How is Machine Learning used to Analyze Big Data?

As technology evolves different areas of experitise tend to compliment one an another is provide a rigid solution. We will need to understand ‘How is Machine Learning used to Analyze Big Data‘ to understand this concept.

Big Data & Machine Learning are the blue chips of the current tech world. The big data is used to extract, store & analyze information out of bulk data sets. While Machine learning is the ability to improve & learn from experience without being explicitly programmed automatically. Machine Learning, when used as a service in data analytics, has helped manage big data better.

Suppose you got into a situation where you’ve to collect vast amounts of data, which is a time-consuming & cumbersome process. You dive deep to gain insights by crunching numbers, by correlating the data & understanding patterns of the data. And thus this data, that you’ve collected will help businesses make quick decisions.

But first, you should know how machine learning is used? Machine learning is used to train machines by making algorithms & feeding them datasets that enable devices in decision making & problem-solving.

What is Machine Learning in Big Data?

Machine Learning algorithms are useful for analysis, data collection & integration. Small businesses that have little incoming information don’t need machine learning. But Machine Learning algorithms are a top priority for large organizations that generates vast amounts of data.

ML algorithms apply to every element of Big Data operation, including:

• Data Analytics
• Data Segmentation & Labeling
• Scenario Simulation

Importance of Big Data Analytics

Big data analytics is instrumental in finding solutions for many problems like time-saving, cost-reduction & lowering the risk in decision making. By combining machine learning & data analytics, organizations can earn a lot by:

• Risk management & calculating risk causes.
• Determining the actual causes of failure in the policies of businesses & eliminating those objects in the future.
• Time-to-time offers for the new & old customers based on their purchases.
• Detecting any fraudulent & mischievous activity using the cross-checking of data.

Uses of Machine Learning in Governance

Machine Learning has helped a lot in the handling of big data in governance as well. In terms of objectives, Big Data analytics in management is quite different from that of businesses.

For instance, the primary goals that governance focuses are the security of fundamental rights, sustainable development, the study of voter’s attitude & behavior, maximum outreach among voters, policy-making, etc.

But as far as businesses are concerned, decision-makers in companies are very limited. But that’s not the case with the government. In governance, different ministries are handled by different ministers. Moreover, sharing & collection of data from various departments is hard & paramount task for governments.

Big Data & Machine Learning use cases.

To give you an idea of how business combines Big Data & ML, we have gathered some examples of Machine Learning & Big Data learning projects.

Market Research and Target Audience Segmentation

Knowing your target audience is one of the critical elements of a successful business. But to make an audience and market research, one needs more than just mere wild guesses & observations. Machine Learning algorithms study the market in the best possible manner and help you understand your target audience.

By using a combination of unsupervised & supervised machine learning algorithms, you can easily find out:

• A portrait of your target audience
• Their preferences
• Patterns of their behavior

This technique is usually prevalent in Entertainment and Media, eCommerce, Advertising, and other such fields.

User Modeling

User Modeling is an elaboration & continuation of Target Audience Segmentation. It forms a detailed portrait of a particular segment by deep diving inside the user behavior. By using machine learning for big data analytics, you can make intelligent business decisions & can predict the actions of users.

One such example of a user modeling system is Facebook. Facebook has got one of the most intelligent and sophisticated user modeling systems. The system has the duty of conducting a detailed portrait of the User to suggest new pages, contacts, communities, ads & ad content.

Recommendation Engines

Have you ever thought about how Amazon shows relevant products & how Netflix able to make on-point suggestions from the get-go? That’s only because of recommender systems. A recommendation engine is one of the most sophisticated Big Data Machine Learning examples. Such intelligent systems can provide a helpful suggestion on what types of products are bought together. Also, these systems point out the content that might be interesting to the User who reads a particular or random article on the internet.

Based on user behavior prediction & on a combination of context, the recommendation system can do these things:

• Play on the engagement of the User
• Shape his experience according to his expressed preferences & behavior on site.

The Recommendation Engines usually apply extensive content-based data filtering to extract insights. And as a result of this, the system learns from the User’s tendencies & preferences.

Predictive Analytics

One of the most fundamental elements of retail is to know what your customer need. And that’s where predictive analytics comes into play. Big data allows you to calculate the probabilities of various decisions & outcomes with a small margin of error.

Predictive Analytics is useful for:

• Assessing the possibility of any fraudulent activity in ad tech projects.
• Suggesting some extra products on eCommerce platforms
• Calculating the probabilities of treatment efficiency for specific types of patients in healthcare.

The best example of predictive analysis is eBay’s system that remains about hot purchases, abandoned purchases, or incoming auctions.

eCommerce Fraud, Ad Fraud

Ad Fraud is one of the biggest problems you have to face in the Ad Tech industry. According to stats, about 10% to 30% of activity in the advertising industry is fraudulent.

By using Machine Learning algorithm, you can fight this problem by:

• Assessing the credibility
• Recognizing the patterns used in Big Data
• Blocking them out of the system before introducing bots or insincere users takes over & trash the place.

Using Machine Learning algorithms, you can watch and track ad activity & block the sources of fraud.

Chatbots

Chatbots or Conversational User Interfaces are one of the most use cases of machine learning & Big Data. A chatbot can adapt to any particular customer’s preferences after many interactions by leveraging machine learning algorithms. The most commonly used AI Assistants in the tech world are Apple’s Siri & Amazon’s Alexa.

Conclusion

The way Big Data is transforming various industries is fascinating. It has a very positive impact on business operations. Big Data, combined with Machine Learning, has many use cases in business, from predicting & analyzing user behaviors to learning preferences.

Hopefully this gives an insight on ‘How is Machine Learning used to Analyze Big Data‘ but we must keep in mind that this the answer to this topic will grow over the coming years.

We will hopefully be revisiting this question (‘How is Machine Learning used to Analyze Big Data‘) in the near future and see how it has evolved.

Subscribe to FinsliQ Blog:

If you have enjoyed and find our blogs informative, then please support the platform by subscribing to our daily newsletters. Benefits of becoming a subscriber:

• Get daily updates with the latest blogs/article
• New updates within the same subject area are release every day (release dates can be found next to the link in the blog)
• Stay up to date with the latest Tech news
• Variety of different types of blogs

Visit FinsliQ | Tech Academy. A variety of course are available in cloud computing, Dev-ops, Cloud Architecture, Cyber Security and much more.

About Post Author

Alam Mohammed

See author's posts