Guide: Machine Learning (ML) vs. Predictive Analytics (PA)

Both predictive analytics and machine learning have become powerful tools in the field of data science. Both can help organizations unlock hidden insights in large, complex data sets and predict future outcomes. Along with providing future predictions, both techniques improve efficiency, strengthen decision making, and gain insights into market trends and opportunities.

However, as the big data space continues to grow — it’s estimated the market will reach $105 billion by 2027 — the importance of both machine learning and predictive analytics can’t be underestimated. While both help organizations gain insights from business data, they differ in how they help you do this.

Below, we’ll explore more of the differences between machine learning and predictive analytics, what each involves, and where the approaches intersect.

What is Machine Learning?

Machine learning is part of the data science family tree. It uses data and algorithms to imitate the way humans learn so it can easily recognize patterns, make predictions, and improve performance. With machine learning, you can more easily identify business opportunities and solve problems faster.

There are three types of machine learning:

• Supervised learning which trains a model with labeled data
• Unsupervised learning discovers patterns in unlabeled data
• Reinforcement learning focuses on improving decision-making through reward-based systems

How Does Machine Learning Work?

Hundreds of new machine learning algorithms are published every day; however, a machine learning algorithm’s learning system contains the same general components to learn them. These include:

• Decision process: The machine learning process starts when an algorithm makes an estimate based on a pattern it finds in labeled or unlabeled data.
• Error function: From here, machine learning evaluates the estimate or prediction it generated. The error function will then compare the model with a known example to determine how accurate the model is.
• Model optimization process: Should the error function determine that the model can be improved to better fit the data points, the model is then adjusted. This helps to rescue any discrepancies.
• Repeat until accurate: The technology will then repeat the error function and model optimization process until a predetermined level of accuracy is met.

The resulting machine learning algorithm can then be used for numerous purposes, including predicting future outcomes, detecting trends, and more.

Who Uses Machine Learning?

Machine learning has found uses across industries — from healthcare and finance to retail and technology. Data scientists, researchers, and engineers typically rely on the approach to unlock insights from datasets. These insights are then used to predict traffic patterns, product recommendations, speech and image recognition, catching security vulnerabilities, as a virtual assistant, and more.

To help you understand how machine learning is used across industries, let’s look at a few real applications of the technology.

• Netflix: The streaming service relies on machine learning to provide a personalized experience to customers. The platform looks at a watcher’s previous viewing history, the viewing history of similar customers, and information about a watcher’s shows and movies to provide recommendations. Netflix’s recommendation system is estimated to drive 80% of hours of content streamed.
• The North Face: The outdoor retail provider used IBM Watson’s machine learning algorithms to learn about shoppers’ preferences (including their budget, likes, and preferences) to help them find the perfect product. Over 60% of North Face shoppers have used the personal assistant during their shopping experience.
• Peoplise: To help companies find the right people for option positions, Peoplise analyzes data from candidates, including their skills and background, and suggests them to recruiters. According to the company, the platform has helped businesses hire 2,000 employees, find 45% more candidates, and speed up the hiring process by three times.

What is Predictive Analytics?

Predictive analytics is a broad term for using data to predict and model the future. It uses statistical techniques, data mining, machine learning, and artificial intelligence to enable companies to anticipate trends and behaviors. It then uses tools to create models based on large historical data sets, which can be used to drive decision making.

How Does Predictive Analytics Work?

Predictive analytics operates through the development and application of predictive models, which rely on advanced statistical techniques, machine learning algorithms, and historical data to forecast future outcomes. Here’s a closer look at the process, its components, and the methodologies involved:

The Predictive Analytics Workflow

1. Define the Problem – The predictive process begins by clearly identifying the problem to solve or the outcome to predict. For instance, businesses often use predictive analytics to identify potential customer churn, forecast product demand, or detect fraud.
2. Data Collection and Organization – Data is the foundation of predictive analytics. Organizations gather structured and unstructured data from multiple sources, such as sales transactions, customer interactions, or IoT devices, and store it in centralized repositories, like data warehouses, for analysis.
3. Data Preprocessing – Raw data is cleaned and prepared to ensure accuracy. This involves addressing missing data points, removing outliers, and standardizing the format. Preprocessing is critical to building a model capable of delivering reliable results.
4. Develop Predictive Models – Data scientists use a range of tools and techniques to create predictive models tailored to the problem. Whether it’s regression analysis, neural networks, or decision trees, the right type of model is selected based on the data and the desired outcome. Machine learning algorithms are often employed to “train” these models, enabling them to detect patterns in historical data.
5. Validation and Deployment – The models are tested for accuracy and refined as needed. Once validated, the models are deployed within business systems to generate predictions in real time or for specific timeframes.
6. Continuous Optimization – Predictive models improve over time, thanks to machine learning. By ingesting new data and feedback, their accuracy and adaptability to changing scenarios grow, ensuring they stay relevant and effective.

