Data Analytics

Which solution is best for your Connected Device- Edge or Cloud Computing_
admin
September 5, 2022
Cloud Computing, Edge Computing, IoT

Which Solution is Best for Your Connected Device – Edge or Cloud Computing?

If you have adopted IoT and are developing an IoT-connected device, you may wish to do some valuable computation to resolve the important issues that have been hindering growth. You might be desiring to install sensors in remote locations, create a device that can do data analytics to watch a renewable energy source, or develop health-related devices that can detect the early signs of diseases.

While creating the IoT-enabled device or IoT solution, at some point, you might get into a dilemma where you have to choose between edge or cloud computation. But what would be best for your device? Where should your device do the valuable computations in the cloud or at the edge?

Selecting between computing on edge or cloud can be an impacting decision, like it can influence a device’s efficiency or cost. Therefore, everyone does great research and thinks twice to avoid the cost of making the wrong decision and then the money spent correcting it.

What is Cloud Computing?

Cloud- It is a collection of servers accessed over the internet. Some renowned cloud providers are Microsoft Azure, Amazon Web Services, and Google Cloud. 

These servers offer on-demand computing resources for data processing and storage purposes. You can easily say that cloud is a centralized platform for storing your files and programs, and you can easily connect any device to the cloud to access the data. Some of the cloud-based services are Dropbox or Google Drive etc. 

Cloud computing is the process of doing computation in the cloud. These computations include data analysis and visualization, machine learning, and computer vision.

What is Edge Computing?

Edge is described as the “edge” of the network that includes devices at entry or exit points of the cloud, but it is not a part of the cloud. For instance, a server in a data center is part of the cloud; however, smartphones and routers that connect to that server are part of the edge. 

Edge computing can be defined as the process of performing computations on edge. In this, the processing is completed closer or at the location where data is collected or acted. 

One example of an edge computing process is object detection attached to an autonomous vehicle. The vehicle processes the data from its sensors and utilizes the result to avoid obstacles. In this process, the data is processed locally rather than sent to the cloud.

What are the points to be considered?

Before opting between edge and cloud computing, a few key questions must be considered.

Quality of Your Device’s Network

Conducting computation on the cloud can be beneficial if you have high bandwidth, low latency, and a sturdy connection to the internet, as you’ll have to send your data back and forth between cloud servers and your devices. If you have to use your device, for example, in an office or home with a steady internet connection, this back and forth can be done seamlessly. In most cases, if computation is conducted on edge, it won’t be affected by the bad or lost internet connection in a distant place. The processing can continue as it is not performed in the cloud. You would never want your vehicle’s objection detection to be failed while driving on the road. It is one of the reasons why autonomous vehicles perform computations like object detection on edge.

How Swift and How Often Does Your Data Need to be Processed?

Edge computing can be best suited in cases where customers demand response times from devices prompt than waiting for it in a decent network connection, such as monitoring components of the device.

The latency of the travel time between the cloud and the device can be minimized or eliminated. It means data can be processed immediately. It implies that if data processing is quick, one can achieve real-time responses from the devices. Cloud computation is also useful when device use is unsteady. For example, smart home devices running computation in the cloud allows sharing of the same computing resources between multiple customers. This decrease costs by restraining the need to provide the device with upgraded hardware to run the data processing.

What Part of Your Data is Crucial to You?

Computing on edge is helpful if you are only concerned about the result of your data after it has been processed. One can only send only important things for long-term storage in the cloud, which may cut down the expense of data storage and processing in the cloud. Suppose you are developing a traffic surveillance device that needs to inform about the congestion situation on the road. You could pre-process the videos on edge- instead of running hours of raw video in the cloud-one can send images or clips of the traffic only when it is present.

Do you know Your Devices’ Power and Size Limitations?

If you think your device will be limited in size and power, provided it has a strong network connection, sending the computing work to be done on the cloud will permit your device to remain small and low-power. For example, Amazon Alexa and Google Home capture the audio and send it to the cloud for processing, letting complex computations run on the audio as it can not run on the small computers inside the device themselves.

Data Processing Model Your Intellectual Property?

If you are creating a device for costumer and the methods you are adopting to process data are part of Intellectual Property, you must rethink the plan to protect it. Placing your IP on your device without a proper security plan can make your device vulnerable to hacks. If you are unaware of resources to secure your IP on edge, it is best to opt for the cloud, which already has security measures.

Final Reasons for Choosing Between Edge and Cloud Computing

Hence, we can conclude that one must consider a few things when choosing between computing on edge or the cloud. In complex issues, you might find the combination of both very beneficial by leaving some parts of processing on the cloud and rest on the edge.

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admin
June 7, 2022
IoT

Role of IoT in Electric Vehicle Monitoring & Management

Today we are witnessing the temperature rise, and one of the major reasons behind this is air pollution. The emission of global house gases is worsening the situation, and its continuity might leave the earth unfit for humans in the coming years.

