What Is Predictive Modeling? An Introduction

Predictive modeling basics

What is predictive analytics? Predictive analytics refers to the application of mathematical models to large amounts of data with the aim of identifying past behavior patterns and predicting future outcomes. The practice combines data collection, data mining, machine learning and statistical algorithms to provide the "predictive" element.

Predictive analytics is just one practice within a spectrum of analytics approaches that include the following:

• Descriptive: As the most basic type of analytics, descriptive analytics identifies a problem or answers the question, "What happened?" However, it can't tell you why something happened, so it's usually used in tandem with one or more of the other types.
• Diagnostic: Diagnostic analytics picks up where descriptive analytics leaves off and makes correlations that explain why something happened.
• Predictive: Predictive analytics takes historical data and identifies patterns that point to likely future events.
• Prescriptive: Prescriptive analytics, the most sophisticated type, recommends the course of action to solve or prevent a problem.

Predictive analytics vs. prescriptive analytics

Our focus in this article is on predictive analytics, which differes from prescriptive analytics.

• Predictive analytics provides a range of potential outcomes based on the available data. It asks, "What can be done?"
• Prescriptive analytics actually suggests actions to take in order to achieve specific goals. It asks, "What should be done?"

Why do predictive analytics matter?

Descriptive and diagnostic analytics tools are invaluable for helping data scientists make fact-based decisions about current events, but they're not enough on their own. In order to compete today, businesses must be able to anticipate trends, problems, and other events.

Predictive analytics builds on descriptive and diagnostic analytics by:

1. Identifying patterns in data outputs.
2. Forecasting possible outcomes and the likelihood that they will happen.

This ability allows businesses to plan more accurately, avoid or mitigate risk, quickly evaluate options, and generally make more confident business decisions. Here are some real-world examples of what predictive analytics can do:

• Help retail businesses predict customer long-term value.
• Assist healthcare practitioners in determining the most effective course of patient treatment.
• Let educators identify students who need more personalized attention.

Predictive analytics in technology & IT

Predictive analytics has been particularly transformative in IT. The increased complexity of architecture sourced to virtualization, the cloud, the Internet of Things (IoT), and other technological advances exponentially increases the volume of comprehensible data, resulting in long delays in issue diagnosis and resolution.

Powered by big data and artificial intelligence (AI), predictive analytics overcomes these difficulties. As it identifies patterns, it can create predictors around IT issues such as:

• Performance issues
• Network outages and downtime
• Capacity shortfalls
• Security breaches
• A host of other infrastructure problems

What's the value of knowing all this? It's clear: improved performance, reduced downtime, and overall more resilient infrastructure.

Predictive models can analyze vast amounts of transactional data to find anomalies or suspicious activities. As a result, it helps in fraud detection and prevention, helping businesses to enhance their security protocols and prevent financial losses.

How predictive analytics models work

Predictive analytics models work by running machine learning algorithms on business-relevant data sets.

Building a predictive model is a step-by-step process that starts with defining a clear business objective. This objective is often a question that helps define the scope of the project and determine the appropriate type of prediction model to use. From there, you'll follow a series of steps as outlined below.

1. Prepare your historical data for statistical analysis. For most organizations, data is spread across many sources such as data warehouses, online databases, and connected devices. It needs to be collected and "cleansed" to remove duplicate, missing, corrupt or inaccurate data, and then organized into a defined format for analysis.
2. Divide data into two datasets: training data and test data. Training data corresponds to known outcomes. It's fed to the machine learning algorithm for evaluation and prediction of new data. The test data will be used to validate that the model can make accurate predictions.
3. Run one or more algorithms against the dataset. Once the appropriate model type and algorithms are decided, the predictive model is built and deployed.

Predictive modeling is an iterative process. Once a learning model is built and deployed, its performance must be monitored and improved. That means it must be continuously refreshed with new data, trained, evaluated, and otherwise managed to stay up-to-date.

(Related reading: continuous data & continuous monitoring.)

Predictive modeling techniques

There are several common predictive modeling techniques that can be classified as either regression analysis or classification analysis.

• Regression analysis examines a dependent variable (the action) and multiple independent variables (outcomes). It evaluates the strength of the relationship between them. This analysis forecasts trends, predicts an action's impact, and determines correlations between actions and outcomes.
• Classification analysis sorts data into categories for more accurate analysis. It uses a few different mathematical techniques, including decision trees and neural networks, as explained below.

Once you decide to use regression analysis, there are several types to choose from. Some of the most common include:

Simple linear regression

The most basic form of regression analysis, linear regression establishes the relationship between two variables.

