Predictive Analysis

What is predictive analysis?  

Predictive analytics is a form of data analysis that looks at current and historical data to make predictions about unknown and future events. It draws on several techniques, including statistical algorithms, data mining techniques, machine learning, predictive modeling, data science tools and artificial intelligence (AI) to: 

• Make useful predictions 
• Identify and assess risk 
• Pinpoint opportunities 
• Guide intelligent decision-making 

Although a predictive analytics tool does not forecast the future with complete certainty, it can assist organizations in envisioning and planning for highly probable behaviors and outcomes while prescriptive analytics looks at those outcomes and finds even more paths of options to consider.  

Organizations can then use the information at their fingertips to anticipate and reduce risk, optimize operations, enhance marketing campaigns, better target customers, and increase revenue. 

Ultimately, predictive analytics empowers organizations to be more forward-thinking and proactive. 

How does the predictive analysis process work?  

When working with predictive analytics tools, you first need to identify what your project will cover or the problem you’re trying to solve. Ask yourself the following questions: 

• What are your business objectives? 
• What outcomes are you looking for? 
• What data sets will you draw from? 
• What are the deliverables? 

Second, you need to collect your data. This is when data mining tools works hand in hand with predictive analytics. Data mining tools gather data from an array of sources and prepare that data for analysis. The data analysis itself involves examining, cleansing (detecting and correcting corrupt data or removing it altogether), transforming and modeling data to find useful information and draw conclusions. 

Next, using statistical models, you use statistical analysis to support and test your conclusions. Then predictive modeling come into play, to produce authoritative models that look to the future. Once you’re implementing predictive models, you can use the results to make more well-informed business decisions in your day-to-day operations. You’ll also want to keep an eye on the performance of your models to make sure they’re providing the expected results. 

Why is predictive analysis important?  

Predictive analytics is increasingly essential as organizations try to successfully plan for the future. They employ this type of analysis to turn data into opportunities and to solve complex problems. Among the typical applications for predictive analytics tools are: 

• Crime detection 
• Operational improvements 
• Social media and marketing campaign optimization 
• Risk reduction 

Crime detection 

Cyber-security concerns are on the rise, but with its pattern- and anomaly-detection capabilities, predictive analytics can help organizations identify possible criminal activity before it happens. 

Operational improvements 

With predictive models, businesses can have greater transparency into their system management, processes, and devices. When you pair this with the associated analytics, businesses can better calculate inventory, management of resources, reduce costs, and ultimately increase operational effectiveness and efficiencies. 

Social media and marketing campaign optimization 

Organizations can use predictive analytics for determining what their customers are buying as well as measuring the social media impact on your brand, your products, and your business. With predictive modeling, businesses can capitalize on their marketing plans and social media platforms to better entice, retain, and grow their audience. 

Risk reduction  

Predictive analytics tools can scan a voluminous number of data sets and scour past trends to pinpoint organizational vulnerabilities now and into the future. Armed with this information, businesses can then take precautionary measures to prevent or mitigate reputational damage. 

How LexisNexis supports predictive analytics  

LexisNexis provides reliable and accurate datasets to help companies make better analysis and business predictions. With Nexis® Data+, users can access a vast repository of over 36,000 licensed sources and 45,000 total resources in more than 37 languages. This extensive database empowers data scientists to access accurate and up-to-date information for their analyses and models, contributing to the success of their predictive analytics initiatives. 

Learn more about Nexis^® Data+