IoT Explained: How it Works?

IoT Explained: How it Works?

The term IoT, expanded as the Internet of Things, refers to the network of billions of physical devices around the globe that are connected to the internet and can collect and share data. The evolving devices with added digital intelligence are now capable of communicating without needing human involvement. In simpler terms, almost any device that can be connected to the internet can be converted into an IoT device. For instance, a light bulb that can be controlled through a smartphone is an IoT device. Similarly, a sensor, a streetlight, or a toy can be an IoT device.

To explain it further, the term IoT is used for devices that do not usually have an internet connection and with added connectivity feature, they are able to function without human intervention. This is why a PC is not considered an IoT device. This article explains the working of IoT.

IoT Working

An IoT system consists of four main components including sensors/devices, connectivity, data processing, and a user interface. Here is what these components do:

  1. Sensors/devices

The sensors or devices are responsible for gathering data from the environment and send it to the system for the processing. The term data here refers to anything ranging from the simple temperature reading to something as complex as a video. Different devices or sensors collect this data depending on what they are meant for. There can be more than one sensors too that form a part of a device. Therefore, anything that collects data of any kind from the environment comes under the category of sensors or devices for IoT.

 

  1. Connectivity

Once the data is gathered, it needs to be sent to the cloud. This is where connectivity is required. The devices/sensors are connected to the cloud using one or more of the methods, for example, Bluetooth, low-power wide-area networks (LPWAN), WiFi, satellite connection, a cellular network, or direct connection to the internet through Ethernet. The method to be used by the IoT device for connectivity is specific, however, the aim is to send the gathered data to the cloud.

 

  1. Data Processing

Once the data has been transferred to the cloud, it is processed there using software that is specially designed for this data. This function can be as simple as checking whether the temperature is in an acceptable range or as complex as identifying the objects in a video. After the processing, if the results are something important like temperature is not in acceptable range or the computer detects something objectionable in the video, it has to be sent to the user so that they can take some action. This is where the user interface is needed.

 

  1. User Interface

The end user is alerted with the important information through some user interface. This can be done by sending an alert message, email, or a notification to the user on their smartphone. It is also possible for the user to proactively check in on the system for any important information through an interface. User interfaces are, again, simple as well as complex depending on the type of device and urgency of information. There are interfaces that will allow you to just check the video, message, or notification while there some complex interfaces that have an added functionality of allowing the user to act on the information received and affect the system. For example, if the temperature of a cold storage goes higher than acceptable, a user might be able to control it from their smartphone.

 

With the continuous drop in the prices of smart devices, sensors, and communication, IoT is increasingly becoming cost-effective. More and more devices are adding to the list of IoT devices and consumers are getting the benefit of it. In the near future, our living and working places will have a huge number of smart products. Right now, the increase in IoT is a topic of hot debate. While some are happy to welcome the technology in their lives while others want their lives to go back to as simple as they were before the dawn of the internet. But, whatever may be the case, IoT is here to stay.

Big Data: Importance and Relevance Today

Big Data: Importance and Relevance Today

The concept of big data was born at the beginning of the 21st century. Since then it has evolved a lot and at present, almost every organization is making use of big data technologies. Data, today, is the gist of businesses and their marketing strategies. Big data analytics are being put to use to gain more perspective and hence, making better decisions towards the business growth.

Big data can be defined as a huge amount of data that is produced by the businesses and the users. It is vast in nature and might be structured or unstructured. The data is analyzed by the industry experts to study trends and therefore, use them for the growth of the businesses.

What makes big data analytics so important?

Big data analytics has revolutionized the world of IT and brought new insights to it. It is increasingly being used by the IT giants to enhance their businesses and up their marketing games. The focus of all businesses is always the customer and big data analytics are helping the business flourish like never before. Big data has huge potential and businesses are more likely to harness it if they understand the applications of it. This article provides insight into the relevance of big data analytics today and how it benefits the businesses.

  1. Data Sciences

Big data analytics is done using various advanced tools and techniques which are then applied to data that is obtained from different sources. Big data is high in volume, variety, and velocity. Data sets are obtained from online networks, web pages, audio devices, video devices, and various other sources. The techniques like machine learning, data mining, natural language processing, and statistics are then used to analyze the data and find the patterns.

Depending on the requirements, the data is then extracted in order to provide certain analysis for the business. Various qualitative and quantitative analysis techniques are used by the business to maximize their profit. Data scientists are present in the organizations who analyze big data and provide insights.

