Predictive Analytics

How IoT Data Analytics Impact your business

What is the Impact of IoT Data Analytics on your Business?

Today, if we observe the trend and business processes, we can express that IoT solutions are changing the way of doing business globally. However, saying that all solutions provide equal benefits would be wrong. We can say that an IoT solution that shares data without analytics is like a symphony playing Mozart without a conductor. This means music is there but with no structure and loses its purpose, beauty, and meaning. We all know that there will be immense flooding of data by IoT, but the absence of a process to properly analyze the data would just cause complexity and noise without proper output.

The Impact of Data Analytics on businesses

Data is compelling and empowers by giving insights into all aspects of the business. It can assist organizations in refining processes, locating missing physical assets for cost saving, or even helping in defining new use cases for already available products. 

In the absence of data, a company can be just reactive or can assume future challenges and results. 

With the data offered by an IoT solution, a company can anticipate the emerging problem before it becomes complicated and resolve it as soon as possible. However, there are IoT data solutions that only offer the data and no other context to make it meaningful. In such cases, IoT analytics comes in as a savior. 

The capability to interpret the data before it comes in front of the user is compelling. For instance, data analytics can help alert a factory manager about the floor problem in real-time instead of waiting and then reading through reports on issues that have already happened. This can reduce time consumption and the possibility of errors. 

Analysis software is available in many forms, from one-size-fits-all products to low-code/no-code solutions to solutions that demand an experienced engineering team to execute and maintain.

Each type of solution has benefits and expenses, and your enterprise must determine the best-fitting solution to get the maximum benefit.

Well-known IoT Data Analytics Solutions

We all are aware that technologies like AWS IoT Analytics, on the one hand, are sophisticated and powerful but, on the other hand, very complicated to execute and demand a highly skilled engineering team having domain expertise. The advantages of the analytics solutions are- it offers customization. Everything needed in your business and unnecessary things to be left out. You can consider AWS IoT products like building blocks: you can get maximum from them, but they demand a lot of planning along with maintenance and oversight.

All businesses cannot afford or consider hiring an expert engineering team to execute these solutions. These businesses are inclined toward adopting a one-size-fits-all solution like Azure provides IoT Central

Azure even provides a solution analogous to AWS, but they are more successful in an out-of-the-box strategy. The straightforward analytics provided by this solution or any other one-sized solution can fulfill the requirements of many businesses. They enable businesses to connect promptly and design their dashboards and alerts within a few days or hours. If your business just needs simple alerting or has a limited number of devices to connect, then opting for this solution would be a great idea and cost-saving as well.

Customizable Solutions

The main challenge with the IoT data analytics solutions mentioned above is that they don’t provide customization options, are costly to scale, and might compel your team to do analytics using a third-party tool (which is no doubt another pricey option). Suppose you own a business having specific analytic requirements and many devices to be connected. In that case, a low-code/no-code solution, like the one proposed by Leverege (running on Google Cloud), could be a terrific middle-ground solution. This type of solution is customizable per the business’s requirement and, in parallel, does not need any technical expertise if it offers an end-to-end alternative and has analytics and an excellent alerting system, even without needing a dedicated and proficient engineering team. 

Irrespective of whatever solution you choose for a business to implement, ensure that a third-party tool to be integrated gives you maximum flexibility and value from the data. Tools like Power BI, Tableau, and Looker can be the best option to support your company in familiarly visualizing your data. If your company has already made a preferred analytics tool list, then it will enable your users to harness their expertise of that tool with new data sources.

Valuable Insights

Till now, hope that you have understood the importance and contribution of Analytics tools. It is essential to obtain the optimum value from IoT solutions irrespective of the products the business chooses. Neglecting these core capabilities may take your business to the loss side as it may miss valuable insights and maximize value. We can simply infer that IoT solutions, no doubt, enhance business operations but remain incomplete.

Data analytics gives direction and beauty to the solutions as it analyzes the data and offers favorable data to businesses to boost operations and amplify outcomes. Today, most companies are embracing the Internet of Things but are unaware of the importance of data analytics and ignore it. They face losses and then switch back to their old processes and operations. Therefore using IoT and offered IoT solutions must be opted for after attaining full knowledge.

Today, IoT is making its space in almost every sector, from smart homes to smart buildings, from smart towns to smart cities, and from smart farming to smart logistics; one can see the influence of IoT in every sector.

Similarly, data analytics is also contributing from its end to add more value to every solution offered by the Internet of Things. For instance, in the baking process, the availability of raw ingredients is insufficient, and it does not come together without a recipe. The recipe brings ingredients together in a beautiful way and offers the best. So, if you are still untouched by the magic of data analytics, then you might be losing a lot of benefits and leverages offered by it.

How will Smart Farming decide the Future of Agriculture

How will Smart Farming decide the Future of Agriculture?

Today, farmers are facing more pressure than ever before. Demand for food is growing as the population increases, whereas agricultural land is used for urbanization. Not just this, the challenges caused by climate change are also decreasing the yield. Sometimes drought, sometimes flood, and unseasonal rain impact the total yearly yield, ultimately causing a shortage of food. 