Popular Predictive Modeling Techniques

Predictive analytics employs various modeling techniques to analyze patterns and deliver forecasts. Some of the most widely used models include:

• Decision Trees – Decision trees segment data into branch-like structures, splitting it based on variables to make predictions. This clear, visual representation makes it easy to interpret results and understand decision paths.
• Regression Models (Linear and Logistic) – Regression analysis evaluates the relationship between dependent and independent variables. Linear regression predicts numerical outcomes, while logistic regression is used for categorical predictions (e.g., yes/no answers).
• Neural Networks – Inspired by the human brain, neural networks are great for handling complex and nonlinear data relationships. They are particularly effective in tasks like pattern recognition and processing vast datasets.

Other Commonly Used Classifiers

To achieve highly accurate predictions, organizations may integrate advanced classifiers into their analytics processes:

• Time Series Analysis: Predicts trends over a continuous timeline, ideal for forecasting market demand or sales. 
• Clustering Algorithms: Groups data with similar characteristics, useful in customer segmentation. 
• Outlier Detection Models: Identifies data anomalies, helpful for fraud detection. 
• Ensemble Models: Combines multiple machine learning algorithms to improve prediction accuracy. 
• Support Vector Machines (SVMs): Useful for classification problems by finding the best decision boundary.

Who Uses Predictive Analytics?

Similar to machine learning, predictive analytics is used across a variety of industries in numerous business processes. It’s due to the technology’s versatility that 95% of organizations are using predictive analytics in one way or another. 

Below are just some of the ways companies are using predictive analytics in their business processes:

• Walmart: Walmart completed a large data mining process to learn all they could about their customers. Once they gathered this data, they used predictive analytic tools to understand buying habits at certain points in time. Surprisingly, they found that right before a hurricane hits, strawberry Pop-Tart and beer sales rose by seven times. They used this information to ensure their shelves were stocked with these items before the hurricane season.
• Healthcare facilities: With over 36 million patients in U.S hospitals, healthcare facilities have access to an enormous amount of data. Rather than looking to improve the patient experience, the data is used in predictive analytics tools to improve diagnoses and predict patient outcomes.
• Cybersecurity: Last year, the average financial loss companies experienced from fraud was $500,000. To avoid this, companies are using predictive analytics to analyze typical fraudulent behavior and training models to recognize these behaviors.

Examples of Predictive Analytics & Machine Learning Across Industries

Both predictive analytics and machine learning have found applications across numerous industries, enhancing processes and outcomes in unique ways. Below are some examples that showcase how these technologies are revolutionizing various fields:

Predictive Analytics in Action:

• Manufacturing: Factory owners implement predictive maintenance by using historical machinery data to forecast potential breakdowns. This allows them to make replacements or repairs before a failure disrupts production.
• Healthcare: Health providers use predictive models to forecast disease outbreaks, enabling governments and organizations to prepare and mobilize resources efficiently.
• Insurance: Insurers leverage predictive analytics to create personalized risk profiles, helping them offer customized payment plans that cater to individual customers.
• Sports Betting: Analytical models examine team performance history to calculate winning probabilities, giving bettors a statistical edge.

Applications of Machine Learning:

• Retail: Companies like The North Face utilize machine learning to recommend products based on shopper preferences, budgets, and past purchase behavior.
• Entertainment: Netflix employs machine learning algorithms to analyze viewing histories and deliver personalized content recommendations, driving 80% of their streamed hours.
• Cybersecurity: ML algorithms detect anomalies in user behavior to recognize potential fraud or security threats, protecting businesses from financial and data losses.
• Recruitment: Solutions like Peoplise analyze candidate profiles to streamline hiring processes, ensuring businesses find the right talent efficiently.

Predictive Analytics vs. Machine Learning

Both machine learning and predictive analytics are used to help make sense of data. However, predictive analytics is a category of data analytics. Machine learning is one of the tools in predictive analytics’ toolbox.  The former uses historical data to make educated guesses about an outcome while the latter provides companies with all manner of classifications, forecasting, and modeling.

Here’s how the two approaches are similar: 

• Used to analyze patterns in large datasets
• Both have an end goal of predictive modeling
• Commonly applied across a myriad of industries
• Use historical data to make predictions about the future

And here’s how they’re different:

• Predictive analytics relies on math and statistical models to make predictions; machine learning automates predictive modeling by training the models through algorithms
• Machine learning evolves from their mistakes and is always getting smarter; predictive analytics only uses data from the past
• Predictive analytics is a practice rather than a technology; machine learning is a technology and relies on algorithms to run

How Predictive Analytics and Machine Learning Work Together

Predictive analytics and machine learning are two complementary approaches that, when used together, create a powerful synergy for data-driven decision-making. While predictive analytics identifies patterns and anticipates future outcomes based on historical data, machine learning enhances this process by automating and refining predictions through continuous learning and adaptation.

