What is Big Data?

Introduction to Big Data

Big Data is a collection of large data sets that cannot be processed using standard computing techniques. It is not a simple technique or method but involves a lot of business and technology fields.

Characteristics of Big Data

There are four characteristics of Big data that differentiate it from the current data, which are given below:

Characteristics of Big Data

Volume: - The amount of data that businesses can collect is important, and as a result, the volume of data becomes a critical factor in Big Data analysis.     

Velocity: - Velocity specifies the speed with which new data is generated in our system. With the help of the Internet, sensors, and machine-to-machine data, we can analyze the Big Data on time.

 Variety: - The generated data is completely heterogeneous in the sense that they could be in different formats such as video, text, database, digital data, sensors, etc. Therefore, understanding the type of Big Data is a key factor in unlocking its value.

Veracity: - Knowing whether available data comes from a reliable source is critical before decoding and executing big data for business needs.

Types of Big Data

Data generated by high-speed masses can be categorized as follows:

  • Structured Data: Structured data is used to refer the data already stored in databases. It is carried out in an orderly manner. It accounts for about 20% of the total existing data, and is most used in computer programming and activities.
  • Semi Structured Data: The Semi Structured data represents a few parts of a data.  Example: XML, JSON data.
  • Unstructured Data: It includes data of various formats: document files, multimedia files, images, backup files, etc.

Source of Big Data

Data usually originates from these three primary sources, namely, internet / social networks, traditional business processes, and the Internet of Things. 

Social Networking sites

Social networks collects data based on human resources, such as:

  • Twitter
  • Facebook
  • YouTube
  • Google
  • E-mail
  • Text Messages
  • Map
  • Instagram
  • Blogs and Comments
  1. Traditional Business Sites

The organizations that provides services or products to their customers are:

  • Commercial transactions
  • Credit cards/Debit Cards
  • E-Commerce
  • Medical records
  • Banking
  • Stock Records
Traditional Business Sites
  • Internet of Things

 IOT (Internet of Things) is defined as the network of interconnected devices, embedded to collect and exchange data, such as:

  • Sensors, traffic, weather, mobile phone location, etc.
  • Security, surveillance videos, and images.
  • Satellite images
  • Data from computer system (logs, web logs etc.)
Internet of Things

Applications of Big Data

The primary purpose of Big Data Applications is to help companies to make more informed business decisions by analyzing large volumes of data. It could include web server logs, Internet click flow information, social media content and activity reports, customer email message, mobile phone call details, and device data multi-sensor.

Domain companies invest in Big Data applications to analyze large data sets, secret patterns, unknown associations, market trends, consumer desires, and other valuable business information. We will study below applications in this topic:

  • Big Data Application in Healthcare
  • Big Data Application in media & Entertainment
  • Big Data Application in Education Industry
  • Big Data Application in Government
  • Big Data Application in Manufacturing

Big Data in Healthcare

Big Data in Healthcare

Healthcare is another industry that generates much amount of data.

Big Data has contributed to medical care in various ways, which are listed below:

  • Big data has reduced treatment costs to decrease the chances of unnecessary diagnoses.
  • Help predict epidemics as well as identify preventive measures can be taken to minimize the healthcare effects.
  • It helps in detecting diseases at early stage and prevents them from getting worse, which in turn makes your treatment comfortable and effective.
  • Patients may receive evidence-based medications that have been identified and prescribed after investigating previous medical findings.
  •  Patients can obtain evidence-based drugs that have been identified and prescribed after researching previous medical results.
  • Examples

Portable appliances and sensors are introduced into the healthcare sector and can provide real-time nutrition to a patient's electronic health record. One Such technology is from Apple.

The Apple technology includes Apple Health Kit, Care Kit, and Research Kit. The main goal is to aware iPhone users to store and access their health records in real-time on their phones.

Big Data in media & Entertainment

Big Data in media & Entertainment

There are many digital devices for entertainment, so a large amount of data is generally expected. This is the main reason for the enhancement of the big data in the media and entertainment industry.

Apart from this, social media platforms are another way to create a large amount of data. The companies and industries in media and entertainment can recognize the importance of this data. They can also take benefit from it, in their growth.

Some of the benefits in the media and entertainment industry derived from big data are given below:

  • Predicts the interests of the public.
  • Optimized or on-demand programming of multimedia streams on digital media distribution platforms.
  • Get information from customer reviews.
  • Effective ads targeting.

Example

Spotify, an on-demand music delivery platform, which uses big data analytics, collects data from all users around the world. It also uses analytics data to provide informed recommendations and music suggestions to each user.

Amazon Prime offers video, music and Kindle books in one stop shop, relies heavily on big data.

Big Data Application in Education Industry

Big Data Application in Education Industry

The education industry is consists a huge amount of data related to students, faculty, courses, results, etc. We can understand that accurate study and analysis of these data can provide insights that can be used to improve the working effectiveness and efficiency of educational institutions.

The following are some areas in the education industry that have been transformed through major data-driven changes:

  • Personal and dynamic learning programs
  • Rephrase the course
  • Classification systems
  • Functional prediction

Example

The University of Alabama has more than 38,000 students and a collection of data. In past, when there were no real solutions to analyze this large amount of data, some solutions were of no use. Now, administrators can use data analysis and visualization of this data to prepare chart patterns of students that revolutionize university operations, recruitment, and retention efforts.

Big Data Application in Government

Big Data Application in Government

Government of any country face a large amount of data almost every day. They have to keep track of the various records and databases of their citizens, their growth, energy resources, geographical surveys, and many more. All of this data contributes to big data. Thus, proper study and analysis of these data helps governments in many ways. Some of the ways are listed below:

  1. Wellness schemes
  2. By making faster and more informed decisions about different political programs
  3. Identifying areas that need immediate attention.
  4. To be updated in agriculture by tracking all current land and livestock.
  5. To overcome national challenges like unemployment, terrorism, exploring energy resources, and much more.
  • Cyber Security
  • Big data is widely used to identify phishing.
  • It is also used to arrest tax evaders.

Example

Food and Drug Administration (FDA) that runs under the jurisdiction of the Federal Government of USA leverages from the analysis of big data to discover patterns and associations to identify and examine the expected or unexpected occurrences of food-based infections.

Big Data Technologies

Big Data technology is important to present a more accurate analysis that leads the business analyst to accurate decision making, greater operational efficiency, reducing costs, and commercial risks. To implement and maintain a wide variety of data, it is necessary to have an infrastructure that can facilitate, manage, and handle large volumes of data in real-time.

Big Data is thus classified into two subcategories:

Operational Big data: Operational Big Data is about ordinary daily statistics that we generate. These transactions can be online transactions, social media, data from a particular organization, etc.

Analytical Big Data: Big data analytics is like an advanced version of Big Data technology. It is more complicated than functional Big Data. Briefly, analytics Big Data is where real performance comes into play, and real-time critical decisions for businesses are made by analyzing operational Big Data.

Challenges of Big Data

Rapid growth in data: The rate of growth at such a high rate creates a problem to look for ideas when using it. There is no 100% efficient way to filter the relevant data.

Storage: Generating such a large amount of data requires space for storage, and organizations face the challenge of managing such comprehensive data without the right tools and technologies.

Data not reliable: There is no guarantee that the collected and analyzed data will be 100% accurate. Redundant data, conflicting data, or incomplete data are challenges that remain.

Computer Security: Businesses and organizations that store such large data (users) can be a target for cyber-criminals, and the data can be stolen. Therefore, encrypting this colossal data is also a challenge for businesses and organizations.



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