To use a simple example, a store could use linear regression to determine the relationship between the number of salespeople it employs and how much revenue it generates.

Multiple linear regression

Multiple linear regression can be used to establish the relationship between the dependent variable and each of the independent variables. A health researcher can use this technique to determine the impact of factors like smoking, diet, and exercise on the development of heart disease, for example.

Logistic regression

This type of regression analysis is used to determine the likelihood that a set of factors will result in an event happening or not happening. A bank trying to predict if an applicant will default or won't default on a loan is a common use of logistic regression.

Ridge regression

This technique is used to analyze multiple linear regression datasets that have a high degree of correlation between independent variables.

Decision trees

A "classification" approach, this technique replicates the decision-making process by starting with a single question or idea and exploring different courses of action and their possible effects through a "branching" process to arrive at a decision.

Neural networks

Modeled on the human brain, this technique helps cluster and classify data to recognize patterns and identify trends that are too complex for other techniques. That's why it's considered a classification analysis.

A retail site that recommends products based on a user's past purchases is one example of neural networks in action.

(See how Splunk can detect suspicious security activities using ML and recurrent neural networks.)

Prescriptive vs. predictive modeling: What's the difference?

Prescriptive modeling is the practice of analyzing data to suggest a course of action in real-time. Essentially, it relies on the insights produced by other analytics models to consider various factors - available resources, past and current performance, and potential outcomes - to propose what action to take next.

In an IT context, for example, prescriptive modeling can:

• Propose infrastructure improvements based on monitoring and maintenance data.
• Enable the system to make the necessary adjustments itself according to a pre-recorded script.

Prescriptive analytics is an extension of predictive analytics. Where predictive analytics can tell you what, when, and why a problem will likely happen, prescriptive analytics goes a step further and offers specific actions you can take to solve that problem. Both types of analytics enable you to make better-informed decisions, but prescriptive analytics pulls the most value from your data, allowing you to optimize processes and systems for the short and long term.

Types of predictive models

There are several different types of predictive analytics models. Most are designed for specific applications, but some can be used in a variety of situations.

Before deep diving into the specific models, we need to understand the differences between unsupervised and supervised models.

• Unsupervised models discover hidden patterns in a dataset by working with data without a label. This is useful in segmenting and clustering data.
• Supervised models, in contrast, depend on datasets with a label. They predict specific output based on the input data.

Since each industry has different data objectives, nature, and challenges, the different types of predictive models have varying applications across different domains. Each type of model has specific tasks like detecting unusual activities, forecasting demands, and so on. Let's discuss the common types of predictive models.

Forecast models

Perhaps the most common types of predictive analytics models, forecast models learn from historical data to estimate the values of new data. Forecast models can be used to determine, for example, things like:

• How many calls a customer service agent can handle in a day
• How many copies of a bestseller a retailer should order for the coming sales period

A real-world example is using predictive algorithms to predict the readmission of patients, as done in the Mount Sinai health system.

Classification models

Classification models use historical data to categorize information for query and response and provide broad analysis to help people take decisive action. Popular across a wide range of industries, they're best used to answer yes/no questions such as, "Is this loan applicant likely to default?"

Clustering models

This model sorts data together around common attributes. One popular application is customer segmentation, where the model can cluster a business's customer data around shared attributes and behaviors. Clustering models use two types of clustering - hard and soft.

• In hard clustering, data points either belong to a category or they don't.
• Soft clustering doesn't put each data point in a separate cluster but rather assigns a probability that a point belongs in every cluster.

Outlier models

Outlier models identify and analyze abnormal entries within a dataset and are usually used where unrecognized anomalies can be costly to companies, such as in finance and retail. For example, an outlier model could identify a potential fraudulent transaction by assessing the amount, time, location, purchase history and the nature of the purchase.

(Related reading: anomaly detection.)

Time series models

This model uses time as the input parameter to predict trends over a specific period. For example, a call center could use this model to determine how many support calls it can expect in the coming month based on how many it received over the previous three months.

(Related reading: time series forecasting & call center metrics to track.)

How to choose the right predictive model

There are a few things to consider when choosing a predictive model:

• What you're trying to accomplish: Forecast models are great for predicting future events based on past ones, while classification models are a good choice when you want to explore possible outcomes to help you make an important decision. The right model will depend largely on what you're trying to learn from your data.
• Amount of training data: In general, the more training data you gather, the more reliable the predictions. Limited data or a few occurrences of whatever you're trying to measure within a dataset may dictate the use of different algorithms, versus a huge dataset with lots of variables.
• Accuracy and interpretability of the output: Accuracy refers to the reliability of the model's predictions, and interpretability is how easy to understand they are. Ideally, your model will have a good balance of each.
• Training time: The more training data you have, the more time you will require to train the algorithm. Higher accuracy also requires a longer training time. These two factors may be the most significant in choosing a model for many organizations.
• Linearity of the data: Not all relationships are perfectly linear, and more complex data structures may narrow down your options to techniques like neural networks.
• The number of variables: Data with a lot of variables will slow some algorithms down and extend training time, which should be considered before choosing a model.

