Published By - Brian Curtis

How is Machine Learning Useful for Predictive Analytics?

Machine Learning Useful for Predictive Analytics

In today’s modern world, many organizations are dependent on big data, which is an untapped resource of intelligence that supports the decision making process and enhanced operations.

However, as data continues to diversify, it has been noticed that more and more organizations are moving towards predictive analytics, to tap the data and get benefit from it at scale.

How Machine Learning can Boost your Predictive Analytics?

It is noticed that nearly 75% of the business leaders say that analytics is the main source of growth of their business. But only 65% of them had accepted that they have predictive analytics capabilities.

Well, let us look into the things that are preventing many organizations from achieving predictive analytics capabilities?

To be precise, the primary thing in this is applying the right set of tools at the right time, which will help the business in pulling out powerful insights from data present in the database.

But, to apply all this and generating value from it, one thing should be kept in mind that a big data system requires the right amount of space for storing information that is processed from the data.

Moreover, by using AI and Machine learning algorithms, businesses can discover new statistical patterns, which also serve as a backbone for predictive analytics.

Working of Predictive Analytics

Predictive analytics, which is driven by predictive modeling is an approach which goes hand-in-hand with machine learning. This is because of the presence of a machine learning algorithm in predictive modeling.

Predictive modeling overlaps with machine learning, and also, its models can be trained over time to respond to new data or values, delivering the results the business needs.

Those organizations that have an abundance of data but are still struggling to turn their data into meaningful insights can get this problem solved by using machine learning and predictive analytics together.

No matter what amount of data an organization is having, if they are not utilizing it and are unsuccessful in converting it into meaningful insights, then this data is of no use.

Use of Predictive Analysis and Machine Learning in Different Sectors

Banking Sector

Here, predictive analytics and machine learning algorithms are used to reduce fraud. Also, they are used together to map the market risks and identify growth opportunities.

Security Sector

Machine learning and predictive analytics play a key role in managing the cybersecurity of an organization.

They are used together to improve the data security of an organization and also in reducing fraud activities and to detect such things which are happening.

Moreover, they also help in improving the services and tracks consumer behavior, which helps in the decision-making process that directly impacts the performance of an organization.

Retail Sector

Predictive analytics and machine learning are used together by retailers to understand consumer behavior, like which consumer-set buys what, when and from where?

All this helps the retailers to plan for placing the appropriate stock and plan for the events and seasonal sale.

It is seen that implementing the solutions of predictive analytics and machine learning can be a boon for any organization. This will help an organization to get insights into the data present in its database.

To get the most out of their combo, an organization needs to ensure they have an appropriate architecture to support such solutions, as well as abundance data.

Well, to implement this, an organization needs to develop a data governance program ensuring that the data which is recorded or stored should be of high quality.

Moreover, the organization’s existing processes will be needed to be modified to ensure that predictive analytics and machine learning can be implemented, which will in turn help the organization to drive efficiency at every step.

Also, to ensure the best model is taken into the process, the organizations need to assess the problems they are facing.

Use Cases of Machine Learning for Predictive Analytics:

Netflix:

Netflix uses the consumers-watching information, to provide them with recommendations of similar tastes and preferences.

This happens because the company wants to keep you engaged on the website/application, thereby making sure that you continue with your subscription plan.

They post such thumbnails on the main screen, which have the highest rate of being opened by the user. This is based on the taste of similar people like you who have used this service earlier.

They use your past viewing data to predict the bandwidth usage to use a server, which improves the load time.

Amazon:

Amazon uses machine learning for predictive analytics to provide you with better search results. Have you ever noticed the “Best Seller” or “Amazon’s Choice” written over some products?

This is because of the use of machine learning for predictive analytics. The products with the most sales and useful reviews are shown to you so that you get influenced quickly and keep scrolling and surfing the website.

This way of making sales, worked big time for Amazon. Also, many products are ranked just because of their sales potential.

Conclusion:

Machine learning for predictive analytics is beneficial as it helps in many ways, like predicting curating consumer behavior, understanding market, getting data insights, etc.

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