sector?

The renewable energy sector is facing a significant transformation, and all credit goes to the power of big data. With its capability to gather, store, and analyze an immense amount of data in real-time, big data provides unprecedented insights into how we generate and use energy.

This has allowed companies in the renewable energy sector to create innovative solutions supporting us to adopt and create a more sustainable future.

So, let’s check how big data transforms the renewable energy sector and how the sustainable future will look!

Big data is a term used to describe the immense amount of data that organizations accumulate and analyze to achieve better insights into their operations. It can be sourced from various sections like:

  • Customer feedback
  • Transactional records
  • Sensor readings
  • Social media posts
  • Search queries, etc

All these together form a data set that can be utilized to make the most suitable decisions on the basis of analysis of correlations, ongoing patterns, and trends.

We can simply say that big data is a way to convert raw data into actionable insights, and this is what makes it so powerful.

Let’s know how big data functions

As we have already discussed, big data is utilized to collect and analyze vast amounts of data in real-time. This enables companies to understand consumer activities and behavior while optimizing their processes as well as operations.

Also, the analysis of big data can assist in identifying patterns that are unintentionally ignored. This is how companies are able to discover new opportunities and develop strategies accordingly. Not just this, big data also empowers organizations to get a better insight into their operations. 

For instance, energy companies can keep track of energy usage and identify areas where improvement is needed and efficiency can be enhanced.

Here, we can place the example of Tesla powerwall. It collects data from its solar panels to observe the production and consumption of electricity in real-time. Tesla’s power wall can be utilized to optimize energy usage by offering customers with customized options.

Three ways through which big data is transforming the renewable energy sector

So, at least, we have some knowledge of big data. Now, let’s find out how it is changing the renewable energy sector.

1. Improved Efficiency:

Big data analysis can support companies by identifying areas where efficiency can be enhanced in energy systems. For example, it helps in reducing wastage and optimizing output. This will ultimately improve the entire profitability produced by renewable energy businesses. This supports both seller and buyer as they can save energy costs and use the same to invest in other green-based projects or initiatives.

The skyrocketing cost of traditional energy sources has unveiled the importance of renewable energy. It has made it more attractive, and the involvement of big data can aid in making it more efficient. Big data will not only make renewable energy more feasible but will also make it a more attractive alternative for buyers.

2. Presaging Demand and Supply:

Big data can also be utilized to foretell the demand and supply of renewable energy.

By analyzing historical data, businesses can understand the market pattern and behavior and can easily calculate the present demand for distinct types of renewable energy resources. Therefore, they can change, shift, or adjust their production as per the need. In this way, companies can target a specific customer base, leading to more conversions and ultimately adding more profits. On the other hand, customers will also get whatever they want, so it turns out to be a win-win situation for everyone involved.

Other than predicting demand and supply, big data are also used to forecast weather conditions, which will allow businesses to plan their production of renewable energy resources.

For instance, the Tesla power wall can forewarn the weather conditions and shift energy production consequently.

3. Automation of a few processes:

In the end, we can say that the most significant advantage of having big data in the renewable energy sector is automation. By automating specific processes, organizations can save time as well as resources while making their operations more efficient.

For instance, solar panel systems can be linked to the internet and designed to adjust their output depending on weather conditions on a real-time basis. In this way, consumers can cut down their electricity bills by generating more energy when the sun is shining bright in the sky.

Besides this, companies can also utilize big data to automate the maintenance of their assets involved in renewable energy systems. By tracking and analyzing real-time data, they can interpret any issues and take action before they turn out to be a significant problem in the process.

Conclusion

With rising global temperatures and increasing greenhouse gases in the environment, it has become necessary to bend toward renewable resources for energy generation. The shortage of non-renewable resources and by-products it offers, like pollution, greenhouse gases, etc, is another reason to shift toward renewable energy resources.

In this initiation, the addition of big data is causing an immense impact on the renewable energy sector. It is making renewable energy more efficient by predicting demand and supply and automating a few processes to reduce time and cost. With the advancement of technology, in the coming years, big data will become an integral part of the renewable energy sector and churn the best result while promising a green and sustainable future.

Industrial Data Help Overcome All Business Challenges

How Can Industrial Data Help Overcome All Business Challenges

Today, if we see the ongoing competition between industrial companies, we can easily underline the challenging hurdles they face to become the best, primarily in operational objectives and in understanding the immense amount of data available to them to decide how best they are achieving those goals.

