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The Uses of Machine Learning and the Benefits for Your Enterprise

uses for machine learning-- illustration depicting artificial intelligence

uses for machine learning-- illustration depicting artificial intelligence

What is machine learning used for?


Machine learning has been around for decades, but in the era of Big Data, this type of artificial intelligence is in greater demand than ever before. Why? Simply put, organizations need help sifting through and working with the extraordinary amount of data that our systems are now continuously generating. With machine learning, companies can build models that process massive volumes of data quickly and “learn” how to use it. Let’s look at some of the uses of machine learning across the business.

 

Machine learning use cases

 

The applications and uses of machine learning are vast and diverse – and they’re all around us, every day. 

 

Recommendations The recommendations provided by popular streaming platforms like Spotify and Netflix are based on machine learning algorithms. By analyzing the songs you’ve listened to or the shows you’ve watched – along with masses of data about other songs, shows and consumer habits – these algorithms identify and suggest additional content you may enjoy.

 

Fraud detection Using machine learning models, banks and other financial institutions can  identify transactions that fall outside typical parameters – such as purchase amount and user location – and alert you when unusual activity occurs.

Search engine results Every time you type a search term into Google, machine learning algorithms analyze your behavior to refine the future delivery of results. For instance, if you spenda significant length of time on a site that wasn’t highly ranked on the initial results page, the Google algorithm will likely bump that page higher for similar or related searches in the future.     

 

Chatbots When you chat with an AI-based assistant to resolve an issue online, a trained machine learning model is at work, providing appropriate responses based on your input. 

 

Spam filters By analyzing characteristics in subject lines, body content and return addresses, machine learning algorithms help protect your in-box from unwanted emails. 

 

Customer retention Service providers rely on machine learning models to identify customers who may be ready to take their business elsewhere. If you’ve stopped using a credit card and suddenly received an email offer for a lower APR, your credit card provider is likely attempting to boost customer retention with the help of a machine learning-based platform.

    

Candidate screening For companies receiving hundreds of responses per job posting, machine learning algorithms can scan resumes for specific keywords and help identify optimal candidates for interviews.

 

Real estate valuation By analyzing available data on a home’s features and the sales of comparable houses in its vicinity, machine learning algorithms estimate the current value of real estate for websites like Zillow and Redfin. 

 

Learning apps Educational tools like the Duolingo language platform use machine learning models to analyze data gathered from users and adjust the pacing of courses as needed. 

 

Medical image processing For radiology, machine learning platforms can be trained to identify potential issues in patient X-rays, flagging them as warranting further attention.

 

What are the benefits of machine learning?

 

The myriad uses of machine learning indicate just how beneficial the technology can be for businesses of all types. No matter where machine learning is used or how machine learning is used, organizations describe its benefits in terms of exponential gains and improvements.   

 

Making faster decisions By allowing businesses to process and analyze data more quickly than ever before, machine learning enables rapid – even split-second – decision making. For example, machine-learning-based software trained to identify anomalies in a company’s security environment can detect a data breach instantly and notify that organization’s tech team. By enabling fast decisions about effective remediation, these platforms can help companies safeguard customer data, uphold their business reputations, and avoid costly corrective measures.

 

Forecasting demand more accurately To compete in a rapidly changing business landscape, companies are under increasing pressure to anticipate market trends and customer behavior. By incorporating machine learning models into their data analytics, organizations gain far more accurate and powerful capabilities for forecasting demand, which translates into more effective inventory management and big cost savings. 

 

Personalizing customer engagement Personalization has also become a critical strategy for competing in today’s marketplace. With machine learning platforms that analyze user behavior and suggest additional products based on purchase history, online retailers interact with customers in a more personalized way and drive more sales. Global giant Amazon is a prime example, with its use of machine learning to create lists of recommended products and feed suggestions to customers.

 

Boosting efficiency The use of machine learning allows organizations to accelerate repetitive tasks and shift human resources to higher value activities. For example, machine learning models can perform exhaustive document searches in a fraction of the time it takes people to perform scanning and cross-referencing tasks. These capabilities allow companies to reduce costs for information retrieval activities related to regulatory compliance and legal research, while also freeing employees to focus their efforts elsewhere. 

 

Managing and maintaining capital assets more efficiently It can be difficult for enterprises to accurately gauge when capital assets will need maintenance work or upgrades, and the costs to do so can be steep. With predictive machine learning models, organizations can collect performance data from equipment and components to monitor their conditions and compute the remaining lifetime of the assets.

 

Learn more about machine learning

 

Starting to think about the uses of machine learning for your enterprise? The first step is making sure that your machine learning model will be consuming clean data sets – the quality of your data correlates directly with the quality of insight you gain. 

 

Working with large quantities of enterprise data will always come with challenges, but to mobilize your business and outpace competitors, you need to unlock its full potential. When you’re ready for machine learning, Matillion is ready to help you transform your data.   

 

Learn more about the “Extract, Transform, Load” – or ETL – process by reading our ultimate guide on the topic or by requesting a demo of the Matillion ETL software platform.  

 

You can also get started with the Matillion Data Loader platform now, for free. Our data integration tool makes it easy to bring  your data into a cloud data warehouse where you can  gain a 360-degree view of all data sources.

 

Or read our case study to find out more about data transformation and machine learning in action. With the Matillion ETL platform, Clutch ingests and transforms massive amounts of the retaildata its customers rely on for business-critical insight.

 

Get Started with Matillion Data Loader