Vehicles are one of the major contributors to air pollution; therefore, it has become a major issue to look after. Today, people and the government are looking for ways to handle this issue. One of the best ways to control air pollution is by replacing fuel-based vehicles with electronic vehicles. Electric vehicles (EVs) are a new and environment-friendly innovation in this direction.

Electronic vehicles are hi-tech machines that collect an immense amount of data to deliver optimum performance. The performance parameters incorporate monitoring speed, mileage, acceleration, battery management, fault alert, charging, and predictive maintenance systems. Therefore IoT plays a crucial role in the monitoring of electric vehicles.

What is the role of IoT in Electric Vehicle Management?

Let’s know each aspect of an IoT integrated Electric vehicle management system and how they help obtain the optimal performance of electric vehicles.

Battery Management System:

The primary function of the Battery Management System is to watch and control the battery’s functioning. This implies monitoring the charging and discharging cycle to ensure battery health and minimize the risk of battery damage by assuring that optimized energy is provided to run the vehicle.

The monitoring circuit in Battery Management System (BMS) monitors the key parameters of the battery, that is, current, voltage, and temperature during charging and discharging conditions. It assesses parameters like power, State of Health (SoH), and State of Charge (SoC) and assures good health based on the calculation. Internet of Things exhibits a vital role in monitoring and controlling as it allows remote data logging facility for battery parameters, conditions, etc. Most EV manufacturers use high-quality Li-ion battery packs as they have a longer life and exceptionally high energy density.

However, there are some drawbacks as well. In situations when battery malfunction happens, the onboard sensor data acquired using IoT can aid in managing the issues. Then, these can operate through AI-based models for performance estimation. Tests can be executed on some Li-ions to evaluate the patterns of partial and full charging and discharging. Models are marked using the data gathered from each step and are integrated with Artificial Intelligence before deploying on a server. The EV sends important sensor data to the server, delivering insights on the next course of action and performance. We can conclude that the server checks the condition of the Electronic Vehicle.

Safety and Smart Driving:

The adoption of Iot also allows real-time monitoring of the vehicles and their parts. It helps in preventive maintenance provided by the technology, which is found to be more reliable by the users. IoT devices attached to EVs can offer the following features to the users.

  • It can measure the exact parameters of the driver like speed, acceleration, and many other things to offer real-time tips to ensure optimal performance.
  • It can prevent theft by real-time tracking, geo-fencing, and immobilization. This ensures better safety and security to diminish the dependence on insurance.
  • It also checks the performance data of the vehicle, based on which EV and battery OEMs can enhance thee products. Here parameters are the range of each charge, use of a vehicle, performance difference based on geography, age, weather conditions, and adjustment in range for each charge over a certain period.

Fault Alert and Preventive Maintenance System:

Electronic vehicles also face technical glitches as other machines do. IoT-enabled fault alert systems can help alert vehicle drivers about the EV faults, providing them time to act and address them before it’s too late. Though EVs are well designed to prohibit errors, sometimes parts might fail or stop.

To anticipate this, AI algorithms and remote IoT data play a vital role. They help alert the EV users and provide them time to resolve the issues before they actually happen. This enhances customer experience as they can rely on it for optimal performance. In addition, it is necessary to know the overall temperature and moisture conditions in various geographies, and keeping a check on remote performance is essential. These factors will help resolve the issue promptly and promises comfort and security to the user.

Telematics Data:

By using  IoT-based telematics technology, data is gathered when linked to the vehicle sensors, shown through widgets, instant notifications, and produce automatic reports.

Let’s look at the useful factors of employing telematics for monitoring distant electric vehicles.

  • Battery Usage Data: Electric vehicles with telematics allow users to track real-time battery usage data. It lets users check important parameters like current, voltage, and temperature to skip battery breakdowns. Battery usage of EVs can be recorded and shared to a remote server that empowers to customize the battery configuration and enhance the best charging practices.
  • Charging Report: The addition of telematics in electric vehicles allows to yield reports on the vehicles’ entire charging sessions, i.e., the entire lifespan. The charging report shows the time duration, location of the charger station, and percentage of charge received by the vehicles.
  • Nearby Charging Stations Alert: Electric vehicle users encounter challenges like knowing state-of-charge(SOC) to schedule when and where to charge. Electric vehicles keep a tap on solutions with telematics and alert the user concerning the vehicles’ low battery level and the informs about the available nearby charging station.
  • Driver Behavior Data: Electric vehicle remote monitoring system with telematics ensures safety by monitoring and analyzing the electric vehicle performance data and also checks the behavioral data of the driver. Telematic provides quick feedback on driver’s behavior changes to fleet managers/owners through IoT enabled smartphone app. This ensures safety and improvement for better output.

Challenges of IoT in Electric Vehicle Management

Let us know some possible challenges of IoT for monitoring electric vehicles.

Cybersecurity:

The generation of the high amount of data and its transit over a network makes this data vulnerable to cyber-attacks and data leakage. Therefore, it is essential to strengthening the IoT networks used in the EV system to ensure no data leakage.