To meet this objective, industrial data management strategies must be adopted to leverage existing assets and systems to unlock the full potential of their plants and drive their businesses forward.

Currently, the flooding industrial data is mostly wasted. In fact, as per the European Commission, 80% of industrial data gathered is never utilized. Asset-intensive organizations need a holistic and integrated solution that offers seamless connectivity across all data sources while providing real-time monitoring capacity to ensure no data is wasted.

With such a broad framework, these companies can maintain asset reliability through predictive equipment failure analysis, reducing maintenance costs and improving overall plant efficiency. Yielding on this vision is a big task today as a flooding amount of data is present. Companies across these sectors have recorded and captured large amounts of data for decades. These data have incredible potential, and using them to good use is far easier than expected.

Unclosing high-potential value use cases that utilize this data in production optimization, machine learning, or emissions tracking needs potent data management strategies. After all, industrial data and systems have traditionally been located in organizational silos, having different pockets of functionality developed by various dealers at different times. This has made data management more difficult and rendered most data unusable at scale.

Going through the Data Lake confusion

To counter the challenges highlighted above, businesses often choose to construct data lakes in which data from different sources is collected.

These data lakes work as potential reservoirs that swiftly accumulate vast amounts of information.

Nonetheless, it is not easy to potentially utilize these data lakes as it requires a workforce skilled in data handling and analysis, ultimately creating a considerable challenge to industrial business. Hiring such highly skilled personnel becomes even more intimidating due to the promptly evolving workforce, where specialized expertise is at a compensation.

Going through this complex system requires a strategic approach, allowing businesses to unveil the full potential of their data lakes and secure a competitive benefit.

The need for real-time data platforms suitable for commercial use

An asset-intensive business offers potential solutions; however, traditional data historians remain key, allowing industrial organizations to access data, know what is relevant, place it into workflows, and make it usable. The market for these assets remains on an evolutionary path globally. As per Mordor Intelligence, it will grow from US$1.15 billion (€1.05 billion) in 2023 to US$1.64 billion (€1.49 billion) by the end of 2028, at a compound annual growth rate of 7.32% during the projection period. 

Today, plant operators and engineers use historians to monitor operations, analyze process efficiency, and look for new opportunities. These are target-oriented systems customized for the operation teams’ benefit. 

With time, there has been an increasing demand for cloud-based applications to aid advanced analytics and quickly scale up. Meanwhile, on the IT side, digitalization teams and products need to be structured, clean, and contextualized data to produce usable insights and expand use case volumes. 

However, different data sources, including historians, offer at-a-glance analyses; their customized nature makes it hard to automate consistency in contextualizing and structuring data.

Enforcing a new solution

The collaboration of plant-level historian solutions and enterprise data integration and management technology allows a uniform confluence of IT, that is, Information Technology, and OT, which is Operational Technology functions. Along with this, we are also noticing the rise of next-generation real-time data platforms, supporting industrial organizations in collecting, consolidating, cleansing, contextualizing, and analyzing data from their operations.

This data foundation shows the beginning point for the industrial organization to optimize processes using machine learning and AI and develop new working methods based on data-derived insights.

Such organizations will be competent in developing current data systems to gather, merge, store, and retrieve data to boost production operations with data-driven decisions or backing performance management and analytics across the business.

This new data consolidation strategy prints a key moment in the evolution of data management. An organization can unveil unimaginable efficiency, innovation, and visibility by centralizing information from different sources into a unified, cloud-based, or on-premises database. The collaboration of batch and event processing delivers track and trace capabilities and authorizes organizations to search into batch-to-batch analysis quickly.

Driving ahead positively

Today, industrial companies face umptieth challenges, including meeting operational objectives, comprehending large amounts of data, and improving asset reliability.

They need a data management approach that uses legacy assets and systems to manage these issues. This approach should have an integrated solution that enables organizations to connect all data sources, access real-time monitoring, boost asset dependability, and increase overall plant efficacy.

Conventional data historians are still crucial to this strategy but must be integrated with cloud-based applications, enterprise data integration, and management technology. This will help companies gather, consolidate, cleanse, contextualize, and analyze data from their operations. This real-time data platform has grabbed a competent place worldwide as companies seek solutions to enhance their operational efficiency and decision-making capacity. Not just this, companies will also be able to update current data systems to gather, store, merge, and get back the lost data. This will ultimately improve production operations with data-based decisions and help in performance management and analytics across the system.

Along with this, companies will also get access to real-time asset performance, track material progress through complicated processes, and interlink people, data, and workflows to support compliance.

