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What is Big Data?
Since the introduction of digital technology, big data has taken hold. It refers to an enormous data set with three distinctive qualities - volume, velocity, and variety.
Big data differs from other sets by its large volume size (volume), rapid rate of change (velocity), and a wide array of structured, semi-structured, and unstructured information sources (variety).
An expansive data set can expose hidden patterns and trends only apparent within an extensive sample size, giving us a full picture.
Big data presents unique challenges when processing and analyzing vast amounts of information, as traditional methods cannot adequately determine its business value.
Organizations used to spend money and time analyzing data to gain insightful conclusions. Now, with advances in computing, large datasets can be combined with powerful analytics for actionable insights from unwieldy datasets.
Big Data Analytics makes large datasets easier to comprehend by providing them in an easy format that's straightforward for people of all levels of intelligence to consume; by employing tools such as artificial intelligence (AI), machine learning, or similar solutions, it may even become possible to gain even deeper insights - big data can even come from Internet of Things devices!
What is IoT Data?
IoT (Internet of Things) refers to a network of physical objects connected by shared networks. Sensors collect data that is then shared with systems that store, manage, and filter it accordingly.
IoT devices range from medical equipment and industrial machinery to wearable technology devices.
IoT devices give companies unprecedented insight into what their connected devices are up to and provide real-time insight.
Furthermore, these IoT devices transmit large volumes of data directly to the internet in real time.
What Is The Impact Of Iot On Big Data?
The Internet of Things, a rapidly growing network of sensors connected around the globe, is making colossal contributions to big data.
Three ways that it affects the field of Big Data:
Generates A Variety Of Data
Connected devices produce data as their reach expands beyond consumer electronics. IoT sensors can collect information such as water levels in farm fields, below-ground seismic conditions, vital signs for remote patient monitoring, and more.
Analyzing specific types of data for a specific use case can provide analysts with greater insights and clarity into situations they face - for instance in smart factories where workers and machines connected to an enterprise combine with information collected on factory floors (bridging OT/IT gaps), leading to increased efficiency and productivity.
Increases Data Volume
IoT sensors generate vast quantities of information. Companies who deploy IoT devices often don't anticipate the massive flood of data they will encounter once their devices have been activated; to make the best use of this information, they require systems that can store and process it - ideally real-time, depending on application - as soon as the sensors go live.
With IoT-enabled devices growing more widespread, there has been an increased need for flexible cloud storage and fast edge processing platforms to manage it effectively.
Real-Time Analytics
Due to machine information collection, IoT data sets are often created quickly, often in real time. Real-time analytics are only possible with machine-driven tools like AI, machine learning, and deep learning, which allow real-time insights that immediately affect IoT devices, users, and organizational decisions.
Here is an IoT example: Traffic lights and cameras connected to the IoT of a smart city show traffic jamming regularly during rush hour; AI tools analyze this data and suggest solutions, like increasing the frequency and duration of green lights displayed during this time each day at exits on interstate exits - this adjustment can easily be made as the traffic lights are linked directly into its network.
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What Is The Relation Between Iot (Internet Of Things) And Big Data?
IoT (Internet of Things) shares many components with big data, making IoT an important source for big data analysis.
Big data and IoT initially existed separately; however, their development has become interlinked as more IoT data becomes generated, requiring traditional storage and analytics methods that become ineffective.
Current IoT device usage data represents a real-time big data set that needs to be made sense of; big data storage and analytics help make sense of all this real-time information, providing valuable insights.
Summary of Relationship at High Level: A network of connected devices with sensors and electronics transmit real-time data directly to the internet.
How Does Big Data Work In Iot Applications?
Big data analytics offers companies an effective solution for making sense of IoT data and information. These solutions help organize unstructured, vast IoT devices' collected information into smaller sets for more in-depth analyses that give insight into their processes and improve decision-making capabilities.
Big data analytics combined with IoT can provide valuable insights.
These include descriptive analytics, diagnostic analyses, predictive analyses, and prescriptive analysis. Descriptive analytics offers real-time insight into device performance; for instance, it could help find connected devices or analyze customer usage before identifying anomalies.
