Big Data is a versatile term that is universally employed for a large set of data, it contains a massive volume of heterogeneous data in the form of structured, semi-structured and unstructured.
You are already familiar with the complete process of how data is withdrawn from different sources and get merged for data processing discussed in our previous blogs, data analytics process.
In this article, we mainly focus on an introductory appearance of the IoT(internet of things) and the multi-faced purpose of the Big data analytics in IoT such as its role, necessity, and challenges.
Assume, Mr Kumar, a 70-year-old, who uses a wearable every time, heads for a morning walk. During his walk in the park, he feels a sudden pain in his mind and is rushed by the nearby people to the nearest hospital. He was cured immediately by doctors who saved him by analyzing his health status and reports which mentioned that he always uses a “wearable”.
“Wearable” is an application of IoT, it is used to keep records of health-related data. So, doctors get statistics of patients and started treatment on time.
IoT is a broad and powerful platform that connects things implanted with electronics, software, and sensors to the internet and allowing it to store data and information. Let’s understand the features and working of IoT.
Every device, sensor, software, area, etc. are connected to each other through the internet in a particular location up to some distant, this interconnection of the complete system can be monitored with smart devices such as mobiles or computers. So, the power to access these things through smart devices is called the Internet of Things(IoT).
It basically refers to the growing network of physical objects that has internet connectivity and the communication happening among these objects and other internet-equipped devices. It helps people to live and work smartly, also gain control over their lives.
IoT enables the business to look in real-time scenario of their company, like, how their company works, passing information into everything from machine efficient to other individual operations.
Machines are designed smart enough to reduce human work and thus devices are made interconnected to share information with the human, cloud-based application and to each other as well in order to fill the gap between physical objects and the digital world. It surely improves the quality and productivity of lives and industries.
The complete IoT-system consists of internet-equipped devices that handle inserted sensors and communication components to store, forward and perform on the extracted data. IoT devices share the data with the cloud to analyze it, also these devices contact each other to act on the obtained information.
An IoT extends a number of advantages to any organizations, such as;
Control their overall business methods
Enhance the customer experience
Save time, energy and money
Increase employee productivity
Unite and adapt business standards
Make better and improved business decisions
Produce more revenue
For example, an air conditioner uses inbuilt sensors to adjust the temperature of a room, it collects the data and information of the outside environment, then increases or decreases the temperature with respect to the outside weather. An amazing application of IoT is “Smart Homes”.
A look of “Smart homes”: connected-devices inside a home
How wonderment if you control the light intensity remotely without switching it off, you can start AC before reaching home to cool down your room, you can monitor the surveillance placed in your home for safety and security purposes, to lock or unlock your home remotely even if you left home, etc.
Smart homes products are assured for saving, time, energy and money. There are many applications in IoT, like smart cities, connected cars, industrial internet, smart retail, energy engagement, IoT in healthcare and IoT in agriculture and many more.
Below is a video showcasing a glimpse of how one’s life is enhanced through Smart Homes.
IoT is used in different industries depending upon the requirements such as in order to operate more efficiently, for a much better understanding of customers and enhancing customer service, improving decision-making to add values in the business.
For example, Amazon Go is using IoT in retail stores that have no cashiers or cash counters. With the help of sensors, customer accounts, online wallets, computers, and machines, Amazon Go monitors customer shopping experience by providing different product counters and make online shopping possible.
In short, IoT is one of the most important techniques for everyone and will continue with the flow as more industries feel the perspective of connected-devices to keep them competitive.
Let’s understand the complete workflow in some piece of information to learn exactly how these two work together;
A company install devices to use sensors for accumulating and transforming data.
An extensive volume of data is stockpiled in the repository, also known as Data Lakes. Both structured and unstructured data in a data lake.
Reports, charts, and other modes of output are generated by AI-driven analytics platform, for example, Google AI platform, TensorFlow, etc.
User devices give more metrics through settings, preferences, scheduling, and other tangible transmissions, those are fed back to the data lake.
