Uses and Benefits of Machine Learning for Your Enterprise

Uses for machine learning 1200


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 technology, businesses can build automated models that process massive volumes of data quickly and “learn” how to use it to solve problems. 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 spend a 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 automated appropriate responses based on your input. 


Spam filters: By analyzing characteristics in subject lines, body content and return addresses, machine learning algorithms use neural networks to 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.


Sentiment analysis: ​​Also called opinion mining or emotion AI, sentiment analysis uses natural language processing and machine learning to understand the underlying sentiment in social media posts. Businesses can use this analysis to discover how people feel about their brand or product.


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 health care companies, machine learning radiology platforms can be trained to identify potential issues in patient X-rays, flagging them as warranting further attention.


Here are some other examples of machine learning.


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 or how it is used, businesses describe its machine learning benefits in terms of exponential gains and improvements.   


Faster decision making: 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 automatically 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, businesses 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 businesses to accelerate repetitive tasks and shift human resources to higher value activities. For example, machine learning technology 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. 


Capital asset efficiency: 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, businesses can automate the collection of performance data from equipment and components and both 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 amounts 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, Matillion is ready to help you with data transformation for machine learning.   


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 tool now, for free. Our data pipeline 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 data transformation and machine learning case study to see acceleration in action. With the Matillion ETL platform, Clutch ingests and transforms massive amounts of the retail data its customers rely on for business-critical insight.