High Cost:

IoT systems in EVs are expensive. They are highly advanced and have high installation and operating costs. Thus, this technology requires more R&D, and the future might provide better and more cost-friendly IoT solutions.

Weighing the Benefits & Challenges:

We can conclude that IoT plays a crucial role in monitoring electric vehicles. The performance parameters enclose monitoring speed, mileage, acceleration, battery management, fault alert, charging, and predictive maintenance systems.

Overall, IoT holds an important place in the success of electric vehicles. However, challenges like cybersecurity should be considered seriously. EVs are innovative steps toward the environment, and their success will promise a better and green future.

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How is Data Science for IoT changing business outlook
admin
May 26, 2022
Data Analytics, IoT

How is Data Science for IoT Changing Business Outlook?

The Internet of Things has been noticed as a shape-changing technology that has changed the shape and working process of everything it has touched, either businesses or our daily lives. It has changed the outlook of every individual living a mediocre life into a smart device-connected life.

IoT connected devices produce tremendous amounts of data wirelessly over the network without any human interference, which is proved to be best for organizations trying to offer the best services to their clients. The only challenge is that IoT generates immense data for traditional data science.

Data Science and How It Applies to IoT

We can simply define data science as a study of processes that assists in extracting value from data. In the IoT system, data is referred to information produced by sensors, devices, applications, and other smart gadgets. Meanwhile, value means predicting future trends and outcomes based on the data.

For instance, suppose you are using a fitness tracker that calculates the number of daily steps. Using this information, data science can predict that:

  • Amount of calories burnt by you
  • How many kgs do you lose
  • When is the possible best time for your workout

This is a simple example of how data science works. Internet of Things is different as it produces high-volume data.

As per the reports, the amount of data to be produced by IoT by 2025 is around 73.1 zettabytes. This will cause trouble for standard data science as it cannot handle it, so it will have to update. Thus, IoT will help data science to go to the next level.

What are the Differences Between Traditional and Data Science for IoT?

There are only a few differences between traditional and IoT-based data science, so here we will check a few critical distinctions.

Data Science for IoT Is Dynamic:

The traditional version of data science is static as it is primarily based on historical information. For example, a company collects data from its clients about their choices and needs. The historical data becomes a base for predictive models that assist the company in understanding its future customers.

On the other hand, IoT changes the dynamic of data analysis as it is all about real-time sensor readings from smart devices. The gathered information permits data science consultants to create highly precise evaluations instantly.

In this case, customer data changes and updates- a feature that is not available in traditional data science. Data science for IoT allows continuous learning, changes with time, and improves operational processes simultaneously.

IoT Drives Larger Data Volumes:

Data science is developing with IoT because of its immense data processing. Here we are not discussing megabytes or gigabytes of data but data science for IoT deals with a massive amount of data that often reach zettabytes.

Better Predictive Analytics Method:

Data science for the Internet of Things is dynamic and wider than the traditional one. Additionally, it also makes a better predictive analytics method.

Thus, data science assists businesses in a great way; using it, businesses can develop better solutions that can diminish operational costs and acquire business growth.

IoT can improve this further through its real-time capabilities. IoT helps make decisions more accurate, assisting companies in identifying new opportunities and improving sales and customer experience while optimizing performance.

The Challenges faced by IoT Data Science:

We all know that data science for IoT holds vast potential, but it comes with challenges. Four major risks have to be overcome before it becomes mainstream.

Data Management and Security:

IoT produces a tremendous amount of data, which also implies that there are high chances of hacking or leaking private information. For example, Suppose hackers hijack the connection between the fitness tracker and doctor’s office app; they can easily access sensitive health records. Thus, it is pretty clear that privacy problems are the major issues with IoT data science.

For instance, many companies often face backlashes for releasing customers’ sensitive information without their consent.

Scaling Problems:

IoT data science is also important, but users often struggle to scale it up to fulfill their demands. When an organization plans to integrate an IoT system or add new sensors to its existing software solutions, it faces some issues and challenges.

Therefore, it is important to prepare for scaling projects in advance. Businesses must set up everything from software to personnel to scale data science processes successfully.

Data Analytics Skills:

Data science for IoT is extensively helpful, but classical data science consultant holds good dominance in the market as IoT analytics is still not very much embraced.

However, this could change soon as more companies adopt IoT technology. IoT scientists will have to add new skills and understand the oddities of the deployment process. For this purpose, they’ll have to learn about the following:

  • Edge Computing: It is defined as the practice of processing data close to the source to improve performance and reduce network congestion.
  • Computer-Aided Design: It is essential to know the logic behind the physical design of a smart device.
  • IoT Computing Frameworks: Data scientists must also employ open-source learning tools to grasp IoT hardware.
Operating Costs:

Another major problem with data science for IoT is the huge cost required to introduce new technology. This is the case for most companies willing to join this latest technology on a larger scale but is restricted by budget.

