Machine Learning: Creating Value from Data

What is machine learning?

Machine learning is a core part of data science. It uses the data organizations collect—combined with algorithms designed to learn patterns—to imitate how humans learn and make decisions. By doing so, machine learning helps organizations implement artificial intelligence (AI) and extract more value from their data.

Trained on large datasets, machine learning algorithms can classify information, make predictions, and uncover insights. Over time, these systems can adapt to new information without direct human intervention, automating tasks and generating faster, more accurate results.

From detecting fraud in financial systems to optimizing logistics and personalizing customer experiences, machine learning transforms raw data into intelligence that drives measurable gains in efficiency, accuracy, and innovation.

Artificial intelligence vs. machine learning vs. deep learning

Artificial intelligence (AI) uses techniques like machine learning and deep learning to solve problems, automate decisions, and reduce human error. It’s the broad goal of enabling machines to think and act intelligently.

Machine learning (ML) is a subset of AI that focuses on teaching machines to learn from data. Instead of following static rules, ML algorithms identify patterns, make predictions, and continuously improve through new information.

Deep learning is a more advanced form of ML that uses neural networks to process complex, high-volume data. Unlike traditional ML, deep learning can automatically identify the most important features within a dataset, making it ideal for image recognition, speech processing, and natural language understanding.

Why is machine learning important for your business?

Machine learning can be a solid foundation for businesses that want to create more value from their data. Its insights improve business intelligence by helping organizations identify patterns and make accurate predictions for data-driven decisions. They can forecast and take advantage of opportunities for growth and profit that have been overlooked. They can also identify unnoticed risks.  

Machine learning fuels artificial intelligence’s automation, helping teams improve processes and complete tasks faster and with fewer errors. Modern organizations collect more data than ever before, and machine learning offers a fast, powerful, and affordable way to turn that data into meaningful value.

Key benefits of machine learning

Machine learning creates real business value by combining automation, accuracy, and speed to improve decision-making across industries. Here are some of its most impactful benefits:

• Automation: Machine learning automates complex and time-consuming tasks like fraud detection, document classification, and customer support chatbots—reducing manual effort and human error.
  
• Data-driven insights: By analyzing massive datasets, machine learning identifies patterns and trends that humans might overlook. This helps organizations make informed, evidence-based decisions.
  
• Improved efficiency: ML optimizes workflows—from manufacturing production lines to logistics and route planning—cutting costs and boosting productivity.
  
• Personalization: Businesses use machine learning to create tailored recommendations, marketing campaigns, and customer experiences based on behavior and preferences.
  
• Risk mitigation: Machine learning enhances risk assessment and fraud prevention by identifying unusual patterns in financial or transactional data.
• ‍Innovation: ML accelerates research and development across industries, supporting breakthroughs in healthcare, climate science, and product design.

How does machine learning power AI and help organizations get value from their data?

The goal of artificial intelligence is to create “smarter” machines, machines that can process, think, and learn as humans do — or better than humans do. Machine learning is one of the many ways data science works to make this goal a reality. 

Machine learning needs data, lots of data. It wasn’t so long ago that the amount of data readily available was fairly small and difficult to process and store. As computer processing and data storage advanced, the number of devices and machines that connect to the internet and stream large amounts of data grew, too. More data and better tools paved the way for machine learning. There is more information for machine learning to learn from than ever before. 

Machine learning powers artificial intelligence systems like automation and speech and image recognition. These tools in turn make it possible for organizations to learn about their customers’ behaviors and desires and then create simplified and personalized processes. This builds brand loyalty and helps close sales. Internally, AI can help employees improve operations by automating mundane tasks and eliminating human error. 

In short, machine learning and AI turn data into valuable actions that save organizations time and money and help them achieve better business results. 

Essentials of a machine learning tool.

For organizations without dedicated data science teams, modern machine learning tools can fill the gap. The ideal solution should simplify setup, ensure data quality, and make insights accessible to everyone. Look for a platform that can:

• Connect data from any cloud, on-premises, or proprietary system.
• Prepare and cleanse datasets for analysis.
• Offer prebuilt and customizable model options.
• Build ML models directly into your data pipelines.
• Write predictive insights back to your source systems.
• Visualize data across teams for easy sharing.
• Enforce data governance through permission controls.
  

Discover automated machine learning (AutoML).

Domo’s Automated Machine Learning (AutoML) helps businesses augment analytics with machine learning insights. With deep integration to Amazon SageMaker Autopilot, AutoML makes machine learning accessible to everyone—regardless of data science expertise. Teams can test models, select the best fit for their data, and share insights in hours instead of weeks.

How do businesses use machine learning and AI?

Machine learning delivers measurable value across every major industry by making processes smarter, faster, and more adaptive. From finance and healthcare to retail and logistics, organizations use ML to gain insights, automate operations, and personalize experiences.

Examples of machine learning in action:

• Finance: Detecting fraud, improving credit scoring, and automating regulatory reporting.
  
• Healthcare: Assisting in diagnosis, predicting treatment outcomes, and improving operational efficiency.
  
• Retail: Personalizing product recommendations and optimizing pricing strategies.
  
• Manufacturing: Monitoring equipment performance, predicting maintenance needs, and streamlining production.
• ‍Logistics: Forecasting demand, optimizing delivery routes, and reducing supply chain delays.

The business value of machine learning

The true value of machine learning lies in its ability to turn data into decisions. By continuously learning from both historical and real-time information, businesses can react faster, reduce costs, and uncover new opportunities for growth.

With the right tools and governance in place, ML enhances every layer of an organization—from forecasting and customer experience to innovation and risk management—transforming data from a byproduct into a strategic advantage.

How will machine learning and AI evolve in the future?

Machine learning and AI are the future of business. They open up new ways to increase business intelligence, productivity, and customer satisfaction. In the coming years, the majority of all companies across industries will incorporate machine learning and AI into their everyday processes to remain competitive and get the most value possible out of their data.