What is Big Data Analytics and Why Do We Need It?

Big data analytics involves the use of advanced analytical methods against extremely massive, multifaceted large data sets, which include semi-structured, structured and unstructured information, sourced from various sources, and in various sizes ranging from terabytes to zettabytes.

It is the size or variety that exceeds the capabilities of conventional databases to manage, capture and process data with low latency. Big data characteristics include large volume, high speed and wide range. Data sources are getting more complicated than the traditional sources because they are powered through artificial intelligence (AI), mobile devices, social media, and the Internet of Things (IoT). For instance, the various types of data are derived from devices, sensors, audio/video, networks files as well as transactional apps, the social media and the web the majority of which is created in real-time and at a large scale.

By using big data analytics, you will ultimately benefit from faster and more effective decision-making, modelling and forecasting of future outcomes, and improve business intelligence. When you are building your solution for big data think about open-source software like Apache Hadoop, Apache Spark and the whole Hadoop community as reliable and flexible storage and processing tools that can manage the amount of data that is being produced in the present.

Big Data analytics is a technique used to discover valuable insights, like undiscovered patterns, inexplicably linked connections, market trends and consumer preferences. Big Data analytics provides various benefits, including better decision-making, and preventing fraud, among other things.

 

What is the Reason Big Data Analytics are Vital?

 

Today, Big Data analytics is the driving force behind everything we do online, in every field.

 

Think of for instance the musical streaming service Spotify as an example. Spotify has 96 million customers who generate huge amounts of data each day. With this information the cloud-based platform creates suggestions for songs – via a clever recommendation engine that is based on user sharing, likes as well as search history and many more.

 

The reason for this is due to the strategies, tools, and frameworks that are the consequence of Big Data analytics.

 

If you’re an avid Spotify user, then you have been to the most popular recommendation section that is based on your preferences, past experiences and many other aspects. The recommendation engine makes use of data filtering tools to gather data and filter it with algorithms. This is exactly what Spotify does.

 

Advantages of Big Data Analytics

 

1. Risk Management

 

Use Example: Banco de Oro, one of the Philippine banks, makes use of Big Data analytics to identify irregularities and fraudulent activities. The bank uses Big Data analytics to identify a set of suspects or the root cause of the issues.

 

2. Product Development and Innovations

 

Application Case: Rolls-Royce, one of the biggest producers of jet engines used by the military and for airlines across the world, employs Big Data analyses to determine how efficient their engine designs are, and whether there is any need for improvement.

 

3. Quicker and Better Decision Making Within Organizations

 

Starbucks uses Big Data analytics to make strategic decisions. For instance, the company uses it to determine whether an area is suitable for a brand new outlet or not. They’ll analyze a range of aspects, including access, demographics, the population of the area and many other factors.

 

4. Improve Customer Experience

 

Delta Air Lines uses Big Data analysis to improve customer service. They analyze tweets to determine their customers’ experiences with regards to their journeys delay, travel time, and other issues. The airline recognizes bad tweets and then takes the necessary steps to rectify the issue. In publically addressing these issues and providing solutions, it aids the airline to build positive relationships with its customers.

 

The Lifecycle Phases of Big Data Analytics

 

Let’s look over the way Big Data analytics works:

 

Stage 1

 

Business case evaluation 

 

Big Data analytics lifecycle begins by preparing a business case that outlines the purpose behind the analysis.

 

Stage 2

 

Identification of the data 

 

This is where a variety of sources of data are identified.

 

Stage 3 

 

Filtering data 

 

All the data in the previous stage is removed to prevent the corrupted data.

 

Stage 4 

 

Data extraction 

 

The data that is not suitable for the program is removed and transformed into a form that is compatible with the tool.

 

Stage 5

 

Data aggregate 

This stage is where data that share identical fields across various datasets are combined.

 

Stage 6 

 

Analysis of data 

 

The data is assessed with statistical and analytical tools to uncover important data.