Automation in DevOps

Adding Automation in DevOps for Thriving AI Models

DevOps, as the name suggests, is a coordinated approach that integrates software development and operations to facilitate seamless software delivery. DevOps breaks down silos and supports a culture of collaboration to achieve faster, more dependable, and higher-quality software releases.

Automation is a crucial pillar of DevOps, as it allows teams to automate different tasks and processes throughout the software development lifecycle. Using automation, DevOps potentially uses Artificial Intelligence to minimize human error, quicken time-to-market, and enhance overall software quality.

But now the biggest question is- How to involve automation in DevOps. Which DevOps processes can be automated? Will it be in parts, or the entire process can be automated? What tools and technologies should be used?

So, here, in this blog, we will discuss DevOps and the importance of automation in DevOps. We will go through different aspects of automation and learn about the benefits offered by automation in DevOps.

DevOps automation utilizes different tools, techniques, and technologies to simplify and automate the duties involved in software development, application deployment, and IT operations.

The primary objective of DevOps automation is to enhance the efficiency, consistency, and reliability of software development and execution processes while minimizing human interventions and errors.

Let us know the Benefits offered by DevOps Automation

DevOps teams can potentially utilize the benefits of automation to revolutionize software development and existing operations. This enables the team to deliver faster, more efficient, and error-free software.

Prompter Time-to-Market:

Automation quickens the complete software development process, beginning from coding to execution. Once liberated from redundant tasks, teams can release new features and updates on time, decreasing time-to-market and staying ahead of others.

Better Consistency and Reliability:

Automated operations confirm consistent and steadfast outcomes. Human interference usually causes errors, whereas automation is the preface of standardized practices, promising fewer errors and a more predictable environment.

Promises reduced Manual Errors:

Human errors are natural in manual tasks. Luckily, using automation practices, DevOps teams will be able to reduce the risk of mistakes caused by human negligence, upgrade software quality, and develop more stable production environments.

Effectual Resource Utilization:

The development team can use resources more efficiently by using automation tools. In fact, infrastructure provisioning and scaling can also be automated depending on the need, optimizing resource allocation and cost-effectiveness.

Scalability:

It is pretty simple to scale applications and infrastructure either up or down, depending on the need. Automated provisioning and configuration allow rapid scaling to manage increased workloads without any human interference.

Improved Cooperation:

DevOps automation facilitates association between development and operations teams. DevOps automation tools offer a standard platform for both teams to work together, lessening communication gaps and supporting a culture of shared accountability.

Faster Recovery and Rollback:

Automation processes’ best part is that they allow quick recovery from failures. Automated backup and recovery approaches can be activated in case of any issue, minimizing downtime and reducing user impact. In the same way, automated rollbacks guarantee that faulty installations can be immediately retreated.

Smooth Predictable Deployments:

Automation in DevOps ensures that deployments are compatible across different environments, relieving the “it works on my machine” situation. This results in smoother transitions from development to production environments.

Infrastructure as Code (IaC):

Automating infrastructure provisioning by utilizing IaC tools streamlines the control of complex circumstances. This also promises easy versioning, tracking, and replicating Infrastructure across diverse stages while lowering configuration import and manual setup mistakes.

Resource Cost Optimization:

Automation supports optimizing resource allocation by permitting resources to turn up or down based on need. This averts over-provisioning and waste of resources, ultimately directing to cost savings.

Compliance and Protection:

Automation facilitates enforcing security and compliance policies throughout the development and deployment procedure. It would be best to reduce manual work in security checks, exposure scans, and access controls to reduce the threat of security breaches.

Continuous Progress:

Automation fosters a culture of endless advancement by allowing teams to collect data and insights on implementation, bottlenecks, and trends. This data can be further used to improve processes and boost overall efficiency.

Which DevOps procedures can be automated?

In a DevOps domain, automation can be executed across the entire software development process, initiating from coding and building to testing, execution, and monitoring.

