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December 9, 2020
Artificial Intelligence

Why do You Need to Switch The AI-Enabled Storage System?

Artificial Intelligence, the term itself defines a lot about its behaviour, work and outcomes. Artificial Intelligence is like magic which allows machines to think like humans. Companies across the world are potentially utilizing this trait to enhance productivity and stay competitive using better insights. The proper use of AI and extracting the most out of the insights it generates can speed up the slow running business or restore the almost destroyed business. Artificial intelligence should be able to extend from start to end, developing its best version in an effortless and cost-effective infrastructure.

But do you know, AI-enabled systems have to face many issues in storage and deployment? Could AI be able to sort out all these challenges?

Well, data is the fuel that powers AI, but sometimes it gets trapped or stored in a way that it becomes challenging or costly to reach, manage or grow. AI depends on data, and its results depend on the quality of data. Organizations must realize the importance of data they generate, their application, removal of undesired data and the lifespan of the data. Businesses should organize the data, including pit-stops for compliance checks, data optimization, data cataloguing, & data governing. All the above are difficult challenges that organizations need to overcome.

At this point, AI-enabled storages come up into the scene. AI-enabled storage facilitates real-time updates regularly from different enterprise data sources. It optimizes data and performs other smart, automated work on it without any human interference.

Now, the question is- What are the benefits of AI-enabled storage? Let’s know about it. As we know, data is the fuel for AI; it means that existing data storage needs to be transformed into an intelligent and automated storage solution. The storage system should be able to do deep learning, and GPU processors should be capable of providing real-time insights.

Here are the key advantages of having an AI-enabled storage system.

Key advantages of having an AI-enabled storage system:

1) Scalable data storage: AI-enabled systems process massive amounts of data in a short period. This amount of data needs a significantly large storage system. Managing a large amount of data sets demands for a storage system that can be extended without limits or auto-scale as per the need. This demand can be fulfilled using AI-enabled cloud storage.

2) Shared Data Stores: Today, shared data has become more valuable than stored data. Thus, in this data-rich as well as the data-dependent world, AI-enabled storage utilizes modern analytics and AI workloads to deliver extendable storage platform to drive downtime to insights for the better business outcome.

3) Data Insights: AI-enabled storage system uses a variety of analytic tools and processes to provide highly efficient, high-performance data insights about billions of objects and files stored. These data insights are highly helpful for taking major business decisions.

4) Reporting and Alerting: AI-enabled storages help in developing reports on data functions and insights produced. They even facilitate the configuration of alerting systems to get instant notifications for any data storage failure or data exception conditions.

  • For example,
    Power BI by Microsoft is one of the smart reporting system facilitated by AI storage. This system supports Learning Data Lifecycles and helps in managing the storage of particular types of data in the best possible way.

5) Failure Forecast: Storage failure can badly affect the productivity of the business. Whenever storage failure happens, one must find out about the lost data and then restore the data through a backup or copy process. This complete failure to restoration process eats time and reduces productivity. In this situation, AI-enabled data storage system can make things easy by detecting and restoring data from the point of failure.

6) Cost-effective: AI-enabled storage system identifies the useful data and analyzes the pattern in which stored data is used. This helps businesses in saving the extra expenditure spent on managing vast terabytes of data and allows to use data storage capacity wisely.

Why is it beneficial to opt sing AI-enabled storage over average cloud storage?

  • AI-enabled storage allows using cost-effective software-defined storage. This enables the storage of data in a way that customers can efficiently access it for different insight-led actions.
  • The other advantage offered by AI-enabled storage over normal cloud storage is that it provides automation of essential functions like infrastructure, capability, and storage management and maintenance.
  • AI-enabled storage holds a more active and manageable structure.
  • AI-enabled storage can smartly administer access rights; it can dynamically re-route data centre data and automatically regulate data centre cooling. Thus it optimizes energy consumption.
  • The AI-enabled storage system consists of highly smart security features to identify data/packet loss during any transition or within data centres. This feature reduces data loss possibilities, raises availability and maintains speed during downtime.
  • Neural storage is another advantage offered by AI-enabled storage. In this storage system, the storage detects and responds to the issues and opportunities without any human interference.

Wrap-up:

Artificial Intelligence is a technology developed to ease the working of productivity or to analyze user behaviour. Data storage has been an issue, and obviously, it plays an essential role in the growth of the business. The introduction of AI-enabled storage has resolved the issue. It has enabled humans to control, monitor and maintain large-scale data storage in scalable and efficient operations. Today, businesses are generating tons of data but segregating useful data and processing them to get actionable information has been a challenging task. However, an intelligent and automated storage system can ease the job by self-predict and analyze real-time data.

It is clear that Artificial intelligence holds a good scope in future and its collaboration with IoT makes it more precious and advanced. The integration will open up new opportunities and growth mediums for businesses and other organizations.

If you are looking for reliable and professional AI service providers or IoT solutions, then connect us. We’ll offer you high-quality services to make your business efficient.

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admin
November 30, 2020
IoT

8 Ways The Internet of Things (IoT) Can Help Grow Your Business

We live in a golden era where every device we use is connected to the internet in one way or another. The network of these connected devices is what we can call the Internet of Things.

IoT impacts the way we live our lives and make various aspects of our life smarter and more efficient. With smart technology being cheaper, we are now seeing more devices built with wireless connectivity embedded. Modern-day IT companies can use IoT technology to make their business operations more streamlined and improve efficiency in ways that were not possible earlier. Here are a few ways how IoT can grow your business today:

1) Improved business insights and customer behavior

Connected equipment in manufacturing, aviation, agriculture, healthcare, supply chain, and many other industries create more data streams and analytics potential. This means that companies are now gaining much greater insights about their day-to-day business operations and learning more about how their customers use their products or services.

In many of the cases, this has been enabled by cloud platforms provided by Microsoft Azure, IBM, AWS, and Google. But there is also a shift towards edge computing in some industries to reduce latency introduced by relying on third-party data centers.

2) Improved inventory management

The use of IoT will dramatically improve small businesses’ ability to monitor and manage their inventory process. Smart devices and IoT are now making it possible to automatically track and collect items and make overall inventory management and logistics more efficient. This whole comprehensive method is more productive than the cumbersome and expensive task where warehouse workers manually do the scanning and tracking of inventory piece one at a time

3) Personalized information and actions

IoT also brings together personal data so that they can deliver a more personalized experience to their customers.