The Bottom Line:

We can conclude that data science for IoT brings a major upgrade to traditional data analytics. It requires efforts and dedication to make data science more robust, powerful, and accurate. IoT can make it possible through data generation abilities. The interconnected devices over the internet constantly communicate to offer businesses a huge amount of user-related data. This allows data scientists to draw relevant conclusions from their databases.

However, the process of deploying data science for IoT is not an easy task, but the benefits it provides negate every challenge. So, we can expect data science for IoT to be a part of the future at a great scale.

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How is IoT Helping The Procurement Team in Improving Productivity
admin
May 13, 2022
Artificial Intelligence, IoT

How is IoT Helping The Procurement Team in Improving Productivity?

Today, almost every device is connected; whether it is your smartwatch, air conditioner, or television, we can say it’s a world where devices are more connected than people. No, doubt these connected gadgets present around us make our lives easier by working systematically. This is possible because of the most popular concept known as the Internet of Things, which can also influence the procurement team.

IoT, a.k.a Internet of Things, can be defined as a network of interconnected computing devices, either mechanical or digital machines. This technology allows transferring data without human-to-human interaction or human-to-computer interaction. Communication is possible using networks and cloud-based systems.

An IoT ecosystem includes web-enabled smart devices that collect, send and work on data collected from their surroundings utilizing embedded systems such as CPUs, sensors, and communication hardware.

IoT devices can exchange sensor data stored in the cloud for analysis purposes or examined locally by interlinking to an IoT gateway or other edge devices.

Besides this, these gadgets can connect with other related devices and respond according to the information they receive from one another. Even individuals can operate the devices for the beginning setup, give instructions, or recover data; the device can perform most of the tasks without human interference.

The Role of IoT in Procurement

Procurement is an important part of the business. It demands the implementation of new technologies to boost productivity, enhance customer service and save costs. As of now, the procurement process is also embracing automation; IoT in this process is one of the most exclusive things happening in the era of digital transformation.

The inclusion of the Internet of Things will provide greater spending visibility and understanding of the supply and equipment used for the procurement process. So, with a proper understanding of what is being used and the requirement specified, the procurement team will have access to optimize catalogs and manage expenditure. Forecasting demands more closely using analytics can significantly improve budget and contract management. This also helps in improving budget and contract management. Despite this, the data generated through IoT sensors and other devices can assist in making informed decisions.

Let’s know how IoT works in procurement.

Traceability of Materials:

A study done by a McKinsey Global Institute shows that by the end of 2025, the Internet of Things’ possible contributions to inventory management, logistics, and supply chain management would reach 560 billion to $850 billion per year. This shows the possible IoT-oriented future awaiting us. Most of the time, IoT contributes to these sections by tracking. IoT sensors can help in making inventory management systems more effective.

For instance, RFID tags connected with IoT devices can track physical inventories and eliminates the need to scan barcodes or labels. In fact, businesses with vast inventory can track the days before items expire using interlinked IoT devices, saving the business from huge losses. IoT also prevents product theft by enabling businesses to know the location of their products.

With the use of machine learning, procurement teams can manage products per demand.

Supply Chain Visibility:

In this process, the procurement team can also potentially use IoT technology. Supply chain visibility, items are documented as transported from the manufacturer to the customer. An IoT-enabled system can read data from various devices like smart tags and sensory data like surrounding temperature and humidity, vehicle speed, and geolocation and accordingly follow the supply chain when connected to it.

The adoption of IoT devices to track inventory and route planning provides the details about where and when items are delayed in transportation. This allows emergency planning and identification of other options to accelerate the supply chain.

Stock Management:

Along with smart shelves and storage bins that inform about the stock levels in real-time and how long the product has been on the shelf, IoT also assists in detecting the pattern of consumption.

For instance, if a product named X is on shelf A and has been the quickest utilized item, IoT sensors will monitor the usage rate and suggest its economic order quantity (EOQ).

This clears how essential procuring an item is, which products are needed, and what amount. Procuring the right inventory quantity reduces costs by lowering waste and the menace of shortage.

Monitor and Alert Maintenance:

The sudden breakdown of equipment in a production unit is the most horrifying dream as it disrupts the business. If the condition of the equipment is not known, things become more difficult and result into process disturbance, indefinite downtime, and even business loss. Regular monitoring of the equipment’s condition through IoT sensors permits the team to watch indicators like vibration, oil, temperature, and performance.

When these indicators go out of range, the sensor alerts the team via computers.

In fact, smart sensors also alert when a machine’s working pattern changes or is about to fail. So this allows teams to schedule the maintenance, decrease the chances of sudden machine failure, and ensure seamless productivity.

Better Decision Making With Predictive Data Analytics:

Procurement teams can predict the future using predictive data analytics and spend analytics. These predictions assist in making critical decisions for designing and executing business techniques. Continous flow and accumulation of data with IoT devices also help create more robust and relevant historical data.

Infact, joining IoT data with additional data coming from other sources can boost business growth.

For example, knowing what quantity of a product is needed can help send accurate requisitions for approvals and create error-free purchase orders.

