What is predictive analytics?

Like Artificial Intelligence, predictive analytics is not a new concept. It has been around for many years and, like AI, it has recently come to prominence through the need to make sense of the vast amount of structured and unstructured data that every enterprise is producing. This blog post examines the practice of predictive analytics and how predictive analytics models can help the business release the value in Big Data and reach better decisions.

What is predictive analytics?

A simple definition of predictive analytics is the use of data analysis, statistical modelling and machine learning technology to ‘predict’ likely outcomes.

So that’s easy then! It’s a little more complex than that because we are dealing with likelihood not certainty. That requires the construction of predictive models. In business, predictive models identify and analyse patterns and trends in historical (and real-time transactional) data to identify risks and opportunities.

Predictive modelling software use known results from existing data to ‘train’ the model to predict outcomes that will occur with new data. The predictive model delivers the results in the form of a probability score showing that something is likely to happen given a certain set of circumstances. It should be clear that, like machine learning, predictive analytics software is adaptable and learns as it goes. The more data the predictive modelling works with, the more accurate the predictions for your chosen predictive analytics solutions will become.

Set in this context, the exponential growth of Big Data is exactly what predictive analytics tools need. It appears almost as a virtuous circle. While companies produce more data, the tougher it is to cope but the better the predictions predictive analytics will provide for enterprises to gather insights needed to make better decisions. Little surprise, then, that the market for predictive analytics solutions is exploding. It is estimated to more than double between 2018 and 2022, reaching $10.95 billion dollars worldwide.

The growth of predictive analytics tools

Predictive analytics encompasses a wide variety of statistical techniques and technologies. For example, the two most common types of predictive analytics models are: classification models that help predict outcomes such as when a component will fail and regression models that help predict a number such as the average time before a component fails.

This largely explains why, for most of the early life of predictive analytics, it was the province of mathematicians and statisticians. The growth of Big Data and the need to exploit the value in the information that companies hold has spurred the rapid development of a new generation of predictive analytics tools that are more interactive and easier for non-statisticians to use. In fact, the best tools of this kind have self-service capabilities to enable users to create their own queries that display results in dashboards and reports that anyone can understand.

You can select a leading predictive analytics platform – such as OpenText™ Magellan™ – that takes information from a wide range of structured and unstructured data sources and applies built-in statistical techniques for profiling, mapping, clustering, forecasting, creating decision trees, classification and association rules, creating regression models, and correlations. OpenText Magellan includes pre-built predictive analytics algorithms and predictive analytics techniques to speed implementation and ensure all users can access, blend, explore and analyse data quickly and easily.

The benefits of predictive analytics

Today, predictive analytics is increasingly used by organizations in many sectors such as financial services, retail, energy, manufacturing and the public sector. Key benefits of predictive analytics software include:

Improved production efficiency

Manufacturing and production facilities are using predictive analytics in conjunction with Big Data and Internet of Things (IoT) devices to dramatically alter their production process. This allows for more effective inventory forecasting and required production rates to meet actual customer demand or predict potential production failures and take the appropriate actions to head them off. In fact, the application of predictive analytics solutions in plant and equipment maintenance – often called predictive maintenance – is one of the key use cases for predictive analytics aiming to ensure optimum performance and uptime.

Improved decision-making

At the heart of predictive analytics is advanced decision support. This delivers insight based on information that you already have. The more information your predictive analytics tool has, the more accurate its predictions, the better decisions that you can make. As predictive analytics software can identify patterns and trends in vast amounts of structured and unstructured data, providing insight that enterprises would previously not had access to. Like Artificial Intelligence, predictive analytics is only as good as the raw materials it has to work with. Data needs to be identified, gathered and cleansed in a way that ensures the integrity and accuracy of the information going.

Enhanced risk reduction

Depending on your industry, predictive analytics software can play a major role in risk reduction. Sectors such as finance and insurance are using predictive modelling to construct an accurate picture of a customer or business, based on all data available. This can then form a more reliable interpretation of that person, business or incident which can be used to make sensible, effective decisions. Predictive analytics is gaining acceptance in many business areas including supply chain risk where it can predict the likelihood of disaster or other events that might cause disruption.

Enhanced fraud detection

Fraud detection was one of the first use cases where predictive analytics software was applied. As predictive analytics is particularly good at identifying trends and patterns in behavior, it can easily spot anomalies that may indicate threat or fraud, which can then be highlighted and prevented. Insurance fraud is one of the biggest crimes in the US, costing at least $80 billion each year, and predictive analytics tools are now being deployed to better detect and flag fraudulent and duplicate claims.

Targeted, personalized marketing campaigns

With a predictive analytics platform, an enterprise can get much more value from its customer data. You know who your customers are, where they are and their buying behaviour and preferences. Armed with this information, they can determine customer responses or purchases, as well as promote cross-sell opportunities. The predictive analytics algorithms can more closely target and personalize marketing campaigns helping businesses attract, retain and grow their most profitable customers.

Why choose OpenText for your predictive analytics platform?

OpenText is at the forefront of the development of a wide range of analytics solutions.

For example, OpenText Magellan is an AI-powered analytics platform with comprehensive predictive analytics capabilities. It delivers a scalable analytics and data visualization platform that enables businesses to design, deploy, and manage secure, interactive web applications, reports, and dashboards fed by multiple data sources.

The solution provides users with a 360-degree view of their business, explores billions of records in seconds and predictive analytics techniques all in a drag-and-drop experience, with no complex data modelling or coding required.

Editor’s note: This is an installment in our “AI Glossary” series of blog posts, offering guidance on key areas of artificial intelligence and analytics. Look for future posts in this series over the months to come.

Zachary Jarvinen

Zachary is the Product Marketing Lead for Analytics and Artificial Intelligence at OpenText. He previously worked at Global Fortune 500 Epson and the U.S. State Department, and was part of the 2008 Obama Campaign Digital Team. Zachary speaks fluent Spanish and Portuguese, and holds an MBA/MSc from UCLA and the London School of Economics.

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