  • Continuous Integration (CI) – It can automatically integrate code changes from multiple developers into a shared storage. Automation equipment like Travis CI, Jenkins, and CircleCI can be employed to start automated builds, run tests, and offer early feedback on code modifications.
  • Continuous Delivery/Deployment (CD) – Automate deploying code shifts to production or staging environments. Automation tools like Kubernetes, Docker, and Ansible will help in application deployment, infrastructure provisioning, and configuration control.
  • Infrastructure as Code (IaC):  Ithelps manage and provision infrastructure employing code. Terraform and CloudFormation tools empower teams to determine infrastructure resources, like servers and networks, as code that can be further versioned, tested, and automated.
  • Automated Testing: Executes tests automatically, confirming that code changes do not cause defects or regressions. This involves unit, integration, and end-to-end tests that can be initiated as a part of the CI/CD pipeline.
  • Monitoring and Alerting – It can keep track of the health condition and performance of applications and infrastructure. When set thresholds are breached, automated alters are initiated to inform the operations team of potential challenges.
  • Log Analysis – Automation tools can filter through logs produced by applications and infrastructure to determine ways, irregularities, and potential problems, assisting in troubleshooting and assertive maintenance.
  • Release Management – Automating the release procedure promises consistency and lowers the threat of human error during the execution of new features or bug fixes.
  • Security and Compliance – It implements security terms and policies, scans code for vulnerabilities, and guarantees compliance with regulatory standards.

Are you Willing to Get Started with DevOps Automation?

DevOps automation proposes a holistic technique to software development and operations, offering benefits that promise faster output, better quality, enhanced integration, lower risks, and potential resource utilization and management. However, it is still wise to go for automation only after enough research and analysis to ensure that it syncs with organizational objectives and is enforced in a way that takes care of both technological and cultural factors.

Why IoT is Becoming the Next Pivotal Technology for Enterprises

Why IoT is Becoming the Next Pivotal Technology for Enterprises

Today, we are well aware with the fact that the Internet of Things is leaving no stone unturned to bring changes and revolutionize different industries. Businesses are embracing IoT with open hands and are spending a reasonable sum on IoT technology to propel growth. Organizations use this futuristic technology to create new use cases, products, and services, to rebuild their business models to fit current market requirements, and to enter new markets.

This blog will show how revenue growth is crucial for businesses spending on IoT technology. We will also know how interest is growing in use cases that help in remote working, augmented reality (AR), virtual reality (VR), and sustainability and how enterprises increasingly view IoT as an option to help companies reach their sustainable goals.

Revenue growth is essential for businesses investing in IoT

Earlier, enhancing operational efficiency was one of the most crucial factors for companies when it was to invest in IoT, but now this has been overtaken by IoT’s potential to push revenue growth.

For instance, enterprises can gather expansive amounts of data in real time using IoT devices and sensors, allowing them to extract valuable insights and make data-driven decisions. IoT-enabled sensors can be implanted in products to collect data on usage, performance, and maintenance needs. The collected data can be used to optimize operations, enhance efficiency, improve customer experiences, and create new revenue streams. The technology allows businesses to provide product-as-a-service models, where customers can opt and pay for the use of the product rather than buying it. This change from owning to usage-based models facilitates organizations to create recurring revenue and embrace long-term customer relationships.

Organizations can also monetize their data by analyzing and churning meaningful information. By providing data-based services, like preventive maintenance, customized recommendations, or remote monitoring, businesses can generate new revenue streams and offer value-added services to their customers.

Recent developments in artificial intelligence (AI) will also act as a booster to help companies in achieving their goals. For example, AI systems empower businesses to unleash the full potential of the vast amounts of data they gather using IoT devices, primarily through predictive analytics, and use the insights generated from analyzing IoT data.

Besides this, organizations can provide better functionalities and offer customized experiences by integrating sensors and connectivity. This opens new product innovation and differentiation opportunities, enabling firms to draft premium prices and captivate new market segments.

By including IoT, companies can enter into untapped market opportunities, develop innovative products and services, and establish a competitive edge in their business.

Interest is growing in backing different use cases

Today, organizations are open to various opportunities unleashed by 5G-based IoT. A noticeable trend in the past year is the firms’ pressing need for a remote working system, virtual reality, augmented reality, and sustainability. These all are seen as significant areas for IoT applications.

Adopting a hybrid working culture in some organizations has made IoT technologies more relevant than before. IoT-integrated devices and applications offer seamless communication, collaboration, and monitoring for remote workers. From smart home offices with connected devices to remote asset management in industrial settings, IoT allows businesses to produce secure and productive remote working environments.

Along with this, the IoT is changing the landscape of AR and VR technologies. IoT devices can offer real-time data and inputs to improve the immersive experience of VR and AR applications. For example, in manufacturing, IoT sensors can gather data from physical objects, visualize in AR overlays for technicians, and help in maintenance, troubleshooting, and training. IoT, VR, and AR intersection produce new avenues for businesses to enhance training, design, and visualization processes, increasing productivity and efficiency.

