As the legendary Yogi Berra memorably said, “Making predictions is hard, especially about the future.

Yet, our understanding of the future isn't entirely uncertain if we presume a degree of stability in the business environment over time. With this assumption and a generally cautious attitude, we can use the process of predictive analysis to make useful inferences about future events, behaviors, and trends based on historical data.  

Predictive analysis is not a new method; mathematicians have been using complex statistical methods to make inferences about data trends for a very long time. However, modern advances in computer power, machine learning, and the rise of big data analytics driven by the massive quantities of data available have created a wide range of new use cases for predictive analysis. These can include fraud detection, merchandise planning, risk mitigation, and generally enhancing services and business performance. The range of possibilities is vast. 

The Mathematics of Predictive Analysis 

The core principle of predictive analysis is the use of known data to train a model that can then be used to predict values based on novel data -- and a wide variety of different mathematical models are used for different purposes. For example, many analyses might require more traditional statistical approaches such as linear or logistic regression, time series analysis, or decision trees. When the data sets become very large and the relationship between inputs and outputs become difficult to discern accurately with traditional methods, mathematicians and computer scientists have developed more advanced neural network algorithms to pick apart highly complex problems.  

 These more complex neural network algorithms are a key part of the many machine learning methods that serve as the foundation of modern artificial intelligence systems. There are a wide variety of model types and training techniques, but, in each case, the model must first be trained with a training dataset and then validated with a separate validation dataset to tune the model’s parameters and provide an unbiassed test of the model’s fit to the data. The model is then exposed to a testing dataset to provide a final evaluation of the model’s capabilities. Once completed, it can make inferences about the future based on routine corporate data. 

Use Cases 

Predictive analysis is used in every major industry. Anytime a company needs the best possible insight into potential opportunities, risks, and rewards when addressing dynamic and complex business problems, predictive analysis can be leveraged. Below, we’ve outlined just a few of the use cases across various industries: 

Manufacturing 

Predictive analysis can be used in manufacturing to analyze machinery maintenance data to anticipate breakdowns and thus optimize manufacturing line uptime. These methods are also used to forecast potential quality issues before products leave the factory floor and analyze production data to create efficiencies and improve throughput. 

Retail 

Predictive analysis can help determine future inventory requirements in retail operations, a key component of an optimized retail supply chain. Careful training of appropriate models can help reduce over and understocking. Properly trained models can also assist with dynamically pricing products based on predicted demand. 

Transportation and Logistics 

Predictive analysis can reasonably outline future traffic conditions while planning logistics to optimize routes and drive efficiency. These sorts of techniques can also help predict vehicle maintenance and improve fleet usage. 

 Energy 

A power company can use predictive analysis to forecast system load by predicting energy consumption and balance load and generation. As with manufacturing, there are many opportunities to anticipate maintenance issues with the many machines that make up the power distribution grid. Effective preventative maintenance driven by sophisticated models of grid function can also prevent outages and optimize uptime. 

 With the effective use of predictive analysis, companies can achieve a competitive advantage by taking a proactive approach. This can reduce costs through efficient operations, increase revenue through targeted marketing and sales efforts, and improve overall customer satisfaction by anticipating and meeting customer needs.  

Aditi Enables Predictive Analysis

At Aditi, we offer a wide array of data and analytics services to empower your business with powerful insights from predictive analysis. Our services include data acquisition and visualization, data governance, and machine learning provided by our teams of skilled professionals. With our extensive experience and expertise with various data and analytics tools and technologies, Aditi can provide tailored solutions to align with your business objectives. Contact us today to discover how we can help you visualize future trends and opportunities.