Diagnostic analytics offers insight into "why" descriptive analytics are conducted. This can be useful when comprehending why certain devices operate in certain ways or produce certain outputs.
Predictive analytics is an extremely beneficial application of big data for IoT. This type of analysis uses machine learning to examine past data to generate probabilities about how a device may function in the future, making this technology especially valuable when servicing IoT devices; organizations can utilize predictive analytics technology to anticipate failures or maintenance requirements before devices stop working properly.
Big data in IoT can be utilized for predictive analytics. This type of analysis offers insight into what can be done to influence something that has already been observed or predicted.
Big Data Analytics And Iot: The Challenges
Data visualization
Data Visualization is critical in IoT Analysis, helping identify key trends. Data visualization must also identify and communicate the best data insights that can guide business decisions.
IoT data is heterogeneous; its various forms include structured, semi-structured, and unstructured files, complicating its visualization efforts further. While theoretically, data visualization should aid trend identification more efficiently, with so many information formats, it becomes harder than expected.
Data Management And Storage
The growth of big data is exponential. Big data storage systems are limited in space today. Therefore, storing and managing such large amounts of data takes a lot of work.
IoT and Big Data Analytics Solutions
The Internet of Things and big data will continue to develop and play an important role in the ability of organizations to make informed decisions.
Internet of Things Examples
IoT devices are likely something you use every day. You may already be familiar with some IoT devices.
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Smart Home Devices. Interactive electronics smart devices use wireless connections to understand user instructions.
Smart home devices, such as thermostats and security systems for the home, can be programmed to perform certain tasks autonomously.
You can program your smart thermostat to automatically adjust to a cool setting when you get home from work.
You may also receive a notification from a camera to inform you that someone is standing at your door while you are away.
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Wearable technologies: Smartwatches are a common example of an Internet of Things.
Fitbits, Apple Watches, and other wearable IoT devices connect with other devices to share data.
They also typically connect to the Internet to track GPS location.
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Personal medical devices: IoT also includes personal medical devices such as pacemakers.
Remote medical devices can monitor and share vital signs or detect early warning signs of health problems.
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Autonomous Vehicles: Self-driving cars and other connected vehicles rely on the Internet for real-time information sharing.
Sensors in the vehicle map its environment, transmit camera footage, and respond to traffic signs.
Three Types of IoT Applications
Billions of connected devices collect and exchange information. This ranges from smart home equipment like smoke detectors and cooking appliances all the way to military-grade surveillance equipment.
Below is a selection of some of the more popular IoT applications.
Consumer IoT
Consumer IoT is a term used to describe personal and wearable gadgets connected to the Internet. The term smart devices sometimes refers to these devices.
Industrial Internet of Things
The industrial Internet of Things is a system of connected devices within the industrial sector. The industrial Internet of Things includes energy management devices and manufacturing machinery.
Commercial IoT
Commercial IoT is the term used to describe the systems and tools that are used outside the home. Commercial IoT is used by businesses and healthcare organizations to audit data and manage consumers.
What is the Internet of Things?
In the following sections, we will break down each component of the Internet of Things.
Internet of Things Platform
An IoT platform manages device connectivity. It can be either a software suite or a cloud-based service. An IoT platform aims to monitor and manage hardware, software, computing abilities, and application layer layers.
Sensor Technologies
IoT sensors (also called smart sensors) convert variables in the real world into data that devices can interpret and use for interpretation or sharing purposes.
There are various types of IoT sensors; temperature sensors detect heat changes and translate this data into useful insights, while motion detectors use ultrasonic waves to track movement before initiating desired responses when the waves become interrupted.
Unique Identifiers
Communication between devices and users is the cornerstone of IoT. Unique identifiers (UIDs), or device context markers, create the framework within the network for successful device interactions by setting its location within its context.
For instance, an IP address can serve as a UID that uniquely identifies an instance ID device and class of devices (type IDs).
Internet Accessibility
Sensors can connect with cloud platforms and devices via various internet protocols. Communication between devices is enabled.