It is crucial to understand that security within IoT has been a cause of concern. However, the emphasis on industry regulation and standards has assured security by design for IoT products, improving organizations’ confidence in exploring more IoT use cases.

IoT is supporting businesses in achieving their sustainability goals

Sustainability is another critical area where the IoT is imprinting its importance. Most organizations agree that emerging technologies are essential in accelerating their green objectives. Key benefits include:

  • Better energy efficiency
  • Enhance measurement of carbon emissions
  • Allows more virtual products and processes

IoT devices and sensors are employed to monitor and optimize energy consumption, reduce wastage, and improve resource efficiency.

For example, IoT-enabled smart buildings can automatically adjust lighting and temperature as per the occupancy, ultimately reducing energy usage and minimizing the company’s carbon footprint.
Besides this, an IoT-based supply chain monitoring system helps optimize logistics, reducing product spoilage and decreasing greenhouse gas emissions.

By interlinking IoT with their operations, businesses can fuel sustainability initiatives, enhance environmental performance, and contribute to a more sustainable future.

Other benefits include- the positive impact on circular business models and the shift to renewable energy sources. Traditional linear business models pursue a “take-make-dispose” strategy, which causes resource depletion and waste generation. However, with IoT integration, businesses can execute circular practices that reduce waste, reuse materials, and extend product lifecycles.

On the other hand, IoT technologies can play a crucial role in integrating renewable energy. IoT-enabled devices can monitor energy consumption routines, product efficiency, and grid dynamics, offering businesses real-time insights into their energy usage. These insights can allow enterprises to discover opportunities for energy conservation, optimize energy generation and storage, and integrate renewable energy sources effectively. Organizations can lower their dependence on non-renewable energy by employing IoT in energy management, reduce their carbon footprint, and contribute to the global initiative toward a clean energy future.

IoT: Critical technology for businesses

Hence, we can conclude that IoT is emerging as a critical technology for businesses, offering them transformative capabilities and opportunities.

The futuristic IoT technology is going to blanket most industries and bring the most favorable changes. Industries are understanding the potential of this latest technology and are embracing it to enhance their operational efficiency. It will not just boost productivity or minimize operational costs but will also aid in improving customer experiences and attaining sustainability goals. Joining the IoT revolution will allow businesses to stay above all competition, be innovative, and flourish in the ever-changing digital landscape.

How Edge Computing is Revolutionizing the Energy Industry

How Edge Computing is Revolutionizing the Energy Industry

The energy industry is experiencing consequential changes as it encounters numerous challenges with an increasing population, like increasing demand for electricity, integration of renewable energy sources, and emerging electric vehicles. The best way to address these issues is by embracing edge computing and using it potentially. It is a distributed computing paradigm that allows data processing and analysis nearer to the source.

In this blog, we will understand how edge computing can change the outlook of the energy industry, making it more reliable, efficient, and sustainable.

Challenges faced by the current Energy Industry

The existing traditional power grid is one of the most necessary infrastructures in our day-to-day lives. It powers our homes, hospitals, schools, industries, and other essential things for our daily routines. However, power grids face numerous challenges because of the increasing electricity demand, the integration of renewable energy sources, and the growing market for electric vehicles. These challenges can be solved using innovative solutions to optimize the grid’s operation, improve its resilience, and diminish energy waste.

What is Edge Computing?

Edge computing can be explained as a distributed computing paradigm that allows data processing and analysis closer to the source. It can be installed in various locations in the power grid, like substations, to process and analyze the data generated by the sensors in real time. Edge can also support optimizing the power grid’s operation, improving its resilience, and cutting off energy waste.

3 essential Pillars of Edge Computing

  • Improved scalability: Edge computing allocates storage and processes it over many locations, reducing the investment cost for infrastructure and capacity for a higher traffic volume or better algorithm.
  • Better data security and sovereignty: As data remains at its original location, the risks for illegal access or theft are decreased automatically.
  • High amount of data processed with less latency: Frequency analysis allows it to work with thousands of data almost instantly, with just milliseconds required for analysis and response. This solved the near real-time use cases- something impossible in Cloud Environments that are more inclined to offline analysis of batch information.

The IDC’s report on ”Edge Computing Solution Driving the Fourth Industrial Revolution”- emphasizes the need for the pillars mentioned above. A survey was conducted in which around 802 industry leaders who adopted Edge Computing – almost 30% stated that their primary motivation was bandwidth costs, 27% data protection, 19% latency constraints, and 12% surveyed were from the energy sector.

