Predictive analysis: What is it and why is it important?

June 28, 2018

For many companies, predictive analysis is nothing new; but it is being increasingly used by various industries to improve everyday business operations and increase competitiveness. 


What is predictive analysis?

Predictive analysis is the use of historical data, machine learning and artificial intelligence to predict future outcomes. The historical data is used to build a predictive model that captures key trends and patterns. Current data is then used on the predictive model to predict what will happen next.

Why is predictive analysis so important?

Using the information provided by predictive analysis, business owners more enabled to develop strategies that can positively affect business operations. Consultants and analysts can use predictive analysis to foresee if any business changes will help to reduce risks, improve operational efficiency, or increase revenue.


Today, businesses have access to huge amounts of transactional data, sales results, customer complaints and marketing information; and are increasingly making more business decisions based on this data.


Predictive analysis can lead to valuable business outcomes, such as:


Fraud detection

The use of predictive analysis can improve pattern detection, allowing for more accurate identification of criminal behaviour. As cybercrime is on the rise, predictive analytics allows for analysis of behavioural patterns in real-time to identify potential cases of fraud or network vulnerabilities.


Targeted marketing campaigns

By analysing historical purchasing behaviours and patterns, predictive analytics can determine and promote additional sales opportunities, allowing businesses to attract, retain and grow their most profitable customers.


Improved operational efficiency

By forecasting required supply and projected demand, predictive analysis allows businesses to effectively manage their inventory levels, improving operational efficiency.


Reducing financial risk

Predictive analysis can be used to systematically reduce risk by using mathematical methods to produce risk ratings for credit scores,


Essentially, predictive analysis answers the question, "What is the most likely outcome based on my current data, and what can I do to change it?"



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