In this way, E-Commerce retailers can leverage the analytics collected based on the customer’s behavior to offer more personalized information and actions. A good example fo this is where there is a car connected, and the driver receives offers that are tailored specifically to him or her. Alternatively, a family with a smart refrigerator can also receive the same tailored experience based on their personal preferences.

4) Efficiency and productivity gains

By connecting a business’s key processes, leaders can easily identify ways to boost efficiency and increase productivity. Thanks to these gains, companies expect that the revenues may get a boost by up to $154 million due to industrial IoT development, according to a recent report by Inmarsat.

Employees at Ford in Spain use a special suit equipped with Ford’s body tracking technology in partnership with Instituto Biomecanica de Valencia. The experiment involves 70 employees in 24 working departments.

The technology works similarly to motion taking systems that record how athletes sprint on turn or actors move and speak. Ford has been using the same technology to design a less physically stressful workstation to boost its manufacturing processes.

By accurately tracking the workers’ movement, it enables data-driven changes to its vehicle production processes, making them safer and more efficient.

5) Asset tracking and waste reduction

Closely linked to efficiency and productivity are the drives to reduce waste for which IoT tracking is integral. The growing use of IoT, with smart devices and sensors, can reduce unnecessary expenses that occur due to operational inefficiencies in the dress collection process. The urban waste collection process is also quite complex and time-consuming as well as resource-consuming

Therefore, using route optimization where municipalities or waste management companies work together and use smart dumpsters can transmit real-time data to waste collectors. The data then can be processed by the IoT company, which can select the best routes for waste collectors with areas that need a cleanup on priority. This results in an efficient pickup process that is more productive so that it can save fuel and workforce costs.

6) Remote working

Remote working is undoubtedly a trend that is becoming more and more popular as the days go by. For almost a decade, the possibility of working from home is more feasible for workers everywhere, and all the credit goes to development in the IoT.

IoT brings efficiency and accessibility on a level that was never seen before, and this has allowed remote working to be a popular option in today’s modern society. With IoT performing many different aspects of standard work practices, it is no surprise that remote working opportunities are flourishing.

7) Improved business security

IoT technology can be used to boost the security of your commercial office building in multiple ways. For example, wireless CCTV systems can be installed everywhere on your business premises and provide you with 24/7 live feed direct to your mobile devices. Many research data suggests that security cameras can be a handy tool against criminals. Security cameras can also come in handy to monetize star performance, and also it lowers the risk of employee theft.

Wireless alarm systems are also another affordable way to improve your business security and protect your equipment. An added benefit of installing IoT based security devices is that they may reduce the cost of your business liability insurance. Today, all the companies should take advantage of IoT devices that can monitor their buildings, boost their workplace security, and can negotiate lower insurance premiums.

8) New business models

While most of the use cases for IoT lies mainly inefficient productivity and process monitoring, and many companies recognized the scope for it to provide them with information about their customers in how they use their products.

We already have been saying internet-connected cars, coffee machines, trains, and all manner of other smart things that can feed used data back to the manufacturers and operators to build services around those products.

While few of these things were initially designed to be connected but added in IoT, there is a new value to them. These can also help improve the future design wire, the heaps of data gathered from real-world usage.

Companies that successfully can integrate IoT with their products have used benefits ahead in the future. It also allows the organization to shift the focus from conventional business models to new revenue streams. The data acquired from the IoT products hold much value. However, more significantly, customers can be given the option to purchase subscription-based services that offer customers a more personalized and tailored experience.

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admin
November 25, 2020
Edge Computing, IoT

How can Edge Computing Change the Outlook of Manufacturing Industry?

IoT, cloud, AI, ML and Edge have been quite familiar terms for technology lovers. There has been a wrong idea or approach that Edge and Cloud are mutually independent. Though they may operate in different ways; leveraging one does not prevent the utilization of the other. In fact, they powerfully complement each other.

Edge Computing in Manufacturing

With the growth and penetration of the Internet of Things in different sectors, the edge computing framework is also findings its way in several sectors. Today, the most promising edge computing use cases are present in the manufacturing industry as it welcomes new technologies, and these advanced technologies effectively improve performance as well as productivity.

IoT is already providing its best for the optimal result in the manufacturing industry; manufacturers are looking for some platform to boost the responsiveness of their production systems. To accomplish this, companies are adopting smart manufacturing with edge computing as its leading enabler.

Smart manufacturing indicates a futuristic factory where equipment can make autonomous decisions based on operations going on the factory floor.

The new technology allows businesses to integrate all steps of the manufacturing process like design, manufacturing, supply chain, and operations. This provides better flexibility and reactivity at competitive markets. But no doubt, this whole vision requires a combination of related technologies like IoT, AI, ML and Edge computing.

One of the critical reason for gathering analytics at the edge of the network is that it enables us to analyze and execute on real-time data without bandwidth costs that come with sending data offsite for analysis.
We all are well-aware that manufacturing is time-sensitive in terms of avoiding the production of out-of-spec components, equipment downtime, worker injury, or death.

In fact, for more complex, longer-term tasks, data can be transferred to the cloud and coupled with other structured and unstructured forms of data. Thus, this supports that the application of these two different computing frameworks is not mutually exclusive but its a symbiotic relationship leveraging the benefits provided by each.

Why businesses need Edge for Manufacturing?

In the manufacturing sector, the purpose of edge computing is to process and analyze data near a machine that require prompt action in a time-sensitive manner. It demands a quick decision right away without any delay. In traditional IoT platform set up, data produced by a device is collected through an IoT device is sent back to the central network server (cloud).

In the cloud, all the collected data is processed in a centralized location, usually in a data centre. This implies that all the devices which need access to this data or use applications associated with it should be connected to the cloud. Thus everything is centralized, and the cloud is easy to secure and control even if it allows for reliable remote access to data. Well, data processing is completed in the cloud; it can be accessed through IoT platforms in several ways, i.e. via real-time visualization, diagnostic analytics, reporting to support better decision making based on real data.

Now, the question which triggers is that, if everything is quite favourable, then why do we need edge computing. The main problem is that the whole process takes time, and the situation turns complicated when there is a need to take prompt decision based on data.

