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BIG DATA ANALYTICS- Obtaining Insights From The Terrabytes.

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  • BIG DATA ANALYTICS- Obtaining Insights From The Terrabytes.

BIG DATA ANALYTICS
Obtaining Insights From The Terabytes

Every organization, whether large or small, possesses the ability to manage a significant amount of data produced by various data points and processes. The data can be managed up to a certain extent via excel sheets, accessing databases, and other such resources. But, when the size of data overpowers the data management capacities of such tools and the rate of human error grows to become unacceptable due to intense manual processing, it’s time to consider Big Data Analytics.

The data explosion is here, with ever-increasing quantities of data being generated every day. The upward trend shows no signs of abating or even slowing. The fact that big data has infiltrated almost every industrial sector today is truly undeniable. It is a major force that drives the global success of businesses and organizations. 

However, it is critical to first understand big data and what is big data analytics.

 

What Is Big Data?

Big Data as defined by Gartner is 

‘High-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.’

Simply put, big data refers to larger, more complex data sets, particularly those derived from new data sources. Since these data sets are so large, conventional data processing tools and web analytics tools can’t handle them. However, these vast amounts of data can be used to uncover valuable information to resolve business issues that previously couldn’t be resolved.

 

The 3 V’s Of Big Data

The three Vs – volume, velocity, and variety spring to mind when we talk about Big Data. With the ever-increasing amount of data, the rate at which it flows through businesses and whole industries is also speeding up.

We are in the middle of a data deluge, which is flooding massive volumes of data at incredible velocities in a wide variety.  Through all this data, information is derived, and the information brings more prospects of innovation. 

1. Volume

What makes Big Data, Big Data? 

Well, the enormous size of data generated by software systems, sensors, devices, transactional applications, web, IoT devices, video & audio, networks, log files, social media, and other sources is incredible. For instance, consider volume from the perspective of social media, which has a significant effect on data. About 2 trillion posts and 250 billion photos have been uploaded since 2016.

2. Velocity

Big data isn’t just big; it’s also rising at a breakneck rate. The flood of data and social media has transformed the way how data is looked at.  There was a phase when we assumed that data from the previous day was fresh and valid. The data is now running almost in real-time, and the update window has shrunk to split seconds. Big Data is represented by this high-velocity data.

3. Variety

Customers, competitors, vendors, and others will generate prescriptive data ranging from structured and readily manageable data to unstructured data that is difficult to process straightforwardly to be used for decision making. The wide range of data formats needs specific processing skills and algorithms. We no longer have control over the input data format, whether it be pure text, photo, music, video, web, GPS data, sensor data, relational databases, documents, SMS, pdf, flash, and much more.

Big Data Analytics 

Why Do We Need Big Data Analytics?

Internet traffic is just a small part of the total amount of data generated and processed around the world and contains both personal and business data. The total volume of data in the world today is somewhere between 10 and 50 zettabytes. An important question is raised here:  What are we going to do with all of this data? What benefit can perpetual data gathering through the web, personal devices, the Internet of Things, and other systems bring? 

“Analyzing the data for gaining insights” is an excellent answer to the question raised. The answers to the questions that will allow better and faster decision-making, modeling, and predicting of future outcomes in the business, government, and society can be sought somewhere in this boundless ocean of data. Another challenge is where do we begin with that kind of data? 

 

What Is Big Data Analytics?

The process of uncovering trends, patterns, and correlations in massive volumes of raw and complex sets of data that contain various types of data (structured, semi-structured, and unstructured data) obtained from a range of sources, with sizes varying from terabytes to petabytes to make data-informed decisions is known as big data analytics. The processes involve well-known statistical analysis methods, such as clustering and regression, and extend them to larger datasets using advanced data analytics tools and web analytics tools

Certainly, there are major benefits to arming yourself with advanced big data tools that have the requisite capabilities to explore Big Data for insights. Those that have the right resources will overcome the Three Vs’ challenges and reap all the benefits they possess. Data Mining, Text Analytics, Predictive Analytics, Data Visualization, Artificial Intelligence, Machine Learning, Statistics, and Natural Language Processing are some of the powerful techniques. Big Data Analytics provides a virtually limitless source of market and informational knowledge, which can contribute to improvements in organizational performance and innovation and expose undiscovered revenue opportunities for businesses in almost every field.