Machine Learning And Artificial Intelligence (Ai)
NLP in IoT devices allows users to interact more easily with the devices and input information. Amazon Alexa is a common IoT device that uses NLP technology.
Machine learning enhances IoT device analytical capabilities.
Edge Computing
Edge computing is a computing framework. Edge computing aims to save resources and improve response times by bringing computational resources closer to the data source, such as data storage.
Edge devices such as IoT gateways are used to achieve this.
Internet of Things: Benefits
Before the IoT's introduction, devices could only collect and exchange information when interacting with humans.
The IoT allows for lower operating costs, improved safety and productivity, and a better customer experience. The Internet of Things has many notable benefits.
- Automation: By eliminating the need for mundane tasks such as turning on the thermostat or locking the doors, you can increase efficiency and quality of living.
- Energy conservation: Automation allows for better management of energy and water consumption without human oversight.
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Big Data Analytics: Internet of Things allows for easy tracking of information that was previously difficult and time-consuming to gather.
You can read more about the IoT and its benefits to certain industries in the sections below.
Internet Of Things And Healthcare
IoT reduces the need for record-keeping in traditional ways and provides real-time notifications to protect patients.
For instance, glucose monitors alert patients or caregivers when their glucose levels are problematic. They can then take the appropriate action.
Internet of Things for Business
IoT has become a business necessity. The IoT allows for collecting and analyzing massive amounts of data in real-time.
IoT devices can also be automated. People can gain greater control over their environment, health, and safety. Smart home security systems, for example, can assess and alert authorities to threats such as burglary or carbon dioxide poisoning.
How Does Iot Affect Big Data?
How do IoT devices and Big Data connect? The data generated by IoT is valuable in terms of Big Data. It is possible because the sensor is connected to a device.
This allows businesses to access detailed information on their devices.
A "smart house" can monitor the following items:
- Temperature and humidity in the home.
- Air quality in the home
- Household energy consumption is divided into three categories: appliances, lighting, and heating.
- Activity levels and behavioral patterns
This data is very valuable for businesses because it's collected and analyzed by computer. Less data is wasted.
Many IoT platforms use machine learning for data stream collection. It will then correlate and analyze all of these data sets.
IoT collects data and analyzes it in real time. It is possible to gain insights faster and with greater accuracy.
Businesses can gain more actionable and useful insights by utilizing the collected data. This can lead to a greater ROI (return on investment).
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How Can Businesses Use The Big Data Generated By Iot?
How can businesses utilize Big Data benefits generated through IoT for their benefit? Many must adapt and evolve their technology to collect large volumes of information and extract insights from Big Data.
IoT devices can send business messages with activity and behavior data that must be stored once received; hence, a platform capable of handling large data sets must be utilized for storage purposes.
Businesses can leverage this information when developing products, analyzing consumer behaviors, reviewing a new product launch strategy, or other purposes. This information can be easily accessed and securely stored by the business at any time, making access easier.
Big Data and IoT Can Benefit Your Business
Now is the time to collect and analyze your data with IoT and Big Data. Using IoT to gather accurate customer behavior information is a fantastic way of gathering accurate information and gaining valuable insight.
Data analytics and IoT have made a dramatic, impactful statement about our ability to interpret large data sets.
IoT integration and Big Data seem poised to become increasingly prominent methods for collecting and analyzing information.
What Are The Benefits Of Iot/Big Data For Industries?
Combining IoT with big data analytics enables organizations to make smarter decisions, improve efficiencies, and anticipate problems more efficiently.
Analytics can reveal patterns and trends, helping businesses better understand customers and enable predictive maintenance. Here are some examples of the impact IoT combined with big data has had across various industries.
Healthcare
Telehealth, telemedicine, and connected monitors will become integral parts of healthcare systems as they gain popularity.
These devices enable providers to remotely monitor patients' readings - from blood pressure meters and glucometers to heart monitors. AI/ML tools can identify patterns or warning signs as sensors collect and aggregate data gathered by sensors, alerting doctors and patients while possibly saving lives.
Healthcare data management is another application of IoT and big data. IoT tracking devices can be used to track equipment and personnel in hospitals.