In the traditional process, the data travels the distance from the edge device back to the cloud, and a slight delay can be critical for taking a specific decision like stopping a machine tool from avoiding breaking. In fact, these IoT connected machines produce a massive amount of data and all the data travelling back and forth between edge and cloud disrupts the communication bandwidth.

The only way to achieve real-time decision making is to adopt edge computing. Edge enabled machines to collect and process data in real-time at the edge of the machine that allows them to respond promptly and effectively.

Edge Use Cases in Manufacturing:

Let’s now check the practical reasons to add edge computing as a necessary thing in manufacturing. There are many business benefits to ensure that all networks are correctly connected to the cloud while providing on-time delivery of powerful computing resources at the edge.

1) Updated equipment uptime:

The adoption of edge computing in manufacturing predicts failure in a subsystem, component or impact of running in a degraded state in real-time. It regularly refines as more data is analyzed and is used to boost operational purposes and maintenance schedule.

2) Decreased sustenance costs:

Better analysis of data for required maintenance means that maintenance can be completed on first visits by providing mechanics detailed guidance about the cause of the problem, required action, what part requires extra attention which ultimately deduces repair cost.

3) Lower spare parts inventory:

Edge analytics models are business-friendly; they can be tailored as per the need of an individual device or system. This implies reading sensors directly associated with specific components/subsystems.

Thus, the edge model describes how the system should be optimally configured to accomplish the business goal, making spare parts inventory more efficient at a minimum cost.

4) Critical failure prevention:

By collecting, analyzing and monitoring data related to components, edge analytics detect a cause for future failure before it affects actualize. This enables early problem detection and prevention.

5) Condition-based monitoring:

The convergence of I.T. and O.T. has allowed manufacturers to access machine data to know the condition of their equipment on the factory floor; either it is new or legacy equipment.

6) New business models:

This is an essential point because edge analytics helps in shaping new business models to catch opportunities. Let’s check an example; edge analytics can enhance just-in-time parts management systems using self-monitoring analysis to predict machine component failure and provides parts replacement notification throughout the value chain. This affirms for a needed maintenance schedule to reduce downtime and parts inventory and ensures an efficient model.

In the CNC machine tool, in-cycle stoppages to the tool are edge decision, whereas end-of-cycles can be a cloud decision. The reason behind this is that in-cycle stoppages require a very low, near-zero, lag time whereas end-of-cycle stoppages have a more lenient lag time. Thus in the former scenario, the machine would have to leverage edge analytics when in-cycle to adapt and shut down the machine automatically to avoid potential costly downtime and maintenance.

Edge and cloud computing

As we already know that IIoT aims to apply the latest analytics to large quantities of machine data to reduce unplanned downtime, reduction in the overall cost of machine maintenance and potentially utilizing the machine learning capabilities. The cloud has been responsible for making this kind of massive data acquisition, transfer, and analysis.

So, if data speed is high and connectivity should be stable and then adopting edge solution is the best option. Therefore it is clear that edge computing will not replace cloud computing but it will complement each other for the optimal result. Thus, integration of edge computing with cloud computing capabilities can enhance efficiency and maximize the productivity of the business.

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admin
November 12, 2020
Artificial Intelligence, IoT

How is Artificial Intelligence Contributing to the IoT Revolution?

Today businesses around the world are extracting the potential outcome by deploying IoT in their business operations. They use it to create new and optimized business possibilities. The firm growth of IoT in various industries is spectacular, whereas AI and its influence on a professional and personal level are considered undervalued. However, combining IoT with rapidly improving AI technologies can design ‘smart machines’ that stimulate intelligent behaviours to make a precise decision without any human interference. Thus, AI-IoT together minimizes chances of error and ensures efficiency along with productivity.

How does AI help in the IoT revolution?

Artificial Intelligence performs smart tasks such as language translation, decision making, voice recognition, etc. and IoT involves a series of interconnected gadgets to transport information over a network. IoT gadgets depend on web connectivity to deliver a fair amount of relevant information about users such as user behaviour, users’ preferences, personal details, etc. It is absolutely wrong to overlook them as they contribute to improving the operations and user experience. However, many organizations have no idea about saving and processing of massive amounts of data which ultimately hinders the growth and potency of IoT.

In this critical situation, AI work as saviour as it helps to store the bulk of data processed by the IoT devices. It analyses and produces a sense of it. Thus, we can conclude that AI is a chief driver which promises to contribute to the exceptional growth of IoT.

Enabling evolving profits for businesses

Earlier, the AI framework was taking care of limited categories of the task. They were not scalable and required human interference. However, advancement in technology has created a successful transformation of AI by involving it into IoT concept. The collaboration of AI with IoT has introduced smart machines which require the least human intervention. The integration of two evolving technology will bring a dramatic change in different industries. Four significant changes that will emerge in businesses after integration of AI & IoT are:

Improved revenue:

Integration of IoT with AI will be profitable for many industries and sectors in terms of generating significant revenues and returns. IoT data providers, IoT gadget, manufactures and companies offering application services based on smart sensors will also enjoy the profits gained by the togetherness of these two highly effective technologies.

Better Safety Standards:

Un-interrupted and regular monitoring allows a business to take prompt decisions to restrict any failure. Up-to-date monitoring improves overall protection and security standards while enhancing productivity. The strict monitoring over different operations even reduces the possibility of harm to lives and assets.

Decreased Expenses:

Assessing the appliances by smart detectors, the sensor installed domestic devices and smart electricity meters cut the extra operational costs in businesses or household works.

Improved user experience:

Smart sensors offer a plethora of opportunities to improve the user experience. These detectors catch the user’s preferences and provide them with options accordingly. For example, smart ac sets the temperature as per the user’s need.

Positive influence on different industries

1) Manufacturing Industry:

Manufacturing sectors like aircraft, automobiles, mining, household appliances, etc. sync smart sensors with their machinery to get future analyses and improve efficiency along with productivity. Industries are endeavouring to develop a wholly autonomous and advanced industrial unit. The installed sensors will assist enterprises in detecting the areas with looming threats, informing about the future maintenance requirement to ensure least machinery falls.

2) Smart Homes:

IoT has introduced advancement in homes too. Today, smart home concepts have taken up speed. Smart homes concept means a house where all the gadgets are connected through a shared network. Integrating IoT with AI will enable devices to interpret the owner’s instruction and make smart decisions, respectively.