Ultimately, you will need to run various algorithms and predictive models on your data. Also, you need to evaluate results to make the best choice for your needs.

Examples of business benefits of predictive modeling

Predictive modeling is important because every business, regardless of industry, relies on data to make better business decisions. Predictive modeling boosts decision confidence by revealing the most likely outcomes of actions under consideration.

Some of the common business benefits can include:

• Improved decision-making: By understanding probable future outcomes, businesses can make more informed decisions. Whether it's about allocating resources, setting up marketing campaigns, or selecting which leads to pursue, predictive insights provide guidance.
• Cost savings: Predictive models can help businesses anticipate and manage risks, reduce waste, and optimize processes. For example, predicting machinery failures can lead to timely maintenance and avoid costly downtimes.
• Increased revenue: By leveraging predictive analytics, companies can better understand customer behavior, segment their market, and target the most promising opportunities. For instance, predicting which customers are most likely to churn allows businesses to intervene proactively.
• Operational efficiency: By predicting demand, businesses can better manage inventory, optimize supply chain processes, and ensure that they meet customer needs without holding excess stock.
• Enhanced customer experience: Predictive models can help businesses understand their customers' needs and preferences, leading to tailored product recommendations, personalized marketing messages, and more effective customer service interventions.
• Risk management: Financial institutions use predictive modeling to evaluate loan risks, insurance claims, and potential fraudulent activities. By predicting which transactions are likely to be fraudulent, businesses can reduce their exposure to financial losses.
• Strategic advantages: Gaining insights into future market conditions, competitive landscapes, and customer preferences helps businesses position themselves effectively. This enables them to gain a competitive edge.

Challenges, risks, and assumptions

Mathematically performed predictions based on datasets are not infallible. Typically, problems with predictive modeling come down to a few factors.

Non-quality data. The first is a lack of good data. To make accurate predictions, you need a large dataset that is rich with the appropriate variables on which to base your predictions. That is not easy to come by for many organizations, as many organizations lack a robust data platform that can correlate all of an enterprise's data, analyze information at a granular level, and derive actionable insights from large datasets. Consequently, small or incomplete data samples can easily result in unreliable predictions.

Past performance does not guarantee future performance. Another obstacle to effective predictive modeling is the assumption that the future will continue to be like the past. Predictive models are built using historical data. However, behaviors often change over time, which may render long-used models suddenly invalid. New and unique variables in different situations in turn elicit new corresponding behaviors and approaches that we can't always anticipate with prior models. Thus, we must constantly refresh predictive models with new data to keep pace with current behaviors in order to make accurate predictions based on them

Model drift. Another common challenge with predictive modeling is model drift. Model drift refers to a model's tendency to lose its predictive ability over time. Statistical shifts in the data usually cause this. If left undetected, it can negatively impact businesses by producing inaccurate predictions.

Getting started

Before starting with predictive modeling, first decide what problems your organization would like to solve. Clarity about what you want to accomplish will yield an accurate, usable outcome while taking an ad hoc approach will be far less effective.

Next, assess any skills and technology gaps in your company. While software solutions do much of the heavy lifting, predictive modeling requires expertise to be effective. Be sure you have the staff, tools, and infrastructure you'll need to identify and prepare the data you'll use in your analysis.

Finally, conduct a pilot project. Ideally, this will be small in scope and not business-critical but will be important to the company. Identify your objective, decide what metrics you will use to achieve it, and how you will quantify the value. Once you have your first success, you'll have a foundation on which to build larger predictive modeling projects.

Should your business rely on predictive modeling?

Predictive modeling is sound data science, but it's not omniscient. Predictive model neither could have forecasted the COVID-19 pandemic. Nor how it would change consumer behavior on such a huge scale, for instance. Those once-in-a-lifetime circumstances aside, predictive modeling is a highly effective way to inform business decisions. You only need to have the right solution and staff in place. Also, you need to continually refresh your model with new data.

With a systematic approach and the right software solution, you can start leveraging the power of predictive modeling. By doing so, you can solve your most vexing business problems and uncover new opportunities.

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