Predictive analytics involves using statistical tools to analyze data to determine the probability of future outcomes. It’s the branch of big data specifically focused on forecasting the most likely result of a scenario given a certain set of conditions. This article looks more closely at predictive analytics to explain how it works, why it’s valuable, and how to use it. It also provides a high level overview of some of the tools involved.
Predictive analytics is not a crystal ball—it can’t tell you with 100 percent certainty who will win this year’s NCAA basketball tournament. However, you could use it to determine the likelihood that a given team would win—predictive research does not deliver guaranteed outcomes, but it can make guesses and hypotheses a lot better.
Retailers use predictive analytics to determine other products that might interest customers based on purchase history, financial firms use it to capitalize on trends in the financial markets, and utilities use predictive algorithms to determine the likely effect of upcoming weather patterns on customers and to forecast their company’s level of readiness.
Predictive analytics can help make businesses better able to anticipate and respond to challenges. In the real world, this can take many different forms—here are just a few examples.
During the 2020 global pandemic, severe supply chain issues affected everyone from manufacturers to retailers to consumers—predictive analytics helps companies mitigate this by identifying likely spikes in demand so they can better manage inventory, shipping, and warehousing.
The 2008 housing market crash touched off a financial crisis and recession in the United States—predictive analytics can help avert a repeat by assessing buyer credit scores and mortgage-backed security risks and forecasting real estate market trends.
Medical providers use healthcare-focused predictive analytics algorithms to diagnose and treat patients based on individual characteristics, and insurance companies use them to analyze historical data for trends that might forecast future events.
Airlines use predictive analytics to set ticket prices based on historical travel data, and hotels and restaurants can use it to forecast how many guests to expect based on past business in order to maximize occupancy and revenue.
Read our Complete Guide to Data Analytics.
Predictive analytics works a lot like other big data analytics projects. First, companies collect a large quantity of data related to the question at hand—the more data, the better. This data often comes from a variety of sources, so companies use data integration tools to combine inputs. Then they use data quality tools to clean and transform that data into a format that allows them to use their predictive analytics tools.
Next comes the process of developing and training a model. It might be a classification model—which sorts like things into groups—or a regression model, which assigns a number or a score to a set of variables. Organizations might use traditional statistical techniques and sophisticated data mining to generate these models, or they might rely on machine learning.
Once data scientists have developed a model they think will perform well, they deploy it into production. They then monitor its performance and make incremental improvements to the model so that it will become more accurate over time.
Predictive analytics is driven by a variety of models. Here are the most common:
Learn more: Data Modeling vs. Data Architecture
Businesses, nonprofits, healthcare providers, and government agencies benefit from predictive analytics in a variety of ways. For example:
From credit scores to actuarial tables, business decision making has been based on predictive analytics data for decades. Vegas casinos have long used a form of predictive analytics to help them decide what kinds of odds to offer on various bets. Predictive analytics is not new—but the sophistication of the underlying technology is.
Artificial intelligence and machine learning (AI/ML) have made it possible to generate insights faster and with fewer inputs and to make actionable data more accessible. The rapid growth of AI/ML tools allow companies of all sizes to incorporate sophisticated predictive analytics into their decision making, glean actionable insights from smaller sets of data and be able to share these insights in real time through dashboards, visualizations and integrations with messaging tools.
The development of more powerful graphics processing units (GPUs) facilitates the kinds of parallel processing activities necessary for predictive analytics and machine learning. At the same time, storage prices have dropped, making it more affordable to store large quantities of data. Companies can store data in the cloud using data lakes for raw data and data warehouses for processed data, and use a data fabric solution to bring all these disparate sources together without having to create duplicate data sets.
In addition to data fabric tools that streamline the data integration process, increasingly powerful machine learning, data mining, and analytics tools allow companies to perform very complex analysis on very large data sets very quickly.
One of the most popular AI technologies for predictive analytics is the artificial neural network. Designed to mimic the human brain, ANNs rely on a layered architecture that yields incrementally improving results.
With data stored in a multitude of formats and locations, data fabric architecture integrates the myriad data pipelines in both cloud and hybrid environments to provide a holistic solution for the ingestion, management, transformation, and analysis of disparate data.