A smart home consists of technologies that aim to make lives comfortable by allowing the owner to regulate the device remotely, regardless of where she/he is currently present. It even ensures better safety and security. For example, an owner can pre-plan the time at which he/she wants coffee, or when TV should turn on to watch their favourite show.

3) Body sensors:

Smart detectors have made the lives of patients and doctors easy. These smart devices sense different activities to maintain good health. Today, pharmaceutical companies are investing in medical sensors that can assist patients in keeping a track on their health. In the covid-19 situation, most of the hospitals are offering virtual consultations to their regular patients. Smart sensors are tracking the health of patients and sending health alerts to the doctors if any changes appear. This technology has allowed everyone to follow social distancing to minimize the chances of Covid spread.

For example, sensors evaluate blood pressure, blood sugar levels, etc. and send a notification if any changes occur.

4) Airlines:

Sensors have also become part of the aircraft to monitor the looming risks and errors and to assure safety before any mishap occurs. These smart sensors predict probable errors and calculate the degree of severity to decrease aircraft downtime and ensure safety. Airline companies also use sensors to detect the maintenance issues that typically cause flight delay and cancellations.

5) Oil Rigs:

Oil industries have to spend a fair amount on obtaining oil drilling machinery. Oil companies can use smart sensors to get a timely notification for servicing and reduce the operational cost. Sensors attached to the machines provide the information about the condition of devices and send the alert for maintenance or servicing before any damage happens to a machine.

Wrapping Up:

IoT and AI are technologies which seem to complete each other through one or another way. This deadly combination will change the outlook of many businesses and industries. It will even improve our personal and professional lives in a very positive way. Thus, undoubtedly this ultimate combination is highly beneficial as it will open up new opportunities for growth and innovations—the addition of AI in IoT concept can ease the business processes, improve productivity and provides enhanced user-experience.

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admin
November 5, 2020
IoT

Plan Your IoT Real-Time Data Streaming Process

IoT is the new demand, or we can say the requirement of technology. The changes, disruptions, upgrading, business demands, customers’ demand, everything queues to urge for the adoption of IoT. However, the adoption of IoT isn’t profitable until you are not stirring the best out of it. Nowadays, the most discussed topic in IoT is IoT real-time data streaming process. IoT data streaming is essential as it provides real-time decision making that is significant to many operations. An organization should have tools to accumulate data from sensors and devices then process it and transfer it to the database for analysis and real-time results.

Data streaming enhances performance and saves from the upcoming mishap as it sends alerts that prompt interference. For example, in a refrigerated truck, if a sensor reads a decrease in the required temperature, then IoT real-time data streaming and AI models will provide alert that the needed temperature is disturbed and it might cause the spoilage of the product.

Why organizations need IoT data streaming?

Well, data streaming is required to attain smooth operations and to get prepared for future challenges. It allows organizations to take prompt decisions and actions.

In short, Organizations use IoT data streaming for:

  • Detecting illegal network access
  • Recognizing upcoming machine failure before it actually occurs
  • Monitor the changes happening in a patient’s health and notify the doctor for the same before any mishap happens

Not just this, real-time data streaming also improves the organization’s competitive position. Let’s check an example of a clothing store. Today, some of the clothing stores are installing smart mirrors in their shops to provide better customer experience. Through smart mirrors, potential customers can virtually try on different dresses or items or get a specific look without any hassle of physically trying them on.

As per the market researcher MarketsandMarkets, the streaming analytics market may grow from $12.5 billion in 2020 and extent to $38.6 billion by 2025 at a compound annual growth rate of 25.2%.

IoT applications and expansion of GPS and geographic information systems that are used to monitor, trace and map events in real-time is essential pillars of data streaming process.

Do you know how the data streaming process runs?

The data streaming process involves three components: software, an operational database and an analytics engine. The operational database runs in real-time, whereas analytics engine extracts the data to provide insights.

For first-time data stream deployment, organizations gather all the three crucial components, which need awareness with the process steps and understanding of the complexities of the tools which are used in each step of the operation.

The first step involves ingesting the IoT data using some message streaming software or message brokers like Amazon Amazon Kinesis Data Streams or Apache ActiveMQ.

Once the ingestion is completed, in the next step ETL tool, i.e., extract, transform and load tool prepares the data for import into an analytics database. This type of process is typically an operational database based on a SQL platform. Organizations must create real-time analytics and machine learning models and applications to obtain useful business insights from gathered data.

It is noticed that many IT departments adopt this methodology, but with the emergence of the latest technology, there are many automated methods and platforms. There are data streaming and analytics services or platforms which simplifies the architecture and mechanics like Splice Machine SQL database and Confluent Platform or machine learning models.

What are the four best data streaming practices?

Organizations that build their process from the start and are looking for an off-the-shelf offering should check the following four best data streaming practices.

  1. Opt narrow business cases: The first practice one should follow choosing business cases which are solely dedicated to IoT data streaming that provides efficiencies, customer satisfaction, cost-saving and increased revenues. For instance, use of IoT data to detect which machines require high priority maintenance and if not provided then will fail on any assembly line, monitor network endpoints to block unauthorized access/ attacks or to trace the positions of the shipment.
  2. Simplify the design: Organizations should simplify the data streaming architecture to accelerate the time from insight to streamed data and cut-off the excess manual-coding. There are tools like Apache Kinesis Data Streams that automates the data ingestion operation, automate the ETL transportation of data into databases and removes the dependency on IT to facilitate the above function through extra coding. Splice Machine database also simplifies the operations as it automatically stores test database sandboxes. Thus, a user has to issue a single command and does not need manually set up the test database by a data analyst.
  3. Clean the data: Everything is essential in the data streaming process. Clean data is equally critical as architecture. The data cleaning process occurs at the time of data ingestion and happens in the processing/working of an ETL tool. Well, if an organization desires to automate portions of these operations, then must contact and work with their vendors and vendor’s toolsets to promise that they are capable of meeting the cleaning needs.
  4. Acknowledge the near-real-time & batch process: It is not suggested to perform every analytical function in real-time. Some data processing are periodically performed, near-real-time intervals like after every half-an-hour. In batch processing that is delivered during the day or overnight is also significant. Before the implementation of the process, organizations should find out which process need real-time and set workflows accordingly.