Big data and IoT has a very cooperative relationship, even if there is the implementation of an AI system for processing of data and making decisions, this will completely making a valuable ecosystem.
Since the data storage serves as the repository and data source, the more IoT devices connected or the more complex AI models, the greater the significance of big data hardware.
In order to identify what is necessitate, which focuses on the emphasis on investing into efficient hardware smartly, or in optimizing the infrastructure design, performance and processing heavily rely on the strength of big data hardware.
We have seen that smart devices are important components in IoT, these devices generate a massive amount of data that needs to be explored and investigated in real-time. This is where predictive and Big Data Analytics come into play.
Moreover, Big data analytics tools leverage IoT for easy functioning but also shows some challenges, Big data is noticeable in IoT due to huge deployment of sensors and internet-applicable things.
Also, data processing in big data is facing challenges due to short computational, networking and storage means at IoT device-end.
Big Data covers a large set of heterogeneous data
When the complete IoT system acts as a data generated source, the role of big data in IoT becomes essential, Big data analytics is an emerging tool for analyzing data created by connected-device in IoT which assist to take the lead to improve decision making.
A large amount of data is collected on a real-time basis and stored, using multiple storage techniques like Microsoft Azure, can be handled under the Big data process. Following are steps that are considered for data processing:
A massive amount of heterogeneous data is created by IoT connected devices which are stored in the big data system on a large scale. This IoT produced big data strongly depends on 3’V factors or characteristics of Big data that are volume, velocity, and variety.
A Big data system is essentially a shared and distributed database, thus the tremendous quantity of data is filed in big data files in the storage system.
Interpreting and examining the accumulated IoT Big data using advanced analytic tools like Hadoop, Spark, etc.
Inspecting and generating the descriptions of examined data for accurate and timely decision-making.
Swift growth in different applications in IoT also give births to various challenges that need to be addressed, in this section we observe the key challenges in IoT with Big data analytics:
Data storage and management: Data produced from internet-equipped devices is increasing at an ever-expanding rate, and storage capacity of Big data system is limited, hence it becomes a most prior challenge to store and manage such a large amount of data. It is necessary to design some mechanisms and frameworks to gather, save and handle this data.
Data visualization: We already know that generated data is heterogeneous data. i.e. structured, unstructured and semistructured in different formats, so it becomes difficult to visualize this data directly. It is required to prepare data for better visualization and understanding for accurate industrial decision-making on time and improving the efficiency of the industry. You can also learn about Types of Data Visualization in Business Analytics.
Confidentiality and privacy: Every smart object into a globally connected network constitutes an IoT system especially used by humans or machines, it adds more attention to privacy and leakage of information. So this crucial data should keep confidential and provide privacy as produced data contains personal information of users.
Integrity: Connected-devices are proficient in sensing, communicating, information sharing, and conducting analysis for different applications. These devices assure users not to share their data indefinitely, data assemble methods must deploy scale and conditions of integrity successfully with some standard procedure and rules.
Power captivity: Internet-equipped devices should be connected to the unending power supply for the smooth and continuous functioning of IoT operations. These devices are limited in terms of memory, processing power, and energy, so devices must be deployed with light-weighted mechanisms.
Apart from these major challenges, Big data analytics encountered other huge challenges as well, for example, device security and backup against attacks as these are the most obvious tools for attacks and give a gateway for wicked activities.
Easy Availability of these devices another challenge, devices must be available for sure due to their critical application nature such as smart homes, smart cities, smart industries, etc.
(Most related: IoT smart city)
To develop efficient and real-time data analysis of globally connected devices, various Big data technologies and tools are easily available sources, we have seen the combined impact of Big data analytics and an IoT in analyzing huge sets of data accurately and efficiently with suitable mechanisms and techniques.
Data analytics also varies with types of data drawn from heterogeneous data sources and interpreted for results. Such a large system is capable of performing well but also faces some issues while data processing.
I expect, this informative article has been able to facilitate a brief view of IoT and the multi-faced performance of big data analytics.
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