What is predictive analytics?

Predictive analytics examines existing data to build models using statistical techniques. Companies use predictive analytics to discover patterns and identify both potential opportunities as well as risks. The goal is to take advantage of data collected in real-time as well as existing historical records to better assess what will happen in the future.

Predictive analytics vs. machine learning

Predictive analytics is a broad term for using data to model the future. Machine learning, however, is a specific technique for analyzing data that uses a computer to “learn” patterns from a collection of data without a manually specified model defined by humans. 

Machine learning is a type of artificial intelligence (AI), and can be used to power predictive analytics, though other techniques could be used to inform predictive analytics besides machine learning (for example, decision trees, which use fixed, pre-specified rules to analyze data).

Why is predictive analytics important?

Predictive analytics are one of the best business intelligence tools for planning any aspect of running a company. Every decision is made better with a more accurate understanding of the future. Predictive analytics provide the foresight to improve outcomes in ways not otherwise possible. Your data is a vast reserve for better decision-making, and not using predictive analytics leaves that powerful resource untapped.

What can you do with predictive analytics?

• Anticipate consumer demand
• Forecast supply availability changes
• Prepare for market shifts
• Allocate resources for potential business risks
• Targeting advertising to highest value consumers
• Identify high-risk patients and provide intervention
• Find children struggling and tailor resources to meet their needs
• Detect anomalies like alerting to fraud
• Optimize energy generation in the face of changing demands and weather conditions

How does predictive analytics work?

Predictive analytics can leverage a number of different technologies. The three most significant examples are:

Decision trees
A decision tree is a technique where each piece of data is analyzed by a series of rules to understand it. The data is tested against a rule and based on the result of that test, the data is then fed to particular additional rules, which in turn determine the next rules to use. This cycle of testing the data to determine which additional rules to apply to it continues until an ultimate classification for the data is found. 

Decision trees are well suited for data that is well-understood and can be classified by subdividing it in predictable ways. They require manual specification of all possible rules to classify the data, which can be labor-intensive. However, this technique does provide very fast and predictable results when analyzing data.

Regression 
Regression analysis is one of the oldest statistical techniques, with origins long predating computers. It uses a series of simple mathematical operations to compute the relationship between sets of data. This relationship can be used to predict what data to expect given any value of interest. 

When this technique is used with one variable predicting the outcome, it is called linear regression. An example would be using the average length of words in an essay to predict how difficult the vocabulary is to understand–longer words tend to be associated with more difficult vocabulary. 

If, however, the predictions are based on more than one variable, it’s known as multiple regression. A multiple regression could be used for predicting risk of heart attack, based on a person’s age, gender, weight, and blood pressure. It uses all these variables together to find the most likely answer for any other combination thereof.

Neural networks
Neural networks are useful for making predictions without understanding the underlying relationship among the variables. They are extremely useful for relationships that are too complex to easily express with other techniques or when there is no understanding of the relationships which would be necessary for other techniques. 

Neural networks are useful for double-checking the predictions of other techniques like decision trees and regression models. 

What should I look for in predictive analytics tools?

The best predictive analytics tools should be accessible for both business owners and data scientists. When everyone can predict potential futures based on past patterns, the entire company benefits from the data.

Domo’s predictive analytics tools offer top-notch algorithms to solve your biggest data challenges. No matter where it is stored, Domo almost certainly has the right tools to connect to your data and prepare it for use by their powerful analytical tools. Building analytics for your data couldn’t be easier with effortless application of even complex techniques such as machine learning. 

SageMaker Autopilot automatically trains on whichever data that you pick and tunes hundreds of machine learning models to find the very best fit for your needs. There’s no need to apply your models to new data as it is collected, either. Domo’s Magic ETL can make inferences in real time as it is received. 

Insights are only as useful as their understanding, so Domo provides a range of tools to present and interact with discoveries and predictions, suitable both for internal presentation and for sharing externally.

How do different industries use predictive analytics?

Almost any industry can benefit from the insights of predictive analytics. Here are a few examples:

• Credit scores from banks and other financial institutions are created through predictive analytics, combining data from a customer’s credit history, loan application, and other data. Scores rank customers’ likelihood of making future payments on time.
• Insurance companies must predict the average costs of claims against a policy in order to set premiums high enough to cover expenses but low enough to be competitive. 
• Marketing campaigns can be assessed for their effectiveness and adjusted for maximum improvement in sales.
• Healthcare can use vast repositories of patient histories to discover patient risk factors, determine previously unknown causes of diseases, assess effectiveness of drugs and techniques, and improve patient care. 
• Social networking is profitable almost entirely because of predictive analytics. The insights into user behavior and preferences provided by these tools are the core product driving profits.
• Sales teams use predictive analytics to identify which opportunities are most likely to become successful sales.
• HR departments identify employees who are most likely to leave the organization with predictive analytics so they can address underlying causes for unhappiness before losing important talent.

How will predictive analytics affect the future?

The power of predictive analytics can truly shape the future of marketing campaigns, insurance policies, business management, and more. It will increase the efficiency of resource allocation, planning, and scheduling. It can also help overcome scarcities, prevent shortages, and respond to crises.

As long as data continues to be collected, predictive analytics will be a great way to use that data to better the future, one set at a time.