Well, to churn profits from IoT, it is necessary to know how to efficiently use it. IoT, along with other technologies, allows you to get a clear vision through data. The useful data enables you to take prompt decisions, and actions to stop the looming loses and mishaps. The IoT data processing is the best way to enjoy the best of IoT and make business operations smooth and efficient. Connect reliable and proficient IoT service providers to know more about IoT, get idea and services for IoT data processing. Your business needs IoT touch; don’t waste your time anymore. Connect us.

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admin
October 29, 2020
IoT, Technology

How AWS IoT Things Graph Simplifies Hardware Integration Process?

Businesses are dependent on raw material; demand in the market and last but not the least, employees. Every operation, strategy, decision making and supervision, everything requires human interference. However, the sudden intrusion of Covid-19 and its ongoing havoc has disrupted regular lives, market and businesses. Business leaders are unable to take any decision without estimating the risk pandemic can cause for workers.

Every time an employee visits the workplace to do some manual task becomes vulnerable to COVID-19. The risk of getting affected by coronavirus has urged businesses to think about software and sensors to automate tasks which were previously operated by humans.

The inclination toward having an automated business process has led to the adoption of IoT. Still, most of the time business owners forget about the most crucial and complex aspect required for the transition, i.e. hardware integration.

No, doubt hardware integration remains one of the most painful processes needed to create customized IoT solutions. In this blog, we’ll check the challenges which emerge while having hardware integration. We’ll also learn about AWS IoT Things Graph and how it helps in making hardware integration easy and painless process.

What are the challenges which emerge during Hardware Integration?

Uninterrupted communication is a critical demand during hardware integration. To get a clear view, let us take an example of an orchestra group. Suppose you own an orchestra consisting of musician belonging to different countries, and all are preparing to play Mahler’s Fifth Symphony. But the catch in this concept is there is no conductor. Absence of conductor would not stop the musicians for performing the piece, but it would not sound great and appealing. Not just this, there would be interruption by hiccups, miscommunication and argument for how the music should be performed.

Well, for mind-blowing performance musicians need alignment, ability to community with each other efficiently, and management of complex interactions between different groups within the orchestra. This implies that the orchestra needs a conductor for a significant result.

Hardware integration encounters similar problems. Your business process consists of a ton of devices that has to work collectively to perform a specific task and get a particular outcome. Furthermore, each hardware vendor speaks in different protocols, so it becomes tough to understand, and you cannot use devices out of the box directly.

Thus, it is evident that the lack of usually shared standards in the industry or business creates confusion for developers.

In the end, developers have to opt for a time-consuming solution. They waste time in repeat integration works for several devices when the devices are fundamentally the same (LED light bulb of different brands). Hardware integration is challenging and complex because you need an expert to understand the working of different parts and how all of them joins to create a big picture.

Any idea about AWS IoT Things Graph and why should you use it for hardware integration?

The best way to define AWS IoT Things Graph is a conductor which IoT space requires to show its best version. AWS IoT Things Graph is a service developed to resolve challenges occurring in hardware integration and helps in faster development of IoT applications. It enables you to connect different devices and web services in a plug-and-play way to end business difficulties.

Getting back to orchestra example, conductor in an orchestra aligns musicians, administers the interpretation and controls the pace of the music, monitors critically and sets a single vision.

The four fundamental features of AWS IoT Things Graph are:

  1. Models
  2. Visual User Interface
  3. Work at the Edge
  4. Monitoring Flows

The model represents things, i.e. devices and web services. Models are an essential feature of AWS as it cuts the time of developers spend on worrying about underlying implementation and allow them to concentrate on using the model abilities. It is often observed that developers have to spend a lot of time ensuring that the messages between various model are communicating correctly or not.

AWS IoT Things Graph simplifies the work of developers and enables comfortable working with devices and services. Additionally, models are like reusable building blocks that developers can share with each other and avoid repetitive integration efforts.

The AWS IoT Things Graph UI lets you determine interaction happening between various models through flow diagram connecting outputs & inputs. It empowers you to design how devices & services should communicate with each other and allows you to create multi-step workflows. So, this gives more time for developers to focus on business logic & functionality than repetitive integration.

In IoT, the biggest challenge is updating the legacy systems with new hardware. Earlier, your approach was limited to a specific ecosystem, but AWS provides you with the freedom to change hardware without rebuilding everything from the start. AWS provides you with flexibility and freedom to emphasis on your application promptly whenever customer demands the better resolution.

AWS IoT Things Graph permits your business to run at the edge with just a few clicks from the AWS IoT Things Graph console. The steps to take up are- define your perimeters, connect models with your real-world devices, schedule when to execute, and station applications to the cloud or AWS Green IoT Greengrass device. Thus, we can conclude that AWS IoT Things Graph charters a path which helps you moulding the concept into a product which is present in the market place.

AWS also allows you to monitor the present condition of the IoT application. This monitoring feature is highly advantageous as it informs you about where any flow or steps fail. You can fix the issue immediately without disturbing any other process. When connected with Amazon Cloudwatch, you can get access to log flow and execution time to check any disruption or steps failure. You can set the metrics which you are concerned about, and you’ll get notified when any changes occur.

Conclusion:

AWS IoT Things Graph is developed to assist application developers and help them in tackling the unique challenges that appear during hardware integration. The adoption of this service ends the long dispute existing between developers and hardware vendors on lack of the standard. It ends the repeated integration efforts and allows developers to concentrate on essential tasks like building features to overcome business issues. IoT is the most critical requirement of the time, but it is hard to implement in the business. AWS IoT Things Graph for hardware integration provides speed, simplicity and customization needed to build IoT application. The use of AWS IoT Things Graph can improve your business operations and assists you in making an idea into reality using IoT. Thus it is a smart decision to use AWS IoT Things Graph for painless hardware integration.

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admin
October 22, 2020
Enterprise Mobility

How to Implement Successful Enterprise Mobility Solutions in Your Business?

The world has turned small through a small device carrying immense capabilities, i.e. mobile phones. In this mobile era, everything is available to your doorstep in a click. As per surveys, most mobile users are spending a minimum of 5 hours a day with a cell phone and occupying 70 per cent of web traffic. The growing interest of people in mobile phones has transformed the business strategies and outlooks too. Most of the companies are planning to digitize their business strategies and addressing mobile phones as the most effective platform for futuristic growth.

