What is predictive analytics?
In simple terms, predictive analytics is a form of analytics that tries to predict future events, trends, or behaviors based on historical and present data. This goal can be achieved in a number of different ways with a number of tradeoffs in terms of accuracy and cost when implementing a predictive analytics system.
Why is predictive analytics important?
Predictive analytics is valuable because it enables organizations to be more efficient and accurate in how they plan for the future. The end result of a properly implemented predictive analytics system will depend on the industry but at a high level here are some common benefits:
Improved strategic decision making - Predictive analytics gives insight into future trends, which can allow business leaders to make better decisions faster, rather than having to be reactive.
Increase operational efficiency - Using predictive analytics can allow businesses to improve their profit margin and efficiency by doing things like predicting equipment failures and reducing downtime.
Improved risk management - By looking at historical data where things went wrong, a business can reduce their risk by finding data that correlates with those bad outcomes and avoid them proactively. An example would be a bad investment in the finance industry.
Happier customers - Predictive analytics can be used in a number of ways to improve customer experience. It could involve predicting potential churn early and reaching out to customers or simply making sure that items are in stock by having more accurate predictions for inventory management.
How does predictive analytics work?
The end goal of predictive analytics is to be able to make accurate predictions based on historical data. Here is a general outline of the process for building a predictive analytics system:
Determine goal for the project - The first step is to identify the problem or opportunity you are trying to address via predictive analytics. Define your goals and success metrics upfront.
Organize and collect data - The next step will be gathering the data that will be used to build your predictive analytics model as well as the pipeline that will eventually be used to send fresh data to your model for generating predictions. This will typically be a combination of public data that is similar to your own, 3rd party data that is relevant to your use case, and your own unique business data for fine-tuning your model.
Process data - Once you have your data one of the biggest challenges is often processing and cleaning data so it can be ready for your model. This can involve removing invalid data, filling in missing data, or transforming data into a standard format.
Develop predictive analytics model - Now that your data has been collected and cleaned, you are ready to actually develop your predictive model. Which model you use will depend on your business requirements in terms of accuracy requirements and what type of modeling you will be doing. A predictive model can be used for detecting trends, classification, clustering, or more. These models can be made using statistical methods or modern machine learning techniques.
Validate results - Creating and deploying your model is just the first step, once the model is live you will need to validate the results to confirm the model works as expected. This generally involves testing against a separate dataset to test accuracy as well as running the model against live production data and testing what the results would be based on the output from the model. If the results aren’t as good as you desired you may need to return to the previous steps and modify things like how data is processed and what type of model is being used.
Deploy to production - If the results generated by your predictive analytics model are accurate and valuable, you can now deploy the model into production where the results the model generates are actually acted upon. This could require having a human in the loop to confirm the action makes sense or be completely automated where action will be taken solely based on the model.
Update and improve model over time - Predictive analytics isn’t a one time deal. You will want to constantly feed your model recent data so it can stay up to date and be aware of potential changes that will need to be integrated into the model. Typical tasks would involve retraining the model, adjusting parameters, or giving it access to additional data to help make more accurate predictions. The entire system can also be fine-tuned over time to be more efficient and affordable.
Predictive analytics use cases
Predictive analytics are useful across almost every industry, but let’s take a look at a few specific examples where predictive analytics is particularly valuable. An ideal use case for predictive analytics is any situation where data is relatively easy to collect and where having more accurate predictions will generate significant business impact in terms of revenue or cost reduction.
In the manufacturing sector predictive analytics can be used to predict and prevent machinery malfunctions before they happen. This reduces maintenance costs and improves the overall efficiency of factories, which results in higher profit margins.
Governments and businesses both take advantage of predictive analytics to help improve the healthcare industry. Governments create predictive models to try and predict and prevent the spread of diseases and also to determine investments in healthcare programs. Hospitals can use predictive models to look at patient medical records to try and create personalized treatment plans.