Introducing Smart Power Grid with Edge Computing

In a smart grid system, multiple sensors are installed to gather data related to the health condition and performance of the power grid. These sensors create immense amounts of data that need to be processed and analyzed in real-time to make reasonable decisions. Rather than sending all the generated data to a centralized data center, edge computing is data processing at the network’s edge, closer to the source.

Edge computing supports optimizing the power grid’s operation, improving its resilience, and lowering energy waste.

For example, a power-consuming company can install edge servers at different locations in the power grid, like substations, to process and analyze the data created by the sensors in real time. The edge servers use machine learning algorithms to anticipate the power demand and supply, detect faults, and effectively manage electricity distribution.

Edge computing also enhances the power grid’s resiliency by allowing autonomous decision-making at the edge in case of network disruptions.

For example, assume any substation loses connectivity with the central control system. In that case, the edge servers can return to a backup mode and continue to function autonomously using locally stored data.

The Advantages of Edge Computing

Edge computing can bring change to the energy industry and make it more reliable, sustainable, and efficient. By opting for edge computing, power utilities can optimize their processes, lower energy waste, and offer higher-quality services to their customers. Edge computing can resolve the power grid’s challenges, such as growing demand for electricity, incorporating renewable energy sources, and emerging electric vehicles.

Besides this, edge computing can allow energy utilities and municipalities to develop and deploy data processed quickly and efficiently, allowing them to churn profit from edge computing solutions more effectively.

Users with no or low programming experience can design and deploy data processes promptly and efficiently using a convenient, low-code tool. This can assist in streamlining the deployment of edge computing solutions and allow energy utilities and municipalities to resolve the power grid challenges.

Main issues of IoT Edge Computing for the Energy Sector?

IoT Edge is powered by solid investment by technology manufacturers in cutting-edge solutions with smaller, lower-powered, and lower-priced microcomputers that can operate as IoT Edge Computing nodes at scale.

Similarly, operating systems and software are also created to give these nodes the capacity to conduct algorithms in a cyber secure way, generally packaged in virtual software “containers” like Docker.

Conclusion

Today, if we look into the energy industry, we’ll find out that the energy industry encounters many challenges, like increasing demand for electricity, incorporating renewable energy sources, and overflowing of electric vehicles in the market. With the increasing population and development of industries, it has become necessary to adopt and embrace technology that cannot just solve the problem but also does not negotiate with the quality of operations.

Edge computing has the prospect of revolutionizing the energy industry, making it more dependable, sustainable, and efficient. By embracing edge computing, power utilities can enhance their operations and reduce the risk of energy wastage.

How to Address Data Management Challenges in IoT Using Fabrics

How to Address Data Management Challenges in IoT Using Fabrics

Whenever we talk about data management, the whole conversation remains incomplete if we do not mention the most important aspect related to data management: the Internet of Things, IoT networks. Today, everything is connected, and all credits go to IoT networks. From smart towns to industrial sensors, our world is interconnected with smart devices, and the volume of data generated has reached unbelievable proportions. This is advantageous for our digital transformation initiatives but carries a parallel increase in vulnerability to data piracy, cyber attacks, and privacy infringements.

The amount of data generated is directly proportional to the higher stakes regarding safeguarding it. This raises the need for data protection measures in IoT ecosystems, which has now become a significant challenge for organizations. It has also necessitated robust data management strategies to guarantee IoT data’s integrity, security, and privacy.

However, enterprises are still making errors. They emphasize more on expanding IoT and are least interested in making the data streams safer and more authentic. More comprehensive IoT networks assure more users and faster streaming, yet they lack in terms of data protection.

Critical data management challenges in IoT

In the domain of IoT, significant data challenges emerge, including security risks, privacy concerns, data authenticity, and data proliferation. Security risks create a constant threat, as IoT devices are vulnerable to breaches, unauthorized access, and tampering, potentially resulting in data leaks and network attacks. 

Safeguarding privacy is crucial due to the collection and transmission of personal data by IoT devices containing sensitive information like location, health data, and behavioral patterns. 

Securing data integrity and authenticity is difficult in IoT environments, as changes often lead to erroneous decisions and compromise system reliability. 

Besides this, the sheer volume of data created by IoT devices can overcome traditional management systems, making it necessary to have sufficient storage, processing, and analysis strategies in a timely and cost-effective way. As per the ‘State of IoT Spring 2023’ report released by IoT Analytics, the worldwide count of operational IoT endpoints rose 18% in 2022, reaching 14.3 billion connections. 