Leaving the old-fashion desktop culture to satisfy business needs, companies are actively adopting a mobile-first approach to the corporate technology stack.

Why do you need Enterprise Mobility?

Enterprise mobility enables a business to use mobile solutions throughout their entire organization. It offers enormous benefits to the organizations and promises unstoppable growth as now a more significant number of consumers and employees depends on mobile devices. After developing a potential enterprise mobility strategy, the next step is to plan and design an enterprise mobility application while ensuring unambiguous app management across the organization.

Chartering a successful enterprise mobility strategy to run a successful business is not an easy task as it requires dedication and forethought. Business differs, so the strategy also differs. Different organizations demand different mobility services. Organizations use enterprise mobility to cut the operational costs, to enhance employees’ productivity and to promote collaboration.

A flourishing enterprise gives priority to personalized objectives to assist the growth of enterprise mobility strategies. Businesses develop goals and purposes while keeping in mind how enterprise mobility apps will influence their:

  • Business
  • Employees
  • Customers

Enterprise mobility strategies ensure,

  1. Increase in productivity to unique levels
  2. Explore more opportunities to become more efficient
  3. Uncompromising security from hackers, malware, and other security issues
  4. Huge reductions in costs
  5. Good portability and Continuous content sharing
  6. Enables data tracking and analytics

Read More: The Ultimate Guide to Mobile App Monetization

Not just this, enterprise mobility solutions opens a route for a business to stay on top in the competition and churn maximum benefit.

Let us know the three irreplaceable ways to launch enterprise mobility solutions. It will empower organizations to meet all upcoming challenges courageously and positively.

1) Be bold while embracing enterprise mobility:

Adoption of modern enterprise mobility solutions is not simple as it demands generous funding and management of upcoming changes. This particular reason forces entrepreneurs to think about the difficult choice between upgrading existing technologies or completely replacing them with a more advanced one.

Thus, choosing between the two requires good courage. Well, the choice might appear harsh and financially painful. Still, the outdated system causes hidden costs along with risks, so the smart choice is to forget obsolete legacy systems and embrace the latest technology.

Once decided to opt for the latest technology, here are the goals to pursue to develop a quality solution:

  1. The first goal is to establish a high level of security and create data protection personalized as per the need of the business.
  2. The second goal includes the introduction of the cloud to your organization. Familiarize cloud technology including its both public and private levels for every user.
  3. The third goal is to provide quick access to data and the availability of regular updates and ensure immediate support.
  4. The fourth goal is to boost profits by subtracting extra expenditure and enhancing employees’ productivity.

2) Charter a strategy for an enterprise mobile application:

Jumping into enterprise mobility approach without any knowledge may lead to a considerable loss. The second success promising way is to know the factors that affect the price of a mobile app. Well, complexity, creators’ business logic and other features intended to be added by a developer increases the cost of mobile app development. Let’s see how different aspects influence the cost of mobile app development.

  1. Functionality: Addition of many functions can complicate the development of a mobile app, but it also promises to improve business processes and make it more efficient, effective and uncomplicated.
  2. Platform: The choice of the platform also determines the time required for the development of a mobile app.
  3. Integrations: An option available for smooth integration optimizes the implementation of enterprise mobility solutions.
  4. App Security: To guard essential company data, it is mandatory to invest in in-app security to eliminate possible vulnerabilities.

Read More: Six Inevitable Steps to Bring Digital Transformation in Your Business

Now, here are a few recommendations which fall in ‘must-follow’ to ensure successful enterprise mobility management strategy in the long run.

  1. Define: You need to have clear and reasonable visions for what you want from your project and how enterprise app will support your business in meeting organizational goals.
  2. Client: While choosing functionalities, think about the needs of customers. Choose functionalities which are user-friendly and can add value to customers.
  3. Minimal Viable Product: Determine a minimal viable product to estimate its prospects on the real market and probable possibilities for high demand among users.
  4. Platform: Find a suitable platform which can meet all your needs without disturbing your already decided budget while considering the population of targeted users.
  5. Budget: Don’t forget to designate one-fifth of the total budget for app maintenance as it can ensure corporate security, effective engagement of users and improved performance of an app.

3) Adopt the newest trends in Enterprise Mobile App Development:

To enjoy the best of the solution, add the newest and most advanced technologies in your enterprise mobility solutions. This will intensify the efficiency of the solution and competitiveness of your organization at the global level. Here is the list of most modern trends and innovations to count on for successful enterprise mobility solutions.

  1. Addition of augmented and virtual reality in your mobile app solution.
  2. Employ m-commerce rather than e-commerce.
  3. Improving supply chain management by adopting an enterprise app.
  4. Addition of AI (artificial intelligence) for in-app automation and better user support.
  5. Adopting IoT technology to enhance app personalization and get better analytics.

Summary

The decision to get a mobile app for business growth can never be regretful. Business mobile apps enable you to manage the different needs of the business, from employee engagement to lead generation and supply chain management. So, never regret your decision and invest some time to draft a well-examined strategy. Don’t miss to add the latest trends to your enterprise mobility solution to make it more efficient and profitable.

If you are still using the old-traditional way to promote your business and expecting for optimal result, then reconsider your decision. Contact us now to get successful Enterprise Mobility Solutions.

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admin
October 12, 2020
Cloud Computing

Why does Your Organization Need to Shift to the Cloud?

Today organization, either small or medium or large, are looking for more business value from their data. Business data has become spinal of businesses because every decision and actions are dependent on it. Thus, the increased dependency has increased the pressure on data executives to access, manage and distribute and analyze the data coming out from different sources before it becomes worthless.

The task of processing the volume data are both challenging as well as expensive with legacy systems, architectures and storages plans while shifting organizations towards cloud migration. The shift to cloud migration can cut off the cost and increase access and viability.

Well, the reason for a shift can be different for different organizations and is based on the organization’s need.