For example, having information on what quantity of a product is being used can help in sending accurate requisitions for approvals and generating error-free purchase orders. This results in an efficient and effective purchase management system. Data collected by IoT can also be used for onboarding suppliers with supplier management solutions to get new products based on previous performance metrics and set criteria.

IoT Procurement Takeaway:

The Internet of Things has become a sensation and is impacting almost every industry. So, it will be smart to invest in this technology and unheave the existing business model.

The procurement team requires a comprehensive IoT framework consisting of machine learning, artificial intelligence, and embedded technologies. These technologies, all together, can bring holistic change and offer maximum benefit.

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How Bioengineering and IoT can Increase Agricultural Production
admin
April 11, 2022
IoT

How Bioengineering and IoT can Increase Agricultural Production?

Today, the Internet of Things is involved in almost everything, which means it is becoming part of every sector, industry, and daily life. Everything would be entirely in vain if we did not utilize this otherworldly technology for the betterment and improvement of agriculture. 

We all know that with the increasing population, we need to improve the agriculture industry to meet the primary demand of every individual. Till now, with the help of digital technology, we have been able to solve some of the most significant issues existing in society, including food insecurity which is prevalent across the world. 

Combining IoT, bioengineering, and urban farming methods can be a perfect and situation-meeting solution to enhance agricultural production in developed and undeveloped countries. Modern food production methods are already failing to meet the rising demands. So, it has become high time for all the agriculture industry leaders to focus on it. 

We all know that the large-scale farming that is crucial to feeding an ever-increasing population is complex and challenging in a number of ways. On one side, agricultural production on a large scale causes a terrible effect on the planet through greenhouse gas emissions, deforestation, and the use of monoculture. On another side, a sudden pest infestation or disease can damage entire crop fields and contaminate the soils for years. 

As per the research conducted by the United Nations (UN), the world’s population will exceed 9 billion by 2050, which means the requirement for 60 per cent more food production to feed everyone. The UN further states that sustainability is a critical component in handling agricultural systems and production in the future. In order to overcome the challenges and meet the needs, agricultural systems managers are trying to reach solutions by switching to technologies.

Using bioengineering, IoT, and other technologies can increase plants’ longevity, distribution, and even natural resistance against different diseases. 

What are the Challenges for Agriculture Industry?

We all know the importance of the agricultural industry. The introduction of agriculture replaced the traditional nomadic cultures and planted the seeds for modern society. The earliest farmers domesticated plants and animals, and with the time being, high crop yield evolved.

In the 21st century, monoculture evolved as a low-cost, high-yield method of agriculture production. Though it provides enormous benefits, still it brings many problems like:

  • The adoption and practice of monoculture involve the continuous plantation of the same crop in the same field for years, reducing the soil nutrients.
  • Monoculture farms are also vulnerable to pests and disease, which sometimes damages the total yield in a single infestation.

However, bioengineering can help in disease eradication; at least, it has been proven in human beings. As per reports published by the University of California Riverside, scientists have been using bioengineering to stamp out deadly diseases like malaria. 

For instance, when genetically modified mosquitoes were released into the wild, malaria cases decreased by 37 per cent. Similarly, bioengineering technology can be employed for agriculture production by leaders in the industry to reduce disease and eradicate pests.

Analyzing Data and Bridging Gaps with IoT:

Innovative bioengineering methods are helpful but cannot eradicate hunger without help from the agricultural IoT. Data shared by IoT can provide valuable insight into what can work and what does not in different locations worldwide. Agricultural sensor technology is already part of countries like Germany, China, and the United States, and we have gathered a lot from the information provided by IoT connected devices.

Smart agriculture devices gather data, from soil quality to moisture content, and it even monitors livestock health. Sensors attached to the equipment can monitor the health of equipment to ensure that machines are running correctly and do not face any mechanical issues. Farmers can use sensors to identify the potential source of waste like water and pesticides and can utilize the data to make informed decisions to reduce unwanted costs without negotiating with the health of the soil. 

Sensors, along with IoT connected devices, can be beneficial for the agriculture industry as they can streamline the process of food from the farm to the dining table. Distribution of the produce is also essential, and IoT helps in this process also.

Reducing food insecurity in urban areas:

Food insecurity is becoming a prominent expression globally, but it can differ at different locations. Generally, city people are not in touch with nature and are deprived of natural food sources; and it is necessary to provide healthy food access to the ever-increasing urban population. With the integration of advanced technology like IoT and bioengineering, some agricultural industry leaders are looking for a solution, and urban farming has come up as an option to combat food insecurity.

For example,
San Francisco has become an example of feeding urban populations where at an indoor farm run by Plenty Inc., crops are grown in a carefully managed environment equipped with plenty of sensor monitors. To save space, crops are planted vertically rather than the traditional way that is in garden beds. Upon harvest, the bulk produced is distributed and sold locally.

Conclusion

The agriculture industry is broad and more complicated than one has imagined. However, advanced technology like bioengineering is at the vanguard of change for agricultural production. Farmers and agricultural industry leaders can utilize data from IoT to make better-informed decisions which ultimately improves crop production and potentially improves public health.

