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What is Big Data Analytics? Definition, Advantages, and Types

  • Ritesh Pathak
  • Jan 23, 2021
  • Big Data
  • Updated on: May 27, 2021
What is Big Data Analytics? Definition, Advantages, and Types title banner

In an era where technology has reached the pinnacle of its use and has completely overpowered our lives, the amount of data exchanged is enormous. 

 

The high volumes of data sets, that a traditional computing tool cannot process, are being collected daily. We refer to these high volumes of data as big data

 

Businesses, nowadays, rely heavily on big data to gain better knowledge about their customers. The process of extracting meaningful insights from such raw big data is reckoned as big data analytics

 

Since traditional computing techniques cannot process these big data, various tools are being leveraged. The tools used for big data analytics have seen increased use in the recent past. 

 

Big data analytics has found several applications in different industries. It has allowed businesses to know their customers better than they know themselves proving the technique to be extremely advantageous. 

 

Through this blog, we will be exploring big data analytics, its different types, advantages of big data analytics, and its industrial applications

 

 

What is Big Data Analytics?

 

The process of analysis of large volumes of diverse data sets, using advanced analytic techniques is referred to as Big Data Analytics

 

These diverse data sets include structured, semi-structured, and unstructured data, from different sources, and in different sizes from terabytes to zettabytes. We also reckon them as big data. 

 

Big Data is a term that is used for data sets whose size or type is beyond the capturing, managing, and processing ability of traditional rotational databases. The database required to process big data should have low latency that traditional databases don’t have. 

 

Big data has one or more characteristics among high volume, high velocity, and high variety. Gartner defines it as: 

 

“Big data 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.”

- Gartner, Research and Advisory Company

 

Big data analytics enables analysts, researchers, and business users to leverage big data, which was previously inaccessible and unusable, for faster and better decision-making. 

 

Recommended blog - Hive Big Data

 

This analytics tool is used by businesses to extract meaningful insights, such as hidden patterns, unknown correlations, market trends, and customer preferences, from a stack of raw and unstructured data.

 

 

4 Types of Big Data Analytics

 

The different types of data require different approaches. This different approach of analytics gives rise to the four different types of Big data analytics. 

 

Big data analytics is categorized into four subcategories that are: 

 

  1. Descriptive Analytics

  2. Diagnostic Analytics

  3. Predictive Analytics

  4. Prescriptive Analytics


 

We will look at them in detail.

 


Types of big data analytics starting from Descriptive Analytics,  Diagnostic Analytics,  Predictive Analytics,  Prescriptive Analytics.

Types of Big Data Analytics


 

1. Descriptive Analytics

 

Descriptive Analytics is considered a useful technique for uncovering patterns within a certain segment of customers. It simplifies the data and summarizes past data into a readable form. 

 

Descriptive analytics provide insights into what has occurred in the past and with the trends to dig into for more detail. This helps in creating reports like a company’s revenue, profits, sales, and so on. 

 

Examples of descriptive analytics include summary statistics, clustering, and association rules used in market basket analysis.

 

An example of the use of descriptive analytics is the Dow Chemical Company. The company utilized its past data to increase its facility utilization across its offices and labs. 

 

Recommended blog - Big Data in Manufacturing

 

 

2. Diagnostic Analytics

 

Diagnostic Analytics, as the name suggests, gives a diagnosis to a problem. It gives a detailed and in-depth insight into the root cause of a problem. 

 

Data scientists turn to this analytics craving for the reason behind a particular happening. Techniques like drill-down, data mining, and data recovery, churn reason analysis, and customer health score analysis are all examples of diagnostic analytics. 

 

In business terms, diagnostic analytics is useful when you are researching the reasons leading churn indicators and usage trends among your most loyal customers. 

 

A use case for diagnostic analytics can be an e-commerce company. Given the situation that the sales of the company have gone down even though customers are adding products to their carts.

 

The possible reasons behind this problem can be: the form didn't load correctly, the shipping charges are high, and not enough payment methods are available. 

 

Taking the help of diagnostic analytics, the company comes out with a specific reason and then works on that to resolve the issue. 

 

 

3. Predictive Analytics

 

Predictive Analytics, as can be discerned from the name itself, is concerned with predicting future incidents. These future incidents can be market trends, consumer trends, and many such market-related events. 

 

This type of analytics makes use of historical and present data to predict future events. This is the most commonly used form of analytics among businesses. 

 

Predictive analytics doesn’t only work for the service providers but also for the consumers. It keeps track of our past activities and based on them, predicts what we may do next. 