Data portals and management systems enable providers to aggregate patient records, including electronic health records (EHRs), thus reducing human error while improving access. Healthcare analytics applications offer predictive recommendations to improve care, hospital administration, and other areas.
Supply Chain Management
Applications of IoT in supply chains include telematics systems and remote monitoring. Artificial Intelligence and machine learning assist in understanding the data generated from these devices.
An American shipping company might operate 1,000 trucks traversing U.S. highways simultaneously. Each container onboard the trucks contains asset trackers, while their vehicle also contains onboard systems.
All this data can be seen on one dashboard using a cloud-based platform; AI/ML tools also help identify trends and alert drivers of potential issues like vehicle service needs or traffic bottlenecks.
Robotics And Autonomous Vehicles
Self-driving vehicles and autonomous robots rely on IoT sensors combined with big data to function safely.
Multiple sensor technologies, such as radar, LIDAR, and GPS, are used in self-driving cars. These IoT devices generate data used by the onboard computer to create a map, chart a route, and avoid obstacles. In this area, real-time data analysis is crucial.
The computer can also learn from past experiences (machine learning) over time.
Industrial IoT
Industrial Internet of Things (IIoT), or Industry 4.0, is an emerging technology that applies IoT across manufacturing, energy production, and construction industries.
With this connected approach, enterprises can monitor machine activity, maintenance requirements, and progress reports and improve overall operational efficiencies. IIoT could revolutionize manufacturing operations while giving organizations access to any data source imaginable.
Data gathering can be challenging in a Smart Factory due to a mix of legacy machines, IoT sensor data, and manual entries.
IoT Gateways equipped with edge analytics are designed to streamline data from various sources - often different formats - and perform initial data cleansing before uploading it to the cloud for further analysis at an enterprise level. Data scientists with AI/ML tools interpret this data and recommend increasing efficiency and productivity.
Agriculture
At a Smart Farm, hundreds or thousands of IoT sensors may be placed throughout an agricultural facility to monitor usage or animal grazing patterns.
The sensors produce data about soil conditions, weather patterns, and irrigation availability - information that can provide insights into precision farming. This approach seeks to cultivate specific sites by applying different fertilization techniques or targeting individual fields with special fertilizing regimens.
Buy It Now
Retailers utilize IoT to monitor assets, supply chain information, and customer behavior. For instance, retailers that source product materials from multiple locations could equip shipping containers with IoT sensors to monitor movement and maintain temperature-controlled environments for materials they source from different suppliers.
Analyzing this data provides insight into ways to streamline sourcing and manufacturing efficiencies to boost efficiencies.
AI & Machine Learning
Artificial Intelligence (AI)
AI (Artificial Intelligence) is the term computer scientists use to create computers that mimic human intelligence.
However, its parameters remain vague and ever-evolving. Playing games such as chess was once considered AI; nowadays, this capability can be found on almost every operating system, and more complex examples include self-driving cars.
Computer scientists also often use AI when discussing various technologies, including machine learning, deep learning, IoT sensors, and computer vision - among others.
Machine Learning (ML)
Tom M. Mitchell defines machine learning (ML) as a subset of AI: it "is the study and application of computer algorithms which enable computer programs to learn through experience automatically." Data analytics often utilize ML for pattern identification; once patterns have been identified, they "learn" from their repetition - such as with streaming services where an algorithm uses knowledge about what you enjoy to recommend movies or songs that might suit you best.
Deep Learning (DL) is a Machine Learning (ML) subcategory. Deep structured learning, commonly called deep learning, does not require domain-specific feature engineering like other forms of machine learning.
"Deep" refers to neural networks with multiple layers used commonly in applications like computer vision and chatbots - the latter application often employs "deep" neural networks with many layers, such as computer vision systems.
Also Read: Application Development For The Internet Of Things And Their Target Users
Conclusion
As more IoT devices become ubiquitous, their data output will increase exponentially. Companies will require analytics and storage tools to take full advantage of it.
Edge computing may become increasingly popular as data processing occurs locally rather than sending everything back to the cloud. Big data and IoT will reveal previously unknown patterns while providing real-time insight that helps organizations and individuals make more informed decisions.