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admin
February 3, 2022
Big Data, Data Analytics, IoT

How to Prevent Data Lake from Turning into a Data Swamp?

IoT devices drive in many opportunities to gather more data than ever before. However, the challenge has changed; it is not about ways to get data but how to store an immense amount of data once it’s gathered. This is where data lakes come in the role. To clarify, a data lake is not just about a cheaper way to store data, but when it is appropriately crafted, data lakes act as a centralized source of truth that offers team members valuable flexibility to examine information that influences business decisions. This is only possible when we potentially utilize data lake practices. Raw data is like crude oil, requiring a thorough refinement process to distil more valuable products like gasoline. In the same way, raw data requires complex processing to get the most beneficial and business-rich insights to take action and measure outcomes.

With the increase in the volume of available data and the variety of its sources continuing to grow, many companies find themselves sitting on the data equivalent of a crude oil reservoir with no feasible way to extract the actual market worth. Traditional data warehouses are like gas stations; data lakes are oil refineries.

Data warehouses are becoming insufficient for managing the flooding business’s raw data. They need the information to be pre-processed like gasoline. Data lakes are the one that allows for the storage of both structured or unstructured data coming from different sources, such as business and mobile applications, IoT devices, social media etc.

Any idea? What does a well-maintained data lake look like? What is the best possible way to lead to implementation, and how do they impact the bottom line?

Explaining Data Lakes: How they Transform business

Data lakes are centralized storage entities to store any information mined to get actionable insights. These contain structured, unstructured, and other information from relational databases like text files, reports, videos, etc. A well-maintained data lake has real prospects to change the outlook of the business by offering a singular source for the company’s data regardless of its form and allowing business analysts and data science teams to extract information in a scalable and sustainable way. 

Data lakes are generally designed in a cloud-hosted environment like Microsoft Azure, Amazon Web Services or Google Cloud Platform. The vision offers compelling data practices that offer noticeable financial edges. These practices are approximately twenty times cheaper to access, store and analyze in a data lake rather than employing a traditional data warehouse. 

One of the reasons behind the domination of data lakes is the design structure or schema, which does not require to be written until after the data has been loaded. Regardless of the data’s format, the data remains as it is entered and does not separate into silos for different data sources. This automatically decreases the overall time for insight into an organization’s analytics. It also offers enhanced speed while accessing quality data that helps to inform business-critical activities. Advantages provided by data lakes like scalable architecture, cheaper storage and high-performance computing power allows companies to divert their shift from data collection to data processing in real-time. 

Rather than investing hours excavating scattered deposits, it provides one source to extract from that ultimately decreases dependency on human resources, which could be utilized to create stronger partnerships across teams. A data lakes give time to your data scientists to explore potential business-critical insights that could advise new business models in the future. 

Best Practices from the Experts

There are challenges in the data lakes process; it acts like a stagnant pool of water-polluting over time if it is not held to the correct standards. It becomes challenging to maintain and susceptible to flooding from insufficient data and poor design.

What to do to set up a supreme system for business transformation and growth?

Here we recommend the following actions to prevent your data lake from turning into a swamp.

Set Standards From the Start

A dynamic structure is the backbone of a healthy data lake. This means creating scalable and automated pipelines, using cloud resources for optimization, and monitoring connections and system performance. Initiate by making intentional data-design decisions during project planning. Mention standards and practices and ensure they are followed at each step in the implementation process. Meanwhile, allow your ecosystem to manage edge cases and the possibility for new data sources. Don’t forget; it is all about freeing up your data scientists from tending to an overtaxed data system so that they can shift their focus on other priority things.

Sustain Flexibility for Transformative Benefits

A healthy data lake exists in an environment that can manage dynamic inputs. This isn’t just about varying sources, sizes and types of data and how it is downed into storage.

For instance, creating an event-driven pipeline facilitates automation that offers source flexibility in file delivery schedules. Setting up a channel with trigger events for automation, based on when a file hits a storage location, eases concerns whenever the files come in. It is necessary to support the data science team’s fluidity around rapid testing, failing and learning to refine the analytics that empowers the company’s vital strategic endeavours, eventually driving unique, innovative opportunities.

Develop the System, Not the Processes

Most people have a misconception that problem-specific solutions may seem faster initially. One of the best things about data lakes is that they’re not connected or centralized around any one source. Hyper-specialized solutions for individual data sources restrict themselves to implementing change and need error management. Besides this, when a particular process is introduced, it doesn’t add value to the system as a whole as it cannot be utilized anywhere else.

Designing a data lake with modular processes and source-independent channels saves time in the long run by facilitating faster development time and streamlining the latest feature implementations.

Handle Standard Inventory to Find Opportunities

Event-driven pipelines are the best option for cloud automation, but the tradeoff demands post-event monitoring to comprehend what files are received and by whom and on which dates, etc.