 

How Does Big Data Analytics Work?

To operationalize the big data, the four procedures applied to large data sets involved in this advanced system of analytics are- 

1. Data Collection

Organizations can now collect both structured and unstructured data from a range of sources, ranging from cloud storage to mobile applications to in-store IoT sensors or even beyond, thanks to today’s technology. Some data will be stored in data centers, where it will be accessible to business intelligence software and solutions. A data lake may be used to store raw or unstructured data that is too varied or complicated for a warehouse.

2. Data Processing

When data is gathered and stored, it should be properly arranged to yield reliable results on analytical queries, particularly when the data is too large and unstructured. Data availability is rising at an astounding pace, leaving data processing, a quite demanding task for organizations.

Batch processing is one option for processing large data blocks over time. When there is a long time between gathering data and analyzing it, batch processing comes in handy.

Stream processing takes small batches of data at a time, reducing the time between data collection and analysis and allowing for faster decision-making. Stream processing is more rigorous and, in many cases, more costly.

3. Cleaning Data

To increase data quality and obtain stronger results, all data must be formatted accurately, and any redundant or irrelevant data must be removed or accounted for. Dirty data can distort and misrepresent, leading to erroneous insights.

4. Analyzing Data

It takes time to transform big data into usable information. Advanced analytics methods can convert big data into big insights.  The following are some examples of big data analysis techniques:

a. By finding anomalies and building data clusters, data mining scans across massive datasets to discover patterns and relationships.

b. Predictive analytics allows future forecasts based on an organization’s historical data, predicting potential threats and opportunities.

c. Deep learning uses artificial intelligence and machine learning to layer algorithms and identifies patterns in the most complex and abstract data, emulating human learning patterns.

 

Types Of Big Data Analytics

There are 4 different types.

1. Descriptive Analysis 

This summarizes previous data in an easy-to-understand format. This allows in the compilation of reports such as a company’s earnings, profit, and sales, among other things. It also helps in the tally of social media metrics.

2. Diagnostic Analysis   

This is done to figure out what caused the issue in the first place. Drill-down, data mining, and data recovery are all examples of techniques used in this type of analysis. Diagnostic analytics are used by organizations because they provide a comprehensive understanding of an issue.

3. Predictive Analysis

This form of analytics analyses past and current data to give future predictions. Predictive analytics makes predictions using techniques such as data mining, artificial intelligence, and machine learning. It predicts consumer and industry patterns, among other things.

4. Prescriptive Analysis

This form of analytics recommends a solution to a specific issue. Both descriptive and predictive analytics are used in perspective analytics. The majority of the time, AI and machine learning are used. 

 

10 Big Data Analytics Tools You Should Know In 2021

If you’re considering making the move to Big Data analytics but aren’t sure which big data tools you can use, check out the following 10 tools that you can put to use-

1. Hadoop

Hadoop is an open-source tool that provides enormous storage for all kinds of data. It has astounding processing power and the ability to deal with innumerable tasks, and it never lets you worry about hardware failure.

2. Xplenty

Xplenty is an elastic and scalable cloud network where you can instantly get access to a variety of data stores and a huge collection of data transformation components.

3. CDH (Cloudera Distribution For Hadoop)

CDH allows you to acquire, store, manage, discover, model, and distribute endless data. It is a fully open-source Big Data tool and has Apache Hadoop, Apache Spark, Apache Impala, and more on its free distribution site. 

4. MongoDB

MongoDB is one of the best tools for dealing with semi-structured or unstructured data sets or those that differ or change frequently. MongoDB is suitable for storing data from mobile apps, content management systems, product catalogs, and other sources.

5. Tableau

Tableau is a tool that provides many comprehensive solutions to assist the world’s largest companies in visualizing and comprehending their data. It produces custom dashboards in real-time, manages all data sizes, and is open to both technical and non-technical users.

6. Qubole

Qubole is highly flexible and has optimized scalability. It is accessible globally in all AWS domains. It administrates, learns, and optimizes its use independently which allows the data team to focus on business performance.

7. Storm

Storm is a cross-platform and open-source tool from Apache which is agile, reliable, and highly scalable. Storm is used by several renowned businesses, including Yahoo, Alibaba, and The Weather Channel, to name a few.