Predictive analytics can be used for marketing purposes to predict trends in consumer demand, improve customer engagement to prevent churn, and improve sales by recommending products that a customer might like based on their past purchases compared to other similar customers.
Supply chain management
Predictive analytics can help with supply chain management by forecasting changes in supply and demand for products by various factors like time of year or location. It can also be used to optimize logistics and manage risk.
The finance industry uses predictive analytics in a number of ways, ranging from predicting stock prices to detecting fraudulent transactions. Banks can use predictive analytics to assess risk for loan applicants by using historical data compared to the applicant’s personal history.
Predictive analytics challenges
While predictive analytics can offer many benefits to a business, implementing predictive analytics can be a challenge. This is especially true if a company does not have the in-house expertise or infrastructure in place. Here are some of the key challenges to consider when getting started with predictive analytics.
To make accurate predictions you will need a large volume of high quality data that is relevant to your use case for predictive analytics. This means you will need to have a way to not only collect data but also have long-term storage in a format that will be easy to access for teams working on creating predictive analytics models.
Integration with legacy systems
Many established businesses will have a large number of systems that may not be seamlessly integrated. This means engineering effort will be required to ensure that data is not siloed and that the predictive analytics team can access the systems and data they require.
Accuracy of results
The biggest challenge with predictive analytics will be creating a model that is able to produce results that are accurate enough to justify the investment made to create them by generating results that are able to drive business value. This will require not only the initial creation of the model but constant updates with new data to keep the model accurate as conditions change.
All of the above problems require highly skilled employees to be solved. These skills are in demand across many industries which means it can be a challenge to attract and retain the workers needed to implement a predictive analytics system.
Another challenge with predictive analytics is to ensure all of this new data being collected and stored is secure. This data can contain sensitive information about customers or about your business so security needs to be a top priority.
Predictive analytics techniques
There are a number of different models available for generating insights via predictive analytics. Which type of model to use for your organization will depend on the type of data you are working with as well as criteria like cost to develop the model and accuracy requirements. Let’s take a look at some of the most common predictive analytics techniques and models.
Machine learning/AI models
In the past classical statistical models have dominated predictive analytics and forecasting due to their ease of interpretation, lower computational costs, and accuracy. However, in recent years ML/AI based models have started to surpass more traditional forecasting methods in terms of accuracy. They also provide the benefit of being easier to generalize for different predictions and require less fine-tuning by highly trained statisticians.
Time series models
Time series models are used to analyze temporal data and forecast future values. They are particularly useful when data shows sequential patterns or seasonality, such as stock prices, weather patterns, or sales data.
Time series models are ideal for data that has seasonal variations and time-based dependencies, making them useful for forecasting. Some downsides of time series models are that they can struggle if the data isn’t in regular intervals and will operate on the assumption that past trends will continue in the future, which can make them inaccurate at predicting drastic changes.
ARIMA and exponential smoothing are examples of time series models. An easy way to get started testing these types of models for predictive analytics is to use a library like Python Statsmodels.
Regression models predict a continuous outcome variable based on one or more predictor variables. They are widely used in predictive analytics, from predicting house prices to estimating stock returns.
Regression models are useful for providing results that are easy to interpret and when you need to identify clear relationships between variables. Some downsides of regression models are that they do require a decent level of statistics knowledge and can struggle with non-linear relationships and datasets with a large number of variables.
Linear and logistic regression are examples of regression models. You can get started with regression models by using the Python scikit-learn library.
Decision tree models
Decision tree models make predictions by learning simple decision rules from the data. They can be used for both regression and classification problems. Decision tree models have the benefit of providing results that are easy to understand compared to machine learning models. A challenge is that they can be easily over or underfit and can be impacted by small changes to data.