One best way to monitor as well as share this information is to establish a summary dashboard of data reports from different sources. Adding alerting mechanisms for processing errors produces a notification when part of the data lake is not correctly functioning as expected. It even ensures that errors and exceptions are detected on time. When an immense amount of data is flooding, it becomes essential to track and handle it in the best possible way.

Right inventory initiatives create stable environments where data scientists feel supported in discovering additional metrics opportunities that can help make more robust business decisions in the future.

Revolutionize Business Intelligence

Data lake revolutionizes business intelligence by chartering a path for team members to peer clean data sources promptly and in the most effective way. A pristine data lake accelerates decision-making, removes struggle, and enhances business model ingenuity. So, we can conclude that prohibiting data lake getting muddied is necessary to get the optimal outcome. One must follow a few data lake practices that can reduce future headaches and keep your data streamlined and humming.

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IoT Data-The key to the smart connected world
admin
August 23, 2021
IoT

IoT Data: The Key to The Smart Connected World

Today, imagination has the power to become real, and all this is possible through the Internet of Things. It has empowered humankind with the ability to convert vision, thoughts and imagination into reality. From smartwatch to smart Tv, smart building to the smart town, every impossible and futuristic dream is becoming part of day-to-day life. So, it is pretty clear that every connected product leads to almost endless possibilities, from enhancing products to creating synergies that almost seemed impossible in the past.

Businesses manufacturing smart products or using IoT to streamline efficiencies are dependent on one thing: data. Data plays a critical role in making the whole IoT system effective and efficient. The data points collected from connected devices communicating to one another create a tapestry of insights for organizations that hold the skill to efficiently and precisely curate and analyze them.

Let’s know about things more closely.

Smarter Products

Usually, there is a lot of guesswork with smart product development. Suppose we summarise the current tech status, then yes. In that case, we are still in the primary stage of IoT, especially in selecting the data connected product development though we are surrounded by an ‘n’ number of IoT connected devices and products. However, manufacturers are learning that smart products offer tremendous insight into which features are used the most, which are sometimes utilized and sometimes not.

No doubt, there is a wealth of information available on smart product relationships. Let us understand this by taking an example of a connected kitchen. In a connected kitchen, multiple devices interact with one another. The data collected shows the quantitative impact that connected products cause on each other, and in some cases, it even identifies relationships that have not been clear at first glance.

IoT data can inform manufacturers when something operates incorrectly, and which factor is behind the emergence of the issues.

For instance, it can check if it is an isolated incident, or the issue arises whenever specific factors leading to the problem occur? This information can highlight anything from fundamental performance problems to possible safety issues. The data permits manufacturers to analyze it and enable them to make intelligent updates to the product or develop a new one, or in some cases, it suggests discontinuing the product altogether.

Safer, More Efficient Production

To utilize the values provided by IoT data to the next meta-level, it is essential to check how connectivity can help in the development of smart products.

The Industrial Internet of Things, i.e. IIoT, is the next new thing of the IoT revolution. In the manufacturing sector, connected devices offer a wealth of information related to certain aspects of smart product development.

As discussed in the smart kitchen example, data from connected devices can inform managers about production delays and the possible reasons for this delay.

Suppose there may be lags from one stage of the manufacturing process to the next, and that may not be detected without the connected data.

Sensors attached to the devices also warn about the part of machinery that needs to be repaired or is about to fail altogether. This gives a manager the ability to address the issues before the fall down happens and prevents the possible production slowdown or failure.

IIoT data can also help in discovering possible manufacturing safety hazards, like dangerous interactions between connected machinery. Besides this, smart wearables can observe the health status of the employees working in the plant. It monitors the vital signs, which can signal possible health issues. By integrating past and present data, it is possible to improve the overall safety of the plant, which ultimately results in better results.

Thus it is clear that either for uniform operation or to ensure the safest possible work environment, IIoT data offers insights into managing a continuous flow of production, which ultimately plays a vital role in delivering a product to market and ensures fulfilment post-launch demand.

Getting Data Where It Needs to Be

Gathering data and churning valuable information from it is a significant step toward market differentiation. The connected devices offer extensive data, and it is crucial to segregate valuable data from the flooding data. 

Now, its time to know the three things that every organization needs to do:

  • Crush the silos: We are crossing the stage where each department had its own sets of data that never went beyond its four walls, and this whole system is essential as it associates with IoT data. The entire system of IoT data is dependent on how data interacts to produce new insights. Today, innovative product manufacturers who embrace IoT on a micro and macro level are the ones who will shine in the coming future and will benefit the most. They will ensure that appropriate data gets to those who want to use it.
  • Share progress updates: When disruptive new insights are available, make sure that stakeholders know them and demonstrate why insights are vital to individual departments as well as organizations. This whole process, which involves sharing information, ensures that everyone is aware of the overall status of the product and its current development stage.
  • Avoid oversharing: It is undoubtedly imperative to enhance data sightlines, but it should not overload stakeholders. There is an immense flow of data that might eat up time in making helpful reports, but it would lead to a delay in the project and might hit critical product development.

