BIG DATA EXPLAINED
The term “big data” refers to data that is so large, fast or complex that it’s difficult or impossible to process using traditional methods. The act of accessing and storing large amounts of information for analytics has been around a long time. But the concept of big data gained momentum in the early 2000s when industry analyst Doug Laney articulated the now-mainstream definition of big data as the three V’s:
Volume: Organizations collect data from a variety of sources, including business transactions, smart (IoT) devices, industrial equipment, videos, social media and more. In the past, storing it would have been a problem — but cheaper storage on platforms like data lakes and Hadoop have eased the burden.
Velocity: With the growth in the Internet of Things, data streams in to businesses at an unprecedented speed and must be handled in a timely manner. RFID tags, sensors and smart meters are driving the need to deal with these torrents of data in near-real time.
Variety: Data comes in all types of formats — from structured, numeric data in traditional databases to unstructured text documents, emails, videos, audios, stock ticker data and financial transactions.
IMPORTANCE OF BIG DATA
The Big Data analytics is indeed a revolution in the field of Information Technology. The use of Data analytics by the companies is enhancing every year. The primary focus of the companies is on customers. Hence the field is flourishing in Business to Consumer (B2C) applications.We divide the analytics into different types as per the nature of the environment. We have three divisions of Big Data analytics: Prescriptive Analytics, Predictive Analytics, and Descriptive Analytics. This field offers immense potential, and in this blog, we will discuss four perspectives to explain why big data analytics is so important today?
- Data Science Perspective
- Business Perspective
- Real-time Usability Perspective
- Job Market Perspective
Big Data Analytics and Data Sciences
The analytics involves the use of advanced techniques and tools of analytics on the data obtained from different sources in different sizes. Big data has the properties of high variety, volume, and velocity. The data sets come from various online networks, web pages, audio and video devices, social media, logs and many other sources.
Big Data analytics involves the use of analytics techniques like machine learning, data mining, natural language processing, and statistics. The data is extracted, prepared and blended to provide analysis for the businesses. Large enterprises and multinational organizations use these techniques widely these days in different ways.
Businesses and Big Data Analytics
Big Data analytics tools and techniques are rising in demand due to the use of Big Data in businesses. Organizations can find new opportunities and gain new insights to run their business efficiently. These tools help in providing meaningful information for making better business decisions.
The companies can improve their strategies by keeping in mind the customer focus. Big data analytics efficiently helps operations to become more effective. This helps in improving the profits of the company.
Big data analytics tools like Hadoop helps in reducing the cost of storage. This further increases the efficiency of the business. With latest analytics tools, analysis of data becomes easier and quicker. This, in turn, leads to faster decision making saving time and energy.
Real-time Benefits of Big Data Analytics
There has been an enormous growth in the field of Big Data analytics with the benefits of the technology. This has led to the use of big data in multiple industries ranging from
- Banking
- Healthcare
- Energy
- Technology
- Consumer
- Manufacturing
How Big Data is Used on Facebook?
The main business strategy of Facebook is to understand who their users are, by understanding their user’s behaviors, interests, and their geographic locations, Facebook shows customized ads on their user’s timeline. How it is possible?
There are around billion levels of unstructured data has been generated every day, which contains images, text, video, and everything. With the help of Deep Learning Methodology ( AI), Facebook brings structure for unstructured data.
A deep learning analysis tool can learn to recognize the images which contain pizza, without actually telling how a pizza would look like?. This can be done by analyzing the context of the large images that contain pizza. By recognizing the similar images the deep learning tool will segregate the images that contain pizza. This is how data Facebook is bringing a structure to the unstructured data.
In Deep Learning There are several use cases are there
Textual Analysis
Facebook uses Deep Text to analyze the text data and extract the exact meaning from the contextual analysis. This is semi-unsupervised learning, this tool won’t need a dictionary or and don’t want to explain the meaning of every word. Instead, it focused on how words are used.
Facial Recognition
The Tool used for this is DL Application, that is Deep-face which will learn itself by recognizing people’s faces in photos. That’s why we’re getting the name of the friends while tagging them in a post. This is an advanced image recognition tool because it will recognize a person who is in two different photos is the same or not.
Target Advertisements
Facebook uses deep neural networks to decide how to target audience while advertising ads. This Artificial intelligence can learn itself to find as much as can about the audience, and cluster them to serve them ads in a most insightful way. Because of this serving the highly targeted advertising, Facebook has become the toughest competitor for the ever known search engine Google.
