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| What Is Big Data Technology What is IOT Software Big Data Analytics |
IoT Software:
What Is Big Data Technology, What is IOT Software, and Big Data Analytics? IOT addresses key areas of networking, embedded systems, partner systems, and middleware. These applications are responsible for collaboration with business systems.
The following points explain the IOT Software application that is responsible for data collection, device integration and real-time analytics and application and process extension within the IOT network.
- Application and process extension:- Application extends the reach of present software and system towards a wider effective network.
- Data Collection:- It manages sensing, filtering, measuring, security and aggregation of data. It works with specific protocols to connect sensors with real-time, machine-to-machine networks. It collects data from multiple devices and distributes it accordingly, over the devices.
- Device Integration:- Software integration relates system devices to create an IOT system. It ensures stable networking among, the devices. They define the software technology of the IOT network.
- Real-Time Analytics:- These applications take input data from several devices and convert it into viable actions and patterns for analysis. They analyse data based on settings to execute automation-related tasks.
Big Data Analytics
Big Data Analytics:-
- Big data analytics refers to the method of analysing huge volumes of big data. It is the process of collecting, organizing and analysing a large amount of data to uncover hidden patterns, correlations, market trends and other meaningful information. It helps an organization understand the information contained in their data and use it to provide new opportunities to improve their business, which in turn leads to more efficient operations, higher profits and happier customers.
- To analyse such a large volume of data, Big Data analytics applications enable big data analysts, data scientists, predictive modellers, statisticians, and other analytical performers to analyse the growing volume of structured and unstructured data.
- It is performed using specialized software tools and applications. Using these tools, various data operations can be performed like data mining, text mining, predictive analysis, forecasting, etc. All these processes are performed separately and are a part of high-performance analytics. Big Data analytic tools and software enable an organization to process a large amount of data and provide meaningful insights that provide better business decisions in the future.
Benefits of Big Data Analytics:-
Big Data Analytics has been popular among various organizations like e-commerce
industry, social media, healthcare, Banking, Entertainment industries, and so on.
E-commerce industries like Amazon, Flipkart, Myntra and many other online shopping sites make use of big data.
What is Data:-
The quantities characters, or symbols on which operations are performed by a computer, may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical or mechanical recording media.
What is Big Data:-
Big Data is also data but with a large size. It is a term used to describe a collection of data that is large in size and yet growing with time.
Examples of Big Data:-
Some examples of Big Data:
The BSE stock exchange generates large data per day.
Social media statics shows that 500+ terabytes of new data get ingested into the database of social media site, that is, Facebook, every day. The data is generated in terms of photo and video uploads, message exchanges, putting comments, and so on.
A single jet engine can generate 10+ terabytes of data in 30 minutes of flight time.
Types of Big Data:-
Big Data are in three forms:
- Structured
- Unstructured
- Semi-structured
Structured:- Any data that can be stored, accessed and processed in the form of a fixed format is termed structured data. It refers to highly organized information that can be readily and seamlessly stored and accessed from a database by a simple search engine. For example, the employee table in a company database will be structured as the employee details, their jobs positions, their salaries, etc. Will be organized manner.
Unstructured:- Any data with no order (unknown form) or structure is classified as unstructured data. For example, e-mail is an example of unstructured data.
Semi-structured:- Semi-structured data is information that does not reside in a relational database but that does have some organizational properties that make it easier to analyze Examples of semi-structured data is a data represented in an XML file.
Characteristics of Big Data:-
- Volume:- The name Big Data itself is related to its size which is enormous. The size of data plays a very crucial role in determining its value of data. Also, whether a particular data can actually be considered Big Data. Hence, "Volume" is one characteristic which needs to be considered while dealing with Big Data.
- Variety:- It refers to heterogeneous sources and the nature of data, both structured and unstructured. Data in the form of e-mail, photos, videos, monitoring devices, PDFs, audio, etc. are also being considered in the analysis applications. This variety of unstructured data has certain issues for storage, mining and analysing, and so on.
- Velocity:- The term velocity refers to the speed of the generation of data. How fast the data is generated and processed to meet the demands. Big Data velocity deals with the speed at which data flows in form sources like business processes, applications logs, networks and social media sites, sensors, mobile devices, etc. The flow of data is massive and continuous.
- Variability:- This refers to the inconsistency which can be shown by the data at times; thus hampering the process of being able to handle and manage the data effectively.