 

  1. Efficient Business Management

Due to a surge in the use of big data by businesses, there is also a rise in demand for the big data analytics tools and techniques. These can be used by businesses to find new opportunities and insights to improve their business efficiency. These tools help in providing meaningful information for improving business decisions. Business can further benefit from it by improving their strategies based on insights and keeping the customers as the focus. Everything adds up to improve the company profits and efficiency.

 

  1. Real-time Benefits

Big data analytics offer real-time benefits too. It has been used across various industries including banking, healthcare, energy, technology, consumer, and manufacturing. Banking is known to make the maximum use of big data analytics. The education sector is benefiting from its new research and analytics tools. It is also an attractive option for students to pursue their studies in. Big data analytics allow a better understanding of consumer needs, which in turn helps the companies to make better products. Therefore, customers are now more satisfied than ever.

 

  1. Job Opportunities

Big data has emerged as a huge industry in itself with a lot of research, investment, job opportunities. People with big data analytics skills are in huge demand and organizations pay them good incentives. IT professionals are now advised to gain big data skills for a good career in the future. Workforce required for big data analytics in all organizations is enormous. The job titles in demand mostly are Big Data Analyst, Big Data Engineer, Business Intelligence Consultants, Solution Architect, etc. Knowledge of big data can provide the professionals with an edge over the others.

The emergence of big data has led to fierce competition and demand for qualified professionals in the field. It holds huge potential and helps business improve their production and profits. Companies can gain a lot of benefits from using it correctly. New tools are emerging every day and there is a lot happening. For more insights, contact our experts at MarkSpace Media.

Data Analytics– Relevance and Importance

Data Analytics– Relevance and Importance

Evaluation of data using analytical and statistical tools to discover some information that is of value and might help businesses in better decision making is known as data analytics. It is done using various methods like data visualization, data mining, business intelligence, and text analytics. These will be discussed in this article in brief. First, let’s take a look at the data analytics process in brief.

  1. It starts with clearly defined objectives where you determine your aim to do data analytics and what kind of data will you need for this.
  2. Next, you start collecting the data from all the relevant sources. Tools for data collection can include surveys, questionnaires, videos, audio, etc.
  3. The collected data is in many formats and is often unorganized. It is, therefore, then organized properly into some consistent format that can be used for the analysis.
  4. Once the data is organized, it is checked for errors, inconsistencies, and redundancies. It is fixed at this stage.
  5. Finally, when you have organized and error-free data, it is used to draw analysis.

Data Analysis Methods

The below section provides insight into different methods of data analytics.

  1. Data Mining

Data mining is defined as the method of analysis that is aimed at discovering patterns in large data. The methods used for discovering these patterns are statistics, artificial intelligence, machine learning, and databases. The technique is used to transform the raw data into understandable business information. Data mining is used for:

–    Detection of pattern in huge volumes of data generally referred to as big data and identify cases that should be excluded from decision making or fraud detection.

–    Learn the purchasing habits of customers. This can be done using machine learning techniques.

–    Clustering for identifying any groups within data that were previously unknown.

–    Classification. It is used to separate the data entries, for example, the way emails differentiate spam and non-spam emails.

  1. Text Analytics

This refers to finding useful information from the text. It is done by taking the unstructured information and processing it to derive any meaningful numerical indices or some other information that can be used by algorithms. It includes collecting information from various sources like web, file systems, and databases, linguistic analysis, pattern recognition, and extraction of the summary. Text analytics is used to:

–    Analyse open ended-survey responses.

–    Analyse documents, email, etc. to filter out spam and unnecessary data.

–    Investigate the competitors by checking their websites.

–    Security applications that can process log files for intrusion detection.

  1. Business Intelligence

Business intelligence uses the data to transform it into actionable intelligence. This means that it analyses the data and uses the results in business decision-making and other business purposes. This allows the organizations to analyze the trends and derive insights from them. Some examples of it include:

–    Operating decisions in an organization like product placement and pricing.

–    Identifying the new markets, demand, and suitability of products in different segments of the market.

–    Budgeting and forecasts.

–    Visual tools like geographical mapping, heat maps, tables, charts, etc.

  1. Data Visualization

It simply refers to the visual representation of data. In data analytics, it means using the tools of statistics, probability, pivot tables, etc. to present the data visually. The advantage of it is that it makes the complex data easy to understand and use. More and more amount of data is generated using the sensors, known as Internet of Things (IoT). This big data is difficult to understand. Ergo, data visualization makes it understandable. It is used in:

–    Extracting summary from raw data.

–    Using bar diagrams to show sales performance of organizations.

–    Using histogram to depict distribution of a variable like income by dividing the range into bins.

Are you using data analytics in your organization? Do you have something to contribute to what has been said? Or, do you need help with understanding this further and how to use it for benefit of your business? Get in touch with MarkSpace now.