8. HPCC (High-Performance Computing Cluster)

HPPC is a solution over an agile, robust, and highly scalable supercomputing platform. It is also called DAS (Data Analytics Supercomputer) and is based on a Thor architecture that enables data parallelism, pipeline parallelism, and system parallelism.

9. Lumify

Lumify is also among the open-source big data tools to analyze and visualize large data. Its notable features include full-text search, 2D and 3D graphical viewings, automated templates, multimedia analysis, real-time project or workplace collaboration, and more.

10. R

R has an open-source, free, multi-paradigm, and diverse software ecosystem and is among the most comprehensive Big Data tools for statistical analysis.

 

How Is Big Data Analytics Used?

You may mix different types of data and sources to make meaningful discoveries and decisions using various analytical techniques. Some of the most popular uses are mentioned below.

1. Development Of Product

When your product is your main business, Big Data is an absolute must. Take, for example, Netflix, which almost everyone is familiar with. How do you think Netflix manages to send an email to you every week with personalized recommendations? Of course, with the help of Big Data analysis. They identify the data of past and current shows that you watched or marked as a favorite and use predictive models to notify you of new shows you might enjoy. 

Other businesses are using additional tools such as social media data, store sales data, focus groups, polls, surveys, and more to determine how to proceed with the launch of a new product and who to approach.

2. Comparative Analysis

When you understand your customers’ behaviors and can track them in real-time, you can compare them to the experiences that other similar products have established, and you’ll be able to see where you stand out from the competition.

3. Machine Learning

Machine learning is really popular today, and everyone wants to learn more about it. We now can develop machines that learn on their own, owing to Big Data and machine learning models that have been developed as a result of it.

4. Customer Experience

Since the market is so huge, it’s difficult for a product to stand out as unique. So, if you want to set yourself apart, make an effort to personalize your customers’ experiences. Big data allows you to collect data from social media, site visits, call logs, and other sources to enhance the experience of engagement and increase the value offered.

5. Scalability And Failure Prediction

It’s critical to know how much of the infrastructure you need to mobilize at any given time, as well as the capacity to foresee mechanical failures. It will be difficult at first to interpret all of the data because you will be overwhelmed with both structured (time intervals, equipment) and unstructured data (log entries, error messages, etc.). However, by taking all of these metrics into account, you can define potential concerns before they become problems and scale the usage of your resources. You can use Big Data to analyze consumer feedback and forecast future demands, letting you know when more resources are needed.

6. Fraud And Compliance

Someone is attempting to imitate your brand, and someone is attempting to steal your and your client’s data.  Every day, hackers are getting more inventive. Security and compliance standards, on the other hand, are constantly evolving. Big Data will assist you in identifying data trends that suggest fraud and determining when and how to respond.

Your data analysts will be able to use your data for a variety of purposes and will be able to relate the various types of data you have. You can use this data to publish official research and increase awareness of your brand.

 

Industry Applications Of Big Data Analytics

Here are some of the significant fields where Big Data Analytics is being used lucratively-

S.No.

Sector

Use

     

1

Media And Entertainment

Recognize the popularity of shows, movies, songs, and other media to provide users with a personalized recommendation list

2

Government

Assists governments in several areas, including law enforcement.

3

Healthcare

Determine how likely a patient is to have health problems based on their medical history.

4

Telecommunications

Boost customer service and forecast network capacity.

5

Education

Based on market preferences, build new and upgrade existing courses.

6

Banking

Customer income and expenditure patterns will help identify which banking offerings, such as loans and credit cards, they would select.

7

E-Commerce

Forecast consumer trends and optimize costs.

8

Marketing

Develop high-return-on-investment marketing strategies that boost revenues.

 

Wrapping Up

The possibilities for using Big Data will be immense if everything around us begins to use the Internet (Internet of Things). The amount of data we have access to is only going to grow, and analytics technology will improve greatly. Big Data is one of the elements that will impact mankind’s future.

All of the big data tools will grow along with it. The infrastructure needs will change over time. Perhaps, in the future, we will be able to store all of the data we need on a single machine with sufficient storage. It’s an indication that everything will possibly become cheaper, quicker, and easier to deal with as a result of this.

One of the things that we at Stallion Infosys are interested in is Big Data Analytics and we surely will stay and keep you updated on the topic.

 

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