What is Big Data Analytics? Types, Tools and Applications

What is Big Data Analytics? Types, Tools, and Applications

The word “data analytics” is trending today. Big Data is a huge amount of data sets that are not allowed to be stored, altered, or investigated via classic tools. Nowadays, there are numerous sources that create data at a very high speed. Social media platforms and other networks are among some of the biggest data sources. For instance, Facebook forms data of more than 500 terabytes every single day, where these data involve everyone’s photos, text messages, voice messages, videos, etc. Moreover, data can be found in several formats. These formats include structural data, semi-structured data as well as unstructured data. Let’s go deeper into the topic and answer the question-What is Big Data Analytics Life Cycle? by discussing big data analytics types, tools, and various applications.

What is Big Data Analytics? and Why It is Important?

In a classic Excel sheet, for example, data is categorized as structured data. However, on the other hand, the data coming from e-mails are classified as semi-structured data, and the photos and videos of yours all belong to unstructured data. When all these data are integrated, we end up with Big Data. Yet, raw Big Data is not useful, so there is something called Big Data Analytics.

Big Data analytics is a series of actions that are used to take meaningful information out. That information includes hidden patterns, unknown correlations, market trends, customer demands. What is more,

Big Data analytics offers many different benefits. It can be utilized to make a better choice, avoid deceptive actions.

The Reason for Big Data Analytics

Big Data analytics feed every single thing that we all do online in every single industry. For instance, the online video-sharing platform Youtube has about 2 billion users, which create a huge amount of data daily. Thanks to this information, you can automatically get suggested videos via the video-sharing platform. These are relied on likes, search history, and shares, and are done with a smart recommendation engine. All of these are done by several tools, frameworks, and techniques, which are all the outcome of Big Data analytics

What are the Benefits of Big Data Analytics?

Below are the four essential benefits.

Dealing with Risk
Innovations and Development of Product
Faster and Efficient Decision Making
Enhancement of Customer Experience

Now let’s take a look at each benefit with examples.

1.Dealing with Risk

Banking companies often use the Big Data analytics process to extract meaningful information and decrease the suspect list and the sources of several other problems. For example, the Oversea-Chinese Banking Corporation (OCBC Bank), uses Big Data analytics to see failed actions and other conflicts.

2.Innovations and Development of Product

One of the greatest manufacturers of jet airline engines throughout the globe, General Electric utilizes Big Data analytics in order to examine the efficiency of engine design and the possibility of potential developments.

3.Faster and Efficient Decision Making

One of the largest coffee companies, Tchibo takes use of Big Data analytics in order to make quick strategic, and efficient decisions. For instance, the company uses it simply to determine whether a certain location would be appropriate for a new coffee shop. In order to do that, the company will examine various effective factors. These include accessibility, population, demographics, etc.

4.Enhancement of Customer Experience

To enhance customer experiences Frontier Air Lines uses Big Data analysis. They analyze tweets to gather information about their customers’ experience taking their journeys, delays, and so on into account. What is more, the airline monitors negative tweets and take action accordingly in order to fix that. Also, as it publicly publishes these problems and related solutions, Frontier Air Lines can build customer relations.

Big Data Analytics Life Cycle

Unlike the previous approaches, today’s big data context is much more sophisticated. The below big data analytics life cycle phases constitute most of the work in a successful project.

Stage 1 – The evaluation of the Business case

Stage 2 – Data identification

Stage 3 – The Filtering of data

Stage 4 – The extraction of data

Stage 5 – The collection of data

Stage 6 – The analysis of data

Stage 7 – Data Visualization

Stage 8 – Final result analysis

Different Types of Big Data Analytics

Let’s take a look at the four types of big data analytics data, and discuss why each is important.

Descriptive Analytics

Descriptive Analytics is an easily readable summary of past data. In this way, reports such as revenue, profit, and sales of a company can be formed. What is more, Descriptive Analytics can also be used to arrange social media metrics in a table form.

For instance, The Dow Chemical Company used Descriptive Analytics and monitored past data followed by boosting the usage of the facility around its office environment. Thanks to Descriptive Analytics, The Dow Chemical Company could recognize underutilized space, which in turn led the company to save yearly about US $4 million.

Diagnostic Analytics

Diagnostic Analytics is made to comprehend the source of a problem. Examples of it include drill-down, data mining, and data recovery. Since diagnostic analytics offers detailed information about the insight of a certain issue, organizations and companies make use of diagnostic analytics.

For example, the report of an e-commerce company illustrates that even tough customers are purchasing goods, the sale of a company is reducing. This can be because of several different reasons such as misleading of a form, expensive shipping fee or lack of available payment choices. In the case like that, diagnostic analytics are used to find the exact reason.

Predictive Analytics

Predictive Analytics is the kind of analytics that use the data in the past and present in order to make the predictive decision for the future. In order to monitor the present data, predictive analytics make use of data mining, AI, and machine. Predictive analytics is used on anticipating customer trends, market demands, etc.

For instance, Payoneer identifies the type of measures they need to take in order to save the clients against failed transactions. By using predictive analytics, the company uses all the past payment data followed by creating an algorithm that anticipates failed actions.

Prescriptive Analytics

Prescriptive Analytics defines the solution to a certain problem, relying on artificial intelligence and machine learning. It also can be used to increase a profit of an airline, where the created algorithm automatically regulates the flight charges depending on factors such as weather condition, customer demand, holidays, oil fees, and destination.

Tools of Big Data Analytics

Below you can look at 5 tools used by Big Data Analytics;

Hadoop: Used to store and analyze data
MongoDB: Used frequently changing sets of data
Talend: Used to combine and manage data
Cassandra: Used to handle a mass of data
Spark: Used to examine big amounts of data

Industry Applications of Big Data

There are industry applications of Big Data. Below you can look at actively used Big Data by some sectors;

Healthcare Media
Entertainment and Telecommunications
Marketing
E-commerce
Education
Government
Banking

Conclusion

Big data analytics allows organizations to analyze their raw data and use it to improve decision making. It is possible to reduce costs, make faster decisions, and identify new opportunities with the help of big data analytics tools and applications. In this article we answer What is Big Data Analytics Life Cycle? by discussing big data analytics tools, types, applications and the lifecycle. If you want to add or share something related why big data analytics matters you can do so through the comments box below.