How can data fabrics handle these challenges?

Data fabrics are essential in allowing scalable data management in IoT ecosystems. They provide valuable support in different aspects of IoT data management. They play a vital role in privacy protection by using data masking techniques that pseudonymize or anonymize sensitive information.

By substituting original values with masked or randomized data, the identity of individuals or devices remains safe, diminishing the threat of data breaches.

Data fabrics also allow access control, restricting access to authorized personnel or systems. Encryption also improves security by shielding transmitted or stored data from unauthorized access. Data fabrics offer an extra layer of security against attackers by integrating encryption with masking.

In addition, data fabrics support data minimization by reducing the amount of sensitive data stored or transmitted, using masked or aggregated data instead.

  • Data integration and aggregation: Data silos create a  significant challenge in IoT, as they can cause data duplication, loss, or inaccessibility by different systems. Data fabrics can support breaking down data silos by offering a unified view of data across the IoT ecosystem. Data is created from different sources and in diverse formats; data fabrics can enable the integration of this data into a suitable view. This allows organizations to comprehend their IoT data landscape and make informed decisions. Data fabrics can collect and merge this data in real-time, offering a compressed and contextualized view of the IoT environment. This collected data can be used for real-time analytics, irregularity detection, and predictive modeling, allowing organizations to derive valuable insights and make proactive decisions.
  • Data processing and analytics: Data fabrics offer processing power, permitting IoT data to be analyzed and changed into actionable intelligence. By using distributed computing and parallel processing, data fabrics can handle IoT data’s high volume as well as velocity. This empowers organizations to conduct complex analytics on the gathered IoT data, like machine learning algorithms, extracting valuable patterns, trends, and correlations. 
  • Data management and quality: Data fabrics offer a management layer guaranteeing data quality, consistency, and compliance. As we know, IoT data comes from different sources and devices, and it is necessary to ensure data integrity and reliability. Data fabrics can implement data management policies, perform data validation and assure data quality standards are fulfilled, thereby enhancing the reliability and trustworthiness of IoT data.
  • Scalability and flexibility: IoT establishment often includes multiple devices creating data at a high frequency. Data fabrics are designed to be scalable and flexible, enabling organizations to manage the high intensity of IoT data and acclimate future growth. They are seamlessly scaled horizontally, adding more resources as required and adapting to evolving IoT infrastructures and data requirements.

Not just this, data fabric tools also enable real-time data processing and help in decision-making. In IoT systems, real-time responsiveness is essential for upcoming maintenance, monitoring, and dynamic resource allocation applications. Data fabrics can process and analyze data in real-time, allowing organizations to take prompt actions based on IoT insights.

Some robust platforms for managing IoT data

For handling IoT data, many platforms offer robust capabilities. One such platform is K2View, a data integration and management solution that allows organizations to merge and manage their data from various sources. Their technique pivots around micro-data management, emphasizing granular test data management instead of replicating entire datasets. This strategy streamlines operations, decreases complexity, and minimizes the risk of data inconsistencies. Organizations can overcome data silos, improve data quality, and achieve valuable insights for informed decision-making using their scalable and flexible architecture. 

For companies planning their AI move, IBM Pak is an available option. It is a pre-integrated, enterprise-grade data and AI platform that assists businesses in accelerating their journey to AI. It offers a unified view of data, streamlines data preparation and control, and allows rapid growth and deployment of AI models. It is also available on-premises or in the cloud.

There are other platforms like Talend, known for its data integration and transformation capabilities. Talend is a data integration platform that gathers, cleans, and converts data from IoT devices. It also offers a combination of connectors to other data sources, making it uncomplicated to build a data fabric. It also offers a set of data integration, quality, administration, application, and API integration capabilities. Their Fabric also supports organizations in getting trusted data promptly, improving operational efficacy, and reducing threats.

The realm of IoT- connecting everything

The Internet of Things (IoT) will become the most powerful domain in the coming years, and data fabrics will be the best solution to encounter and subdue data challenges. They empower businesses to break free from silos and gain a holistic view of their digital landscape. With the help of data fabric, real-time insights become the standard, promoting intelligent decision-making and growing businesses into new frontiers. With the adoption of this paradigm, data fabrics come out as beacons driving organizations to the vast intricacies of IoT data and unlocking endless opportunities.