 

"The purpose of predictive analytics is NOT to tell you what will happen in the future. It cannot do that. In fact, no analytics can do that. Predictive analytics can only forecast what might happen in the future, because all predictive analytics are probabilistic in nature."

- Dr. Michael Wu, Chief AI Strategist, PROS

 

 

Predictive analytics uses models like data mining, AI, and machine learning to analyze current data and forecast what might happen in specific scenarios.  

 

Examples of Predictive analytics include next best offers, churn risk, and renewal risk analysis.

 

We can take the example of PayPal (Stripe vs PayPal) to understand how businesses use predictive analytics. 

 

The company determines the steps they need to take the steps to protect their client’s fraudulent transactions. It uses all past payment data and user behavior data to predict fraudulent activities. 

 

 

4. Prescriptive Analytics

 

Prescriptive analytics is the most valuable yet underused form of analytics. It is the next step in predictive analytics. The prescriptive analysis explores several possible actions and suggests actions depending on the results of descriptive and predictive analytics of a given dataset.

 

Prescriptive analytics is a combination of data and various business rules. The data of prescriptive analytics can be both internal (organizational inputs) and external (social media insights).

 

Prescriptive analytics allows businesses to determine the best possible solution to a problem. When combined with predictive analytics, it adds the benefit of manipulating a future occurrence like mitigate future risk. 

 

Examples of prescriptive analytics for customer retention is the next best action and next best offer analysis. 

 

A use case of prescriptive analytics can be the Aurora Health Care system. It saved $6 million by reducing the readmission rates by 10%. 

 

Prescriptive analytics has good use in the healthcare industry. It can be used to enhance the process of drug development, finding the right patients for clinical trials, etc. 

 

Talking of analytics in healthcare, read our blog on the role of big data in the healthcare industry

 

 

 

Advantages of Big Data Analytics

 

Big Data Analytics has proved advantageous to businesses. They are using Big Data Analytics in various ways. The advantages it offers have made it one of the most sought modern-day technologies.

 

Let us look at the four advantages of big data analytics offers. 

 

1. Risk Management

 

Big Data Analytics offers crucial insights on consumer behavior and market trends that help businesses to assess their position and progress. 

 

Also, they are able to foresee any upcoming risks taking the help of predictive analytics, and mitigate that risk backed by prescriptive analytics, and other types of statistical analysis techniques

 

It may interest you to also read about the 5 ways in which businesses are using big data analytics.

 

 

2. Product Development and Innovations

 

Big Data Analytics also helps businesses to decide on the manufacturing and nodding for a product to go ahead in the market.

 

Customer feedback on a product is a part of big data. This data is then leveraged by businesses to assess the performance of their product and henceforth decide whether it is to be continued or stopped. 

 

Talking about innovations, the insights collected are key to innovations. They can be used to tweak business strategies, marketing techniques, and many more. 

 

The list of examples of this advantage of big data can go on forever because businesses these days heavily rely on market insights to form any sort of business strategy. 

 

 

3. Quicker and Better Decision making

 

The world has become faster and so has become the process of decision making. Big Data Analytics has fueled the process of decision making. Nowadays, companies don’t have to wait for days or months for a response.  

 

The reduced time of response has also led to increased efficiency. Now, businesses don’t have to suffer big losses if their product or service is not being liked by customers as they can rework their business model, making use of the technique. 

 

 

4. Improve Customer Experience

 

When businesses can analyze customer behavior so often, they can improve the customer experience and that too on a personal level. 

 

Diagnostic analytics can be used to find solutions to problems being faced by the customer. This will result in a better-personalized experience eventually reading to an improved customer experience. 

 


5. Complex Supplier Networks

 

Through big data, companies provide supplier networks, also called B2B communities, through a larger degree of precision. Big Data analytics allows suppliers to escape the constraints they encounter. It allows suppliers to adopt higher levels of contextual intelligence, enhancing their success.

 

 

6. Focused And Targeted Campaigns

 

Big data can be adopted by platforms for offering tailored items for the target market. Rather than wasting money on unsuccessful advertising campaigns,. Big data aids businesses in executing a sophisticated analysis of customer trends. This includes keeping an eye on assessing online purchases as well as point-of-sale transactions.

 

These insights in turn enable companies to generate lucrative, specific, and targeted campaigns, letting companies meet customer expectations and generate larger brand loyalty.

 

 

Conclusion

 

Going through the advantages offered by big data analytics, you may be able to discern how crucial it has become for businesses. It offers a solution to every business problem that may arise.  

 

The different types of big data analytics enable businesses to process and make use of the stack of raw data they collect on a daily basis. 

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