What is Big Data Analytics:-
- Big Data refers to data that exceeds the typical storage, processing and computing capacity of conventional databases and data analysis techniques. As resources, Big Data requires tools and methods that can be applied to analyse and extract patterns from large-scale data.
- Big Data Analytics refers to the process of collecting, organizing, and analysing large data sets to discover different patterns and other useful information.
Types of Big Data Analytics:-
There are five types of big data analytics:
- Descriptive Analytics: The simple way to define descriptive analytics is that answers the question "what has happened?". In this type of analytics, the data comes in real-time for insights on how to approach the future. The main objective of descriptive analytics is to find out the reasons behind success or failure in the past. A business learns from past behaviours to understand how they will impact future outcomes. It is leverage to understand the overall performance of the company at an aggerate level and describe the various aspects.
- The example to explain descriptive analytics are the result that a business gets from the web server through the Google Analytics tools to help understand what actually happened in the past and validate if a promotional campaign was successful or not based on basic parameters like a page view.
- Diagnostic Analytics:- It consists of asking the questions; why did it happen? Diagnostic analytics looks for the root cause of a problem. It is used to determine why something happened. This type attempts to find and understand the cause of events and behaviours.
- Prescriptive Analytics:- Prescriptive analytics adds the spice of manipulating the future. It advises on possible outcomes and results in action that are likely to maximize key business metrics. It basically uses simulation and optimization to ask What should a business do?
- Optimization that helps achieve the best outcomes.
- It helps understand how to achieve the best outcomes and identify data uncertainties to make better decisions.
- Predictive Analytics:- It consists of asking the question. What is likely to happen? It uses past data to predict the future. It is all about forecasting. Predictive analytics uses many techniques like data mining and artificial intelligence to analyse current data and make scenarios of what might happen. Organizations like Walmart, Amazon and other retailers leverage predictive analytics to identify trends in sales based on purchase patterns of customers, forecasting customer behaviour, forecasting inventory level, and predicting what products customers are likely to purchase together so that they can offer personalized recommendations, predict the number of sales at the end of the quarter or the year.
- Advantage of Big Data:-
- One of the biggest advantages of big data is predictive analysis. Big Data analytics tools can predict outcomes accurately, thereby, allowing businesses and organizations to make better decisions, while simultaneously optimizing their operational efficiencies and reducing risks.
- By harnessing data from social media platforms using Big Data analytics tools, businesses around the world are streamlining their digital marketing strategies to enhance the overall consumer experience. It provides insights into the customer's pain points and allows companies to improve upon their product and services.
- Keeping irrelevant data is a curse for the database since it will make the filtering process complicated. But the statistics say, around 43% of companies are having tools which are unable to filter junk data. A simple thing like filtering the customers from web analytics will be able to provide insights for the efforts of your acquisition.
- Big Data analytics could help companies generate more sale lead which would naturally mean a boost in revenue. Businesses are using Big Data analytics tools to understand how well their product/services are doing in the market and how the customers are responding to them. Thus, they can understand better ways to invest their time and money.
- Using Big Data Application:-
- The people who are using Big Data know better what Big Data is. Let's look at some such industries.
- Healthcare:- Big Data has already started to create a huge difference in the healthcare sector. With the help of predictive analytics, medical professionals are now able to provide personalized healthcare services to individual patients. Apart from that, fitness, wearable, telemedicine, and remote monitoring-all powered by Big Data and AI- are helping change lives for the better.
- Academia:- Big Data is also helping enhance education today. There are numerous online educational courses to learn from. Academic Institutions are investing in digital courses powered by Big Data technologies to aid the all-around development of growing learners.
- Banking:- The banking sector relies on Big Data are fraud detection. Big Data tools can efficiently detect fraudulent acts in real-time such as misuse of credit/debit cards.
- Manufacturing:- According to Global Trends Study, the most significant benefit of Big Data in manufacturing is improving the supply strategies and product quality. In this sector, Big Data helps create a transparent infrastructure, thereby, predicting uncertainties and competencies that can affect the business adversely.
- IT:- One of the largest uses of Big Data, IT companies around the world, are using Big Data to optimize their functioning, enhance employee productivity, and minimize risk in business operations. By combining Big Data technologies with ML and AI, the IT sector is continually powering innovation to find solutions even for the most complex of problems.
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