Big data analytics is the specialized techniques and technologies used to process a large set of data. On-hand database tools are practically incompatible with processing such a large set of data. That’s where come big data analysis techniques that are useful to solve practical business data problems. Some of these real-life problems are like –

-Building email list

– Identifying user preferences from the applications like Netflix.

– Determining content strategy by analyzing the users

And many more.

There are different big data analysis techniques, among which we have discussed the 10 most popular ones, which have already gained quite a popularity.

1. Association rule learning
2. Genetic algorithm
3. A/B Split testing
4. Edge analytics
5. Machine learning
6. Sentiment analysis
7. Social network analysis
8. Descriptive analysis
9. Prescriptive analysis
10. Predictive analytics

Now let’s explore each of the above ones.

Big data analysis techniques

1. Association rule learning

Association rule deals with co-relations between variables in large databases. It consists of various interesting techniques. Through these techniques, a business can determine which products are mostly purchased from the large data history of purchasing. Major supermarkets first used this technique to discover relations between products.

Association rule learning helps in:

• placing products in appropriate proximity to each other to increase sales
• analyze biological data of the users to uncover new relationships
• exploring web server logs to extract information about visitors to websites 
• Identifying malicious activities from the monitor system logs

This technique is excellent for increasing sales and forming customer incentive programs. 

2. Genetic algorithm

Genetic algorithm revolves around the mechanism followed in natural evolution, mechanisms like mutation, inheritance, natural selection, etc. Here it is not gene instead business. In this case, the mechanisms evolve solutions to problems for optimization. In a nutshell, it mimics biological evolution.

Genetic algorithms are being used to:

· schedule doctors for emergency duty

· determine what will be the optimal materials and engineering practices as return combinations for fuel-efficient cars

· generate “artificially creative” content such as jokes

Here are a few examples to showcase genetic algorithms in action:

• Used to schedule which doctors will be working in emergency rooms at any given time.
• Used to develop content like jokes and puns from artificially creative sources.
• Create business processes that mimic the way a buyer moves through every step of the buying process.

Since the solution used to merge artificial intelligence with current data, no matter how much data you are dealing with, the system will automatically organize, categorize and discover correlation.

3. A/B Split testing

This is a handy big data analytics technique for web designers. A/B testing is a very basic and randomized control experiment. In this technique, two versions of a variable are experimented with in the same way to find out which one is performing better in a controlled environment.

For web designers for UX design, such testing turns out very useful as it gives them an insight on how to improve certain features like the layout so that the application gets more consumer response. Furthermore, since we are talking about Big data, the tester receives more meaningful and statistically significant data.

4. Edge analytics

Edge analytics is a comparatively new concept, and it under evolution. However, once it is perfected, it will revolutionize the big data analysis mechanism. In this technique, the data is analyzed the moment it is collected or at the edge, so you immediately get a complete analysis. This can be really useful for IoT devices, especially security cameras, or navigation devices, etc.

For large retailers, it is an added advantage as they will be able to analyze points of sale. It helps them to either up-sell or cross-sells immediately. 

5. Machine learning

Machine learning is the statistical method where software can learn from data. Without explicit programming, machine learning enables computers to learn focus on making predictions. Here the known properties learned from sets of “training data” are used for analysis.

Machine learning is a sub-component of Artificial Intelligence. It can be defined as the algorithms that parse data sets and then learn from them to apply what has been learned to make informed decisions. In machine learning, the computer program learns from experience by performing some tasks and seeing how those tasks’ performance improves.

It is the state-of-the-art field of AI that is used extensively in developing tools for industry and society. The machine learning algorithms focus on solving real-world issues by automated tasks across industries. These may range from on-demand music services to data security services.

From a data analysis perspective, you can use Machine learning to:

• distinguish between spam and non-spam email messages
• determining user preferences and recommendations based on this information
• determine the best content for engaging prospective customers
• determine the probability of winning a case, and setting legal billing rates

6. Sentiment analysis

Sentiment analysis is a prevalent method for social media analysis. It helps researchers to determine the sentiments of the audience concerning a topic.

Sentiment analysis is being used to help:

• analyzing guest comments in a hotel for improving service at a hotel 
• determine what consumers really think based on opinions from social media

7. Social network analysis

Social network analysis, as the name suggests, is a technique to study interpersonal relationships. It is now being applied to analyze the relationships between people in many fields and commercial activities. In this case, nodes represent individuals, whereas ties represent the relationships between the individuals in a network.

Related post – How Big data is affecting Social media

Social network analysis is being used to:

• see how people from different populations form relations with outsiders or, in more technical terms, ties.
• Determine the importance or influence of a particular individual within a group
• Determine the minimum number of direct ties required to connect two individuals
• understand the social structure of a customer base

8. Descriptive analytics

Descriptive analytics answers what exactly happens to data. By definition, descriptive statistics are the term given to the analysis of data that helps describe and summarize data in a meaningful way. For example, you can find patterns emerging from the data. This is useful for KPI tracking. The goal is simple – you have a clear set of key performance indicators, and descriptive statistics will show you the outcome based on the company’s actual performance.  

It’s an excellent method for periodical reports, sales overviews, and other types of primary analyses

9. Prescriptive analytics

The primary purpose of business intelligence is to find ways to make better and more accurate decisions. This where prescriptive analysis proves so valuable but also difficult. It’s a combination of descriptive and predictive analyses. 

By definition, prescriptive analytics is the area of business analytics dedicated to finding the best course of action for a given situation. This methodology’s downside is the logistics, as prescriptive analytics usually requires a lot of manpower and substantial budgeting. 

10.Predictive analytics

Predictive analytics provides a real-time report on customer behavior. Instead of working on historical data now with AI and machine learning, businesses can predict customer behavior in the future. This is a beneficial method in the e-commerce sector. During online shopping, predictive analytics tools register data about the online shopping behavior of the customers. This includes their shopping habits, shopping frequency, like products, etc. This helps in predicting which product will work in the market and for how long. Here mainly, consumer data is used to predict customer behavior.