Likewise, Behind the Facebook business model, there are a lot of interesting data handling methodologies are there, and there are a lot of controversial things behind Facebook business flow. But, we don’t want to focus on those things.
Benefits and Importance of Hadoop — Big Data Platform
Hadoop is an open source platform that provides excellent data management provision. It is a framework that supports the processing of large data sets in a distributed computing environment. It is designed to expand from single servers to thousands of machines, each providing computation and storage. Its distributed file system facilitates rapid data transfer rates among nodes and allows the system to continue operating uninterrupted in case of a node failure, which minimizes the risk of catastrophic system failure, even if a significant number of nodes become out of action. Hadoop is very valuable for large scale businesses basing on its proven benefits for enterprises given below:
Advantages for Enterprises:
- Hadoop provides a cost effective storage solution for business.
- It facilitates businesses to easily access new data sources and tap into different types of data to produce value from that data.
- It is a highly scalable storage platform.
- Unique storage method of Hadoop is based on a distributed file system that basically ‘maps’ data wherever it is located on a cluster. The tools for data processing are often on the same servers where the data is located, resulting in much faster data processing.
- Hadoop is now widely used across industries, including finance, media and entertainment, government, healthcare, information services, retail, and other industries
- Hadoop is fault tolerance. When data is sent to an individual node, that data is also replicated to other nodes in the cluster, which means that in the event of failure, there is another copy available for use.
- Hadoop is more than just a faster, cheaper database and analytics tool. It is designed as a scale-out architecture that can affordably store all of a company’s data for later use.Advantages for Enterprises:
- Hadoop provides a cost effective storage solution for business.
- It facilitates businesses to easily access new data sources and tap into different types of data to produce value from that data.
- It is a highly scalable storage platform.
- Unique storage method of Hadoop is based on a distributed file system that basically ‘maps’ data wherever it is located on a cluster. The tools for data processing are often on the same servers where the data is located, resulting in much faster data processing.
- Hadoop is now widely used across industries, including finance, media and entertainment, government, healthcare, information services, retail, and other industries
- Hadoop is fault tolerance. When data is sent to an individual node, that data is also replicated to other nodes in the cluster, which means that in the event of failure, there is another copy available for use.
- Hadoop is more than just a faster, cheaper database and analytics tool. It is designed as a scale-out architecture that can affordably store all of a company’s data for later use.
Demand for Hadoop:
Low cost implementation of hadoop platform is attracting the companies to adopt this technology more conveniently. As per a report by Allied Market Research, The market for Hadoop is projected to rise from a $1.5 billion in 2012 to an estimated $16.1 billion by 2020. Significantly noted, the data management industry has expanded from software and web into retail, hospitals, government, etc. This creates a huge demand for scalable and cost effective platforms of data storage like Hadoop. Let us take a look at how Hadoop helps in providing excellent analytics services.
All business Data Captured and stored Successfully:
The enterprises and organizations estimated that they use and analyze less volume of info and most amount of it goes wasted. The reason being the organizations lack the analytics capabilities. It is a bad practice to term the data as unwanted as any part of data can be put to good use by the organization.
So, it is necessary to collect and keep all the data in well manner. With its capabilities of handling large volume of data, Hadoop helped the companies to store and analyze the high volume of data successfully.
Hadoop makes data sharing with its high sharing Ability:
The organizations use big data to improve the functionality of each and every business unit. This includes research, design, development, marketing, advertising, sales and customer handling. Sharing is difficult for to share across different platforms. Hadoop is used to create a pond. It is a repository of various sources of data, intrinsic or extrinsic sources of data.
Continuity and Stability:
It is generated continuously. Be it your social media presence, mobile platforms and other related services. These activities generate data on each second and the volume of it is huge. The solutions need to be scaled quickly and in a cost effective and secure manner.
Hadoop supports Advanced Analytics:
As compared to the traditional tool, Hadoop provides more accurate facts and figures. Hadoop supports advanced features like data visualization and predictive analytics in order to provide and represent the useful insights in a best graphical manner. It can help to optimize the performance using a single server and handle huge volume of information.
Hadoop is considered affordable for both enterprise and small business which makes it an attractive solution with endless potential. With the passage of time, companies and enterprises are getting closer to Hadoop. They are moving to implement big data to support the marketing and other efforts and resources.
