predictive modeling 7 top predictive analytics use cases: Enterprise examples

Predictive analytics vs. machine learning

Machine learning lends itself to various applications, while predictive analytics focuses on forecasting specific variables and scenarios. Learn what they can do when combined.

Predictive analytics and machine learning help companies make better decisions by anticipating what will happen. Both approaches can predict future outcomes by analyzing current and past data. As such, the terms machine learning and predictive analytics are sometimes used synonymously, but although related, they belong to two different disciplines.

Predictive analytics or predictive modeling, as it's sometimes called, is a type of analysis that uses techniques and tools to build predictive models and forecast outcomes. Methods used in predictive analytics include machine learning algorithms, advanced mathematics, statistical modeling, descriptive analytics and data mining. The term predictive analytics designates an approach rather than a particular technology.

Machine learning (ML) is a type of artificial intelligence that creates computer algorithms designed to become more accurate as they process or "learn from" large volumes of data. Machine learning's ability to learn from previous data sets and stay nimble lends itself to diverse applications. Fraud detection, spam filtering, malware detection and image analysis are among the many applications of machine learning used by businesses.

Predictive analytics combined with machine learning is a powerful way for companies to get value from the massive amounts of data they collect and generate in running their operations.

Here is a brief definition of each term, followed by their chief differences, their use cases in the enterprise and some of the benefits and challenges they present for businesses.

What is predictive analytics?

Predictive analytics is a form of advanced analysis that builds upon two earlier types of analytics which typically were done with human coding -- descriptive and diagnostic analytics. Businesses use descriptive analytics to document, for example, how many items were sold yesterday; diagnostic analytics "slices and dices" that information to find out, for example, why fewer items were sold yesterday than the day before.

Predictive analytics uses measurable variables to predict the likely behavior of people and things, such as the buying habits of an individual consumer, or when a machine will need maintenance, or to forecast sales. In addition to classical statistical techniques such as linear and logistic regressions, predictive modeling uses a repertoire of techniques that are also used in machine learning. These techniques include decision trees, neural networks and support vector machines.

Predictive analytics vs. AI chart

Expertise in these sophisticated techniques means that predictive analytics has commonly been the domain of professional data scientists, data analysts and statisticians. As leading business intelligence vendors introduce advanced analytics and AI capabilities into their self-service BI platforms, predictive analytics is becoming more accessible to business users, i.e., "democratized."

A crucial piece of deploying predictive analytics is strong business leadership, said Gartner analyst Andrew White. This is because the first step in a successful predictive analytics deployment is to define the business objectives and goals for the project. The next step is identifying the right data for the project and determining which of the various analytical techniques should be used to build the predictive model. Data quality is paramount, especially when the model is being trained on smaller data sets, so effective data management is an essential component of predictive analytics.

"You need to have ownership or leadership around prioritizing and governing data as much as you have the same for analytics," White said, "because analytics is just the last mile."

What is machine learning?

Artificial intelligence is the replication of human intelligence by machines. It includes a range of diverse technologies in addition to machine learning, including natural language processing, machine vision and robotics. These diverse technologies each replicate human abilities but often operate in different ways in order to accomplish their specific tasks.

Machine learning is a form of AI that allows software applications to become progressively more accurate at prediction without being expressly programmed to do so. The machine learns by detecting patterns within the data sets. The algorithms in machine learning programs and software are created to be versatile and allow for developers to make changes via hyperparameter tuning. ML is the foundational basis for deep learning and neural networks, the advanced techniques that are used in applications such as autonomous vehicle operation and financial forecasting.

Machine learning can increase the speed at which data is processed and analyzed, making it a useful technology for predictive analytics programs. Using machine learning, predictive analytics algorithms can train on even larger data sets and perform deeper analysis on multiple variables with minor changes in deployment.

Machine learning and AI have become enterprise staples, and the debate over their value is obsolete, said Gartner analyst Whit Andrews. In years prior, operationalizing machine learning required a difficult transition for organizations, but the technology is now successfully implemented across industries due to the popularity of open source and private software machine learning development.

"Machine learning is easier to use now by far than it was five years ago," Andrews said. "And it's also likely to be more familiar to the organization's business leaders."

What are the key differences between predictive analytics and machine learning?

As noted, predictive analytics uses advanced mathematics to examine patterns in current and past data in order to predict the future.

Machine learning is a tool that automates predictive modeling by generating training algorithms to look for patterns and behaviors in data without explicitly being told what to look for.

Here are some key differences:

  • ML is trained via supervised and unsupervised learning and is the foundation for advanced technologies such as deep learning and autonomous vehicles.
  • Predictive analytics builds on descriptive analytics and diagnostic analytics and is a steppingstone to prescriptive analytics.
  • Machine learning algorithms are designed to evolve and improve as they process more data, without being programmed to do so.
  • In predictive analytics, data scientists sometimes run the model manually.
  • ML works best when given very large data sets. Once a machine learning algorithm is trained on clean, high-quality data, it can be applied to so-called messy data.
  • Predictive analytics depends upon having data that is accurate and complete to build models.

Just as the debate over the value of ML and AI in business has become obsolete, enumerating the differences between ML and predictive analytics has become something of an academic exercise. As ML has become more understandable and widely employed in business, it has become an integral feature of predictive analytics.

Use cases using machine learning and predictive analytics

Enterprises have established successful applications for prediction, be they by machine learning or predictive analytics. Here is a sampling:

  • Retail and marketing organizations use prediction models extensively to fine-tune strategy. Predictive analytics is used to spot website user trends, hyperpersonalize ads and target emails. Massive amounts of data collected from points of sale, retail apps, social media, in-store sensors and voluntary email lists provide insights on sales forecasting, customer experience management, inventory and supply chain.
  • Manufacturers use prediction models to monitor their equipment and machinery and predict when they need to replace or repair valuable pieces.
  • Healthcare organizations use prediction models to extrapolate the course of diseases based on past data.
  • Financial services companies use prediction models in their risk management programs to calculate when customers are likely to default on a loan and to detect fraud.
  • HR information systems use prediction models to identify candidates and predict when employees might be preparing to quit.

Benefits and challenges of using predictive analytics and machine learning for businesses

Machine learning algorithms can produce more accurate predictions, create cleaner data and empower predictive analytics to work faster and provide more insight with less oversight. Having a strong predictive analysis model and clean data fuels the machine learning application.

While a combination of predictive analytics and ML does not necessarily provide more applications, it does mean that the application can be trusted more. Splitting hairs between the two shows that these terms are actually hierarchical and that when combined, they complete one another to strengthen the enterprise.

Challenges. While the techniques associated with both predictive analytics and ML are becoming embedded in software and result in so-called "one-click" forecasting, enterprises will face the usual challenges associated with getting value out of data, starting with the data. Corporate data is error-prone, inconsistent and incomplete. Finding the right data and preparing it for processing is time consuming. Expertise in deploying and interpreting the predictive models is scarce. Moreover, predictive analytics software is expensive, and so is the processing required to make effective models. Lastly, machine learning technologies are evolving at a rapid pace, requiring continuous scrutiny on how and when to upgrade to newer approaches.

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