The first wave of predictive analytics brought a lot of general-purpose tools to the market. These tools were powerful but often required a data scientist to operate them well. The next generation of tools is more tailored for the needs of specific industries or specific use cases, and they are designed to be used by ordinary business professionals who don’t have specialized or advanced degrees.
While cloud dominates predictive analytics, some jobs are moving out to the edge of the network, particularly in Internet of Things (IoT) environments. In some cases, analyzing data where it is generated and transmitting only the most important insights back to the cloud can prove more efficient and effective.
Data management teams are starting to adopt the DevOps and microservices practices that have transformed other parts of IT. As data pipelines become more complex and more critical to business success, enterprises are looking for ways to improve collaboration and streamline processes.
The combination of high-profile data breaches and the implementation of the European Union’s General Data Protection Regulation (GDPR) has enterprises more concerned than ever about privacy, security, and compliance. More enterprises now have a chief data officer responsible for overseeing big data efforts, including predictive analytics.
Predictive analytics is closely related to several other big data technologies, including machine learning, AI, data mining, and prescriptive analytics. Here are the differences.
Machine learning is the branch of computer science that gives systems the ability to learn without being explicitly programmed. These techniques can be very helpful in building, training and improving predictive models. However, machine learning can also be used for tasks other than predictive analytics.
Machine learning is a subset of artificial intelligence, which attempts to create systems that are good at the kinds of thinking and tasks that humans have traditionally been better at than machines. For example, voice recognition, image recognition and robots are all examples of artificial intelligence. As with machine learning, artificial intelligence can be used for creating predictive analytics models, but AI can also be used for many, many other things.
Data mining is the process of finding patterns and relationships in data. It is often part of the predictive analytics process. Organizations will use data mining to find patterns in historical data, and then predictive analytics goes the next step of using those patterns to forecast what is likely to come next. Companies can use data mining to help with predictive analytics, or they can use data mining alone.
If you think of predictive analytics as taking data mining to the next step, you can think of prescriptive analytics as the next step beyond predictive analytics. Prescriptive analytics not only tell you the likelihood of future outcomes but also the likely results of actions you might take in reaction to those future events.
Essentially, it tells you both what might happen and what you should do about it. Few prescriptive analytics solutions are on the market today, but many industry watchers believe they will become more common in the coming years.
Predictive analytics are used across a broad range of industries and businesses. Some of the most common use cases include the following:
Data scientists and business analysts use both traditional statistical techniques and more advanced predictive algorithms for predictive analytics. Here are the most commonly used predictive analytics algorithms.
| Neural Networks | A neural network is actually a series of connected nodes (or algorithms) that can recognize patterns in data and output predictive insights. |
| Decision Trees | Decision trees are similar to flow charts in that they take a set of data and use a series of decisions to classify the data into smaller subsets, which can then be used to generate predictive insights. They can be used for classification and regression models. |
| Bayesian Analysis | The Bayesian approach uses past probability data to help infer future probabilities of an event. |
| Random Forest | Random Forest is a machine learning algorithm that combines multiple decision trees to produce a single insight. |
A very long list of startups and more established vendors offer predictive analytics tools. In addition, many comprehensive data analytics platforms include some level of predictive analytics capability. Here are some of the most popular software platforms on the market that includes predictive analytics tools:
Read more: Top 8 Predictive Analytics Tools
Predictive analytics takes the guesswork out of the equation for businesses in many industries by analyzing historical data and other information to forecast future behaviors or trends. This makes planning easier and limits risk.
Because predictive analytics can be used to plan inventory, establish pricing, and forecast customer turnout, it can increase profits. It can also drive marketing campaigns based on what it determines about past customer behavior and how that is likely to translate into future sales. It can even mitigate the risk of lending to certain borrowers or detect fraud.
New technologies like artificial intelligence and machine learning are changing the way predictive analytics works, making it more powerful, more accurate, and more accessible to enterprises.
Read 5 Ways Brands Can Better User Data Analytics to see other enterprise approaches to incorporating data analysis into all aspects of their work.
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