For example, a predictive analytics model might forecast customer behavior based on past purchasing trends. By integrating machine learning, this model can evolve and improve its accuracy over time, using new data to adjust its parameters. Machine learning also enables predictive analytics to handle more complex datasets, such as unstructured text or images, making it possible to incorporate techniques like sentiment analysis or facial recognition into the predictive framework.

This partnership not only increases efficiency but also provides deeper insights, allowing businesses to approach challenges with greater precision and adaptability.

Benefits and Challenges of Predictive Analytics and Machine Learning

Predictive analytics and machine learning (ML) offer immense potential to transform business operations, making them indispensable tools in the field of data science. Before fully integrating these technologies, it’s essential to understand their key benefits and challenges.

Benefits:

1. Enhanced Decision-making: Predictive analytics provides organizations with data-driven insights, helping leaders make informed and confident decisions. By forecasting future trends and possibilities, businesses reduce uncertainties and identify new opportunities.
   • Example: A retail company can predict customer purchasing behaviors and adjust inventory accordingly, minimizing overstock or shortages.
2. Efficiency and Automation: Machine learning enables automation of routine tasks and complex data processing. Models monitor dynamic data streams and identify patterns in real-time, generating actionable insights.
   • Example: Banks leverage ML to automatically detect fraudulent transactions, reducing risks while saving time and resources.
3. Improved Marketing Strategies: By analyzing customer behavior with predictive analytics, businesses can create optimized, targeted marketing campaigns for maximum engagement.
   • Example: Predictive models can identify high-value customer segments, helping marketers tailor promotions that drive conversions.

Challenges:

1. Expertise and Resources: Both predictive analytics and ML require significant human expertise, including data scientists and analysts, to deploy and manage effectively. Building and maintaining models can strain smaller teams or organizations with limited capacity.
2. Monitoring and Benchmarking: Continuous oversight is critical to ensure models work accurately and adapt to business changes. Questions leaders need to ask include:
   • How will I know if my model is performing well?
   • What steps should I take if the model’s predictions misalign with real-world outcomes?
3. Data Quality: Both tools are only as good as the data they work with. Clean, relevant data is essential for accurate analysis and modeling. Poor-quality data can lead to incorrect predictions and flawed decision-making.

By carefully evaluating these benefits and challenges, businesses can set themselves up for success with predictive analytics and machine learning.

Conclusion

Machine learning and predictive analytics are both valuable tools in the world of data analysis — each with their own unique strengths. Machine learning is a versatile learner, capable of adapting to different tasks and learning from diverse data. Predictive analytics uses past data to forecast specific outcomes, making it particularly useful for making informed decisions about the future. 

While machine learning is broader and more adaptable, predictive analytics is specialized in foreseeing particular results. Regardless, both approaches can help you sharpen decision making, increase efficiency, and drive business goals forward. 

To help you get started with machine learning, check out Domo’s Machine Learning playbook. You’ll learn how to properly prepare data, run a model in a ready-made environment, and visualize it back in Domo to simplify and streamline the process.

You can also check out Domo’s ETL’s tools which are built into the solution, help integrate, clean, and transform data — one of the most challenging parts of the data-to-analyzation process.

Predictive Analytics vs. Machine Learning FAQs

• What is the difference between predictive intelligence and machine learning?

Machine learning is an adaptive learner that relies on algorithms to learn how to predict patterns and behaviors. Predictive intelligence relies on past data to make predictions and make decisions.

• What is predictive analytics in machine learning?

Predictive analytics in machine learning uses historical data and statistical algorithms to predict future outcomes. It leverages patterns and trends identified in this data to make informed forecasts and assist in decision-making processes. Essentially, it’s a branch of machine learning focused on anticipating specific results based on the analysis of previous information.

• Is machine learning the core of predictive analytics?

Yes, machine learning is often at the core of predictive analytics. Predictive analytics relies on machine learning algorithms to analyze historical data, identify patterns, and make predictions about future outcomes, forming a crucial component of the predictive modeling process.

• Is machine learning for prediction only?

No, machine learning is not solely for prediction. Machine learning can be used in a variety of tasks, including classification, clustering, pattern recognition, and decision-making

• Which is better: data analytics or machine learning?

It depends on the specific goals of your organization and the task at hand. Predictive analytics is valuable in extracting insights from existing data while machine learning is better suited for tasks requiring automated learning, prediction, and decision-making based on patterns in data. The effectiveness of each depends on the nature of the problem and the desired outcomes.