Smart and Sustainable Livestock Management supported by IoT

IoT-Enabled Livestock Management: A Smart and Sustainable Approach

Food is a basic need for every living being, and the importance of the agriculture industry and livestock farming cannot be underrated. The demand for food, rabbit and poultry meat, has been increasing day by day, and the reason behind this increase is population growth, changing diets, and affordability. This has led to an increase in the number of poultry and rabbit farms worldwide and improved the sensitivity toward animal welfare issues.

Many farm owners have adopted standardized farming management practices to meet the growing meat demands. Many others have added new technologies and innovations like smart farming methods using the Internet of Things and machine-to-machine solutions in their livestock management process.

Adopting the latest farming technology, smart sensors, and livestock monitoring solutions can assist farmers in efficiently managing their resources and enhancing productivity. This adoption also guarantees minimum wastage and less energy consumption.

Value Proposition

Traditional methods of livestock management include inspecting each animal for signs of injury and diseases. Farm owners with large livestock farms often fail to detect ill cattle and face loss. This monitoring method is time-consuming, labor-intensive, costly, and highly erratic.

As per research conducted by Oklahoma University, lung lesions and scarring are found in 37 percent of cattle that had never been diagnosed as sick, and in a trial at the Meat Animal Research Center found that 68% of steers tested showed signs of past respiratory infection.

Although the animals can recover independently, studies show that once cattle have been ill, they cannot catch up to the rest of the healthy herd in health or value.

IoT Data for Livestock Houses and Management

Farmers can optimize their processes, improve animal welfare, enhance traceability, and increase overall productivity by adopting IoT technology offering real-time data on environmental aspects like temperature, gas levels, and humidity.

Such IoT solutions are constructive for monitoring ammonia levels, which causes severe eye irritations and respiratory problems in animals and humans as well. By keeping these aspects under control, farmers can improve their cattle’s health and well-being and enhance the final product’s quality.

Enhanced Farming Practices

Using low-cost and durable smart sensors in an IoT solution for farming is an efficient and cost-effective way to gather and analyze data. The IoT sensors can be implanted in different locations around the livestock houses to gather data on different aspects like temperature, humidity, water quality, gas levels, and many other things.

The sensors can also be installed to gather data in real-time, guaranteeing that the farmer has access to real-time information about their farm’s current status, environment, and operations.

The consolidated and easy-to-install feature of the sensors makes them a feasible option for farmers who wishes to speed up their farming practices without disrupting their procedures. The advanced battery-backed system guarantees that sensors are durable, skipping the frequent battery replacement and maintenance requirements.

IoT Platforms for Livestock Houses

The gathered data is then processed and analyzed using an IoT platform, which can provide insights and actionable suggestions to the farmer. The information can be accessed using mobile devices, authorizing the farmer to monitor their farm from any location at any time. The data provided is in an easy-to-understand format means farmers do not need specialized technical knowledge to understand and utilize the information.

What are the Benefits of IoT-Enabled Livestock Management?

  • Monitor the health and vitality of livestock in real-time, allowing farmers to immediately treat animals and prevent the spread of illness or disease.
  • Track grazing animals to know their grazing patterns and activities and prevent loss. 
  • Collect and analyze past data to identify and understand trends in cattle health or track the spread of illness.
  • Monitor the heat period or birth time, avoiding the loss of new calves and optimizing breeding practices.

Revolutionizing the Farming Industry to Boost Productivity

The success of IoT in reducing disease and mortality rates in livestock houses, increasing output, and optimizing overall operations shows its potential for enhancing farming practices.

The data gathered using the sensors can be utilized to determine the pattern and make informed decisions to enhance productivity, lower costs, and guarantee animal welfare.

Overall, employing IoT technology in farming is the most futuristic approach to overcome the food crisis and counter the increasing demands of a growing population. It has the potential to revolutionize the industry and address some of the challenges encountered by farmers, like limited resources, growing demand, climate change, etc.

We all are very well aware of the significant issue of this era: climate change. The changing climate is hammering productivity and leading to the food crisis. On the other hand, the uncontrollably growing population is amplifying the issue. The only possible way to control both major issues is to adopt the most potent solution that can resolve both issues hand in hand. The adoption of IoT in farming is the most extensive way to overcome both challenges. Not only in farming, but IoT can also be used to control carbon emissions and contribute to slowing down the rapidly changing climate. IoT also helps provide a better lifestyle by offering smart houses, cities, buildings, hospitals, and the list goes so on.