 

Stage 7 

 

Visualization of data

 

Utilizing tools like Tableau, Power BI and QlikView, Big Data analysts can produce graphs that show the results of their analysis.

 

Stage 8

 

Final results of analysis 

 

The final stage of the Big Data analytics lifecycle, which is where the outcomes of the analysis are shared with business stakeholders , who are able to take appropriate actions.

 

Different Types of Big Data Analytics

 

Here are the four different types of Big Data analytics:

 

1. Descriptive Analytics

 

This summarizes the past data in an easy-to-read format that anyone can easily comprehend. This assists in creating reports on a company’s sales, revenue, profit etc. It also aids in the calculation of social media’s metrics.

 

Use Case how Chemical Company analyzed its historical data to improve facility utilization throughout the office and lab. By using analytical descriptive data, Dow was able to find spaces that were not being used. The consolidation of space allowed the company to save almost US $4 million per year.

 

2. Diagnostic Analytics

 

This process is used to discover the root of the issue in the beginning. Techniques such as drill-down, the use of data as well as data retrieval are a few examples. Companies use diagnostic analytics as they provide a comprehensive understanding of a specific issue.

 

Example: An online business’s report shows that its sales have dropped; however, customers are still adding items to their shopping carts. This could be due to many reasons, such as the form did not load correctly, or the shipping costs are too high, or there’s no payment options that are sufficient. This is why you could utilize diagnostic analytics to pinpoint the cause.

 

3. Predictive Analytics

 

This type of analysis examines the past and current data to predict the future. Predictive analytics utilizes information mining AI and machine learning to analyse current data and make predictions for the future. It is a method of predicting customer trends, market trends and more.

 

Utilization Cases: PayPal will determine what measures they must use to protect their customers from fraudulent transactions. Utilizing advanced analytics that are predictive, the business makes use of all historical transaction data and user behaviour data to create an algorithm to predict fraud.

 

4. Prescriptive Analytics

 

This type of analysis lays out the best solution for a specific issue. Perspective analytics is a combination of predictive and descriptive analytics. The majority often, however, it depends on AI and machine learning.

 

Prescriptive analytics can be used to boost the profit of an airline. This kind of analytics can be utilized to create an algorithm that can automatically adjust the fares for flights depending on a variety of factors like demand from customers and weather conditions, destinations holidays, the price of oil.

 

Big data is often described by 4Vs

 

Volume is the large volume in data (Size of the data)

 

Velocity is the rate at which data is created, stored, analyzed, and then utilized.

 

Variety is the term used to describe the various sources of data, as in the different kinds of semi-structured, structured and unstructured data. Today, data is sourced from a variety of sources, including emails, social media apps, wearable devices phones, smartphones, as well as IOT-connected appliances.

 

Veracity is the degree of uncertainty in data. Veracity is the level of quality as well as accuracy and reliability of data.

 

Big Data Industry Applications

 

Here are a few areas where Big Data is actively used:

 

Ecommerce:

 

Predicting the trends of customers and optimizing prices are just a number of ways that E-commerce makes use of Big Data analytics

 

Marketing marketing :

 

Big Data analytics helps to create high-return marketing campaigns that result in increased sales

 

The field of education is used to create new courses and enhance existing ones according to market needs

 

Health 

 

With the aid of medical records of patients, Big Data analytics is used to predict whether they will suffer from medical issues.

 

The Media and Entertainment 

 

Used to comprehend the need for music, shows, films and more, to provide an individualized recommendation list for its users

 

Banking 

 

Income and spending habits help determine the probability of selecting the best banking offer, including credit cards and loans

 

Telecommunications is used to forecast capacity of networks and enhance the experience of customers

 

Government 

 

Big Data Analytics for Government Big Data analytics helps governments in the field of law enforcement, among other things.

 

Conclusion:

 

There are today thousands of sources which generate information at a speed. The data sources are available throughout the globe. One of the biggest databases of this data is social media networks and platforms. Let’s take Facebook as an example. Facebook produces more than 500 terabytes of data each day. This includes photos, messages, videos, and much more.