Wrap up:

IoT holds a great future; in fact, we can say that IoT is the future. Coming decades will be transformative for innovative product manufacturers who comprehend the art of IoT and its data analysis. These future-oriented companies will offer products featuring more personalized, powerful, and intuitive services than ever before. These IoT-based organizations will create a new digital ecosystem where everything will be interconnected and inform each other. So, be ready to see a world where devices communicate and offer the best possible assistance and solutions.

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How Operational Analytics Helps Businesses in Making Data-Driven Decisions
admin
February 1, 2021
Big Data, Data Analytics

How Operational Analytics Helps Businesses in Making Data-Driven Decisions?

With the adoption of the latest technologies in businesses and growth in disruptive technologies, cloud computing and IoT devices are causing immense data generation than ever before. However, the challenge is not collecting data but using it in the right way. Thus, businesses have found an option to analyze the data most potentially. Organizations are using futuristic analytics features to understand the data. Operational analytics is one of the popular solutions to upheave business.

Nowadays, data is increasing tremendously. Every time a user interacts with the device or website, an immense amount of data is produced. At the workplace, when employees use company’s device like computer, laptop or tablet, then the data produced by them is also added in the company’s data house. The generated data turns useless if not used appropriately.

Operational analytics is at the initial stage of getting the place in the business industry. A survey by Capgemini Consulting states that 70% of organizations prioritize operations than customer-focused operations for their analytics initiatives. Nevertheless, 39% of organizations have widely combined their operational analytics initiatives with their processes, and around 29% has achieved the target from their endeavours.

Any idea about operational analytics and how it works?

Operational analytics can be defined as a type of business analytics which aims to improve existing operations in real-time. The operational analytics process involves data mining, data analysis, business intelligence and data aggregation tools to achieve more accurate information for business planning. We can say that operational analytics is best among other analytic methods for its ability to collect information from different parts of the business system and processes it in real-time, enabling organizations to take a prompt decision for the progress of their business.

How Operational analytics helps in business?

Operational analytics allows processing information from various sources and answers different questions like what appropriate action a business should take, whom to communicate and what should be the immediate plan etc. Obviously, actions taken after considering operational analytics are highly favourable as they are fact-based. Thus, this analytics approach fully automated decision or can be used as input for management decisions. Operational analytics is used in almost all industries.

We can have a look at some of them:

  1. Today, banks use operational analytics to segregate customers based on aspects like credit risk and card usage. The data provided helps the bank to provide customers with the most relevant products that fall under the customers’ personalized category.
  2. Manufacturing companies are also taking advantage of this beautiful technology. Operational analytics can easily recognize the machine with issues and alerts the company on machinery failures.
  3. Adding operational analytics in the supply chain enables an organization to get a well-designed dashboard that provides a clear picture on consumption, stock and supply situation. The dashboard displays critical information that can examine and promptly coordinate with the supplier on a supplemental delivery.
  4. Operation analytics is also active in the marketing sector as it helps marketers segregate customers based on shopping patterns. They can use the data to sell related products to target customers.

What are the benefits of operational analytics?

Adoption of operational analytics brings many benefits for businesses. It imprints a positive impact on the entire enterprise.

Speedy decision-making:

Businesses that have already adopted operational analytics enjoy the privilege of making decisions in real-time based on available customer data. Previously, companies were restricted to decide on annual or half-yearly or quarterly data. Adopting operational analytics has empowered companies by providing the data in real-time, which ultimately helps in changing the processes and workflow. A recent study has proven that improving operations can make a US$117 billion increase in profits for global organizations.

Improved customer experience:

Operational analytics works as a real-time troubleshooter for companies. For instance, if a shopping site or an air travel company encounters money transaction problems, then operations analytics immediately finds the issue and informs that the payment portal of the app is corrupt. It notifies the employees for the same and clears it quickly.

Enhanced productivity:

Operational analytics has allowed organizations to see the drawbacks that hinder the growth and disrupts the workflow. Businesses can streamline the operations and process, depending on the data.

For example, suppose an organization follows a very lengthy process to authorize something. In that case, the company can detect the issue, remove it, or change it to online modes to simplify the process.

Operational analytics software:

Operational analytics software supports organizations to achieve visibility and insight into data, business operations and streamlining events. It empowers an organization to make decisions and promptly act on the insights.

Some of the famous operational analytics software are:

  • Panorama Necto– Panorama Necto is renowned as a business intelligence solution that caters enterprises with the latest ways to cooperate and produce unparalleled contextual links.
  • Alteryx– This software helps operations leaders and analysts in answering strategic investment questions or critical process in a repeatable way.
  • Siemens Opcenter– Siemens Opcenter is considered as holistic Manufacturing Operations Management (MOM) solution that allows users to execute a plan for the whole digitization of manufacturing processes.

Conclusion

We can now conclude that businesses are welcoming operational analytics to improve workplace efficiency, drive competitive advantages, and provide the best customer experience.

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