In the coming years, agricultural enterprises must operate in new and adapted ways to achieve sustainability and the capability to survive. The latest and most advanced technologies, falling under the roof of  Smart Farming, provide opportunities for farmers to overcome unprecedented challenges while passing the agricultural way of life to future generations. This has led to a boost in the yield along with the profit. 

Unfortunately, local farms, including some in the world’s most fertile lands, are also declining today. Therefore, creating a viable business is becoming challenging, and farmers must find ways to work more smartly instead of shedding sweat in the field. 

Historically, agricultural structures demand immense amounts of time, money, and effort to order, unload, store, plant, monitor, cultivate, and harvest crops. 

Advanced smart farming technologies provide new methods to simplify the workflow and enhance operations, leading to robust businesses.

IoT’s Shining Agricultural Future

The Internet of Things (IoT) is the soul of this transformation process. Intelligent sensors are implanted throughout a farm that behaves like the eyes and ears of the farmers. It collects information about crops, the equipment’s condition, and performance. One of IoT technology’s most crucial characteristics is its ability to expand visibility to new endpoints. IoT empowers farmers with real-time monitoring and analytics systems through which they gain more insights into their operations and the capability to handle them more efficiently.

Let’s know some of the ways through which smart farming improves agricultural enterprises and will continue to do so in coming years:

1: Monitoring Crop Growth

IoT in the agriculture industry acts like a superhero; it assists farmers in improving the crop’s quality and the land’s fertility. Sensors integrated with IoT systems collect nutrient density information, allowing farmers to adjust the amount of fertilizer to be used. If any symptoms of infestation are observed, using pesticides can eliminate the invasion. In actuality, data-driven agriculture always empowers farmers because the information received permits adjusting activities imaging the current conditions, handling the crop cycle more effectively, achieving efficiency, and enhancing crop growth.

2: Improving Greenhouse Operations

Greenhouse IoT sensors improve visibility and automate traditional manual functions, making the entire process more efficient and automated. They gather and transfer real-time data, like air pressure, humidity, temperature, soil conditions, and light levels. In fact, water consumption can be managed with SMS or email alerts if any defects occur. In modified systems, adjustments are made automatically. The processes turn out to be more cost-effective and accurate, and operational performance improves.

3: Tracking Water consumption

Agricultural IoT supports farmers in monitoring water tank levels in real time by tracking the amount of water being used and the amount of water left in the tank. Smartphones provide intuitive reports that analyze trends and make recommendations in many cases. Farmers can alter water usage as needed, making irrigation more efficient and economical.

Predictive Equipment Maintenance

Farmers can monitor their land, vehicles, and other assets through IoT. Farmers wish to reduce downtime as the equipment is essential to daily operations. It offers a clear view of the device’s performance, which updates as the equipment operates. Advanced data analytics provides them with the required management insights. Artificial Intelligence and machine learning measure an asset’s typical efficiency and wear and tear based on metrics like vibration analysis, oil analysis, and thermal imaging.  

Predictive maintenance models depend on complicated algorithms to determine when an asset must be serviced, fixed, or retired. These insights come before the device stops performing. This assures that operations and processes at farms are continuous and seamless. The other advantages include lengthened machinery lifecycles, reduced downtime, and productive outcomes.

Smart Livestock Management

We all know that livestock management and monitoring demand good quality of time, effort, and money. Traditionally, employees are hired on farms to look after the health or injury of the cattle. The process is undoubtedly costly, highly unreliable, and inefficient.

IoT solutions terminate speculation when diagnosing an animal’s health by monitoring temperature, heart rate, blood pressure, etc. The information is sent through the wireless network to an application in real time.

Farmers can then access the mentioned information using mobile devices:

  • Each animal’s health condition and location in its herds can be checked from anywhere at any time.
  • Farmers can also check the peak mating time and season of the cattle.
  • Farmers receive alerts if a metric falls outside of the normal range.
  • Most suitable gazing spots can be easily determined.

Benefits to Agriculture and Personalized Services

By embracing smart farming technology, farmers can develop more suitable approaches, and this proficiency changes the relationship between customers and retailers. The farmers can emphasize consumers’ interest in sustainability and eco-friendly food while focusing on creating new strands of their products. They can then charge a premium for their products, enhance revenue and create a more substantial business.

Smart sensors automatically observe every facet of everyday agricultural work. This advanced technology allows farmers to automate real-time data collection, boost production volumes, cut down costs, reduce waste, and much more things that increase productivity. These many advantages are compelling farmers to spend on technology.

According to the worldwide smart farming review, investment in this field will increase from $3.715 billion in 2022 to $7.040 billion in 2026, a CAGR of 13.65 percent. It means farmers will have added hundreds of millions of IoT sensors to improve their everyday operations.

Steering Efficiency

Smart farming technology provides businesses with new ways to increase agricultural efficiency, reducing costs and boosting revenue. In other words, smart farming technology is most significant for the growth of modern farming, increasing the yield to meet growing demand and will keep agricultural industries viable in the future.

How is Data Science for IoT changing business outlook

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.