Gradient boosting model
Gradient boosting involves creating an ensemble of prediction models, general decision tree models. These models can be extremely accurate and have been used in recent years to win many machine learning competitions. Gradient boosting is good at providing accurate predictions for data with non-linear relationships between variables and datasets with high dimensionality. One weakness is that they can be overfit when they aren’t tuned properly and are more of a black-box compared to traditional statistical models. XGBoost and LightGBM are libraries that can be used to create gradient boosting models.
Random forest models
Random forests are similar to gradient boosting in that they are ensemble models that use decision trees for making predictions. The main difference is that gradient boosting models generally use far more decision trees and they are also trained sequentially so that errors from previous trees can be corrected. In comparison, random forest decision trees make predictions independently and then the final prediction is created by aggregating those predictions. This makes the results easier to interpret because each decision tree’s prediction can be analyzed. You can test out random forest models on your data using a library like scikit-learn.
Clustering models like k-means clustering can be used for grouping data points together. While this is generally used for data analysis, these clusters can also be used as input features for predictive models like the ones mentioned above. Cluster modeling can help to identify hidden patterns or relationships in your data, but to work they do require a way to measure how similar data points are and the number of clusters generally has to be chosen ahead of time.
Future trends in predictive analytics
The predictive analytics landscape is changing rapidly as technology advances and impacts all industries. Here are a few trends to look out for in the future:
Increased demand for real-time data - To get the most accurate results possible, models need to be updated as frequently as possible so they aren’t out of sync with reality. This means that real-time data and systems that support it will become increasingly important.
Prescriptive analytics - The next step beyond predictive analytics is known as prescriptive analytics, which involves actually taking action based on a predicted outcome before it has happened to try and influence the outcome itself. This means moving from predicting what will happen to how we can make the outcome happen.
Synthetic data - Data is the key to making accurate predictions, the problem is that many businesses haven’t been collecting the data they need. A number of tools have been created to create “synthetic” data which can be used to help get a predictive analytics system off the ground with data that is artificially created to mimic the use case.
Further adoption of machine learning and AI - While most businesses are still relying on traditional methods for creating predictions, cutting edge practitioners are using ML/AI to win competitions due to their accuracy. In the future these types of cutting-edge methods will eventually reach businesses to solve real-world problems.
Easier to use predictive analytics tools - Currently implementing and using predictive analytics requires specialized skills. But domain knowledge is very important to making accurate predictions. Future tools will focus on usability and allow non-technical users to make predictions based on their data. This will make implementation cheaper and more affordable while also resulting in more business value.
Predictive analytics vs predictive maintenance
Predictive analytics is a broad field that uses statistical algorithms, machine learning, and data to anticipate future events across many domains. It identifies patterns in historical and current data to predict future trends, behaviors, and activities. Predictive analytics is used in numerous industries like finance, healthcare, marketing, and more, to make informed decisions and proactive strategies.
Predictive maintenance, on the other hand, is a specific application of predictive analytics in the field of maintenance and asset management. It uses predictive analytics techniques to anticipate when equipment might fail or require maintenance. By analyzing data from sensors, logs, and historical maintenance records, predictive maintenance models can forecast equipment failures before they happen. The goal is to perform maintenance just in time to prevent failures, improving efficiency and reducing downtime.
In short, predictive maintenance is a subset of the broader predictive analytics ecosystem.
Traditional statistical models vs machine learning and AI models for predictive analytics
More traditional techniques like regression models and decision trees have been used for decades for predictive analytics. This is due to their simplicity, lower computation requirements, and ability to show the relationship between specific variables and see the impact of how changing these variables impacts business outcomes.
In recent years AI/ML techniques like neural networks and gradient boosting have grown in popularity for predictive analytics use cases. The main reasons for this are that ML techniques can work better with higher dimensional data where relationships between numerous variables are harder to define. These AI/ML models can learn from data without requiring explicit tuning and can find relationships between variables that aren’t obvious, which results in higher accuracy.
Some downsides of AI/ML for predictive analytics are that they tend to require larger amounts of hardware for computation, and that they are also harder to interpret in terms of how they output results, in some ways acting as black boxes.