Here the list of few common reasons for shifting to the cloud:

  • Consumption-based model: The consumption-based model is one of the beneficial advantage offered by the cloud. The high price of storage, servers and operations designed for on-premises implementations are the reason for the switch to clouds. Cloud provides a utility-based model which allows user to pay for ‘what and when’ it is used.
  • Value-added insights: Aquiring value-added insights from the data is a goal of organizations. The available ongoing costly solution requires high maintenance due to the complex and massive data flow. The traditional data warehouse solutions are not capable of meeting the needs of an organization and does not provide value-added insights for better results.
  • Reaching end-of-life/end-of-support: The traditional platforms and technologies are not capable of reaching end-of-life/end-of-support or scale-up. Cloud Migration that results from data warehouses is capable of reaching the end of life/support by the provider.
  • Leveraging analytics and AI/ML: Old platforms and warehouse solutions disappoint in leveraging analytics and AI/ML for extracting better business insights. They fail to support organizations in meeting the set objectives and goals. On the other hand, they require a high cost of maintenance too.

Well, if you have decided for cloud migration, then let’s get to ‘where’ and ‘how’ questions directly.

From where to start for migration?

Suppose an organization has made a mind for cloud migration, then the most important question which strikes in mind is ” where to start?”. Cloud migration is no different from any large transformational initiatives as it involves a logical starting point. The already existing data warehouse is actually a starting point because it stores a large amount of data.

Read More: Six Inevitable Steps to Bring Digital Transformation in Your Business

How to start?

The database migration or complete end-to-end cloud migration requires a whole focus on the source of the transformational initiative.

Some of the success factors which can help in cloud migration are as follows:

A) Developing a strong business use case resonates across the organization. It ensures clarity, provides vision, offers strategic guidance for initiative and provides a methodology to measure success. A well-designed business use case involves different technologies, platforms and IT. It provides a common framework to deliver optimal value.

B) Keeping knowledge of current state and future state while sharing a common perspective across the organization is essential. This involves cross-checking the technical architecture, understanding the cultural and political dynamics present around the initiative. When everything is clear that is a solid understanding of the current state and its requirement, future goals and objectives and existing gaps, then a precisely planned roadmap can be efficiently followed for near-term or long-term value.

C) While undertaking a data-driven program, one should follow the set standard and requirements for collection, identification, storage purposes. Data governance involves unstructured data, semi-structured data, structured data, registries, taxonomies and ontologies as it contributes to organizational success through regular and compliant practices. Guidelines from administration need to discuss all types of new data requirements that must be included as a part of any new program. Thus this must be addressed at the source to ensure that the resulting insight can be trusted to assist the organization in achieving value from the investment made.

D) Precise planning and keeping an eye on future guarantees success. Today, new tools and technologies are available in the market. Creating a well-planned data strategy allows organizations to scale-up and nurture their investment in a most-favourable manner. Data strategy recognizes the critical skills required and what is needed to achieve business objectives, plans and strategies.

A complete data strategy will also help in overviewing data management and assures that all essential steps involved in the modernization process are followed. The modernization process involves data migration, cleansing, standardization, and governance.

To confirm long-term, scalable and sustainable data program, an organization must go for single approach instead of being involved in different projects for the same purpose. Disparate projects do not lay the required right foundation for business transformation. An enterprise data warehouse implementation or modernization is not an easy task. Still, a precisely-designed, well-socialized, futuristic strategy supported by the right level of capability can assist organizations in achieving their objectives.

Prompt Softech is an IT company working with a motive to elevate businesses through innovative and smart technologies. The IT company assists businesses which require cloud migration. The Softech company gives priority to the current need of businesses and delivers the most fitting solutions. If you wish to benefit more from the data, then must switch to the cloud and enjoy the advantage by generating the business value data.

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admin
September 30, 2020
IoT

IoT-Enabled Smart Collar to Improve Cattle Management

The present COVID-19 situation has gripped the crowd-culture and demands for the minimalist culture, which means the least human-interaction, the more safety assurance. Corona-virus has limited the gathering at everyplace, either its an office or a shopping mall. In this situation, it has become a more challenging task for a rancher to keep a track on every cattle with least number of helpers.

Earlier, in the presence of labours, it was not easy to keep a track on a large herd because cattle management includes a lot more than you think. The day usually consists of feeding, cleaning, vaccination, health monitoring and anytime heat stress, anytime artificial insemination requirement and anytime calving. Farmers have to be all-time available to run a successful cattle farm.

Today, the Internet of Things has made many jobs easy in different sections. The need for time and the existing loopholes in cattle management has led to the innovation of IoT-enabled collars. The smart collar is the outcome of the collaboration of advanced technologies.

The IoT-based and cloud-connected collars monitor rumination, movement, activities, body temperature of the cattle. It collects the data and stores it on the cloud for analysis of pattern and behaviour.

How smart collar helps you in managing cattle remotely?

The GPS enabled cattle tracking collar provides the real-time location of your cattle. The collar sends the signal every 15 minutes at your livestock management dashboard. This process allows you to track the movement and location of your cattle at any time from anywhere. The IoT-enabled collar collects and stores the data on the cloud for future purposes. Farmers even install specific geofences and alerts to get the notification if cattle move out of a designated area.

If you own a large farm, then it would have become a more difficult task in this pandemic situation with less number of helper. Maintaining a record of each cattle becomes an arduous task. A bit of careless attitude might end up into a loss of 3,000-3,500 bucks per missed heat per animal. The sensor-enabled smart collar helps you to skip from all the undesired loss.

How IoT-enabled smart collar works?

  1. The smart wearable catches the movement and records the data. It predicts the estrus based on the change in behaviour, movement and activity pattern.
  2. The IoT-enable collar compares the daily activity of cattle and notifies if any change in pattern appears.
  3. The smart collar also compares the movement of each cow with the heard and alerts if any change shows.
  4. The IoT-enabled collar detects the patterns which only appears at the time of estrus and inform promptly.

Read More: Why is IoT Impossible Without an Open Ecosystem?

Smart collar curtails the undesired losses:

The reproduction cycle of a cow is very critical. For instance, missing the single heat stress of a cow can disturb a whole reproduction cycle and cause considerable loss. Suppose, if a farmer misses the heat stress many times or faces failure in artificial insemination, then, this situation might turn out to the worst result.

Therefore, this new data storing wearable tag eases the management work by notifying about the peak heat-stress period.

Usually, cattle management solutions include smart collars, receiver unit and antenna.

  1. It offers remote tracking of cattle and informs about its current location.
  2. It records and stores ambient temperature, rumination profile, cattle movements on the cloud.
  3. It alerts for oestrus period and animal health.
  4. It sends a timely alert for general health, lameness, oestrus, digestive health etc.

Reasons to have an IoT-enabled cattle management system:

The most straightforward reason is to minimize the loss of cattle, maintain good health and yield benefit. The other reasons are:

  • Cattle management solution can monitor ‘n’ number of cows.
  • It stores data and allows farmers to access it anytime from anywhere.
  • It reduces dependability on labour.
  • It provides a highly accurate result.
  • It alerts farmers if any cow is ill or lost.

Undeniable Advantages:

The inclusion of a cattle management solution can help farmers in all aspects. It improves farm management, increases the breeder’s efficiency and optimizes the input costs like labour, hormonal treatment, semen etc.

It lowers the dependency on human resources and assures for no human error. Farmers can access the data and can edit it as per the existing situation. The solution also helps the farmer in tracking and recovering stolen cows.

Conclusion:

The introduction of smart cattle management has solved the problem of poor breeding performance. The IoT-based cattle management solution is economical, robust and user-friendly. It has confirmed a positive impact on the growth of yield and health of cattle. It has reduced livestock theft and can count stock within a matter of minutes. The cattle management solution keeps a track on every individual cow and alerts the farmers if any change occurs. The adoption of revolutionary innovation in cattle management can change the outlook of livestock management. It contributes to saving money, efforts and time as well.ive

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admin
September 22, 2020
Mobile Apps

The Ultimate Guide to Mobile App Monetization

The emerging competition and latest technology introduction recommend having an eCommerce app for the business. Most of the companies turn their vision into a reality by developing a mobile app but is it enough?

If your e-commerce app is not making money, then it is an absolute waste of money, because every investment needs to prove its worth.

However, the biggest obstacle in converting an app into money is the lack of knowledge and guidance. So, consider reading this blog to know “How to make money with an app.”

How to Make Money With an App?

Mobile App monetization involves two strategies-Direct Monetization and Indirect Monetization.

Direct monetization is a simple and most popular one. Many companies adopt it, whereas indirect monetization is a bit complex.

1) Direct Monetization:

In this monetization strategy, money is generated directly from an app. If someone downloads your business app from the Apple App Store or Google Play Store, then the money is generated from the app. When a customer uses your business app to buy something, then the sale is generated from your app.

2) Indirect Monetization:

This monetization strategy is complex but famous for software products. You can make money by developing an app, but the actual money amount is not bound to the app. In this you are making money either app is used or not.

Top 6 App Monetization Models and Strategies:

There are six popular strategies to generate money with an app.

A) Paid Downloads:

This model is the quite obvious and simplest one to generate money with an app. You can charge a fee to download the app, but the charges should lie in the range that users are in use to pay.

Though the paid-download model is a straightforward app monetization model, the drawback is fewer downloads. Many people prefer free apps instead of a paid app. However, paid-users will be highly interested and will try to get the optimal result. If users are using the app often, then you could benefit more from them with other app monetization models.

B) In-App Purchases:

The in-app purchases monetization can be used for paid as well as free apps for physical and virtual products.

For instance, most of the gaming app uses this in-app purchase model for virtual coins or experience upgrades. Players spend money to purchase weapon, vehicles, map, lifelines etc.

Pokemon Go, one of the popular games among kids, has generated more than $3 billion in revenue after being a free app.

Pokemon Go is an excellent example of a free mobile app that makes money from in-app purchases. The game sells “PokeCoins” which is a virtual currency to pay for upgrades within the app and improves the gaming experience.

But this method is not just limited to gaming apps, and many e-commerce websites can build an app in the same model to increase mobile sales. Suppose if a customer purchases something like shoes, books, t-shirts etc. which you are selling online from your app, then it falls in this model only.

C) Subscriptions:

This model is used to generate recurring revenue with an app. You can customize the subscription package of your e-commerce app on Google Play Store and Apple App Store. In this model, a user needs to sign up for the subscription, and he’ll be charged on a recurring basis until he doesn’t cancel it manually.

Most of the subscription apps are on a monthly basis, but you can customize it to annual or quarterly billing cycles as well.

The subscription model can be used for a wide range of industries. For example, if you have built a fitness app, then you can set up a monthly subscription to access workouts, videos, and training. In fact, this model is fit for small businesses like local dry cleaner to offer pick-up and delivery cleaning services.

D) Freemium Model:

This monetization model lies in between subscription and in-app purchases. The word freemium is a combination of two words-Free + premium = freemium. This implements that you need to offer your app for free to use this freemium monetization strategy. The next step is offering a different version of your app, that is free & premium.

In this model, the app offers a free version having basic features, and the upgraded one is the paid version to deliver supreme experience.

For example, a music app offers unlimited songs to users, but it is full of advertisement and download is not free. To enjoy advertisement free and free song downloading, the user needs to take a subscription. Another way to allure users is by providing a free trial of the paid version. This offer increases subscription chances and enhances user-experience.

E) Advertisements:

Advertisement is another way to generate money through your app. In this method, you sell space available in your app for advertisements. Now you might be recalling the ads you have seen in websites or apps. App advertising can be of different types and sizes like banner ads, video ads, pop-ups, native ads, interstitial ads etc. There are different revenue models available in this strategy:

  • Cost-per-install (CPI)
  • Cost-per-click (CPC)
  • Cost-per-action (CPA)
  • Cost-per-mile (CPM)
  • Cost-per-view (CPV)

The payment depends on the type of advertisement and other factors.

F) Product Extensions:

This strategy falls under indirect monetization and is absolutely a great way to generate revenue for your business, service or product, but as we have already shared, money won’t be generated directly to actions within the app.

For example:

QuickBooks is an online accounting software with more than 7+ million customers worldwide. This paid platform administers more than 50% of the accounting software market share. Anyone who buys this software gets free access to iOS and Android mobile app. This app does not generate revenue directly but having this software provides a native mobile experience which adds value to the product.

In fact, having a product extension also helps in differentiation you from the competition.

Wrap Up:

Though app monetization remains one of the tough tasks for developers and business owners, but it is one of the important purposes behind the development of an app. The primary step involved in the monetization process is to understand the available ways for it and figuring out which one will suit best for your business.

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